From a3f4f23a02f568a999e0116454d16ccfbea0c297 Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Wed, 13 Feb 2019 22:36:42 +0530 Subject: [PATCH 01/11] Intro to Pandas Solved --- IntroToPandas.ipynb | 1394 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1394 insertions(+) create mode 100644 IntroToPandas.ipynb diff --git a/IntroToPandas.ipynb b/IntroToPandas.ipynb new file mode 100644 index 0000000..c03f9a7 --- /dev/null +++ b/IntroToPandas.ipynb @@ -0,0 +1,1394 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "IntroToPandas.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "z5pOXU4Zr0hC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "f9ae84cb-a59e-47c8-b96a-2ab8a7111d9b" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'0.22.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "metadata": { + "id": "Hv_S4B7UuiBC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "bc40960b-808e-4cdb-e147-c54a6087d83f" + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "id": "j2xU2104uZvM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "3f8a6bf0-6e8d-400d-a2db-55fdb69064d1" + }, + "cell_type": "code", + "source": [ + "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n", + "population = pd.Series([852469, 1015785, 485199])\n", + "\n", + "pd.DataFrame({ 'City name': city_names, 'Population': population })" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -114.31 34.19 15.0 5612.0 1283.0 \n", + "1 -114.47 34.40 19.0 7650.0 1901.0 \n", + "2 -114.56 33.69 17.0 720.0 174.0 \n", + "3 -114.57 33.64 14.0 1501.0 337.0 \n", + "4 -114.57 33.57 20.0 1454.0 326.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 1015.0 472.0 1.4936 66900.0 \n", + "1 1129.0 463.0 1.8200 80100.0 \n", + "2 333.0 117.0 1.6509 85700.0 \n", + "3 515.0 226.0 3.1917 73400.0 \n", + "4 624.0 262.0 1.9250 65500.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "mxxIvr1Ivpjy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 396 + }, + "outputId": "1a08c7c1-5ebc-4f54-9a9c-54657817c353" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 6, + "outputs": [ + { + 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "iHToL0NUv2uX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Accessing Data " + ] + }, + { + "metadata": { + "id": "Cp4yJYY_v7BX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + }, + "outputId": "cfc95238-5121-4f4b-f89c-95c2aad0d453" + }, + "cell_type": "code", + "source": [ + "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n", + "print(type(cities['City name']))\n", + "cities['City name']" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "Name: City name, dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "6LlUbKOZwrHZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "db016877-aa9a-4642-e396-abd00bf00a7a" + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "c9BM3FGSww_t", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 128 + }, + "outputId": "18e5f007-05da-4859-d3fa-c7372c853af9" + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulation
0San Francisco852469
1San Jose1015785
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "id": "4iAUOjJYw4F0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Manupulating Data\n" + ] + }, + { + "metadata": { + "id": "Nl6BaGzGw-mM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "bc35f117-0a8d-46e5-ddfe-e052bc805dfc" + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 852.469\n", + "1 1015.785\n", + "2 485.199\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "nprpnbHfxV4k", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "4409ba67-c913-42aa-8aed-107b85745d44" + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 13.655892\n", + "1 13.831172\n", + "2 13.092314\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "id": "WW97qJ60xZk8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "04c2707a-d687-48cd-fa56-2ebd91f1ea0b" + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "id": "Wxbos2l4ykq1", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "652d36d4-89ac-48ef-bd50-656df5d245b4" + }, + "cell_type": "code", + "source": [ + "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n", + "cities['Population density'] = cities['Population'] / cities['Area square miles']\n", + "cities" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation density
0San Francisco85246946.8718187.945381
1San Jose1015785176.535754.177760
2Sacramento48519997.924955.055147
\n", + "
" + ], + "text/plain": [ + " City name Population Area square miles Population density\n", + "0 San Francisco 852469 46.87 18187.945381\n", + "1 San Jose 1015785 176.53 5754.177760\n", + "2 Sacramento 485199 97.92 4955.055147" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "id": "Izy0OEx9y2yM", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Exercise1" + ] + }, + { + "metadata": { + "id": "Q39AJvWBy7Dc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "outputId": "65068c35-da34-44b1-fe63-31700fc4412b" + }, + "cell_type": "code", + "source": [ + "cities['Wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityWide and has saint name
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
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..............................
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "count 17000.0 17000.0 17000.0 17000.0 17000.0 \n", + "mean -119.6 35.6 28.6 2643.7 539.4 \n", + "std 2.0 2.1 12.6 2179.9 421.5 \n", + "min -124.3 32.5 1.0 2.0 1.0 \n", + "25% -121.8 33.9 18.0 1462.0 297.0 \n", + "50% -118.5 34.2 29.0 2127.0 434.0 \n", + "75% -118.0 37.7 37.0 3151.2 648.2 \n", + "max -114.3 42.0 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income median_house_value \n", + "count 17000.0 17000.0 17000.0 17000.0 \n", + "mean 1429.6 501.2 3.9 207.3 \n", + "std 1147.9 384.5 1.9 116.0 \n", + "min 3.0 1.0 0.5 15.0 \n", + "25% 790.0 282.0 2.6 119.4 \n", + "50% 1167.0 409.0 3.5 180.4 \n", + "75% 1721.0 605.2 4.8 265.0 \n", + "max 35682.0 6082.0 15.0 500.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "mO6JkcYP8vgS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Build the first Model" + ] + }, + { + "metadata": { + "id": "VBIJrJMq81eP", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Step 1**" + ] + }, + { + "metadata": { + "id": "P1eYJExF85Gn", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the input feature: total_rooms.\n", + "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n", + "\n", + "# Configure a numeric feature column for total_rooms.\n", + "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "cBpT4KlC9DUT", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Step 2**" + ] + }, + { + "metadata": { + "id": "kjRin-pz9E8H", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the label.\n", + "targets = california_housing_dataframe[\"median_house_value\"]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Cuoiaxki9L_a", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Step 3**" + ] + }, + { + "metadata": { + "id": "vOVTC0IX9Nvj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + }, + "outputId": "70c767a3-c4f3-4e0c-da27-27511fe30e28" + }, + "cell_type": "code", + "source": [ + "# Use gradient descent as the optimizer for training the model.\n", + "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n", + "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + "\n", + "# Configure the linear regression model with our feature columns and optimizer.\n", + "# Set a learning rate of 0.0000001 for Gradient Descent.\n", + "linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + ")" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "VEk96_RT-TQZ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Step 4**" + ] + }, + { + "metadata": { + "id": "BvwfXivj9T33", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "GkP2ThpD-f92", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Step 5**" + ] + }, + { + "metadata": { + "id": "L1NFZrNi-cuN", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = linear_regressor.train(\n", + " input_fn = lambda:my_input_fn(my_feature, targets),\n", + " steps=100\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "gJbxV-Nw_biQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Step 6**" + ] + }, + { + "metadata": { + "id": "A7S0BNdn-jti", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "3c2d5f51-e10e-447c-817d-cdf2572fd388" + }, + "cell_type": "code", + "source": [ + "# Create an input function for predictions.\n", + "# Note: Since we're making just one prediction for each example, we don't \n", + "# need to repeat or shuffle the data here.\n", + "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n", + "\n", + "# Call predict() on the linear_regressor to make predictions.\n", + "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + "\n", + "# Format predictions as a NumPy array, so we can calculate error metrics.\n", + "predictions = np.array([item['predictions'][0] for item in predictions])\n", + "\n", + "# Print Mean Squared Error and Root Mean Squared Error.\n", + "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n", + "root_mean_squared_error = math.sqrt(mean_squared_error)\n", + "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n", + "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mean Squared Error (on training data): 56367.025\n", + "Root Mean Squared Error (on training data): 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "dK3O7Yrp_jea", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "6dc41f5d-48d1-4477-b629-81b6919a829f" + }, + "cell_type": "code", + "source": [ + "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n", + "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n", + "min_max_difference = max_house_value - min_house_value\n", + "\n", + "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n", + "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n", + "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n", + "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Min. Median House Value: 14.999\n", + "Max. Median House Value: 500.001\n", + "Difference between Min. and Max.: 485.002\n", + "Root Mean Squared Error: 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "k3ZZ84GD_ql6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "5ce94150-964f-400a-c48b-0f610971d157" + }, + "cell_type": "code", + "source": [ + "calibration_data = pd.DataFrame()\n", + "calibration_data[\"predictions\"] = pd.Series(predictions)\n", + "calibration_data[\"targets\"] = pd.Series(targets)\n", + "calibration_data.describe()" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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predictionstargets
count17000.017000.0
mean0.1207.3
std0.1116.0
min0.015.0
25%0.1119.4
50%0.1180.4
75%0.2265.0
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" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 0.1 207.3\n", + "std 0.1 116.0\n", + "min 0.0 15.0\n", + "25% 0.1 119.4\n", + "50% 0.1 180.4\n", + "75% 0.2 265.0\n", + "max 1.9 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "id": "NO4J_Joq_vDB", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "sample = california_housing_dataframe.sample(n=300)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vNhCwxBG_zdf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + }, + "outputId": "b7190a3e-daff-45f3-e56d-7144ec5d4bd3" + }, + "cell_type": "code", + "source": [ + "# Get the min and max total_rooms values.\n", + "x_0 = sample[\"total_rooms\"].min()\n", + "x_1 = sample[\"total_rooms\"].max()\n", + "\n", + "# Retrieve the final weight and bias generated during training.\n", + "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n", + "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + "# Get the predicted median_house_values for the min and max total_rooms values.\n", + "y_0 = weight * x_0 + bias \n", + "y_1 = weight * x_1 + bias\n", + "\n", + "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n", + "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n", + "\n", + "# Label the graph axes.\n", + "plt.ylabel(\"median_house_value\")\n", + "plt.xlabel(\"total_rooms\")\n", + "\n", + "# Plot a scatter plot from our data sample.\n", + "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n", + "\n", + "# Display graph.\n", + "plt.show()" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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qa6kraQ/X54mI0o/DJo2QWw+3mAwoLTKn9dSxWOvznGYnIkoPjsg1Qq4wjMcXwJoV05Fv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\nO5Gs4S5vt2gQBwCvLxB+PXenD6cb2zGsrBDWAlP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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "UZ5G33kD_8zG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Tweak the Hyperparameters" + ] + }, + { + "metadata": { + "id": "LEZ76-Ns_21T", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]]\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label]\n", + "\n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Output a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "n21giKIkAKOZ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Task 1- Achieve an RMSE of 180 or below" + ] + }, + { + "metadata": { + "id": "URi-m8QTCZEU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "2f6ec861-a648-45d6-a051-f58a7c410ae6" + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00001,\n", + " steps=100,\n", + " batch_size=1\n", + ")" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 236.32\n", + " period 01 : 235.11\n", + " period 02 : 233.90\n", + " period 03 : 232.70\n", + " period 04 : 231.50\n", + " period 05 : 230.31\n", + " period 06 : 229.13\n", + " period 07 : 227.96\n", + " period 08 : 226.79\n", + " period 09 : 225.63\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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max189.7500.0
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" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 13.2 207.3\n", + "std 10.9 116.0\n", + "min 0.0 15.0\n", + "25% 7.3 119.4\n", + "50% 10.6 180.4\n", + "75% 15.8 265.0\n", + "max 189.7 500.0" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 225.63\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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IiIiIVEFBfl48dWsnBkQ24FjKGV5YFMfWn48bHZaIiEiFUlJCREREpIpys5i5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\npFMSdF3ChaqEbaoAVRUju+ONdvpXQ+/40u+8MyQcE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predictionstargets
count17000.017000.0
mean87.2207.3
std71.9116.0
min0.115.0
25%48.2119.4
50%70.2180.4
75%104.0265.0
max1251.9500.0
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" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 87.2 207.3\n", + "std 71.9 116.0\n", + "min 0.1 15.0\n", + "25% 48.2 119.4\n", + "50% 70.2 180.4\n", + "75% 104.0 265.0\n", + "max 1251.9 500.0" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 175.66\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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XJSFRzdWv5cszI8PR6dS8v+4oh06lODskIYQQotJJUkIIIYRwIFPqFU7eN4nz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V8Hdm/coDaxVFSVE2dlth30c/5qqGtj4hiJVdkGxo/UDnkxQlFUSk818mGBnQMlTmQZjAYtq5Ym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QyTaCARVzzM4buzvo6k5kRSyYZ2XzujRWrXB/pHr9hqYAf3qtmcp9Xk72jwpVFCgusCYDEZ60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\necfppl5meSYmOWDAlAhKeDwe2tv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predictionstargets
count17000.017000.0
mean168.2207.3
std135.1116.0
min0.415.0
25%93.0119.4
50%137.3180.4
75%202.5265.0
max4198.6500.0
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" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 168.2 207.3\n", + "std 135.1 116.0\n", + "min 0.4 15.0\n", + "25% 93.0 119.4\n", + "50% 137.3 180.4\n", + "75% 202.5 265.0\n", + "max 4198.6 500.0" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 184.64\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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KUr/U67ZdZfSLD+XhX3dvkSVmhRC+a38iIF/sp0LcQgghRFsmOYZtVGudUtFi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predictionstargets
count17000.017000.0
mean168.0207.3
std134.9116.0
min0.415.0
25%92.8119.4
50%137.2180.4
75%202.3265.0
max4193.5500.0
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" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 168.0 207.3\n", + "std 134.9 116.0\n", + "min 0.4 15.0\n", + "25% 92.8 119.4\n", + "50% 137.2 180.4\n", + "75% 202.3 265.0\n", + "max 4193.5 500.0" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 184.56\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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CiAZX0+Sy3Z9JCYDbe4Sy93gOP+1Io3NrqZYQQlz75JaQuKia4RJOGsldCdHY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\nU1M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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "bE7iVWoyC5kt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 2089fd02f816acd4e7c7828abb94f6fbf017ef96 Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Thu, 14 Feb 2019 00:23:08 +0530 Subject: [PATCH 03/11] Synthetic and Features and Outliers --- SyntheticFeaturesAndOutliers.ipynb | 1214 ++++++++++++++++++++++++++++ 1 file changed, 1214 insertions(+) create mode 100644 SyntheticFeaturesAndOutliers.ipynb diff --git a/SyntheticFeaturesAndOutliers.ipynb b/SyntheticFeaturesAndOutliers.ipynb new file mode 100644 index 0000000..ecac3a9 --- /dev/null +++ b/SyntheticFeaturesAndOutliers.ipynb @@ -0,0 +1,1214 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "SyntheticFeaturesAndOutliers.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "9GwUSJzHIHp5", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U5LKCCSkJmim", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Synthetic Features and Outliers" + ] + }, + { + "metadata": { + "id": "V7WpWDUGJyZx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Setup**" + ] + }, + { + "metadata": { + "id": "m56tVhvKJk90", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "87ed7be0-187e-486f-fb15-446a55e3bf60" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import sklearn.metrics as metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))\n", + "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n", + "california_housing_dataframe" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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5366-118.234.039.01099.0263.0787.0269.03.8194.6
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10273-120.038.924.01669.0422.0589.0281.03.0100.8
8084-118.433.925.03521.0852.01524.0764.03.8361.3
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17000 rows × 9 columns

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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "13466 -122.0 37.3 52.0 777.0 148.0 \n", + "3913 -118.0 34.1 33.0 1994.0 405.0 \n", + "5853 -118.2 34.0 33.0 1134.0 375.0 \n", + "10617 -120.5 34.6 8.0 2482.0 586.0 \n", + "5366 -118.2 34.0 39.0 1099.0 263.0 \n", + "... ... ... ... ... ... \n", + "10950 -120.9 37.6 22.0 1440.0 267.0 \n", + "14434 -122.1 37.9 27.0 744.0 214.0 \n", + "8656 -118.5 34.4 16.0 8726.0 1317.0 \n", + "10273 -120.0 38.9 24.0 1669.0 422.0 \n", + "8084 -118.4 33.9 25.0 3521.0 852.0 \n", + "\n", + " population households median_income median_house_value \n", + "13466 362.0 144.0 4.0 262.5 \n", + "3913 993.0 403.0 3.8 163.9 \n", + "5853 1615.0 354.0 2.1 141.7 \n", + "10617 1427.0 540.0 3.1 120.4 \n", + "5366 787.0 269.0 3.8 194.6 \n", + "... ... ... ... ... \n", + "10950 774.0 249.0 4.0 204.3 \n", + "14434 295.0 169.0 2.7 350.0 \n", + "8656 3789.0 1279.0 6.8 323.3 \n", + "10273 589.0 281.0 3.0 100.8 \n", + "8084 1524.0 764.0 3.8 361.3 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "id": "LyBvoWggJ1YB", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "6TUpHAWEJ6Ci", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \n", + " Returns:\n", + " A Pandas `DataFrame` containing targets and the corresponding predictions done\n", + " after training the model.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label].astype('float32')\n", + "\n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + " \n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Create a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n", + " \n", + " return calibration_data" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "w6ftq31tKBpd", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Task 1" + ] + }, + { + "metadata": { + "id": "IFHhMe9KJ9-M", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1074 + }, + "outputId": "c9a31fbe-995b-4e09-c469-ee3dfaca3a9e" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] =(california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n", + "\n", + "calibration_data = train_model(\n", + " learning_rate=0.00005,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\"\n", + ")" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 237.51\n", + " period 01 : 237.49\n", + " period 02 : 237.46\n", + " period 03 : 237.44\n", + " period 04 : 237.41\n", + " period 05 : 237.39\n", + " period 06 : 237.36\n", + " period 07 : 237.34\n", + " period 08 : 237.31\n", + " period 09 : 237.29\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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predictionstargets
count17000.017000.0
mean0.3207.3
std0.1116.0
min0.115.0
25%0.2119.4
50%0.3180.4
75%0.3265.0
max6.2500.0
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" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 0.3 207.3\n", + "std 0.1 116.0\n", + "min 0.1 15.0\n", + "25% 0.2 119.4\n", + "50% 0.3 180.4\n", + "75% 0.3 265.0\n", + "max 6.2 500.0" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 237.29\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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SJ9gm53NmtroHZD7eA/vjPbA/3gPLMChB1ARd2c9suQJFqkqIRbUPs8dyruP8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\n3XffNdhea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predictionstargets
count17000.017000.0
mean139.1207.3
std64.4116.0
min31.015.0
25%113.8119.4
50%136.9180.4
75%156.6265.0
max3072.6500.0
\n", + "
" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 139.1 207.3\n", + "std 64.4 116.0\n", + "min 31.0 15.0\n", + "25% 113.8 119.4\n", + "50% 136.9 180.4\n", + "75% 156.6 265.0\n", + "max 3072.6 500.0" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 138.42\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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nqnNf1r/ZnMZ7n51Ar9fw+APN6NUt2Nsh1RlWm8JHS0/y7Y/p6HUa\nbhkdzYghkehkdYS4AI1Gw/gBLXE4FX7YfYrXvtjD1PGd8TPJ9E4IIUTtIZ9addDSzYfZtDPV87PN\n8b+ifxm5ds/vJgxqVeOxico589bHWJOOEnHbGCzdOlZoDN3+rWjTUlCatsMd06FyAakq5J4sqiNR\nLxyMNdNtIcem5WCaCZ1WpUMDG0YfeSezOVTeW2XlxFk33droGTPAhLYOJSQUt8qiL06y+tuiDhsz\nHmpBrHTYqDL7k/KZ/14yZ9MdNG1kZvJdMTRrIp0VROk0Gg23xMVid7rZ/scZ5i/by5RxnWRVpBBC\niFrDR6byoqrYnQqJSWmXfFxiUjqj+7aQSUstYjuawsl/v48hMoxGMyZVaAxN1hl0ezaj+gXguur6\nyt9mYc0ERz4Y6hUVt6wBVqeG30+bUVVoF2WnntE3Wn/anSrvr7KSfNpNl1g94wfWrYTE+R02GjUw\nM3NKC+mwUUUcTjeLV5xi1YZzaIBRQyO58YYGGAw+svxH+DytRsMdw1rjdCnsPJTGguX7eGh0Rwx6\neQ0JIYTwfZKUqGNy8u1k5tov+bisPBs5+XYiguUqXG2gqirJ0+eg2h00ff4x9IEV6GyhuNBvW4bG\nreDsMQLMlbzC7akjoYeghjVSR8L539afTreGK8LshPj7RqcNh1Plg9U2jp5y06mlnpsGm+pUMcK/\ndtiY9mAz6vnLx0dVOHK8kNffS+bESRtRESYm39WU1i1rpnONqFt0Wi33/KMdjuX72Hskg7e//p37\nR7RH7yv3tgkhhBAXIZ9UdUxQgImQwEtfvQy2mD0FMIXvO/np1+Ru/Y36g/oQPGxghcbQ7f0ebdZZ\nlJZX4m4UW7mAStSRaFiUmKhmbhX2nzVT6NTSKMhJwyBXte+zLJwulQ/X2jicqtC+uY6b40x16v7/\n5BOFTJt1iKPHrQzqE8rTj7SUhEQVUBSVL1ad5olZBzlx0kZ8/zBee661JCREpeh1Wh4c2Z42TYNJ\n/DOd99cewO32jdVkQgghxMXIzLKOMRl0dGkVXqKmxIV0aRUmt27UEs6MbPY/Pgetvx9NZz9RoaKJ\nmrQUdH9sQa1XH1e3IZULyAt1JFQV/kw3kmXVEervokWoo9r3WRYul8qidTaSUhTaxOiYOMSMTld3\nEhLnd9i4ZXQ0o4ZKh42qkHraxuvvJXP4WCGhwQYm/bMpndtXsuCsEP9l0Ot4aHQHXl26hx37z2LU\na7ltSOs6dTuZEEKIukWSEnXQ+AEtAdh7JIO0LCsmY1HyweFUCLaY6dIqzPOYsrA7FWkn6kUnnv83\nzoxsmvzrEUyNoso/gNOBfttXoIKz92gwVHKFjBfqSKTm6Dmda6CeUaFNpG+0/lQUlY/X2ziQrBDb\nRMdtQ83o61BCYv33aSz8tKjDxmP3N6N3d+mwUVlut8q679L4ZNlJHE6Vvj1DuGtCIwLqyUexqFpm\no54pYzsxb0kiW/aexmjQMWHQFZJUFEII4ZNkJlQH6bRaJgxqxb2j/TiSnOG5TaO8iYXi1qKJSWlk\n5tqlnagX5G79jfQv1hDYpR2Rd4yv0Bj6xG/R5mXiatsbNTKmcgF5oY5EeoGOIxlGjDo3HRrY8YW6\nbYpb5dMNNv44qnBFYx3/HG7GoK8bk/3zO2wEWvQ8+bB02KgK59LtvPHBcX4/mI8lQMeUu5vQU1qp\nimrkb9YzdXxnXlqcwHe7UjEZdIzu21wSE0IIIXyOJCXqMLNRX6KQZXmLWv61tai0E61ZbpudY9Pn\ngFZLx7efx6Ev/5+r5tRhdId24A4KR+lcsVoU/wvIBTn/fT3UUB2JPLuW/WdNaDXQPsqOWe/9e6Pd\nbpXF39rZe1ihRUMtd9ShhITNrvDau8n8migdNqqKqqps3prJ+5+fwGpz071zEA/c1oT6QQZvhyYu\nAwF+Bh4b35kXFyey7pfjmAxaru/dzNthCSGEECX4wDVH4YtKay2amJSO3ekbXQ/qslPzP8B+NIXI\nu24kqGu78g9gt2LYvgJVo8XVezToKvElSFUh91RRYqKG6kjYXRr2nTbhVjW0ibATaHZX+z4vxe1W\nWbLJzu4kFzENtNx5vR9GQ91ISGRmOZj54p/8mphDhzYWXnyqlSQkKik7x8mcN47ynw+PA/DQHU2Z\n8VBzSUiIGhUUYOLxGzsTGmhmxZZjfPtrirdDEkIIIUqQlRK1VHXXeSittai0E61+hYeOcPo/H2Fs\nGEWjx++r0Bj639aiKczF1WkAamjDSgaUUaN1JBQ37DttwqFoaR7iIDzA+0kwt6ry5WY7uw66aBKp\n5e5/+GEy1o2ERPKJQl54/QjpmU4GXhPKvbc2xuAL98nUYtt3ZvH2xyfIzXfRvnUAD93RlIgwSfII\n7wgJNPP4TZ158bMElmw+jNGgo1+XSn4uCCGEEFVEkhK1zF/rPARbjLRuGsKEwVfgb6q6q2/FrUUz\nLpCYkHai1Ut1u0l+/AVUl0LT2U+gq1f+5I/2+B/oju3BHdoQpf21lQvIWQgF52qsjoSqwoFzJvId\nOqIsThrXd1br/soWk8ry7+38ut9Fowgt94zww2yqGwmJhH1FHTasNumwURUKCl0s/CyVH7dnYjRo\nuOOmRgwbGI62DrWJFbVTRLA/j93YhZcWJ/DJhkMYDVp6tW/g7bCEEEIISUrUNn+t85CZ5+Dn38+Q\nkJTGNR0bVFkRytJai0o70eqV9tkK8nfuJXj4QIIH9yn/ANY89DtWoer0RbdtaCvxXHmhjsTRTAPp\nBXrqmxVahTu83mlDVVVW/uRg++8uosO03DvCD786kpAo7rCh02l47L5m9L5KCi9Wxu4/cvnPB8fJ\nyHLSspk/k++KoVEDs7fDEsIjOqweU8d3Zu7iRN5fewCjXke31hHeDksIIcRlTpIStUhpdR5sDqXK\ni1AWtw1NTEonK89WoXaionwcZ9M58cIb6Cz1aPp/j5V/AFVF/8vXaOyFuLoNRQ0Kr3gwXqgjcSpX\nz4lsI34GN+2ibHj74rKqqqze6mDrHidRoVruHemHv7n2JyQUt8rHX5xklXTYqBI2u8KiL06y/vt0\ndDq4aUQDRg+LQleHWsSKuqNJpIVHxnfi5SW7eWfVHxj0Wjq1rJn2zkIIIcSFSFKiFimtzkOxxKR0\nRvdtUSUrGYpbi47u26Ja61eI/0l55hWU3HyazpmOMar8CQXtkQR0qYdwRzZDaX115YIpriNhrJk6\nEmdzVP5MM6LXqnRoYMPbLzVVVVn3s4MfE51EBmu4b6SZAL/a/yXz/A4bDRuYeHpKSyloWQkHD+cz\n/73jnD5np3G0mcl3x9CiqdTbEb6tRXQQU8Z05LUv9rBgxe9MGduRtjEh3g5LCCHEZUoqmdUixXUe\nSlNchLIqmQw6IoL9JSFRzbK/20rm6o0EXNmRiImjyj9Afhb6nd+gGkw4e40CTSX+vB3n1ZEIrP46\nEgUODduTitp9to+y4W/wfuvPDTscbN7lJKy+hvtG+WHxr/1vl5nZzhIdNl56KlYSEhXkcLr59KuT\nPDUniTNpdm6Ij+DlZ1tLQkLUGrFNgpk0ugOgMv+rvfyZmu3tkIQQQlymav8s+zJSXOehNFKEsnZS\nCq0kz3gJjV5HzLwn0ZS3LojqxvDzcjROO67uQyGgfsWDcbsgt+bqSDgU2HfajFOB2AgH9f283/pz\n468ONv7qJDRQw/0j/QisV/vfKpNPFPLErIMcOV7IwGtCefqRFtTzl8VyFZF8opC7H03gq7VnCQ81\n8vy0K7h9XCOMhtr/OhGXl/bNQrn/hva4XCr//nIPx07nejskIYQQlyGZQfkQu1PhXFYhdufF2x+O\nH9CSQd0aYTZeeNWCFKGsnU7OewdH6mmi7r8V/9blr9mhO/gL2rPJKI1a427epeKBlKgjEVHtdSTc\nKvxxxozNpaVNQ4iyuKp1f2WxeZeD9b84CLYUrZCob6n9b5MJ+3J4ck4S6ZlObhkdzYP/bCItPytA\nUVS+WnuGx//vEEeSC7iubxivPdeGdrEWb4cmRIV1aRXO3de3xWZXeHXpblLP5Xs7JCGEEJcZuUzm\nA/7a5jMk0ESXVuEX7KRRXOdhRJ/mfL4xiYMpWWTl2aUIZS1WsO8gZ977HFNMIxpOubPc22tyzqFL\n2Ihq8sfV44bK3WpRoo5EaMXHKQNVhUPnTOTYdITXc9GukYH09Grd5SX9mOhg7TYHQQEa7h/lR0hg\n7f/ivv77NBZ+dgKdVjpsVMapszbmv3ecQ0cKCA4y8OSUWFo2NXo7LCGqxNVtI3G4FD5cd5CXl+5m\n+s1diQqRW5GEEELUDElK+IC/tvnMyLVfspOGv0nPncPbYncqpGVbQVUJD/avknagouaoikLytNmg\nKMS8OAOtXznbB7oV9NuWo3G7cPYYC34BFQ+mhutIHM82cDZfj8Wk0DrCjkbj3S94W/c4WLXFQWA9\nDQ+M8iM0qHb/Lf21w8aMh5rTumUlXh+XKVVVWf99Oou+OInd4eaaq4K5+5bGtGgWTFpanrfDE6LK\n9OkYjcPp5rONScz7PJHpN3clvL6ft8MSQghxGZCkhJeV1ubzUp00FLebr348UqYVFsI3nf3wCwr2\n7Cd09BCCri1/twzdvh/RZpxEad4Jd5O2FQ+kRB2JRtVeR+Jcvo7kTCMmvZsOUTZ0Xn65bv/dyYof\nHVj8Ndw30o+w+rX77+evHTZmTm5JVITUmimv9EwH//nwOHv+yCOgno5Jd8RwzVXSoUDUXQOvbITD\npfDl90eYuziRJyZ0IUwSE0IIIaqZJCW8rLQ2n8WdNCKCL7yEsiIrLITvsJ88Q+pLb6ELDqLJvx4t\n9/aajJPo9v2I6h+Iq/uwigeiqpB78rw6EtW7ZDfHpuXAORM6jUqHKBtGL78L/brfybLNdgL8itp+\nRobU7oREZraT2a8f4cjxQjq0sTDtgWYE1JO3+vJQVZUff8lk4aepFFoVunYI5MHbmxASLLdriLpv\nyNVNcbncrNhyjLmfJzJtQhfCgiQxIYQQovrU7tl3HVBam8/SOmlcaoVFacUyhfepqsrxp+biLiik\nydOTMYSW8z5/lxP9tq/QqO6i9p/GSkwYCzPAUQDGgGqvI2F1avj9tBlVhbZRdgJM3m39ueugky82\n2fE3w70jzUSF1u4iscdTrX/rsCEJifLJyXUy981jvL7wOG63yv23NWHmlBaSkBCXlet7N2NEn2ak\n59iYuziRjBybt0MSQghRh8ls1cuK23yev+KhWGmdNNKyCsm4xAqLRlUaqahKWd98T/a3P2HpdSVh\n468v9/a63ZvQ5qShxF6N2qBFxQNxFJxXRyK6WutIuIpbf7o1XBFmJ9Tfu4mzxCQnn2+0YzbBvSP8\niA6r3QmJhH05vPzWMaw2N7eMjmbU0Eg01VwXpK75NTGbNxelkJProm2rAB66o6nc9iIuW//o3QxU\nWLn1GC8tTuCJCV0JDSpn3SMhhBCiDCQp4QOKO2YkJqWTlWcrtZPG+Z06Lqa0FRbC+1y5+RyfOQ+N\n0UDMizPK/cVRc+YYugPbcQeG4up6XcUDcbuKbtuAaq8j4Vbhj7MmCp1aGgY5aRjk3dafew+7WLzB\njskA94zwo1FE7U5InN9hY+p9UvegvAoKFT74/ASbt2Wi12u4fVxDhl8XgU4rSR1xefvHNc1Qga+3\nHmPu50WJiZBASUwIIYSoWpKU8AHFbT5H921BTr6doADTRVdI/LWOxIWUtsJCeF/qiwtwnkmj4WP3\n4tcypnwbO2wYfl4OGnD1Gg36Ci4pr8E6EqoKh9ONZFn1hPi7aBnqqLZ9lcXvR118st6GQQ933+BH\nk8ja+7eiuFU++fIkX284R2CAnhkPS4eN8tp3II83PjhOWoaD5k39mHxXDE0ayv3zQhS74ZpmqKrK\nqm3JzF1cVGNCEhNCCCGqkiQlfIjJoLtoUUsovY4EQIjFRNfY8AuusBC+IX/XPs4tWoa5ZQwNHryt\n3Nvrd61HU5CNq31f1PDGFQ+kML3G6kiczNFzKtdAPaNC20h7dXcaLdWBZBcfr7Oh18Jd//AjpkHt\nTUjY7Ar/fjeZHdJho0LsdjeffHWStZvS0Gph3D+iGDu8AXq9rI4Q4q+KEhOw+mdJTAghhKh6kpSo\nRUrr1KHRwJRxnWgULldJfZXb6eLYtBdAVWk27ym0pvKtctCeOIju8C7cwVEoHftVPBBHARSk1Ugd\nifQCHYczjBh1bjpE2dF7sbTuoRQXH621odHAndebad6w9iYkpMNG5SQdLWD+e8mcPGOnYQMTk++K\n4Ypm9bwdlhA+S6PRMKJP0a0ca35OZu7niTwxoSvBFkmECiGEqDyZxdYixZ06LlTgMsRiJlx6ifu0\nM+98ivXAYcInjMBydZdybeu25qP/5WtUrQ5X79Ggq+Cfbg3Wkci3a9l/1oRWA+2j7JgN3uu0cfiE\niw9WF1WPv+N6My0b1963vuOpVmb9+zDpmU4GXBPKfbc2xuDNbE8t4nS5+XL1Gb5aewa3G64fHMHN\no6MxGeX8CXEpGo2GkX2KbuVYu/04cxcnME0SE0IIIapA7Z2ZX4Yq2qlDeJ/teCqnXl2IPiyExjMf\nLt/Gqopt0zI0tnxcXeNQg6MqFoSqQk7N1JGwuzTsO23CrWpoF2kj0Oyutn1dytGTCu+vtqGq8M/h\nZmKb1N63vcTfc5n35lHpsFEBx1OtzH8vmaMpVsJDjTx0R1M6tLF4OywhahWNRsOoa5sDSGJCCCFE\nlam9s/PLVHk6dQjfoKoqydNfxG2z0+yVp9HXDyzX9tpje3H9uQd3RFOUNr0qHkhhOjirv46E4oZ9\nZ0zYFS3NQhyEB3iv9WfyaYX3VllxueG2oWbaxNTetzzpsFExiltl1YZzLF5xCpdLZeA1odxxUyP8\n/SSJK0RFFCcmVBXW/XL8v7dydKG+dP0SQghRQbV3hn6ZKk+njtLYnUqlthdll7FiA7k//kJQv56E\njIgr38YFOeh/WwMGI85eo0BbwWXmNVRHQlXhwDkT+XYdURYnTeo7q2U/ZZFyVmHh11acLpg4xEz7\n5rXz7c7tVvlYOmxUyJlzdua/n8yBPwsICtTz4O1N6N65vrfDEqLW02g0jO7bHFVV+WZHiqf4pSQm\nhBBCVETtnKWLS3bqKPbX5IPidrN082ESk9LIzLUTEmiiS6uijh26in7hFRflysoh5dlX0JpNxLw4\nvXxL7VUVw/aVaBw2zIPGYrdU8Mr4+XUkgqq3jsTRTAPpBXrqmxVahTu81mkj9ZzCuyut2J1wc5yJ\nji1r51ud3e7mtYXH2JEgHTbKQ1VVNv6YwYdLU7HZ3fS8sj73TmxMUKDB26EJUWdoNBrG9GuBCqzf\nkcK8zxOZdlMXgiQxIYQQopyqdaZus9kYPnw4DzzwAD179mTatGkoikJ4eDjz5s3DaDSyatUqFi1a\nhFarZdy4cYwdO7Y6QxyUVDUAACAASURBVLpsKG43C1fuY9uekyWSD6qq8t2uk57HZeTaPTUqJgxq\n5a1w66wTs+bjysii8VMPYWrSsFzbapN+Q3v6MO7oKzB06AXp+eUP4Pw6EgERYKi+OhKnc/WcyDbi\nZ3DTLsqG1ksJiVPpCu+stGKzw03XmejSqnZ+Ec3MdjJn/hEOJxfSvnUATzzYXDpslEFmloMFH6WQ\nsC+Xev46ptwdw7U9gqX2hhDVQKPRMLZfC1Bh/a8pzP08kWkTuhJUr3zdpYQQQlzeqvXS+FtvvUVQ\nUBAA8+fPZ8KECSxevJimTZuybNkyCgsLWbBgAR999BGffPIJixYtIjs7uzpDqjXsToVzWYXYnRW7\nH3/p5sOs2nKUjFw7Kv9LPmzbd+aCj09MSq/wvsSF5f6SQNrnX+PX9goi77m5XNtqcjPQ71qPavTD\n2XNExb9QnV9Hwq/66khkWbUkpRnRa1U6RNnw1h1BZzIU3l5updAG4waZuLJ17UxIHE+18sSsgxxO\nLmTANaE882hLSUiUwZYdmUx+5gAJ+3Lp1M7Cv/+vDX17hkhCQohqpNFoGNu/Bdd1b8zpjELmLk4g\np8Dh7bCEEELUItU2yz1y5AiHDx+mX79+AOzYsYPnnnsOgP79+/PBBx/QrFkzOnTogMVSVAG9a9eu\nJCQkMGDAgOoKy+dVxe0VdqdCYlLaBX9nc1w48ZCVZyMn316mW0LEpbntDpIffwE0GprNewqtoRx/\nam43+p+/QqM4cfYcAf7lK4zp4akjYYDAhtVWR6LQoeGPM2YA2kfZ8Dd6p/XnuSw3b6+wUWCDMQNM\nXNW2diYkzu+wcfOoaEYPkw4bl5Kb7+LdT1L4f/buOzyqMv3/+PtMn/ReIQkt1FAtFBFBUFCB0BEr\nIuraV1Z0Lfv97a5rb9hdFSsiSBdBEAULTYHQSwBJIb1nkkw/5/dHgA2YMiEzySR5XtfFdYXJnDnP\nzCQnc+7zPPdn6++l6HUq7r6lI9deFSZeN0FoJpIknWu4vfH3TF5anMKjNw4QMyYEQRAEl3isKPHC\nCy/w9NNPs2rVKgDMZjM6XfUfp9DQUAoKCigsLCQk5H/r5ENCQigoqP1kuqbgYB80mqZfig0P9744\nuA9WHTgv8vPsDAcfo465yUkuPUZOYSXFJmuj9hsWZKRLQigGXeu8Gutt72Xqv9/CcjKd+HtvptM1\nQxq1rfW3TVgLMtF0H0DAZcPO3d6Y5yg77JScPI6MRFB8N7Q+nnl9rHaF3w8qOGS4tItEQrhvkx7v\nYt/HvCIH/11VhKlK4dYbAhh9edPG4Un1PcdV67N57b0TqNUS/5zfk6uHRzTjyNynOX8ft+8q4vk3\nUikqsZHUM4AnH+5Bhxhjs+zb2447ntAenqPgHmcLE4oC3+/K5OUzhYkAUZgQBEEQGuCRM9BVq1bR\nv39/OnbsWOv3FaX2K6l13X6hkpKqix7bWeHh/hQUmJr8OO5ktTvZui+r1u9t3ZfNuMs6upSU4bQ7\nCfbTUWz68/RJvVaF1S7/6fa+XUIxlZnxrlfENd72XpqPp3Hi+ffQRkcQ9tCdjRqbVJyDdtt6MPpR\n2W8slWe2bdRzVBQozQCHHfwiKa0EKt3/+sgK7Ms2UGlVExdkwxc7LtQU63Sx72NRmczby82UVShM\nGK6jX2fZq34eaqrrOdaesGH02udRn+b6fTSbnXy85DTf/1yERi1xy9QYJo6NRK1yNMv+ve244wln\nn6MoTAiukiSJmVd3RUFh067TvPTVmcKEjyhMCIIgCHXzSFFiy5YtZGZmsmXLFnJzc9HpdPj4+GCx\nWDAYDOTl5REREUFERASFhYXntsvPz6d///6eGFKrUFZhpbi89hkOjVleodeq8TXWXpQI9tfTMz6Y\n/SeLKTFZCPY3MCAx7Ny0S6FpFEUh7fFnUWx24p95FLV/I6IbnY7qZRuyE/uQSaC/yKU05/WRuMjE\njgYoChzL11FmURPu66BTSMtEf5aYZN5dUV2QuH6YjhEDWt8H3/MSNqL0PPlwV6JFwka9Dh0z8eZH\n6eQV2kjoYOShufEkdBRLzwTBG0iSxI1XdwMFNu0+fW4phyhMCIIgCHXxSFHi9ddfP/f1m2++SWxs\nLCkpKWzYsIGJEyeyceNGhg8fTr9+/XjqqacoLy9HrVazZ88ennjiCU8MqVUI9NMTEqCnqJbCRLC/\nweWYLavdSZWl9pPE3GIzdodM365hjB7UgZAAg0uzLwTXFH61BtP2PQRdO4KQcSMbta1634+oSvJw\ndr0EOfYik1CaqY9ERqmWvAot/nonPSKsLRL9WVYh8+5yMyUmhbGDdYwa1Po+8JaU2Xl2gUjYcJXN\nLrNoeTbffJ+PBEy5PpIZE6PRakScsSB4E0mSuHF0NxQFfthz+txSDn9RmBAEQRBq0Wyffh944AEe\ne+wxlixZQkxMDMnJyWi1WubNm8ecOXOQJIn77rvvXNPL9kivVTMgMfy8nhJnDUgMq7V4YLU7Kauw\nEuinP/f9+mZcQHWfis17slCrJBED6kb2wmIy/r0Ala8P8c882qhtpfwM1Id/RfELxnHJ2IsbgNMB\n5Wd+dgJjQeWZYlN+hZpTxTr0Gpk+UVbULXA+WF4p884KM0XlCmMu0zLmstb3QTf9tJn/LDhJQZGN\nUcNCuOe2OHFyXY+TaVUs+DCNzGwL0ZF6HpwTT4+ujZiJJAhCs5IkiVljuqGg8OOeLF5avJdHb+wv\nChOCIAjCn3i8KPHAAw+c+/rjjz/+0/fHjh3L2LEXeRLWBp1dRpGSWljv8or6Ujrqm3FRU0pqIVNG\ndBEzJdwk4/+9hrO0nLh//Q19bJTrG9qtaLctBwXsQyeD9iKm7itKdUFCdoJfJGg9M5W93KLiaL4e\ntVQd/anXNH/ShqmqeslGYanCqEFarr289X3AFQkbrnM4FJavy+Xrb3JwOmHcqHBunRaDQS+OW4Lg\n7SRJ4qYxiSjA5j1ZvPzVXh69cQB+xtaZjiQIgiB4hpgn7GXUKhWzRicyZUSXP82AqGnJjydqTekA\nmDU6kX7dwvhxd+1NM88SMaDuU/bTDopWrMe3fy8iZ09r1LaaPRuRTMU4eg1DiUy4uAFUFYK9yqN9\nJMx2iQO5BmQFkqKs+OmbvyBRYVZ4b6WF/BKFEQO0XDdU1+pO5jdsKeC/X2SiVknMuyeBKy7zzPvV\nFmRmm3njw3ROpFURGqzlgTvi6df7IiNyBUFoEZIkcfOYRFBgc0oWLy9O4W+iMCEIgiDUIIoSXkqv\nVddZLLDanaSk1h5zcHb2gyunaY3pUyHUzVllIe3x50CtJuHFJ5HUrl/BlbKPo079DTkwAmf/qy9u\nALYKj/eRcMhwMNeA3SnRNcxKqK/T7ftoSJVF4f2VZnKLZK7op2X8Fa2rICHLCm8vPMniladrJGyI\n5Qe1kWWFbzcV8MXyLGx2hauGhHDnTR3w9RF/sgShNZIkiZuuSURRFLbszeblr1L420xRmBAEQRCq\niU94rVBBSVWdSzNKTBYKSqrYe7yw1u/XVFefCqFxsl//EGt6FlH33IJvn+6ub2g1o922EkVS4bhi\nCqgv4sOZ0wHlZ2bEBHbwSB8JWYHDuXoqbSpiA+x0CHS4fR8NMVsV3l9lJrtQZkiShuQrW1dBQiRs\nuC6/0MqbC9M5eLSCAD8ND9/VkSGDglt6WIIgNJFKkrj52u4owE+iMCEIgiDUIIoSrUjNPhJ1CfY3\ngCTV2+gyyE/HJT0iRAyoG1QdOUHue5+j6xBN7N/uatS2mt/XIplNOPpdjRIS0/id/6mPhLHxj+GC\nE4U6is0aQnwcdAn7c8ysp1msCv9dZeZ0vsxlvTRMvkrfqgoSJWV2nn3jJCdOVTEgKZBH7ooXCRu1\nUBSFH34tYuHi05gtMpcNCOQvt8YRFChOWAShrVBJErdc2x1FgZ/3ZfPKV3v524398TWI33NBEIT2\nTHwybkUu7CNRmwGJYYQHGeuOFvXT8//uuFR0v3YDRZY5Nf8/KA4nCc8/jtrH9aKAKv0g6lP7kUM7\n4Owz/OIGUFlwpo+Ev8f6SJwu05BdrsVXJ9Mr0oqqmWsBVpvCB2vMZOTJDOqhYdooPapWVJC4MGHj\n6Xm9KS2tbOlheZ2SMjvvfprB73vL8DGqeGBOPCOHhrSq4pMgCK5RSRK3ju0OKPy8L4eXv9rL32aK\nwoQgCEJ7JooSrUR9fSQAgv109EwIIXl4p3qjRQf1CBcFCTfJ/2w5lbsPEDJhDEGjhrm+odmEZuc3\nKGoNjmGTL27Jha2iurmlSgsBMR7pI1FUqeZEoQ6tWiYpykJzp1Xa7AoffWMmLUemf6KGmaP1qJq7\nKtIEew+W89K7f1Bl/l/ChlYrIj8vtG1XCe99loGpwklST38euCOe8FBxjBKEtqy6MNEDRYFf9udU\nz5iY2R8fUZgQBEFol0RRopUoq7DWuyRDVhS2H8zlWEYJAxLDGT8sARmJfan5lJisdUaLChfHllvA\n6efeQh3oT9y/5rm+oaKg2b4ayVqF45LrUALDG79zpx3KPNtHosIqcThPj0qqTtowaJs3acPuUFi4\n1sLJLJm+XdTMuqZ1FSQ2bink/S8yUKskHrk7geGXi4SNC1VUOvhgUSY/7yhBp5W4c1YHxo0Kb1Xv\nsyAIF08lSdw2rrow8euBHF5Zspd5M0RhQhAEoT0SRYlWItBPX+eSDICySjvwv2jQX/fnYLU7CfbT\nMbh3FLPGdMNHL/7Qu0v60y/hNFWS8OIT6CLCXN5OdWIP6qxjyFGdcfa4vPE7VpTqxpaK5/pIWB3V\n0Z9ORaJXpIUAg+z2fdTH7lD4eK2F45lOendWc/NYA+pWcqIqywqfLcti9Xf5ImGjHnsPlvPWx+kU\nldjp1smHh+5MIDba0NLDEgShmakkiduv64GCwtYDubyyZN+ZwoT4eCoIgtCeiKN+K1HfkozaWGzV\nkY3FJhvbDubiY9Awa3SiJ4fYbpRs/JmSb3/E79J+hM9Kdn1DUwmaXetQtHrsQyeBdBFT+c/2kdB7\npo+EU4aDuXqsDhWdQmxE+DVv9KfDqfDZOgvHMpz0TFBz61gDanXrKEhYrTKvf5jGjt2lImGjDhar\nk0+XZvHd5kLUapg1KZrJ10W1mvdYEAT3U0kSs8f1BAW2HsytMWNCfEQVBEFoL8QRvxU5u/Riz7F8\nik2NS0FISS1kyoguIgK0iZyVVaQ/8QKSVkOnl55EUrlYWFBktNtXIDls2IdOBt+gxu+8Zh8Jf/f3\nkVAUOJKvx2RVE+lvJy7I7tbHb4jDqfDFdxYOpzlJjFNz23UGNJrWcbJaM2GjTw8/Hruvs0jYuMCR\n4xW88VE6uflW4mINPHRnAp3jfVp6WIIgeAGVSmL2dT1RgG0Hc3l1aXVhwqgXx1FBEIT2QBztWyGn\n3Pj1/SUmC2UVViKCxUlAU5x+8V1s2XnEPDwHY2Jnl7dTH9mBKi8NZ8eeyJ37N3q/TrvN430kThVr\nKazUEGhw0j3c5onemXVyygrvfV3KgZNOunZQc8cNBrStpCBRM2Fj5LAQ/nJbHNrm7grqxex2mcWr\nclj9XR4KkDw2ghsnxaATTT8FQahBpZK447qeKApsP5TLq0v28ogoTAiCILQL4kjfirgSCVqXYH8D\ngX5iKnlTVO4/Qt5HS9B3jiPmwTtc3k4qzUed8j2K3hfH5RMaP8NBUTCdPnGmj0SUR/pI5JRryCjV\nYdTK9ImyNGv0pywrLP7eSsoxB51jVNwxvvUUJGombMyaFM3UG6JEjGUNpzKqWPBhGumnLUSG63hw\nTgK9EkWPDUEQaqdSScy5viegsP1QHq8u3csj00VhQhAEoa0TR/lWoqFI0LPUquq+ABcakBgmlm40\ngeJwcOpvz4As0+n5v6MyuFjgkZ1oti5Hkh3YB08D40WckFUWYK8ynekjEdz47RtQYlaRWqBDo1JI\nirLQnD8msqKw5IfqgkS3OC23X6dDr20dJ/UiYaNuTqfCyvV5LFmdg8OpcM1VYdw+PRajQRyDBEGo\nX3VhohcKsONQHq8t3cdfp/cThQlBEIQ2TBzhW4mGIkGD/HT0Tghh+tXd+GbrKVJSCykxWUQUqJvk\nfvQVVQePETb9BgKuuNTl7dQHfkJVnI2z8wDkuF6N37G1uo+ESqtH9kAfiSqbxKHc6tSD3lEWfHTN\nF/0pKwrLfrSy64iDuEgV824JodJU2Wz7v1iyrPD5sixWnUnYePyBzvTsJq7+n5WZXcX/vZhK6slK\nggO13Dc7jkF9A1t6WIIgtCIqlcSd1/cCBXYczuO1r/fx12miMCEIgtBWiaN7K1FfJGiQn45/3nEZ\n/j46AGaNTmTKiC6odVqcNruYIdFE1tM5ZL34HprgQDo+/bDL20mFp1Ef+AnFJxDHpdc1fsdOe3X8\nJxIBHbtSWuHegoHdCQdyDThkie7hVoKNzRf9qSgKK7dY2XnIQYdwFXMnGvExqKg0NdsQLopI2Kib\nLCt8t7mAz77OxmqTGX55MHNv6oi/n/gzIwhC46lUEnNu6ImsKPx2JJ/Xv66eMWHQiWOKIAhCWyOO\n7K1EfZGgl/SIOFeQqHn/8DBfCgq8/CzPyymKQtoTLyCbLSQ8/zjaUBdTMxz26mUbioxt6CTQGRq7\n4+qCxJk+ElqjH1S4772UFTiYa8BsVxEXZCM6wOG2x26Ioiis/sXGtgMOosNU3JVsxMfg/Us2LkzY\nmH9vZ3HCfUZhsY23Fqaz77CJAH8ND9wRz7DL3L/USBCE9kWtUjF3fPUsw9+O5PP60n08LAoTgiAI\nbY44qrcSTllGURQMOjUWmxMAg07N0KQosTTDg0rW/kDZpl8JuOIyQqde7/J26pTvUZUX4ug+GCW6\nS+N3XFkA9iqP9JFQFDhWoKPMoibM10GnkOaL/lQUhbVbbfyy105UiIp7ko34Gr2/IJGRZeaZ10XC\nxoUUReGn7cV8sOg0VWYng/oG8PS8XijOxkUWC4IrXnzxRXbv3o3D4eDuu+8mKSmJ+fPn43Q6CQ8P\n56WXXkKn07FmzRo+/fRTVCoV06dPZ9q0aS09dKEJzhYmFAV+P5rP61/v5+FpfUVhQhAEoQ0RR/RW\nYsmPJ/hhd9Z5t1lsTlSShFolTo48wVFmIv3pl5D0OhKef9zlVAUp9w80R7cjB4TiHDim8Ts+00cC\nlRY80Ecio1RLnkmLv95Jzwhrs0V/KorC+u02tuyxExEscc9kA34+3l+Q2HuonJfeEQkbFyort/Pu\nZxns3FOGQa/i3tvjGD08lLAQPQUFoighuNeOHTs4fvw4S5YsoaSkhEmTJjFkyBBmzZrFuHHjePXV\nV1m2bBnJycm8/fbbLFu2DK1Wy9SpUxkzZgxBQS7OchO8klql4q4J1c0vdx3NZ8HX+3l4Wj/0OrE8\nVRAEoS0QZ7OtQH3JGymphVjtzmYeUftw+rm3sOcXEfvwHAyd41zbyGZBu20FiiThGDoFNLqGt6mp\nRh8JAjuAyr0fuPIr1Jwq1qHXyPSJsqJuxiPAxt/s/LDLTligxD2TjPj7eP/hZ+NPhfz7tRPY7QqP\n3JXAtPHRoiAB7NxTyoNPH2HnnjJ6Jfrx+r96MubKMPHaCB5z6aWXsmDBAgACAgIwm83s3LmTq6++\nGoCRI0eyfft29u3bR1JSEv7+/hgMBgYOHMiePXtacuiCm6hVKu4a34tLuodzLLOUBcv2YbWJzz+C\nIAhtgfefFQj1Jm+UmCyUVdSdyiFcHNPv+8j/bDnG7p2J+sutLm+n2bUeqbIMZ58rUcI7Nm6n5/WR\niAStsZGjrl+5RcXRfD1qqTr6U69pvqSNTb/b2LjTRkiAxD2TjQT6efehR5YVPl16mnc/zcDXR80/\nH+3G8MEi8rOyyskbH6Xx/Ft/YDY7uX1GLP+e343IcNHsU/AstVqNj48PAMuWLePKK6/EbDaj01UX\nfkNDQykoKKCwsJCQkP/9roaEhFBQ0HCcttA6aNQq7prQm0HdwzmacaYwIS7MCIIgtHpi+UYrEOin\nJ9hfR7Hpz1Oig/z0BPqJEwJ3km120ub/B4CEF55ApdO6tJ0q8wjqk3uQQ6JxJl3V+B1X5p/pIxHg\n9j4SFrvEgVw9sgJJUVb89M1XkNi8x8b67TaC/SX+MtlIsL93FySsVpkFH6axfXcpMZF6nnq4C9GR\njWxU2gbtP2LizY/SKCy20zneyMN3JtAx1r2FM0FoyKZNm1i2bBkLFy7kmmuuOXe7otR+TKvr9pqC\ng33QaDyzDCA83N8jj9vePTVnMC9+vovtB3J4d/Uhnp5zea09JsTr37LE69+yxOvf8sR74DpRlGgF\n9Fo1vsbaixK+Rq2I/HSz3Pc+x3zsD8JvmYz/Zf1d28hSiWbHahSVunrZhrqRv1rWCqgqArUW/KPd\n2kfCIVdHf9qdKrqGWgn1bb6rSj/vtbH2VxuBvtUFiZAA7y5IlJTZee6Nkxw/VUXv7n48dp9I2LBa\nZT5flsW3PxSgUsGMCVFMvSEajUYs1RCa1y+//MJ7773Hhx9+iL+/Pz4+PlgsFgwGA3l5eURERBAR\nEUFhYeG5bfLz8+nfv/7jeElJlUfGGx7uLxKwPGj22O5YrQ72pBbwj/e28eDUvud9HhKvf8sSr3/L\nEq9/yxPvwZ/VV6Tx7jMEAajuKVFlqT0hocpiF1MX3chyKpOs1z5EGxFKxycecG0jRUGzcw2SpRJn\n/9EowZGN22nNPhIB7u0jIStwOE9PpU1FTICd2MDmi/7cut/O6p9tBPhK/GWKkdBA7z7cZGSZeeyZ\nYxw/VcVVQ0P4v3ld231BIvVkJY/8vyN8+0MBsdF6XniyOzOTY0RBQmh2JpOJF198kffff/9c08qh\nQ4eyYcMGADZu3Mjw4cPp168fBw4coLy8nMrKSvbs2cMll1zSkkMXPESjVnHPxN4M6BbGkfQS3ly+\nH5v4PCQIgtAqte9P3G5ktTspq7AS6Kd3+8yF4nILRXX2lLBSVmElItjHrftsjxRFIe2x51CsNuL+\n9Tc0ga5NuVKd2oc64zByRDzOnkMbu1MoP32mj0SU2/tInCzSUVylIcTooGuYrdmSNnYctLNiixU/\nY3VTy/Ag7y5IiISN89kdMkvX5LLi21wUYPw1Edw0OQa9zrvfR6HtWrduHSUlJTz88MPnbnv++ed5\n6qmnWLJkCTExMSQnJ6PVapk3bx5z5sxBkiTuu+8+/P3F9Nm2SqNW8ZfkPry76iApxwt5c/l+HpjS\nF52YQSoIgtCqiKJEEzllmSU/niAltYDicishAXoGJIYzY1RXt0V1btqVWef3gv0NoqeEmxQtX0f5\nr78RePUwQsaPdm2jyjI0v32LotFhHzoZGvueV+aD3eyRPhKnyzRklWnx1cn0irKiaqZz7N+P2Fn2\noxVfA9wz2UBkiHefyG78qZD3P89ApZJ45K6Edt/QMv20mQUfpnEqw0x4qI4H58TTp4c4qfNWVqvM\nuh8L+OGXQu68qSP9ewe09JA8YsaMGcyYMeNPt3/88cd/um3s2LGMHTu2OYYleIGzhYl3Vh5k74lC\n3lxxgAcmJ7X0sARBEIRGEEWJJlry4wk27Tp97v9F5dZz/581OrHJj2+1O9l/sqjO73eJDaCg1Ex4\nkFH0lmgCe1EpGf/vNVRGAwnPPubaVXJFRrttJZLdgv3yCeDfyJNZq8ljfSSKKtWcKNShVVcnbWia\nqS6w+6idJd9bMejhnklGokO992dSlhW+WJ7NyvV5+Pup+fsDXejZza+lh9VinLLCmg35fLkyG4dD\nYfTwUGbP7ICP0Xvfw/bMbpf5/ucilq3NoaTMgY9RjUHv3QVAQfAUjVrFvZP+V5h4a8UB/nl3I2cu\nCoIgCC1GFCWawGp3kpJae9RYSmohU0Z0aXKhoL44UIDfjuTz25F8DDo1w5KimHl1N7fN0GhPMv+9\nAEdxKR3/8TD6jjEubaNK/R1V7kmcMd2QuzVyzbLTDuXZeKKPRIVV4nCeHpUEfaIsGLTNk7Sx77iD\nxd9b0evg7klGYsK992RWJGycLyffypsfpXHkeCVBARruvT2OS/sHtfSwhFo4nQqbtxWxdE0uBUU2\nDHoVU2+IYuK1Efj5ij/pQvt1dsbE2ysPsP9kEf/6aAd33dALo178XgiCIHg7caRugvoKBiUmi1t6\nPQT66QkJ0NfZU+Isi83JD7uzkCTJLTM02pPyrbsoXPoNPn26E3XnTJe2kcqL0OzegKIz4hgyqXGz\nHGr2kfB3bx8Jq0PiQK4BpyLRK9JCoEF222PX58BJB198Z0GngbuSjXSM8N6CRGmZnWdFwgZQ3Udl\nw5ZCPl2ahcUqM+SSIO65JY4A//b5engzWVbYtquExStzyM6zotVIjL8mgsnXRRIU4FpssSC0dVqN\nivsmJfHe6uoeEy8tTuGv0/vh76Nr6aEJgiAI9RCfPJugvoKBu3o96LVqBiSGn7dEpD4pqQUuz9Dw\nZHPO1kK2WDn12LOgUpHw0pNIGhd+JWQnmq3LkZx27EMngU8j19vX7CNhcF8fCacMB3P1WB0qOoXY\niPBrni7kh085+Hy9BY0G5iYbiY/y3p+ljCwzz7x+koIiG1cNDeHe2+PQNtfaFi9TVGLj7Y8zSDlY\njq+Pmr/elcDwy4PbdYNPb6QoCrv2lfHlyhzSMs2o1XDNVWFMuyGKsBBxoiUIF9JqqpdyLNn8B5t+\nz+C5L/Ywb0Z/QgPb72w4QRAEbyeKEk1QX8FgQGKY2070Z4zqClQvCSkqt9R73+IL0jhqKzw0R3PO\n1iL7jY+x/pFB5J034tevl0vbqA/9iqowE2dCEnJCI5tpnesjoXNrHwlFgaP5ekxWNZF+duKCao+Q\ndbejaQ4++daCSgV3TjDSKdp7CxI1EzZuTI5m2vj2mbChKAq/7izhv4syqah0MqBPAPfNjiM0WJzg\nepv9h8tZtCKb1D+qkCS4akgI0ydGEx0hmhsLQn3UKhUPzuiPRgXf7czg2S92M29Gf2LCfFt6aIIg\nCEItRFGiiWoWIkJe3QAAIABJREFUDEpMFoL9DQxIDDt3uzuoVSqmXtWZI+nFDd43xF9PoJ8epyzz\nwaoDbN2Xda7w0LdLKKMv6cimXZlsTsk+t427m3O2FubUP8h5+xN00ZF0mH+PS9tIxTmo929GMfrj\nuOyGxu3Qg30kThVrKajUEGhw0j2ieaI/UzMcfPytBUmCOeMNdIn13oKESNioVm5y8P7nGWzbVYpe\np+LuWzpy7VVh7bI4482Onqhg0YpsDh6tAGDIoCBuTI6mY6x7I4MFoS2TJInpI7vi76Pl680nee6L\n3Tw8vR9dYgJbemiCIAjCBURRoonUKhWzRicyZUQXjy6F+M9ne8gqqGrwfgMSw9Fr1Xy5KfVPqSCb\nU7LZnJJdZzSku5pztgaKLHNq/rModgfxz85H7efC1ROnA83WZUiyE/uQZNA3ol+IokDZ2T4S0aB1\n3zTS3HINGaU6DBqZ3lGWZon+PHnaycK1FhSluiDRraN3HkouTNh4/P4u9Epsnwkbu/aV8c4n6ZSU\nOejR1ZcH58S36+ae3uiP9Cq+XJnN7v3lAAxMCmDW5Bi6xDetN5EgtGfjLo/Hz6jlk/VHeXnxXu6f\nnETvTu2zMC0IguCtvPNMohXSa9VNbmpZF1OVjayCivr3r1NxRVI0M0Z1rTcVBECuI4zBXc05W4OC\nxaup+G0vwdeNJPjaES5to973I6rSfJzdLkGObeSMksp8cJztI+G+VINSs4pjBTo0KoW+0RZ0zVBP\n+iPbyYffmJFluP16A93jvfMwYrXJLPhAJGxUmZ18/NVpNv1ShEYjceu0GCZcG4m6OapXgksys80s\nXpXD9l2lAPTu7sdNk2PadUStILjT8L4x+Bm0vLv6EK9/vY+543txWc/Ilh6WIAiCcIZ3nk0I5zmd\nX1FnIeGsJ28eRIeI6oaLRWVV9caI1sVdzTm9nS2/kMxn3kDl50v8vx91aRspPx31oV9R/IJxDBrb\nuB16qI9ElU3iYG71SXbvKAs+Os9Hf6bnOPlwtRmHE24bZ6BXJ+88hNRM2OiV6Mfj97fPhI2Dx0y8\n+VE6+YU2EjoaeXhuAvEdxBIAb5Gbb2XJmhx+3l6MrEC3Tj7MmhxDv17+YkmNILjZgMRw5s3oxxvL\n9/P+6kNUmu2MHNihpYclCIIgIIoSrUKHCD9UUt0zHEL8dYTXmN3gaozohfp3C20XSzcy/u9VnGUm\n4v8zH110RMMb2K1ot62o/nLYFNA2onDjoT4SdiccyDXgkCW6h1sJNno++jMzz8l/V5uxO+DmsQb6\ndPHOw0dmlpl/n03YGHImYUPbvhq42uwyi5Zn8833+UjA1BuimD4hqt0mjXibohIbX3+Ty6ZfCnE6\nIb6DgVmTYri0f6AoRgiCB3WPC2b+jQN5belePt+YiqnKzvhhCeL3ThAEoYV551mFcB5/Hx2x4X5k\n5te+hGNg94jzigmNjRE9y/PX2Vte6eZtFK/eiO/APkTcOsWlbTR7NiCZinH0ugIlIt71nXmoj4Ss\nwKFcA2a7io5BNqIDHG553Pqcznfy/iozVjvMukZPv27eeejYd6icF9t5wsaJU5Us+DCd0zkWoiP1\nPHRnAt27iI7z3qCs3M6KdXl8t7kAm10hOlLPjcnRDLs0GJVYTiMIzSI+yp+/3zyIV5bsZdWvpzBV\n2blxTDdU7exvhSAIgjfxzjML4U+evHUgz3y2m9P5leduU6ngsp4RJA/v9Kf7zxjVFR+jjq37shuM\nET1r3/Eipl3lbLOzJZxVZtIefx5Jo6bTi08iqRt+nlL2cdSpvyMHReDsP6pxO/RAHwlFgdQCHaUW\nNWG+DjqHeD76M6ewuiBhscLMMXoGdtd6fJ8X4/ufqxM2JEnir3clcGU7S9hwOBSWrc3h67W5yDJc\nf3U4t0yNRa8XsyNaWmWVg9Xf5fPN9/lYrDLhoTqmT4hi5NBQ1GpxIiQIzS0yxIe/3zyIV5fu5Yc9\np6mw2JlzfU80anG8FARBaAmiKNFKqFUqesQFU2l2UGKyotOoUKlgx6F8jmeWMSAxnBmjuqJWqc7d\nf25yEuMu60hxuYVNu0+z/0QRxabqxITatPVGl1mv/BdbZjbR992GT69uDW9grUK7bSWKpMIxbAqo\nG3Ey7qE+EpmlWnJNWvz0TnpGWD0e/ZlbJPPeSgtVFph+tZ5LenpfQUIkbFQ3SlzwQTon06sIDdby\n4Jx4+vYKaOlhtXtmi5N1PxSwcn0elVVOggI03DI1hjFXhrW7JUWC4G2C/fU8ftNAFny9n52H86i0\n2LkvOQl9c3SMFgRBEM4jihKtxJIfT5y3HMPm+F8PgaJy67nvzRp9fiqEXqsmOtSXW67pjnWkk4JS\nM68v3UuxyfanfbTlRpeVB4+R+98v0cfFEvPXuS5to/ltLZLZhKP/1SghMa7vzGmD8ixAgkD39ZEo\nqFDzR7EOvVomKcqKpy/oFJTIvLfSTIVZYcpIPZf39r6ChNUms+DDNLbvKiU6Us/T7SxhQ5YV1m7K\n54tl2dgdClcNDeHOWR3w9RGH9pZks8ts2FzI8nW5lJU78PNVc+u0GK4bFSFmrgiCF/E1aJk3sz/v\nrjrI/pNFvLwkhYem9sPP6H1/7wRBENoy8cm1FWgo4vOslNRCpozoUufyC71WTYdwPwZ2j6i138SA\nxLA2uXRDcTpJm/8fcDpJeP7vqH0aPmlVpR1AnXYAOawDzt7DG7EzBcqyQJGrZ0ho3HOCXFyhcCRf\nj0pSSIq2otd4tgNIYanMuyvMmKoUkkfoGJrkfR/QSsvsPPfmSVL/qE7YeOz+zgS0o4SN/EIrb3yU\nzqFjFQT4a5h3WxyXD3Rf3KzQeA6Hwo+/FrH0mxyKSuwYDSpmTIhi/DWR+Pq0vWOrILQFeq2a+ycn\n8fG6I2w/lMcLi/bwyIz+BPu3zYs0giAI3qj9fIJvxcoqrC5FfLq6/GLGqK5AdRGjxGQh2N/AgMSw\nc7e3NXmffE3l3sOEThpL4FWDG96gyoRm5zcoam31so3GzHSoyDvTRyLQbX0kLHaJvRkKsgJ9oqz4\n6T2btFFcXl2QKKtUmHCFjuH9dB7d38XIzDLzzIKT5Be2v4QNRVH44ZciPlp8GotV5vIBgdxzWxxB\nAd5XOGovnLLCLzuLWbI6l9x8KzqdRPLYCCaNiyLAX/yZFQRvp1GrmHNDL3yNWjbtOs2zn+9m3sz+\nRIW0zeWsgiAI3kZ8WmoFXI34rLn8wmp3klNYidP+58aVapWKWaMTmTKiC2UVVgL99G1yhgSALTuP\n08+/gzoogLh/PtLwBoqCZscqJJsZ+6XXowSEub4zqwnMxW7tI+GQ4UCuHosduoTaCPN1Nvkx61Ni\nqi5IlFYoXDdUx4iB3leQqE7YOEWV2cnM5Gimt6OEjZIyO+98ks6ufeX4GFU8OCeeq4aGtJvn720U\nRWHHnlIWr8ohM8uCRi0xblQ4U2+IIiRIFIkEoTVRSRI3Xt0Nfx8dK3/+g+e+2M1fp/cjIUr05xEE\nQfC0RhUlUlNTycjIYPTo0ZSXlxMQIA7UzcHViM8BiWFo1BJfbkolJbWAYpOVEH/9n5pg1nzcttrU\n8qz0p15Crqyi08tPoQ1rOI1BdWI36qxU5KjOyN0vc31Hf+oj0fSr9ooCh/P0VNrUdImEDr6ejf4s\nq6guSBSXK4wdrOPqS7yvIFEzYePhuQmMGNJ+Eja2/l7C+59nYKpw0renP/ffEU94qPe9R+2Boijs\n2F3Mux+f5GR6FSoJRl0RyowJUUSEiSnfgtBaSZLE+KEJ+Bu1fL7hGC9+mcIDU/rSMz64pYcmCILQ\nprlclPjkk09Yu3YtNpuN0aNH88477xAQEMC9997ryfEJZ1y45EJ3ZmaD1eYkJOB/yy8ubIhZXxPM\ntq5k/RZKvtuC/+CBhN04seENTCVodq1H0RqwD50MkouFBUWBstNu7yNxokhHcZWGYKOD/glaigrd\n8rC1Kq+sLkgUlSmMvlTLmMu862S3PSdsVFQ6+GBRJj/vKEGnk5h7UwfGjgxHpRKzI1rCoWMmFq3I\n5sjx6njmKy4LZubEaGKj20+DVUFo664aEIufUct/vznEa0v3cveEPgzqHt7SwxIEQWizXC5KrF27\nlqVLl3LbbbcBMH/+fGbOnCmKEs2ktiUXwHnLL+priNlQE8y2xmmqIO2pF5F0WhJeeKLh6e2yjHbb\nciSHrbog4Rvo+s4q8sBhcWsfiawyDVllWny0Mr0jragkzxUJTFXVsZ8FpQojB2kZO9i7ChLtOWEj\n5WA5by1Mp7jUTmJnHx68M4HYqPbx3L3N8VOVfLkim72HTAAMuyyUKdeF0ymubc82E4T26pIeERgN\nGt5afoB3Vh3gtrE9uLJfI5K4BEEQBJe5XJTw9fVFVWNKukqlOu//QvO4cMlFza/ra4jpahPMtuL0\nC+9iz8kn5pG5GLslNHh/9dHtqPLTcXbsidy5v+s78kAfiaIqNccLdWhVCknRFjQerCNVmhXeX2kh\nr1jmyv5arh+q86r+BO01YcNscfLp0iw2bClEo5a4aXIMk8ZFolZ7z3vTXqSfNrN4ZTY7U8oA6NfL\nn1mTYhg2OIqCAlMLj04QBE/qnRDC/FkDeG3pPj5Zf5QKs51xl8d51d9JQRCEtsDlT/dxcXG89dZb\nlJeXs3HjRtatW0eXLl08OTahkepriFmzCWZbV5FykLyPl2LoEk/MA7MbvL9Umoc6ZROKwRfH4Imu\nFxY80EeiwipxOFePJEGfaAtGreeiP6ssCu+vMpNTJDOsr5YJw72rIFEzYWPEkBDuaycJG0eOV7Dg\nwzTyCmzExRp4eG6CuBrfAnLyLHy1OodfdpagKNCjqy83TY6hTw//lh6aIAjNqFN0AH+/eSCvLNnL\nsi0nMVXZmDayKyov+nspCILQ2rlclPjHP/7BZ599RmRkJGvWrGHQoEHcdNNNnhyb0Ej1NcQckBjW\nLpZuyHYHaY8+C4pCwotPoNI3sBRBdqLZugJJdmAfPB0Mvq7tyAN9JGwOOJBrwKlI9IywEGjwXPSn\n2arw39VmsgpkBvfRkDzCuwoS7TFhw26XWbwqh1Xf5QEwaVwkNyZHt4tCjDcpKLKx9Jscfvy1CFmG\nznFGZk2OYWBSQJv/GRQEoXbRob48cfMgXlmylw2/ZVJRZee2cT3QqMXxWRAEwR1cLkqo1Wpmz57N\n7NkNX3kW3MNqdzY6svPChpjB/v9rgtke5H3wJVWHUwmbOYGAIYMavL/6wBZUxdk4uwxA7tjT9R2d\n7SNhcE8fCadcXZCwOlQkBNuI9Pdc9KfFpvDBajOZeTKX9tQwZaTeq674bPq5kPfaWcLGqYwqXv8g\njYwsC1EReh6cE0/Pbu2jkae3KC2zs+zbXDZsKcThUOgQbeDGSdEMHhgkmooKgkBIgIG/3zyI15bu\nY+vBXCotDu6Z2Ptc43FBEATh4rlclOjVq9d5V4kkScLf35+dO3d6ZGDtmVOWWfLjiepYz3IrIQF1\nx3peqGZDTLVOi9NmbxczJACsGVlkvfw+mtBg4p5+qMH7S4WnUR/4GcU3EMcl1zViR+Vu7SOhKHC0\nQI/JqibSz0F8sL1Jj1cfq13hwzVm0nNlBnXXMP1q7ylIyLLC58uyWLEuDz9fNX9/oO0nbDidCivW\n5bJkTQ5OJ4wdGcat02IxGtrH76w3MFU4WPVdHt9uKsBqk4kM0zFjYjRXDglBLYoRgiDU4GfU8uiN\n/Xl75UH2nijk1SV7eXBqX3wM2pYemiAIQqvmclHi6NGj57622Wxs376dY8eOeWRQ7V1jYz1rm1Gh\n16oJD/NtN43YFEUh7e8vIFusJLz8FJrgBtIzHHY0W5cjKTK2IZNB5+LyC6cNyrM510fC1djQeqSV\naCmo0BBocNI9wuqOXpm1stkVPlpj4VS2TP9uGmaM0XvNFWCrTeb/XjzC5q0FREfqeerhLsS08YSN\nrFwLb3yYRuofVYQEabn/jngG9Alo6WG1G2azkzXf57NmQx5VZpmQIC23z4jl6uGhaDViSrYgCLUz\n6DQ8NLUvH649zG9H8nnhyxQemd6v3fTtEgRB8ISLamOv0+kYMWIECxcu5K677nL3mNq1xsR6NmVG\nRVtTvHojZZu3ETBiMKGTxjZ4f3XK96jKC3H0GIwS3dm1nZzXRyLGLX0kck0a0kt0GDQyvaMseKpG\nYHcoLFxr4WSWk6QuamZdo/eaq8Cl5Xaee6P9JGzIssL6Hwv4bFkWNpvClYODmXtTR/x82+5z9iZW\nm8z6HwtYsS4XU4WTAD8Nt8+IZuzIcPS69nXcFATh4mjUKu4a3xtfg5bNKVk8+8Vu5s0cQESQsaWH\nJgiC0Cq5/Cl42bJl5/0/NzeXvLw8tw+ovWtMrGdjZ1S0VfaSMtL/8QqSQU/Cc4832IxOyvkDzdHt\nyAFhOAeMcX1HNftIGJveR6LUrOJYvg7NmehPnYdm7DscCp98a+F4ppNendTcPNbgNdGSNRM2rr0q\ngjk3xrTpxo55BRb++coJ9h8x4e+n5qE74xh6SXBLD6tdsDtkNv1cxNff5FJSZsfHqGbWpGhuGB2B\n0SiWywiC0DgqlcTN1yTi76NlzdY0nvt8N4/M6E/HiLa97FAQBMETXC5K7N69+7z/+/n58frrr7t9\nQO2dq7GersyocKeLabrZXI78/WUchcV0+Pv9GBI61H9nmwXt9hUokgrHsCmgaSCd4yzL2T4S+uo+\nEk1UZZc4mFs906J3pAVfnWeiPx1OhU/XWzia7qRHvJrbxhnQeElBYv/hcl54+0zCxsRo7pvTjcLC\nipYelkcoisLmbcUsXHyayionl/QL4N7b4wkOFOuQPc3pVPhpezFL1uSQX2jDoFcx5fpIksdGitkp\ngiA0iSRJJA/vjL+PjkXfp/L8oj08NLUviR2bfuFCEAShPXH5E9lzzz3nyXEIZ7ga6+nKjIoGTs9d\n4u1LREw7U8j8aCnGnl2JuufmBu+v2bUOqbIMR9JVKGEuvkJOG5jc10fC7oQDOQYcskRiuJVgH89E\nfzqdCl98Z+HwKSeJHdXcfr0BjcY7ChI1EzYemhvPVUNC22zcYmm5nfc+zWBnShlGo5r7Zsdx9RVt\n9/l6C1lW2L6rlMWrssnKtaLVSIwfE8Hk6yMJChDFIEEQ3OfqQR3wNWr4aO0RXlmyl78k96F/17CW\nHpYgCEKr0WBRYsSIEfV+eN6yZYs7xyPgWqxnfTMqAn31GPXuuQLozUtEZKuNU/OfBUmi04tPotLW\n/5xVmUdQn0xBDonBmTTCtZ0o8gV9JJrWyEpW4FCuAbNdRccgGzEBjiY9Xl2cssKXG60cOOmkS6ya\n2TcY0HpBQUKWFRatyG43CRs7dpfy7mcZlJsc9O7ux/97tDcalefSVYTqWSm79pXz5cps0jLNqFRw\nzYgwpo2PIizExZlRgiAIjTS4VxS+Bi1vrzzAW8sPMPu6HgxLavrMSkEQhPagwTPXL7/8ss7vlZeX\nu3UwQrWasZ51LZmob0ZFSYWVf33yO8P6xTJ+SNxFz2hoTNPNlpDzzmdYjp8i/p5Z+A1Kqv/O5go0\n21ejqDQ4hk0GtYtFm4p8t/WRUBRILdBRalET5uugc4hnTk5lWWHJ91b2HnfQKUbFnPEGdNqWL0hY\nbTJvfJjGtl2lbT5ho7LKwYdfnmbLtmK0Gok7Znbg+tHhREYaKCgQRQlP2X/ExKIV2aSerESS4MrB\nwcycGE10G/05EwTBuyR1DuVvMwew4Ot9fPTtESrMdq69LK6lhyUIguD1Gjwzi42NPff1iRMnKCkp\nAapjQZ955hnWr19f63Zms5nHH3+coqIirFYr9957Lz169GD+/Pk4nU7Cw8N56aWX0Ol0rFmzhk8/\n/RSVSsX06dOZNm2am55e66bXqs81taxNzRkVReWW875XVG5lzS9/UGW2XfSMhsY03Wxu5pPpZL+x\nEG1kGN2feYRSWz13VhQ0O9cgWStxDBqLEhTp2k7c3Ecis1RLrkmLn85JTw9Ff8qKwtIfrew+5iA+\nSsWdE4zodS1fkCgtt/Pcm3+QerKyzSds7DtUzpsL0ykqsdM1wYcH74ynY4zoyO5JR09U8OXKHA4c\nqY5AHjwoiBuTo4mLFa+7IAjNq2tsII/fNJBXluxlyY8nqDDbmXxlZ7FkTxAEoR4unxU888wzbN26\nlcLCQuLi4sjMzOSOO+6o8/6bN2+mT58+zJ07l6ysLO644w4GDhzIrFmzGDduHK+++irLli0jOTmZ\nt99+m2XLlqHVapk6dSpjxowhKEg0CWrI2RkV44cm8H8Lf6O04s9n5k2Z0eBq083mpigKaY8/h2K1\nEf/Mo2gD/aHAVOf9Vaf2oc48ghwRj7PHENd24uY+EgUVav4o1qJTyyRFW1F7oB2HrCgs32zl98MO\nOkaomDvRiMELChI1EzZGDAnhvtvj2mTChtUq89myLNb9UIBaDTMnRjPl+iiv6ePRFp3KqGLRimx2\n76+etTegTwA3TY6hS0LLFEsFQRAAYsP9eOLmQbyyZC/fbk/HVGXn1mu7o/KSKG5BEARv43JR4sCB\nA6xfv55bbrmFzz//nIMHD/L999/Xef/rrrvu3Nc5OTlERkayc+dO/vnPfwIwcuRIFi5cSKdOnUhK\nSsLf3x+AgQMHsmfPHkaNGnWxz6ndMVsdlNVSkICmzWhwtelmcytcuhbT1l0EjRlO8HUN/JxUlqH5\nbS2KRod96GRwZSmLm/tImKwqjuTrUUmQFG1Fr3F/0oaiKKz6ycaOgw5iwlTclWzEqG/5Dz8XJmxM\nnxDVJq8WHTtZyRsfppGdZ6VDtIGH5yaIE2MPOp1jYfHKbLbtKgWgV6IfN02OadP9SQRBaF3Cgoz8\n/eZBvLZ0Hz/vy6bSbOeuCb3QarwrwUwQBMEbuFyU0OmqG4TZ7XYURaFPnz688MILDW43c+ZMcnNz\nee+995g9e/a5xwkNDaWgoIDCwkJCQkLO3T8kJISCgtr7GJwVHOyDxg0H9fBw/yY/RnOx2ByUlFsJ\nDtBj0GnOu91kdaDXqbHYnH/aLizISJeE0PO2aYz7pw/Ax6hjx8EcCkvNhAUZGdwnmjvG90bticv9\nDbAWFJPy7wWofX0Y+N6/MEYEALW/l4oiU/XT5zjtVgxjZhDYOd6lfZhy0rA4LBiCwvGP7dik8VZZ\nFXYcVJAVGNZdIibY96Ifq66fV0VR+HK9ia377XSM1PD4HaH4+7T8TIS13+fw0tsnUUnw9CM9uHZk\nw8tmWtPvJIDdLrNwcTqLlmegKDAjuQN33ZyAXl/38am1PceL4annmJ1r5uPF6WzYkocsQ4+u/tx1\nSwKXDghukWKXeC8FQahPgK+O+bMG8Oby/exOLeC1pft4YEpftzUjFwRBaCtcPip26tSJRYsWcckl\nlzB79mw6deqEyVT3lPmzvvrqK44cOcKjjz6KovzvCnHNr2uq6/aaSkqqXB12ncLD/SmoZ8q/t7gw\nkjPIT0//xDBmjOrCsi1/kJJaUOvyirP6dgnFVGamKc80eVgC4y7reF7TzeLiyiY84sU7+eC/sReX\nEvfPR6gw+lNRYKrzvVQd3YE2IxVnbCJlkb3rXeJxjqUcyvNArceiDcXShJ8RhwwpWQYsdjVdQq1o\nHQ4aqLfVqa7nqCgK326zsXm3ncgQFXPG67BUVmJpmbcHqG60+eXKbJZ/W52w8fj9nend3afB37fW\n8jt5VvppM69/kEZappmIMB0PzImnT3d/ysvrPj61tud4MTzxHItLbHy9NpdNPxfhcCrExRqYNSmG\nywYEIkkShYUVbt2fK9rTeykKE4Jw8Yx6DX+d3o//rjnM7tQCXlycwl+n9SPAV6QBCYIgnOVyUeJf\n//oXpaWlBAQEsHbtWoqLi7n77rvrvP/BgwcJDQ0lOjqanj174nQ68fX1xWKxYDAYyMvLIyIigoiI\nCAoLC89tl5+fT//+/Zv2rNqQCyM5SyqsbN6Txe9H8qgw1x8nadSrSR7e2S3jaKjpZnMo+3knRcvW\n4dO3J5F3zKj3vlJ5IZo9G1F0RhyDk3Gpq6TjTB8Jqel9JBQFjuTpqbSpiQ6w0yHQM9Gf3+2oLkiE\nB0ncM8nQ4jMkzkvYiNDz1F/bXsKGU1ZY/V0ei1fl4HAojL4ylDtmdMBoFFNy3a3c5GDFulzW/1iA\nza4QHaFnZnI0wy4LRi3WZguC0EpoNWr+ktyHzzYc5ed9OTy3aA/zZvQjLFA04xUEQYBGFCWmT5/O\nxIkTuf7665kwYUKD99+1axdZWVk8+eSTFBYWUlVVxfDhw9mwYQMTJ05k48aNDB8+nH79+vHUU09R\nXl6OWq1mz549PPHEE016Um1FfZGcDRUkAKw2JxVVNnzawDRB2Wwh7fHnQKWi04tPIqnrOQGUnWi2\nLkdy2rEPnQQ+LlzlU2QoP9NHIqDpfSROFukoqtIQbHTSLczmkaSN73+zsel3O6GBEn+ZbCTAt2UL\nEu0hYSMnz8IbH6Vz9EQlwYEa7r09nkv6Bbb0sNqcyionqzfk8c3GfCxWmbAQLdMnRDNyaKhoHCoI\nQqukUkncNrYHfkYd63ak89wXe3hkej9iw0UvHEEQBJfPGB577DHWr1/PpEmT6NGjBxMnTmTUqFHn\nekRcaObMmTz55JPMmjULi8XCP/7xD/r06cNjjz3GkiVLiImJITk5Ga1Wy7x585gzZw6SJHHfffed\na3rZ3tUXyemKsCBjiyVkuFvWgo+wpp0m6u6b8O3bo977qg/9gqrwNM6EJOSEJNd2UJEHDgsYgqr/\nNWWsZRpOl2nx0cr0irTgiQu6P+yy8d0OGyEB1QWJQL+WLUhkZpv5z+snySu0ceXgYO6fHd+mEjYU\nRWHDlkI+WZKF1SYz7NIg7rolrs0VXVqaxerk200FrPouj4pKJ0EBGm6aHMO1V4W1qZ8nQRD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y+88MLlHo4gNEuxEYE8O60vMeEB/LIjk09XHMTlbh4rYwVBEGri9Sxo4MCBLF++nLS0NLRaLYmJ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naucu3bEBy/AA+ytWcYO2C01fBx/4nLzfn8w/rErGYpR5docXzO2sX1HI4vmF2Bz5DA4N897W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\noUMHkyO+MZs2beLkyZPMmTPHsezOO++ka9e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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "XU_3BLNxLWk4", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Task 2" + ] + }, + { + "metadata": { + "id": "qFRgB1HDLYw6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "outputId": "f1c99c9b-6154-4d6e-83bc-e621e08685e3" + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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pOX938tpsajrf0YeevjCcDmuiGsLgitlxXb0RHHy3NeWxlDa+Dp7GUyr3AwB9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\nmOAusye9M4knFSSrQKB1UgG/SZLoimE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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "2VmPzDvSLnrE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Solution**" + ] + }, + { + "metadata": { + "id": "kL4GywkZLkYs", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 337 + }, + "outputId": "773040a3-c875-4ea1-ae7e-6dc5c4173e5d" + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ixyNg+wefYfehs9HbO1xetgYnojFBtYFIp9Xi5mum4OBxB1zuzGfQ7W0+j1tX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\nn36KhoYGnDx5Eo8++igaGhrS+RJJMep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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "NYaIXYERL0Az", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "c6638e7d-8d42-46dc-df02-d6cdd79a1c1a" + }, + "cell_type": "code", + "source": [ + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "c_zAX54CMEeS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Task 3" + ] + }, + { + "metadata": { + "id": "AdlzHSLKL-qj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "3628a514-ea45-44cc-f505-856641269514" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 10))\n", + "\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Gmpub4/b6KRvU3/fRpZi4m2++Wc8++6wkKTs7W0NDQxobG0twVcnn888/17Fj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\n4tXPowAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "dk4cJ2ObMV0m", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Solution**" + ] + }, + { + "metadata": { + "id": "bhSI5BSUMRev", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "690ed724-e4cd-47e7-c3e7-57e218388f94" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 6))\n", + "\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "kMYGd3a0MZfT", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "ee41a92b-b517-4882-8e7d-f8a452497b56" + }, + "cell_type": "code", + "source": [ + "calibration_data = train_model(\n", + " learning_rate=0.05,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\")" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 212.76\n", + " period 01 : 189.06\n", + " period 02 : 167.52\n", + " period 03 : 147.36\n", + " period 04 : 131.32\n", + " period 05 : 120.49\n", + " period 06 : 115.95\n", + " period 07 : 112.36\n", + " period 08 : 110.36\n", + " period 09 : 109.63\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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predictionstargets
count17000.017000.0
mean195.9207.3
std53.9116.0
min45.415.0
25%162.7119.4
50%195.4180.4
75%223.2265.0
max512.0500.0
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P45kTZGlBIWqraMdujsx5gvJDxzB3aEPsS48RMmKQd4OddvS7NqDftw0FDbXr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\nJSIiIiIieTEmJKL7cuTIEfzlL3/p8bbnnnvugZ/3q6+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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jvFrI3YKMeyo", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "62d1cb2b-5b55-4701-8c75-1ed17fb4e67c" + }, + "cell_type": "code", + "source": [ + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Ggx9doXquQp+wSP/GHkmfsNz+CzvtiMQS8Ab5nEUBi74VnYoBX3LjlqCO2mKK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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "m0dZeJydM0G3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 507d3f983e5f9462e19a6edbc0635cc394e4b44f Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Thu, 14 Feb 2019 00:44:23 +0530 Subject: [PATCH 04/11] Validation Set Programming Exercise. --- ValidationSetProgram.ipynb | 1364 ++++++++++++++++++++++++++++++++++++ 1 file changed, 1364 insertions(+) create mode 100644 ValidationSetProgram.ipynb diff --git a/ValidationSetProgram.ipynb b/ValidationSetProgram.ipynb new file mode 100644 index 0000000..023a40d --- /dev/null +++ b/ValidationSetProgram.ipynb @@ -0,0 +1,1364 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "ValidationSetProgram.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "vkU2picdNrj_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "_DrlYKubORzG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Validation" + ] + }, + { + "metadata": { + "id": "QIlzxlgjOVkB", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Setup**" + ] + }, + { + "metadata": { + "id": "8R8l3C4FOJEZ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "# california_housing_dataframe = california_housing_dataframe.reindex(\n", + "# np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "iHMlinDEOaRF", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "uYRvNJm4OeNF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "c051ce14-af4e-43df-a51b-e4e772f8ad9a" + }, + "cell_type": "code", + "source": [ + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_examples.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 229.5\n", + "std 122.5\n", + "min 15.0\n", + "25% 130.4\n", + "50% 213.0\n", + "75% 303.2\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "E0-DK5pBOv9N", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Task1" + ] + }, + { + "metadata": { + "id": "xx1y5CmyO-q2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's check our data against some baseline expectations:\n", + "\n", + "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n", + "\n", + "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n", + "\n", + "If you look closely, you may see some oddities:\n", + "\n", + "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n", + "\n", + "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n", + "\n", + "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n", + "\n", + "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source." + ] + }, + { + "metadata": { + "id": "Xds6c4DZPAww", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Task 2" + ] + }, + { + "metadata": { + "id": "jyImGYEsO74b", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 498 + }, + "outputId": "00bf0f3f-51b6-4ab9-a5a3-2e8cfa434b5c" + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(13, 8))\n", + "\n", + "ax = plt.subplot(1, 2, 1)\n", + "ax.set_title(\"Validation Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(validation_examples[\"longitude\"],\n", + " validation_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n", + "\n", + "ax = plt.subplot(1,2,2)\n", + "ax.set_title(\"Training Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(training_examples[\"longitude\"],\n", + " training_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n", + "_ = plt.plot()" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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TIKCfnQcdHliVoCvR99ieQx5pt5OrLjTYsDVOOqOZN7uIYOD09lRfs8Bi6/4M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\n84fNE9buX9z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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "o2mM53xMPYNU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Task 3\n", + "The code that randomizes the data (`np.random.permutation`) is commented out, so we're not doing any randomization prior to splitting the data.\n", + "\n", + "If we don't randomize the data properly before creating training and validation splits, then we may be in trouble if the data is given to us in some sorted order, which appears to be the case here." + ] + }, + { + "metadata": { + "id": "JVw966gtPoda", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Task 4" + ] + }, + { + "metadata": { + "id": "IfaryPeLPmdz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "PYOWwVrUPu9c", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "QDaxyQI5PycG", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(\n", + " validation_examples, validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "XfZDgdimP5BM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 741 + }, + "outputId": "788f60a4-e874-4896-8957-82dedd717459" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n", + " learning_rate=0.00002,\n", + " steps=1000,\n", + " batch_size=5,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 201.28\n", + " period 01 : 181.50\n", + " period 02 : 169.48\n", + " period 03 : 163.17\n", + " period 04 : 161.04\n", + " period 05 : 160.94\n", + " period 06 : 161.32\n", + " period 07 : 161.64\n", + " period 08 : 162.63\n", + " period 09 : 164.03\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Yim/OLsCGK1vwoc9UyGX6m/shIiIyJoJYnqETA6PP/YaGtl/yl4hgHI87Df/6\ng9Cjtq/UcSRlaLWhEqyL4WJtDBdrUz6SzcDQ8xv8zxl6d9zYh/R7GVLHISIiMghsYAwcB3qJiIge\nxQbGCHCgl4iIqDQ2MEZAJsgwouFQCBB4hl4iIiKwgTEa7po66Ojsg9jsePwVc1zqOERERJJiA2NE\nXvDoBwuFOXbc2MuBXiIiqtHYwBgRS6UFBnn6Ia/oHjZf2yF1HCIiIsmwgTEynZzboo7aFWfunuNA\nLxER1VhsYIxMyUDvEA70EhFRjcYGxgiVGui9c0zqOERERFWODYyR0g303uQZeomIqOZhA2OkONBL\nREQ1GRsYI1Z6oPe61HGIiIiqDBsYIyYTZBipO0PvVg70EhFRjcEGxsi5aWpzoJeIiGocNjDVAAd6\niYiopmEDUw1YKi3wAgd6iYioBmEDU0105EAvERHVIGxgqokHB3rX8wy9RERUzbGBqUZKBnrbIi77\nLgd6iYioWmMDU8284OmnG+hNu5cudRwiIiK9YANTzViacKCXiIiqPzYw1VBH57ZwU9dGyN3zuJgU\nIXUcIiKiSscGphqSCTKMbDQUJjIFVvz9C+5kxkodiYiIqFKxgamm6qhdMbbJSNwrysfiCys5D0NE\nRNUKG5hqrLW2BYZ49kfavXQsDluJvMI8qSMRERFVCjYw1VyvOl3R2bkd7mTF4qe/f+H5YYiIqFpg\nA1PNCYKAgAZD0MS2IS4lX8bGq9sgiqLUsYiIiJ4LG5gaQC6T4+Vmo+Fi6YQjMSfwZ/RhqSMRERE9\nFzYwNYSZQoU3WkyAlVKDzdd24FxCuNSRiIiInhkbmBrERmWNN7xehqlciZ8v/Yab6beljkRERPRM\n9NrABAUFYcSIEfD398fevXsBAKtXr0bTpk2RnZ2t227btm3w9/fHiy++iI0bN+ozUo1XW+2Ml5uO\nRmFxEZZcWIWk3GSpIxEREVWYQl8vfPLkSVy9ehXr169Hamoqhg4dipycHCQnJ0Or1eq2y8nJwcKF\nCxEcHAwTExMMHz4cvXv3hrW1tb6i1XjN7BtjRMMhWHd5MxaFrcA078mwMDGXOhYREVG56W0FxsfH\nB/PmzQMAaDQa5ObmomfPnnjnnXcgCIJuu7CwMDRv3hxqtRoqlQqtW7dGaGiovmLRP3xdOqBnnS64\nm5OIZeGrUVBcKHUkIiKictPbCoxcLoe5eclv9cHBwejSpQvUavUj2yUlJcHW1lZ329bWFomJiWW+\nto2NORQKeeUGfoCDw6M5q6NX7UcgqzgTp+6cw+83t2BKu/GlmktDVFNqY2xYF8PF2hgu1ub56K2B\nuW///v0IDg7GihUryrV9ec6hDlU9AAAgAElEQVRRkpqa87yxnsjBQY3ExEy9vb6hGek5HAkZyThy\n+zQsBQ0GevSROtIT1bTaGAvWxXCxNoaLtSmfspo8vQ7xHjlyBEuWLMGyZcseu/oCAFqtFklJSbrb\nCQkJpWZkSL+UchO83mI87FS22HVrP07GhUgdiYiI6Kn01sBkZmYiKCgIS5cuLXMg18vLC+Hh4cjI\nyEB2djZCQ0PRpk0bfcWix1ArLTHJ62WYK8zwS2QwLqdckzoSERFRmfS2C2nnzp1ITU3F22+/rbuv\nXbt2OHXqFBITE/Hqq6+iZcuW+OCDDzBt2jRMnDgRgiBg8uTJT1ytIf1xtNDiteZj8cP55Vh2cTWm\neU+Gk0UtqWMRERE9liAa4YVx9LnfsKbvlzwdH4qfL62DrcoG73lPgZWp4TSTNb02hop1MVysjeFi\nbcpHshkYMj5tHVtjYN0+SMlLxdILq5BflC91JCIiokewgaFH+Ln3RHvHNridGY1Vf/+GYrFY6khE\nRESlsIGhRwiCgJcaDUMDm3oIS/obm6/tkDoSERFRKWxg6LEUMgVebRYIR4taOBB9BIfuHJM6EhER\nkQ4bGHoicxMzTGoxAWqlJYKvbEN40iWpIxEREQFgA0NPYWdmizdaTIBCpsCKi78gKuOO1JGIiIjY\nwNDTuWlqY0LTl1BQXIjFF1YiJS9V6khERFTDsYGhcvFyaIZh9QciIz8Ti8NWIrcwV+pIRERUg7GB\noXLr7toZXV07IjY7HsvD16KouEjqSEREVEOxgaFyEwQBw+u/gOb2jRGZehXrLm8q19XDiYiIKhsb\nGKoQmSDD+CajUFvtguNxZ7D39kGpIxERUQ3EBoYqTKUwxRstJsDG1BrbbuxGyN3zUkciIqIahg0M\nPRMrUw3e8JoAlVyFNZfW41raTakjERFRDcIGhp6Zi6UTXmk+BsUQ8eOFn5GQkyh1JCIiqiHYwNBz\naWzbAC81HIbswhwsCluBrPxsqSMREVENwAaGnltH57bo69YDibnJWBq+CgVFBVJHIiKiao4NDFWK\ngR594K31wo3021gTsQHFYrHUkYiIqBpjA0OVQibIENg4AB5W7jibEIbtN/ZIHYmIiKoxNjBUaUzk\nJni9+Thozeyx9/ZBHIs5JXUkIiKqptjAUKWyVFrgDa+XYWFijnVXNiMi+YrUkYiIqBpiA0OVTmtu\nj9ebj4dMkGH5xTWIyYqTOhIREVUzbGBILzyt3TG2cQDyiu5hUdgKpN1LlzoSERFVI2xgSG+8a7XE\nYI9+SLuXjiVhK5FXeE/qSEREVE2wgSG96u3WDR2d2iI6KxYr//4FRcVFUkciIqJqgA0M6ZUgCBjZ\ncCga2zbAxeRIBF/dDlEUpY5FRERGjg0M6Z1cJsfEZmPgbOGIwzHHcTD6iNSRiIjIyLGBoSphplBh\nktfLsFKqsenaDpxPvCh1JCIiMmJsYKjK2Kis8YbXyzCRm2DV37/hVkaU1JGIiMhIsYF5QHZeAWKT\nsqSOUa3VVrtgYtPRKCwuxJKwVUjKTZE6EhERGSE2MA/YePA6JgcdQHQCmxh9ambfGAENBiOzIAuL\nw1YgpyBH6khERGRk2MA8oHUDBxQWiVi1KwLFxTxSRp+6uHZEj9q+iM9JwLLwNSgsLpQ6EhERGZFn\nbmBu3br11G2CgoIwYsQI+Pv7Y+/evYiLi0NgYCBGjRqFqVOnIj8/HwCwbds2+Pv748UXX8TGjRuf\nNdJza+Fph26tXXEzLhP7QqIly1FTDK03AF4OzXAl7Tp+jfydh1cTEVG5ldnATJgwodTtRYsW6f7+\nySeflPnCJ0+exNWrV7F+/XosX74cs2bNwvz58zFq1Cj8+uuvcHNzQ3BwMHJycrBw4UKsWrUKa9as\nwc8//4y0tLTn+EjP55XBzWBpZoLNh28gIS1Xshw1gUyQYXyTkXDT1Map+LPYdWu/1JGIiMhIlNnA\nFBaWXtY/efKk7u9P+23Zx8cH8+bNAwBoNBrk5ubi1KlT6NmzJwCge/fuOHHiBMLCwtC8eXOo1Wqo\nVCq0bt0aoaGhz/RhKoOVpSlG9aqP/MJirN4dyVUBPVPKlfhPi/GwU9lgx819OBV3VupIRERkBMps\nYARBKHX7wR/mDz/2MLlcDnNzcwBAcHAwunTpgtzcXCiVSgCAnZ0dEhMTkZSUBFtbW93zbG1tkZiY\nWLFPUcnaNamFFp52uHQrFUfDeSVlfdMo1Zjk9TLMFGb4JTIYV1KvSx2JiIgMnKIiGz+taXmc/fv3\nIzg4GCtWrECfPn109z9pZaM8Kx42NuZQKOQVzlJeWq0GU19qjSlfH8CGg9fR3ccNNhqV3t6PAAcH\nNd43ex1fHv4Byy+uxsxe78NV4/TY7cjwsC6Gi7UxXKzN8ymzgUlPT8eJEyd0tzMyMnDy5EmIooiM\njIynvviRI0ewZMkSLF++HGq1Gubm5sjLy4NKpcLdu3eh1Wqh1WqRlJSke05CQgJatmxZ5uumpurv\nsFsHBzUSEzMhABjWxRO/7LuC+etCMWloc729J5WoJXPG6IbDsTpiPb48uADvt5kCtdJS9/j92pBh\nYV0MF2tjuFib8imryStzF5JGo8GiRYt0f9RqNRYuXKj7e1kyMzMRFBSEpUuXwtraGgDQsWNH7Nmz\nBwCwd+9e+Pr6wsvLC+Hh4cjIyEB2djZCQ0PRpk2bin5Gveje2gX1XK0QcjkRoVek3a1VU7Rz8kZ/\n915IzkvBkgurkF9UIHUkIiIyQGWuwKxZs+aZX3jnzp1ITU3F22+/rbtv9uzZmDFjBtavXw9nZ2cM\nGTIEJiYmmDZtGiZOnAhBEDB58uSnNkdVRSYImNCvEf634jTW7L2MRnWsYa4ykTpWtde/bm8k5aXg\ndHwofr70GyY2GwOZwFMWERHRvwSxjKGTrKwsBAcHY/z48QCAdevW4bfffoObmxs++eQT2NvbV1XO\nUvS57Pa4Zb3tx25i85Gb6NrSGeP8GuntvelfhcWFWHB+Oa6m3UDP2l0wrP5ALrkaKNbFcLE2hou1\nKZ9n3oX0ySefIDk5GQBw8+ZNzJ07F9OnT0fHjh3x5ZdfVm5KA9avvRtcHCzw1/lYXI5KlTpOjaCQ\nKfBa87GoZa7Fn9GHcfjOcakjERGRASmzgYmOjsa0adMAAHv27IGfnx86duyIkSNHlhq8re4Uchkm\n9GsMQQBW7opEfkGR1JFqBHMTc0zyehlqE0tsuLIVp++clzoSEREZiDIbmPvncQGA06dPo3379rrb\nz3JItTHzcNagd5vaSEjNxdZjN6WOU2PYm9ni9RbjoZAp8O2xH7Hz5j4Ui8VSxyIiIomV2cAUFRUh\nOTkZUVFROHfuHDp16gQAyM7ORm5uzTvN/lBfD9hbqbDnVDRux3PfZVWpa1UH77T+D+zMS87Wu+TC\nKl7BmoiohiuzgXn11VfRv39/DBo0CJMmTYKVlRXy8vIwatQoDBkypKoyGgxTpRzj+jVCsShi5a4I\nFBVzJaCquGlqY3afj9DYtgH+To7E7DPzEZ0ZI3UsIiKSSJlHIQFAQUEB7t27B0vLf08odvToUXTu\n3Fnv4Z6kqo9CethPOy7hWHg8hnfzRP/2bnrLQqU5OKhxNyEdO27uw+5bf8JEpsDIhsPQ3skwzhtU\nU/FoCsPF2hgu1qZ8nvkopNjYWCQmJiIjIwOxsbG6Px4eHoiNja30oMZiRI/60FgosfXoTdxN4a6M\nqiQTZBjk0Rf/+WcuZk3EBvx2eRMKiguf/mQiIqo2ylyBadSoEerWrQsHBwcAj17McfXq1fpP+BhS\nr8AAwJnIBCzechENa1vj/VGtIKthQ81SeLg2iTnJWHZxNWKy4uCmqY1XmwXCRmUtYcKaib9JGi7W\nxnCxNuVT1gpMmWfinTNnDrZu3Yrs7GwMGDAAAwcOLHXl6JqsTUMHtKpvj3NXk3A4LBbdWrpIHanG\ncTC3w3vek/Hb5U04HR+K2WfmYULTUWhkW1/qaEREpGdl7kIaPHgwVqxYge+//x5ZWVkYPXo0Xnnl\nFWzfvh15eXlVldEgCYKAMX0awsxUjo0HryE1857UkWokpVyJsY1HYESDocgtzMOC88ux59YBHmpN\nRFTNlesCM05OTpg0aRJ27dqFvn374osvvpB0iNdQ2KhN8WL3esi9V4S1ey/jKfPQpCeCIKCLawe8\n0/o/sDLVYNuN3VgWvga5hTXvUH8iopqiXA1MRkYG1q5di2HDhmHt2rV4/fXXsXPnTn1nMwpdvJzR\noLY1zl1NwtnLvGK1lOpaueFDn6loYO2JC0l/Y86Z+YjJipM6FhER6UGZDczRo0fxzjvvwN/fH3Fx\ncZg9eza2bt2Kl19+GVqttqoyGjSZIGB8v0ZQyGVYu+8KsnILpI5Uo6mVlpjS8hX0rtMNibnJ+CZk\nAc7En5M6FhERVbKnHoXk7u4OLy8vyGSP9jpfffWVXsM9iSEchfSwHSdu4fe/bqBzcye8PKBx5Qej\nCtfmfEI41kRsQF7RPXR17YRh9QZAIStzbp2eAY+mMFysjeFibcrnmY9Cun+YdGpqKmxsbEo9dufO\nnUqIVn30bVsHZyIScDQ8Du2a1kJTdx6tJbWW2uZwsqiFZRfX4K87xxCdeQcTm42BtamV1NGIiOg5\nlbkLSSaTYdq0afj444/xySefoFatWmjbti2uXLmC77//vqoyGgWFXIYJ/RtDJgj4eVck7uXzitWG\noJaFFu95T4G31gs30m9j9pl5uJp6XepYRET0nMpcgfnuu++watUqeHp64s8//8Qnn3yC4uJiWFlZ\nYePGjVWV0Wi4OarRt21t7DoVhc1HbmBkT56PxBCoFKaY0HQU6lq5YdO1PzD//DIM9uyHnrW71Lir\nqhMRVRdPXYHx9PQEAPTs2RMxMTEYO3YsFixYgFq1alVJQGMzuHNdaG3MsC8kGjfjMqSOQ/8QBAHd\na3fG1Favw9LEApuv7cBPF9cir7Bmn8+IiMhYldnAPPzbqZOTE3r37q3XQMZOaSLHeL9GEEVg5c4I\nFBbxhGqGpJ51XXzoMxWeVnVxLjEcQSELEJ99V+pYRERUQeU6D8x9XG4vn0ZuNuji5Yw7idnYdfK2\n1HHoIVamGkxt9Rp61PbF3ZwEBIX8gNCEC1LHIiKiCihzBubcuXPo1q2b7nZycjK6desGURQhCAIO\nHTqk53jGK6C7J8KuJ2H78Vto00gLJzsLqSPRA+QyOfzrD4K7pg7WRm7ETxfX4lbtLhjs2Q9ymVzq\neERE9BRlNjC7d++uqhzVjrnKBGN6N8TCzeFYtSsS00e35hWrDZB3LS84WzpiWfhq/Bl9GFGZdzCh\n6WhYmT753ANERCS9MhsYFxdeYfl5eDd0gHdDB5y9nIhD52LQo7Wr1JHoMZwsauH9Nm9ibcRGnE8M\nx5wz32Nis0B4WrtLHY2IiJ6gQjMwVHFjejeAuakCGw9dR0oGj3gxVGYKFV5pNgZD6w1ARn4Wvj+3\nBIeij/ECnUREBooNjJ5ZWZpiRI96uJdfhNV7eMVqQyYIAnrV6Yq3Wr0KC4U5Nl7dilWXfsO9onyp\noxER0UPYwFSBzi2c0NjNBheuJ+NUBA/ZNXQNbOrhw7ZTUVfjhpC75/FNyAIk5PBK40REhoQNTBUQ\nBAHj+jWCUiHDr/uuIjOHv9EbOmtTK7zd+nV0de2I2Ox4zDnzA8IS/5Y6FhER/YMNTBXRWpthiK8H\nsnILsO7Pq1LHoXJQyBQIaDAE45qMRJFYhB/Df8bW67tQVMzrXBERSY0NTBXq7eMKd0c1Tvx9F+E3\nkqWOQ+XU1rE13m8zBfZmdth7+yAWhv2EzPwsqWMREdVobGCqkFxWcsVquUzA6t2RyL1XKHUkKicX\nSydMb/MWmts3weXUa5h9Zh5uZURJHYuIqMZiA1PFamst0a99HSRn3MOmwzekjkMVYG5ihteaj8UL\nHn5Iv5eB784uxpGYEzyyjIhIAnptYK5cuYJevXph7dq1AIDr169j9OjRGDNmDGbMmIHCwpIViG3b\ntsHf3x8vvvgiNm7cqM9IBmFQR3c42prjwNk7uBaTLnUcqgCZIENf9x6Y3HIiTBWmWHd5M9ZEbEB+\nUYHU0YiIahS9NTA5OTmYOXMmOnTooLvvm2++wWuvvYa1a9fCyckJu3btQk5ODhYuXIhVq1ZhzZo1\n+Pnnn5GWlqavWAbBRCHH+H6NIAJYtSsSBYW8YrWxaWzbAB/6TEUdtStOxZ/Ft2cXIimXc01ERFVF\nbw2MUqnEsmXLoNVqdffdvn0bLVq0AAD4+vri2LFjCAsLQ/PmzaFWq6FSqdC6dWuEhobqK5bBaFDb\nGt1buSA2KRs7TtySOg49A1uVDd5t/QY6ObfDnaxYzD4zHxeTIqSORURUI+itgVEoFFCpVKXua9Cg\nAf766y8AwJEjR5CUlISkpCTY2trqtrG1tUViYs04adjwbp6wUZtix4nbiEnkUS3GyERuglGN/DGm\n0YsoKC7A4gsr8ceNvSgWuapGRKRPZV7MsbJNnz4dn376KTZt2oS2bds+dvixPAORNjbmUCjk+ogI\nAHBwqLorEU8JaImZP53C2n1XMedNX8hlvGJ1WaqyNhXxgkMPNKtdD98e/xG7bu1HXF4s3mr/MixN\nLaSOViUMtS7E2hgy1ub5VGkD4+TkhKVLlwIoWYFJSEiAVqtFUlKSbpuEhAS0bNmyzNdJTc3RW0YH\nBzUSEzP19voPq+tggbaNtTgdkYD1uyPQ26d2lb23sanq2lSUGjZ4v/WbWPX3bzgffwnv7/4SrzQP\nRB119b4KuaHXpSZjbQwXa1M+ZTV5VXoY9fz583Ho0CEAwKZNm9CjRw94eXkhPDwcGRkZyM7ORmho\nKNq0aVOVsSQ3qlcDWKgU+P3wdSSl5Uodh56DhYk53vCagP7uvZCSl4Zvzy7C8dgzUsciIqp29NbA\nXLx4EYGBgdi8eTNWr16NwMBAdO3aFQsWLIC/vz+0Wi26desGlUqFadOmYeLEiZgwYQImT54Mtbpm\nLatpLJR4qVd95BcU42desdroyQQZBnj0wRteE6CUmeCXyI34NTIYBTzUmoio0giiEf601Oeym1TL\neqIo4rsNYbh4MwUTBzRGp+ZOVZ7B0BnjkmtSbgqWh69GdFYs6qhd8EqzsbAzs5E6VqUyxrrUFKyN\n4WJtysdgdiHRkwmCgLF+DWFqIse6P68iI5tXrK4O7M1s8a73ZLR3bIOozBjMOTMPEclXpI5FRGT0\n2MAYEHsrMwzr4oHsvEL8up8/5KoLpdwEYxq/iJcaDsO9ontYGPYT/rixB7mFeVJHIyIyWmxgDExP\nb1d4OGtwOiIB568mPf0JZBQEQUBnl/Z413sSrE2tsOvWn5hxbBa2XNuJtHu8nAQRUUWxgTEwMpmA\nCf0aQS4TsGbvZV6xuppx09TGf9u+gxc8/GAiV2Bf1CF8cnw21kRsQFz2XanjEREZDfmnn376qdQh\nKionR3/zIRYWpnp9/fLQWChRXCwi7Foycu8VwquevaR5DIUh1KYymMhNUM+6Lrq6dIStmQ3ic+7i\ncuo1HI45gaiMO7BRWcPG1BqCYBwnNawudamOWBvDxdqUj4WF6RMfq9IT2VH5DejgjpDLiTh4Lgbt\nmtRCg9rWUkeiSmYiN0En53bo4OSD8KQI7I86hIvJEbiYHIG6mjroVacrWjg0hUzgQikR0cP4f0YD\nZaKQYXy/RhAArNwViYLCIqkjkZ7IBBm8HJpimvdkvNt6ElrYN8XNjCgsu7gGM09+gyMxJ5HPc8gQ\nEZXCXUgPMaRlPVuNCtm5BQi/kQxRBJq42z79SdWYIdVGX2xV1mhTqyW8tV4oLC7EtbQbuJB0Ccdj\nT6NQLISThSOUchOpY5ZSE+pirFgbw8XalE9Zu5C4AmPghnX1gJ1Ghd2nohB1lyc9qikcLbQY3Xg4\nPu/4Efq4dUehWIjtN/ZgxvFZCL6yDcm5qVJHJCKSFBsYA6dSKjDOryGKikWs3BWJouJiqSNRFbIy\n1WCwZz980fG/GFZvIMwVZjh45yg+PTkHK//+FdGZsVJHJCKSBId4jUAzDzt0aOqIE3/HY9+ZO/Br\nV0fqSFTFVAoVetbpgm6unRBy9zz2R/2FkLvnEXL3PBrZ1Ecvt65oZFPfaI5cIiJ6XmxgjMTInvUQ\nfiMZW47cQOsG9tDamEsdiSQgl8nRzskbbR1b41LKFeyP+guRqVcRmXoVrpbO6F2nK1ppW0Auk0sd\nlYhIrzjE+xBDHawyNZHDRmOK0xEJiEnKRsdmjjXut21DrY0UBEGA1twe7Z280cyuEXIL83Al9TrO\nJYbjVHwoBAhwtnSEogoaGdbFcLE2hou1KZ+yhnjZwDzEkL+pXOwtcCs+E3/fTIGdRgU3xydfpbM6\nMuTaSMna1AqttS3Q1rE1ikUR19Nv4WJyBI7EnMC9onw4WzjCVK7U2/uzLoaLtTFcrE358CikakIQ\nBIzt2xAqpRzrD1xDWtY9qSORAbE3s8OIhkPwRcf/on/d3hAEAbtv/YkZx2fht8jfkZCTKHVEIqJK\nwxWYhxh6V2xmqoCZqQJnryQiKT0PbRvXkjpSlTH02hgKpVyJBjae6OraEdamGsRlxSMy9RoO3zmB\nmKw42KpsYKOyqrT3Y10MF2tjuFib8uGlBKqZbq1ccOrSXZy9nIizlxPh3dBB6khkgJRyJbq4dkQn\n53Y4n3gR+6P+wvnEizifeBGeVnXR260rmto14qUKiMgosYExQjJBwPh+jfC/Faexdt9lNHazhrnK\nsM7OSoZDLpPDu5YXWmtb4GraDeyLOoRLyZdx/cJNOFrUQq/aXdDGsRVMZPzfAREZD+5CeoixLOup\nzZWAIOD81SRk5RagZf3qvwpjLLUxVIIgwM7MFm0dW6OlQzPkFxXgatp1hCX9jROxZ1AsFsPZshZM\nZBVrhlkXw8XaGC7Wpnw4xFtN9WtXB64OljgcFoeI2zy1PJWfi6UTxjYZgc87fIgetX2RV5SHLdd3\nYsaxWdh07Q+k3UuXOiIRUZnYwBgxhVyGCf0bQRCAn3dF4l4Br1hNFWOjsoZ//UH4ouP/YbBHPyjl\nSvwZdRifHJ+N1ZfWIzYrXuqIRESPxV1IDzG2ZT0btSly7xXiwo1kFBWLaFq3+l6x2thqY0xM5Cbw\ntK6Lrq6dYKeyQXxOAi6nXsORmBOIyoiGtakGtiqbx548kXUxXKyN4WJtyodHIVVzQ309EHolEXtO\nR6FtYy3cHTVSRyIjZSJToKNzW7R3aoOLSRHYF/UXLiZH4mJyJNw0tdG7Tjd4OTTlkUtEJDmuwDzE\nGLtihVwGZwcLHL8Yj5uxGejcwgkyWfW7zIAx1sZYCYKAWhZadHT2QWPb+sgpyMGV1OsITQhDyN1z\nkMvkcLJwhFwmZ10MGGtjuFib8uGlBCrAWL+ptNZmSE7PQ/jNFChNZGhQ21rqSJXOWGtj7GxU1vCu\n1RLeWi8UioW4lnYTF5Iu4VjsKRQWF8LDvjYKeVJog8R/M4aLtSmfshoYQRRFsQqzVIrExEy9vbaD\ng1qvr69P2XkF+L9lp5CTV4jPJ7aFo231umK1MdemOkm/l4m/7hzD4ZgTyC3MhYlMAVdLZ9RWu8BV\nXfJfJwtHnlfGAPDfjOFibcrHweHJ1/xjA/MQY/+mColMwKItF9GgtjU+GNUKsmp0xWpjr011k1eY\nh+Oxp3E26Tyi0mNRLBbrHpMLcjhZ1NI1NXXULnCxdNbrRSXpUfw3Y7hYm/Ipq4Hhr0jVjHdDB7Sq\nb49zV5Nw+HwsurVykToSVVMqhQo96nTBCO8BiI1PQVz2XURnxiAqKwZ3MmMRkxWLO1mxQFzJ9gIE\naM0dUPufVZrali6orXaGuUn1WikkoqrBBqaaEQQBY/o0RGRUGjYeuoamdW3hYG0mdSyq5kzkJqij\ncUUdjSs6/XNfUXER7uYkIjozBtFZMYjOjMGdzDjczUlAyN3zuufaqWz/bWrULnC1dIGV6ZN/6yIi\nArgL6RHVZVnvcFgsVu2KhK3GFO+NbFUt5mGqS22qm4rUpVgsRlJuSkkzkxVb0txkxiCrILvUdlZK\n9T+7n1z+Wa1xfuJ5aOjJ+G/GcLE25cNdSDVQFy9nZObk4/e/bmD22rN4d0RL1KnF32pJWjJBBq25\nPbTm9vCu5QUAEEURaffS/1mp+bepuX/+mfvMFWb/ztRYljQ2Dub2PCcNUQ3FBqYaG9DBHWamCqzd\newVBv57DOwFe8HSxkjoWUSmCIMBGZQ0blTVaODTV3Z+Zn4U7mbG63U/RmTG4nHoNl1Ov6bZRypW6\nI6BqW94/AqoW5DK5FB+FiKqQXhuYK1euYNKkSRg/fjzGjBmDM2fOYO7cuVAoFDA3N0dQUBCsrKyw\nfPly7N69G4IgYMqUKejatas+Y9UoPVq7QqWUY8WOSHyz7jze9G+OJu7V93IDVH2olZZobNcAje0a\n6O7LLczTNTV3MktWa25lROFG+i3dNgpBDmdLx1IzNS6WTlDKK3aVbSIybHprYHJycjBz5kx06NBB\nd99XX32Fb775Bh4eHliyZAnWr1+Pfv36YefOnVi3bh2ysrIwatQodO7cGXI5f4OqLB2bOcHURIGl\n2y7i+40XMGlIM7Ssby91LKIKM1OoUN/GA/VtPHT35RcVIDY77p9VmpKmJjYrDlGZMbptZIIMjuZa\n3Xlqals6w1XtDDMFB9yJjJXeGhilUolly5Zh2bJluvtsbGyQlpYGAEhPT4eHhwdOnToFX19fKJVK\n2NrawsXFBdeuXUPDhg31Fa1G8m7ogKnDvfDDpgtYsCkcrwxsjPZNHaWORfTclHITuGvqwF1TR3df\nUXER4nMSEJV5/+inkvma2Ox4nI4P1W3nYGYHV7WLbqbGVe0MtdJSio9BRBWktwZGoVBAoSj98v/9\n738xZswYaDQaWFlZYdq0aVi+fDlsbf/dpWFra4vExMQyGxgbG3MoFPpboSlr6tmYdXNQQ+tgic+X\nn8SyPy7BxNQEfh3cpXguIwUAAB48SURBVI5VIdW1NsbOEOviCGu0xL+7n4rFYsRnJeJmahRupkbr\n/pxLuIBzCRd025mbmMHWzBp25tawMbOGnZmN7ratWckftaml0RwRZYi1oRKszfOp0iHemTNnYsGC\nBfD29sacOXPw66+/PrJNeY7qTk3N0Uc8ANX/0DYHSyXeG9kK364/j4XBYUhIzkK/dm5SxyqX6l4b\nY2VMdTGBORqYNUIDs0aAc8n/b1Ly0nBHNygci5S8VKTkpOFORtwTX0chU8BKqYG1qRWsTTWwVln9\n8/eSPzamVtAo1ZIPExtTbWoa1qZ8DOYw6suXL8Pb2xsA0LFjR2zfvh3t27fHzZs3ddvcvXsXWq22\nKmPVOG6Oanw0pjW+WXceGw9eR+69Igz1rWs0v1ESVRZBEGBnZgM7Mxt4OTQr9Vh+UT7S7qUj7V7G\nP/9N//d2Xsnfb6TfgojH/9IlQIBGaQmrfxqaf/+rgY2q5La1qRUvr0D0jKq0gbG3t8e1a9dQr149\nhIeHw83NDe3bt8fKlSvx5ptvIjU1FQkJCahXr15VxqqRnOws8NHokibmj+O3kHuvEC/1ql+trp1E\n9DyUciW05g7Qmjs8cZui4iJk5Gc+odFJR1peOmKz4xGVeeeJr2GmMPu3sXmk0bGGlakGFgpz/oJB\n9BC9NTAXL17EnDlzEBMTA4VCgT179uCzzz7DjBkzYGJiAisrK8yaNQsajQYBAQEYM2YMBEHAp59+\nCpns/9u79/Co6juP459z5sx9JpfJDZKQkIQil0AggNyRLqhVK24RCMWkdbftrkt9urXUirYUu/Sy\nccvqgpRabZ+lqEsQraKiaBUQuUsghEBAIERIIBeSkMtk7rN/zGQykwtGwsyZST6v58kz9/E7HiDv\n/M7JDN+YKhTiY7RYmZ+LtVuO46Ojl2GxOfDwPaOg4P9/oj5RiArfe9j0xu12o81u7h43fsHTaG1C\nddvVXp9DKUqBYaOOCQweTTSMSoPsu6yIQokfJdDFYNwv2dpux7Nbj6PiSgsm35aAf1kwFpIi/CJm\nMG6bSMDtcmtYHFZc722XlbUJTdZmtNhab7jLKlodFRA2Q2JMcFoFqBUq75c68FTqvMx3NA4t/r3p\nmxsdA8OA6WKw/qFqtzrwP9tO4OylJmRnmvDDb42DWhleP80N1m0T7rhdQsfpcuK6rdnvWJymbsFz\n3doMh9v5lZ9bKSpvGDkahdrvtp4jqPPUc54rQr3j35u+CZuDeCl8adUSfrIkB3948yROnL+GZ4uO\n40eLcqDT8I8IUbhQiAqYNLEwaWJ7vY/L7fLtslLoXKhtuA6rwwqr0wqr09bzqcPmO29xWtFovQ6r\n0wqX29WveSVR6jmKOiJH6mVlyO9U4xdQGknDlSLy4Xcn8lEpFXh04Tj86e1T+Ky8Fv+15Rh+siQH\nRh1/S4IoUoiCCKPKAKPK4PkpX3FzP+W73W443E6/wOkpgLznHV1vC7yfxWFFs7UFVmf9Ta0OdRAg\nQK/UQa/Uw9DlVK/UwaDUw6DS+91HDy2jZ8BiwFAASSHikQVjsUmlwN4TV1D46jGsyJuAWKNa7tGI\nKIQEQYBSkKAUJRiU+lv2vA6XA7YeIsfqtMHS40pRZwS12dvQajejzd6GWnNdr8cDBbwOX/R0Rk1H\n7PjHkEGlh17SQa/SQydpGT0RgAFD3YiigIfvGQWNSsKHn13Cf75yFD9dOhEJMfzcGCLqH0mUIIkS\ndEpdv57H5Xah3WEJiBrfqa0Nbb7rPOdb7W2oNdf3OXp0Sq03cvxWd7pEj++U0SMLBgz1SBAELJ03\nAlq1Atv3XcTvXj6KFUsnIiX+1v0kRkR0s0RB9K2s9PWtT11uFywOC1p7iJ42u9kbPv63taGu/Vqf\njgUKjJ6eVnsCV36UBhesTgdUopLv8XOTGDDUK0EQ8I+zM6FVSyj6+BwKXynGirwJSB/Cz+8gosgj\nCiJ0Sh10Nxk9HSs53aLH0Rk/bXZzn6MH8ISP50Blte9AZY1CDbXUcdlzm1ah6X6dpPEd6KxRqKFS\nqAbVKhADhr7U3benQaNS4K/vn8Ez/1eMf1+Ug5HDen/jLiKigcI/evrKEz1Wb/T4h0/nri2Xwonr\n5lZYHZ7f/LI4rGixt6K+/Vq/DnT2/MaWpjOKOoJIoYbWd50aGm8QaQNiqfO+Gin83xuIAUN9cseE\nFGhUEl565xT+u+g4Hl04DtmZcXKPRUQUdjzRo4VOqQUQ3+N9bvQ+MHaXwxc2VqcV7Q6L70Bmi9MC\nq8OKdqfVL34svl+Bt3ivM9vb0WBphN3luOnXoRK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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "j6_KRQfkQa70", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Solution**" + ] + }, + { + "metadata": { + "id": "QAAmHBoXP8o2", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model of multiple features.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(\n", + " validation_examples, validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "BG1sFBRkQiU8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "54d94887-ee46-4a45-c3bc-c850624c88d8" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " learning_rate=0.00003,\n", + " steps=400,\n", + " batch_size=4,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 211.18\n", + " period 01 : 197.14\n", + " period 02 : 184.93\n", + " period 03 : 175.35\n", + " period 04 : 170.21\n", + " period 05 : 166.92\n", + " period 06 : 164.38\n", + " period 07 : 162.35\n", + " period 08 : 161.18\n", + " period 09 : 161.01\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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7D17o/AwtnJux99JBlsV9TpmhzNJlCSGEEFUmAaaBcbZxYlrYk/h7+BGfmcD8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\nIXRl4ZPrP0FgDajapo5gb00g1FTWt552QT6AlEQb2RmJJMZ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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ABCPkNsgRIm-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Task 5" + ] + }, + { + "metadata": { + "id": "eW7odXrvRVOi", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "c3f9d6dd-dfc3-4a1a-ba1e-7d3d49da0969" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_test_input_fn = lambda: my_input_fn(\n", + " test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 162.45\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "Pm4bCwf4Rfg6", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Solution**" + ] + }, + { + "metadata": { + "id": "X5fQ-24HRXcq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "fd80acb8-6702-483e-f32a-b070b248cffe" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_test_input_fn = lambda: my_input_fn(\n", + " test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.5f\" % root_mean_squared_error)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 162.45190\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "9H2BNfiDRlFZ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 62bc1760a60cfb2cc8ee3701d0cae7caad39b266 Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Thu, 14 Feb 2019 01:04:35 +0530 Subject: [PATCH 05/11] Feature Set Programming Exercise --- FeatureSetProgram.ipynb | 1507 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 1507 insertions(+) create mode 100644 FeatureSetProgram.ipynb diff --git a/FeatureSetProgram.ipynb b/FeatureSetProgram.ipynb new file mode 100644 index 0000000..74c16b1 --- /dev/null +++ b/FeatureSetProgram.ipynb @@ -0,0 +1,1507 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "FeatureSetProgram.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "ux-g-4pzSleS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "cMkkkYICS9v4", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Featured Sets" + ] + }, + { + "metadata": { + "id": "JxWqKx08TFey", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Setup**" + ] + }, + { + "metadata": { + "id": "5MS91BmbS7S2", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "lHlrEdI9TLXw", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mUBOEG9JTPk2", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "b3752055-3f4f-4b3c-e285-fc215456a5df" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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target-0.1-0.00.10.10.0-0.00.10.70.21.0
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms \\\n", + "latitude 1.0 -0.9 0.0 -0.0 \n", + "longitude -0.9 1.0 -0.1 0.0 \n", + "housing_median_age 0.0 -0.1 1.0 -0.4 \n", + "total_rooms -0.0 0.0 -0.4 1.0 \n", + "total_bedrooms -0.1 0.1 -0.3 0.9 \n", + "population -0.1 0.1 -0.3 0.9 \n", + "households -0.1 0.1 -0.3 0.9 \n", + "median_income -0.1 -0.0 -0.1 0.2 \n", + "rooms_per_person 0.2 -0.1 -0.1 0.1 \n", + "target -0.1 -0.0 0.1 0.1 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "latitude -0.1 -0.1 -0.1 -0.1 \n", + "longitude 0.1 0.1 0.1 -0.0 \n", + "housing_median_age -0.3 -0.3 -0.3 -0.1 \n", + "total_rooms 0.9 0.9 0.9 0.2 \n", + "total_bedrooms 1.0 0.9 1.0 -0.0 \n", + "population 0.9 1.0 0.9 0.0 \n", + "households 1.0 0.9 1.0 0.0 \n", + "median_income -0.0 0.0 0.0 1.0 \n", + "rooms_per_person 0.1 -0.1 -0.0 0.3 \n", + "target 0.0 -0.0 0.1 0.7 \n", + "\n", + " rooms_per_person target \n", + "latitude 0.2 -0.1 \n", + "longitude -0.1 -0.0 \n", + "housing_median_age -0.1 0.1 \n", + "total_rooms 0.1 0.1 \n", + "total_bedrooms 0.1 0.0 \n", + "population -0.1 -0.0 \n", + "households -0.0 0.1 \n", + "median_income 0.3 0.7 \n", + "rooms_per_person 1.0 0.2 \n", + "target 0.2 1.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "VwXj07qJTci7", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "uOtYECW1ThXU", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + "\n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TeHdjxXrTkSN", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "uoFXGz63ToxQ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 758 + }, + "outputId": "25e67cae-4b86-46ba-c40f-5ff56765a060" + }, + "cell_type": "code", + "source": [ + "#\n", + "# Your code here: add your features of choice as a list of quoted strings.\n", + "#\n", + "minimal_features = [\"median_income\", \"latitude\",]\n", + "\n", + "assert minimal_features, \"You must select at least one feature!\"\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "#\n", + "# Don't forget to adjust these parameters.\n", + "#\n", + "train_model(\n", + " learning_rate=0.01,\n", + " steps=1000,\n", + " batch_size=50,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 121.10\n", + " period 01 : 114.68\n", + " period 02 : 111.86\n", + " period 03 : 109.18\n", + " period 04 : 106.78\n", + " period 05 : 104.30\n", + " period 06 : 102.40\n", + " period 07 : 100.46\n", + " period 08 : 98.65\n", + " period 09 : 96.70\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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VAkwJ0NQ/hIa+d3HRchqPoDPM/eMYcQmp9i5LRETEbhRgSgCTycT9de/B2eKM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\namLDhg1s27aNffv2kZubi81m47333mPTpk1s27Y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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "QSiF6XZdUJcF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "###**Solution**" + ] + }, + { + "metadata": { + "id": "pPhrDWSjUTJ7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "089695bb-a20e-405f-8d16-1ee4ec142497" + }, + "cell_type": "code", + "source": [ + "minimal_features = [\n", + " \"median_income\",\n", + " \"latitude\",\n", + "]\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "_ = train_model(\n", + " learning_rate=0.02,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 126.83\n", + " period 01 : 117.01\n", + " period 02 : 115.96\n", + " period 03 : 115.92\n", + " period 04 : 113.94\n", + " period 05 : 112.23\n", + " period 06 : 110.76\n", + " period 07 : 109.76\n", + " period 08 : 109.27\n", + " period 09 : 108.61\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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pri5HRESk2VPAaQTd2oVgLWoFwF4dphIREXE6BZxG4GE10yu8K4bdTOKZfa4u\nR0REpNlTwGkk13WNwp4fSlZ5JhklWa4uR0REpFlTwGkkPTuEYCrUYSoREZHGoIDTSDysFnqGdMMw\n4OvTOkwlIiLiTAo4jSgu5hrshcGklp6koKLQ1eWIiIg0Wwo4jahnx1BMBS0B2Jd5wMXViIiINF8K\nOI3Iy8NCTGBXAHak7nVxNSIiIs2XAk4jG9SlI/aSAI4UHaGsqszV5YiIiDRLCjiNrHd0KORHYmDn\nQE6Kq8sRERFplhRwGpm3p5Vo/xgAtp/SYSoRERFnUMBxgUHRXbCXe5Ocl0yVvcrV5YiIiDQ7Cjgu\n0LdTOEZeJFVU8H3uEVeXIyIi0uwo4LiAr7eVdt6dAdh+ao+LqxEREWl+FHBcZEh0D4xKD/ZlH8Ru\n2F1djoiISLOigOMi/btEYM+PoJxiThSecnU5IiIizYoCjov4eXvQ2jMagG0ndTWViIhIQ1LAcaHB\n7Xth2MzsydDTxUVERBqSAo4LxXZphb0gnCIjlzPFGa4uR0REpNlQwHGhAF9PIi0dAN30T0REpCEp\n4LjYoGt6YxgmEtP2uboUERGRZkMBx8XiurbFXhhMrv0M+eUFri5HRESkWXBqwElJSWHMmDGsWLEC\ngLS0NGbOnMn06dOZOXMmmZmZAKxdu5bJkyczZcoU/vSnP523nt/85jdMmjSJhIQEEhIS2LRpkzPL\nblSBfp6E0R7QYSoREZGG4rSAU1JSwqJFi4iPj3dMe+GFF5gyZQorVqxg7NixvPHGG5SWlvLHP/6R\nN998k3feeYetW7dy6NCh89b30EMPsXz5cpYvX86IESOcVbZLXNe6NwDbUxVwREREGoLTAo6npyfL\nli0jIiLCMe3xxx9n3LhxAAQHB5OXl4ePjw9r1qzB398fk8lEUFAQeXl5zirLLQ3pGo29uAUZlaco\nrSp1dTkiIiJNntMCjtVqxdvbu8Y0X19fLBYLNpuNlStXMmnSJAD8/f0BSE5OJjU1lT59+py3vhUr\nVnDnnXfy4IMPkpOT46yyXSI4wItAW1sw2fn69HeuLkdERKTJszb2Bm02G/PnzycuLq7G4atjx47x\n8MMP89xzz+Hh4VFjmZtuuomgoCC6devGa6+9xksvvcRjjz12wW0EB/titVqc9hkAwsMDGnR9QzsO\nYG3Wfnae/pbbBoxs0HVfTRq6L9Jw1Bv3pL64L/XmyjR6wFm4cCHt2rVj9uzZjmlnzpzh/vvv5w9/\n+APdunU7b5lzg9CoUaN44okn6txGbm5Jg9Vbm/DwADIzCxt0nde17ciHp3w4Zj/E6fRcPMyN3pom\nzxl9kYah3rgn9cV9qTf1d6F5D7K8AAAgAElEQVQg2KiXia9ZswYPDw/mzJlTY/pvf/tbnnjiCXr0\n6FHrcg888AAnT54EYMeOHXTu3NnptTa2sCAfAiqvwTBXsTctydXliIiINGlOGybYv38/S5YsITU1\nFavVyrp168jOzsbLy4uEhAQAoqOjmTFjBomJiSxdutSx7MyZM4mKimLDhg3MmTOHadOm8atf/Qof\nHx98fX1ZvHixs8p2qT7hPdlalsIXx3YxsHVPV5cjIiLSZJkMwzBcXURDc/awnrOGDtNzi3ly52Ks\nFjMvjH4Cs0n3YbwUGtJ1X+qNe1Jf3Jd6U39ucYhK6hYZ7IdPeWts5jKSso66uhwREZEmSwHHzfQI\nqT7J+rND37i4EhERkaZLAcfNjOvaD8Nm4VBRMs3w6KGIiEijUMBxM63DAvEqa0mlpZBjuaddXY6I\niEiTpIDjhroGdgVg46FEF1ciIiLSNCnguKExXQZg2E0k5el+OCIiIpdDAccNRbcMw6MsnDJrNmcK\ns11djoiISJOjgOOmOvl3AWB98tcurkRERKTpUcBxU6M7DwRgf85BF1ciIiLS9CjguKluUa0wlwVT\nZDlDbkmRq8sRERFpUhRw3JTJZKK9TydMJoMNybqaSkRE5FIo4LixER0HALAn8zsXVyIiItK0KOC4\nsX7XtMdU4Uee6RTF5WWuLkdERKTJUMBxY2azmTae0ZgsNjYm73F1OSIiIk2GAo6bG9quPwCJaftc\nXImIiEjToYDj5uI6xECVFzkcp7yyytXliIiINAkKOG7OYrbQ0tIBPCrYlLLf1eWIiIg0CQo4TUBc\nmz4AbE/d6+JKREREmgYFnCZgWKfeYLOSYTtKZZXN1eWIiIi4PQWcJsDL4kGoqS14lbDt8PeuLkdE\nRMTtKeA0EbGtegGw5bguFxcREbkYBZwmYlTnfmA3kVp5GJvd7upyRERE3JoCThPh5+lLIFHgk0/i\nkROuLkdERMStKeA0If0iegLwxdFdLq5ERETEvSngNCGjO1c/fPNE6SHsdsPF1YiIiLgvBZwmJMQn\nCD97OHa/bPadSHN1OSIiIm5LAaeJ6RXaHZPJ4PNDu11dioiIiNtSwGliRnUaCMDh4mTshg5TiYiI\n1EYBp4lpHRCJtz0Qm28GyaeyXV2OiIiIW1LAaYK6BnXDZLHzWYoOU4mIiNRGAacJGtmx+mqq5Pxk\nDB2mEhEROY9TA05KSgpjxoxhxYoVAKSlpTFz5kymT5/OzJkzyczMBGDNmjXcdttt/PSnP+Xdd989\nbz1paWkkJCQwdepU5s6dS0VFhTPLdnsdg6/Bw+5DlV8aR9LyXV2OiIiI23FawCkpKWHRokXEx8c7\npr3wwgtMmTKFFStWMHbsWN544w1KSkp4+eWXefPNN1m+fDn/+Mc/yMvLq7GupUuXMnXqVFauXEm7\ndu1YtWqVs8puEswmM50CYjB5VPJp8reuLkdERMTtOC3geHp6smzZMiIiIhzTHn/8ccaNGwdAcHAw\neXl57N27l169ehEQEIC3tzf9+/dn166ad+rdsWMHo0ePBmDkyJFs27bNWWU3GcM69APgQM5BHaYS\nERH5EavTVmy1YrXWXL2vry8ANpuNlStXcv/995OVlUVISIjjPSEhIY5DV2eVlpbi6ekJQGho6Hnz\nfyw42Ber1dIQH+OCwsMDnLr+ixke0p+/7Xubct9UCivsRLcJcmk97sLVfZELU2/ck/rivtSbK+O0\ngHMhNpuN+fPnExcXR3x8PB988EGN+RcbjajPaEVubskV1Xgx4eEBZGYWOnUb9dHON5ojpoO8tz2R\nGcOvc3U5LucufZHzqTfuSX1xX+pN/V0oCDb6VVQLFy6kXbt2zJ49G4CIiAiysrIc8zMyMmoc1oLq\nkZ+ysjIA0tPTz5t/tRrSrvow1Z7M73SYSkRE5ByNGnDWrFmDh4cHc+bMcUzr06cP+/bto6CggOLi\nYnbt2sXAgQNrLDdo0CDWrVsHwPr16xk6dGhjlu22+kR0xWSYKfNOJTWr2NXliIiIuA2nHaLav38/\nS5YsITU1FavVyrp168jOzsbLy4uEhAQAoqOjeeKJJ5g3bx533303JpOJ+++/n4CAAA4ePMiGDRuY\nM2cODzzwAAsWLOCdd94hKiqKm2++2VllNyneVm+ivNuRajrK5oOHmBre19UliYiIuAWT0QyPbTj7\nuKU7HRvddHwb7x5+D9/s3jz70+muLsel3KkvUpN6457UF/el3tSf25yDIw2rf6ueYEChx0lO6zCV\niIgIoIDT5LXwDCDcMwpzQC7bko67uhwRERG3oIDTDFwb1QeTCXae3ufqUkRERNyCAk4zMLBVLwDy\nLSdId/I9gERERJoCBZxmIMI3jEBLKObAbHYkpbq6HBEREZdTwGkmBrTshclsZ/uJ/a4uRURExOUU\ncJqJ2Fa9Acg2HScrr9TF1YiIiLiWAk4zcU1Aa3zM/liCMtiZdMbV5YiIiLiUAk4zYTKZ6BveE5O1\niu3HD7i6HBEREZdSwGlGzh6mSrcfJaegzMXViIiIuI4CTjPSKagDHiYvzEEZJCZluLocERERl1HA\naUYsZgs9Q7th9ipj29FkV5cjIiLiMgo4zczAltU3/TtdcZjcwnIXVyMiIuIaCjjNTLfQGMxYMAdn\nsCsl09XliIiIuIQCTjPjZfGkS1BnzL5FbD902NXliIiIuMRlB5xjx441YBnSkM4epjpedoiC4goX\nVyMiItL46gw4d911V43Xr7zyiuP3xx57zDkVyRXrGdYNMGEOSmfX9zpMJSIiV586A05VVVWN19u3\nb3f8bhiGcyqSKxbg6U87/7aY/fPYkXzc1eWIiIg0ujoDjslkqvH63FDz43niXga27IXJBIeLDlFU\nWunqckRERBrVJZ2Do1DTdPQO7wmAOTid3bqaSkRErjLWumbm5+ezbds2x+uCggK2b9+OYRgUFBQ4\nvTi5fGE+IUT6tOSMPZ2dKacZ2ifK1SWJiIg0mjoDTosWLWqcWBwQEMDLL7/s+F3c24DInqw9dobk\nvBSKy/rg5+3h6pJEREQaRZ0BZ/ny5Y1VhzhB7/CerD22EVNQOnu+z2Jwr1auLklERKRR1HkOTlFR\nEW+++abj9b/+9S9uuukm5syZQ1ZWlrNrkyvUxr8VgR6BWAIzSUw+4+pyREREGk2dAeexxx4jOzsb\ngKNHj/L888+zYMECBg0axO9///tGKVAun8lkon9kL0zWKg5kfU9pedXFFxIREWkG6gw4J0+eZN68\neQCsW7eO8ePHM2jQIG6//XaN4DQRfcJ7VP8SmM7eQ+qZiIhcHeoMOL6+vo7fd+7cSVxcnOO1Lhlv\nGjoGtsfH4oMlOIOvkzNcXY6IiEijqDPg2Gw2srOzOXHiBLt372bw4MEAFBcXU1pa2igFypWxmC30\nCe+BybOc/WeOUFahw1QiItL81Rlw7r33XiZMmMCkSZOYNWsWgYGBlJWVMXXqVG6++ebGqlGuUO8f\nDlMZLdLYdyTHxdWIiIg4X52XiQ8fPpwtW7ZQXl6Ov78/AN7e3vz6179myJAhjVKgXLluIZ2xmqzY\ngzNITMogtmuEq0sSERFxqjoDzunTpx2/n3vn4o4dO3L69GmionR33KbA0+JJj9AY9hrf8e3R45RX\ndsPLw+LqskRERJymzoAzatQoOnToQHh4OHD+wzbfeuutOleekpLCrFmzmDlzJtOnTwfgrbfeYsmS\nJezcuRM/Pz/279/PkiVLHMscOnSIl19+mf79+zumJSQkUFJS4jjpecGCBfTs2fMSP+rVrU94T/Zm\nfYctII39R3IYEBPu6pJEREScps6As2TJEv773/9SXFzMxIkTufHGGwkJCanXiktKSli0aBHx8fGO\nae+//z7Z2dlERPzvEEnPnj0dd0wuKChg1qxZ9O3b97z1LV68mC5dutRr23K+HmFdMWHCEpzON8kZ\nCjgiItKs1XmS8U033cTf//53XnjhBYqKipg2bRr33HMPH3zwAWVlZXWu2NPTk2XLltUIM2PGjOHB\nBx+84CXmr7/+OjNmzMBsvqSHnEs9+Hv40TmoI2b/fPYcO0Vllc3VJYmIiDhNnSM4Z7Vq1YpZs2Yx\na9Ys3n33XZ566imefPJJEhMTL7xiqxWrtebqz56oXJuysjK2bNnC3Llza52/dOlScnNziY6O5v/9\nv/+Ht7f3BdcVHOyL1ercc0zCw5vew0YHdehPyu7DVPqlcSqnjGt7tHR1SQ2uKfblaqHeuCf1xX2p\nN1emXgGnoKCANWvWsHr1amw2G//3f//HjTfe2KCFbNy4kREjRtQ6enPnnXcSExND27Ztefzxx3n7\n7be5++67L7iu3NySBq3tx8LDA8jMLHTqNpwh2qcTAJbgDD7deZwOEX4urqhhNdW+XA3UG/ekvrgv\n9ab+LhQE6ww4W7Zs4T//+Q/79+/n+uuv55lnnnHaeTCff/45d9xxR63zxo4d6/h91KhRrF271ik1\nNHch3sFc49+ak8Zpdu9Po8rWFatFhwNFRKT5qTPg3HPPPbRv357+/fuTk5PDG2+8UWP+4sWLG6yQ\n/fv307Vr1/OmG4bBXXfdxdKlS2nRogU7duygc+fODbbdq02f8B6cLEqlwieNA8dy6R0d6uqSRERE\nGlydAefsZeC5ubkEBwfXmHfq1Kk6V3z28u/U1FSsVivr1q1j0KBBbN26lczMTO6991769u3L/Pnz\ngerDYOeeo7N582ZOnTrF1KlTmTJlCjNnzsTHx4fIyEgeeOCBy/qwUn1X4w+PrsccnEFicoYCjoiI\nNEsm49yb2/xIYmIiDz74IOXl5YSEhPDXv/6Vdu3asWLFCl577TU2b97cmLXWm7OPWzblY6OGYfDE\ntiVkFedjOnA9L8we3mwOUzXlvjR36o17Ul/cl3pTf5d1Ds6f/vQn3nzzTaKjo/n000957LHHsNvt\nBAYG8u677zqlUHEuk8lEn/CefFq2mTLPDJJP5tGjff3ubSQiItJU1Pm/7mazmejoaABGjx5Namoq\nd955Jy+99BKRkZGNUqA0vLMP37QEp/P+5iPsSsmkolL3xRERkeajzhGcH9+Qr1WrVjWuaJKmqWNg\nO/w9/CgJzeLwN/m8tHofXh4W+nQKZWBMBL06huLlqWdViYhI01Wv++CcdaE7EEvTYjaZ6R3Wna1p\nX/Pzn7Yk7aQXiUkZ7DxY/eNpNdOrYygDuobTJzoMH69L2k1ERERcrs7/cu3evZsRI0Y4XmdnZzNi\nxAgMw8BkMrFp0yYnlyfO0ju8B1vTvub7sr3cPuRWJg+P5mRGEYnJGSQmZfJNSvWP1WKiZ4dQBsSE\n069zGL7eHq4uXURE5KLqDDiffPJJY9UhjaxrcGeCvAJJTN/DvqwDxEb2Y2jreG4dFs0tQztyOquY\nxORMEpMz2HMoiz2HsrCYTXRvH+IIOwG+nq7+GCIiIrWq8zLxpkqXiddPYUURX53eyZbU7eSW5wHQ\noUU7hraOo39Ebzws1aM1adnFfPND2DmRXgSA2WSia7sgBsZE0K9LOIF+rg87zaUvzZF6457UF/el\n3tTfhS4TV8C5DM1tx7Mbdr7LTmJz6jYOZqdgYODn4Ut8q1iGRMUR7vu/mwFm5JXyzQ+HsY6mFQBg\nMkGXNkEM7BpB/y7hBAd4ueRzNLe+NCfqjXtSX9yXelN/CjgNqDnveFmlOWxJ3c62tK8pqiwGoFtI\nF4a2jqdnaFcs5v9dXZWVX8qu5EwSUzI5dCrfMb1T60AGxoQzICaC0MALP/W9oTXnvjR16o17Ul/c\nl3pTfwo4Dehq2PEq7VXsydjHl6nbOJx/DIAgr0CGRF3HoKhrCfRqUeP9uYXl7ErJJDEpg5RTeZzd\nqzq0avFD2AknItjXqTVfDX1pqtQb96S+uC/1pv4UcBrQ1bbjpRalsSV1OzvP7KLMVo7ZZKZPWA+G\nto6nS3D0ebcPyC+uYHdK9Tk7ScfzsP+wi7WN9GdATAQDY8JpFerX4HVebX1pStQb96S+uC/1pv4U\ncBrQ1brjlVWV8XX6Hr5M3UZqURoAkb7hDGkdR1zLAfh6nD9CU1hSwZ7vs0hMzuTAsRxs9urdrXW4\nHwN/CDtRYX4Nco+lq7UvTYF6457UF/el3tSfAk4Dutp3PMMwOFpwnM2ntrM7Yy9Vhg0PswcDIvsw\nrHU87VpcU+tyxWWV7Pk+i2+SM9l/NIcqmx2AliG+DOwazsCYCK6J8L/ssHO198WdqTfuSX1xX+pN\n/SngNCDteP9TVFHMtrSv2ZK6nayyHADaBrRmaOt4Bkb2xdNS++XjpeVV7D2cxTdJmew7kk1FVXXY\nCQ/yrh7Z6RpB+5YBlxR21Bf3pd64J/XFfak39aeA04C0453PbthJyvmeL1O3sy/rAAYGPlZvrms5\ngKGt42npF3HBZcsrbOw7kk1icgZ7D2VT/sODP0NbeP1wzk4EHVu3wHyRsKO+uC/1xj2pL+5Lvak/\nBZwGpB2vbrlleXx1egdfnd5JQUX136lLUDRD28TTJ6xHjUvNf6yi0sZ3R3Mcd1AuLa8OO0H+no4T\nlDu3CcJsPj/sqC/uS71xT+qL+1Jv6k8BpwFpx6sfm93G3qzv+PLUNlLyDgPQwjOAQVHXMiTqOoK9\ng+pcvrLKzoFjOXyTnMnu7zMpLquqXoevB/1/CDsxbYOwmM2A+uLO1Bv3pL64L/Wm/hRwGpB2vEt3\npjiDLanb2X4mkdKqMkyY6BnWjaGt4+kW0hmzyVzn8lU2O0kncklMymRXSiZFpZUA+Pt40K9zGAO7\nRjB0QFvycosb4+PIJdJ3xj2pL+5Lvak/BZwGpB3v8lXYKkhM38uXqds4UXgKgDDvEIa0jiO+VSz+\nnhe/P47NbiflZD6JyRnsSs4kv7iixnyzyYTZXP1Pk9mE2WTCYjZhNuF4fe57zOYffmqb7vhnzWUt\n5rPr5vxlzebq6eesw/TDOi2O17Ut+79teXla6NcpHC/PCx/Oa0r0nXFP6ov7Um/qTwGnAWnHaxjH\nC07yZep2EtP3UGmvxGqy0C+iN8PaxNOhRbt6XUFltxscSq0OO+m5ZZSXV2I3wG4Y2O0//BhG9bSz\nv9sNDMPAZv/fdMM4O6/mso3z5TDAWonJsxSTZxkmr1JMlir8yzqQMKIv/bqEN0oVzqTvjHtSX9yX\nelN/CjgNSDtewyqpLGH7mW/Ykrqd9JJMAFr7t2Jo6zhiI/vhba3f86yc0Zfagk+t4ejHQcrx2qCi\nqor8ynzyK/IorCwgvzKfwsp8CqsKKKoqoKiqEBtV52/bZqHyVGd6BvRn2pgYwoJ8GvSzNSZ9Z9yT\n+uK+1Jv6U8BpQNrxnMMwDFJyD/Nl6jb2Zn2H3bDjZfHk2pYDGNo6jtb+repc3hV9sRt2CiuKyS3P\nJbcsn9yyXHLK88gtyyOnrPqfhZVFF1ze38OPYO8gQryCCPau/gnxDqa4soT/HvqYUlsp9qJAjJO9\nmNS/N+OubYvVUvf5Su5I3xn3pL64L/Wm/hRwGpB2POfLLy9g6+mdbDm9g7zy6ieVdwxsz9DWcfSL\n6I2H2XreMs7oS7mtgtwfgkpOeW6N4JJTnkdeWR5Vhq3WZa1mK8FegQR7BzsCTMjZEPPD6wvdCBGg\nsKKI/3z/AV+n7wbDRGVae8LL+3Dn2G7EtA1u0M/pbPrOuCf1xX2pN/WngNOAtOM1Hpvdxv7sJL5M\n3cbBnBSgetQjvlUsQ1pfR5hPqOO9l9oXu2GnoKLQEVhyy/8XXs6OxBRXllxw+QBPf0K8gmsNLiHe\nwfh7NMwztg5kJ7MyaTW55bnYy3ypPNaDuLY9mDKyEy38LhyQ3Im+M+5JfXFf6k39KeA0IO14rpFZ\nks2W09vZlvY1xZUlmDDRLaQLQ1vH0TOsG5ERgTX6UlZVVh1Yzgku1a9zfwg0+dgNe63b8jB7EOId\nTLBXoCO8nDsSE+wViIfFo7E+OuW2Cj46sp7PTn6JgUFVZhQeGT2ZPKQbw/pGXfQuz66m74x7Ul/c\nl3pTfwo4DUg7nmtV2irZnbmPzae2cbTgOADBXkH0i+pORkGuI9CUVpXWurwJEy08A84JLkHnjcT4\nWX0bZPSloZ0oOMXbSas4VXQao9KTyhNdaesVw4xxXWkbWfuX3B3oO+Oe1Bf3pd7UnwJOA9KO5z5O\nFZ7my9Rt7EzfTYWt+n44XhbP6tGXc0/e9TobXoIJ8mqBtZZzeJoKm93G56e28OGR9VTaK7HlhVF1\nvDuje8Vw89AO+Hi532fTd8Y9qS/uS72pPwWcBqQdz/2UVZVh+FRglHjgY/V2y9GXhpZVms0/k1aT\nlPs92C1UnuqEX1Fnpo7pysCYcLf6G+g7457UF/el3tTfhQJO07veVKQW3lZv2ga1xtfDx63+w+5M\nYT6hzO57DzO6346flzcebZMpa7eZv6zfyp/+vZeM3AufIC0i0twp4Ig0YSaTiWtb9uex6x7mupYD\nMPsV4N1jG0lVW3nk9W2s2XKUyqraT6QWEWnO3O9gvYhcMn9PP+7s/jNiW/bjX0mryWp1DEIzWPNt\nNtsOpJNwfRe6tw9xdZkiIo3GqSM4KSkpjBkzhhUrVjimvfXWW/To0YPi4v899blHjx4kJCQ4fmy2\nmjdOS0tLIyEhgalTpzJ37lwqKmo+XFFEqnUL6cJvr3uIsW1HYPYsw6trIrlB2/njqp38dc135BeV\nu7pEEZFG4bQRnJKSEhYtWkR8fLxj2vvvv092djYRERE13uvv78/y5csvuK6lS5cydepUbrjhBp5/\n/nlWrVrF1KlTnVW6SJPmafHk5k4TGBDZh5VJqzhBKr7BWSQei+HbZZncOqwTI/u1xmy+Os5VEpGr\nk9NGcDw9PVm2bFmNMDNmzBgefPDBSz4JdMeOHYwePRqAkSNHsm3btgatVaQ5uiagNQ8PmM1tnW7E\nw8PAM3ofdNzJys17WPRWIkfTClxdooiI0zhtBMdqtWK11ly9v79/re+tqKhg3rx5pKamMm7cOO66\n664a80tLS/H0rL4lfWhoKJmZmXVuOzjYF6vVcgXVX9yFLksT11JfzvezyImM7BrH3xJXsocD+Pb+\nitSTnXjqrXxuiO9IwoTu+Ps4/67M6o17Ul/cl3pzZdziJOP58+fzk5/8BJPJxPTp0xk4cCC9evWq\n9b31uW1PrpMvj9X9CdyT+nJhJjy5p9sMvgnZw7vfr8HeNhnvyDN8vKeQLXtP87NRnYjrHum0S+zV\nG/ekvrgv9ab+3Po+OHfccQd+fn74+voSFxdHSkpKjfm+vr6UlZUBkJ6eft45PCJycSaTiYEt+/FY\n3K+JazUQm1c+3j23Ux62j2Uffcsf/7WHtOzii69IRKQJcHnAOXLkCPPmzcMwDKqqqti1axedO3eu\n8Z5Bgwaxbt06ANavX8/QoUNdUapIs+Dn4UtCtynM6fsLwnxCMEceJaDfNpLzUnj87ztZvfkIFZW2\ni69IRMSNOe1RDfv372fJkiWkpqZitVqJjIxk0KBBbN26lT179tCrVy/69u3L/PnzefbZZ9m+fTtm\ns5lRo0Zx3333cfDgQTZs2MCcOXPIyMhgwYIFlJeXExUVxeLFi/HwuPA5A3pUw9VJfbl0FbZKPj62\nkY0nvsBu2DHnt6H4cGfC/QOZNjaG3tGhDbId9cY9qS/uS72pPz2LqgFpx3NP6svlSy1K4+2Dqzhe\neBIrXpQe6UJVVhQDYiK4Y3RnQlp4X9H61Rv3pL64L/Wm/tz6HBwRca3W/q14eOD9TO78E8wWA4+O\n+wjqvZtdx47x27/tYN3OE9jseuSDiDQdbnEVlYi4ntlkZuQ1Q+gT3oN3kt9jf3YSfn2ysad15p3P\nK/lq3xnuHB9Dp9aBri5VROSiNIIjIjWEeAfzy9538fMe0/D19MFolUTIgK9JLTnF08u/4c2Pkygq\nrXR1mSIiddIIjoicx2QyMSCyD91COvPeobVsTduJd4/teOV3YvO+KnalZDJlZCcG92rptHvniIhc\nCY3giMgF+Xr4Mq3bZOb2+z8ifMMoDzxEcOx2Kn3T+Pvagyx5exepmUWuLlNE5DwKOCJyUV2Co/l/\nsQ8yvv1oKijBHJ1IZN+DpJzJ4Ik3vubdzw9RXqF754iI+9AhKhGpFw+LB5M6jmNARPVTyo8WHCdw\nwBlI68bHO+zsPJjO1LFd6Nc53NWliohoBEdELk2Uf0seGjCLKV1uxmyGipZ7aHXtPvIqc/nzf/ax\ndNW3ZOWXurpMEbnKaQRHRC6Z2WRmeJtB9A7rzjsp77Mv6wA+vdPxL+jOniQ7B47n8JPBHbg+9hqs\nFv1/lIg0Pv2bR0QuW7B3EP/Xawb39EzAz8OX/IB9tIz7Bs8WBazadJgn3via5BO5ri5TRK5CCjgi\nckVMJhP9Inrx6HUPMzjqOvJt2dijv6L9gOOk5eSzZOVu/rjiGzLydNhKRBqPnkV1GfSMEPekvriH\n73OP8M/k/5Bekom/NQDL6d6cORaAxWxieN8oJg3uQKCfp6vLFPSdcWfqTf3pYZsNSDuee1Jf3Eel\nvYp1xz5j/fHPsRk22vt1JuPANWSne+LlYWFs7DWMv7Ytvt46DdCV9J1xX+pN/SngNCDteO5JfXE/\nacXp/Ct5NYfyjmLCxDWeXUjb34aCPA/8vK1MjG/P6AGt8bBaXF3qVUnfGfel3tSfAk4D0o7nntQX\n92QYBqm2Eyzf9R6nik5jNplpbe7KyW9bUVrsQXCAFzcP6cCgXi2xmHVaYGPSd8Z9qTf1d6GAo/Fh\nEXEqk8lEv1Y9iYq9ht0Z+/jw6DpOlhzA2jOFLkZ3ju6N4I2Pk/hk5wluHRZN/y5her6ViFwxBRwR\naRRmk5kBkX3oG96THWe+4aOjGzhZ/i1+/bwIrejO0b1hvPzePjpGtWDy8Gi6tgt2dcki0oQp4IhI\no7KYLQyKupbYyH58eXo76459xmnrboKv9SWgqBtH9tv4wz9307NDCLcNj6Zdy9qHn0VE6qKAIyIu\n4WHxYNQ1QxnUKpbPT/45b6cAACAASURBVG5h44nNpPt8Q0R8AJ453difZGf/0Ryu7RbBLcM6Ehns\n6+qSRaQJUcAREZfytnpzQ4cxDG0Tz4bjm/ji1FcUtthJy8HBGGkx7Dxo8E1yJkP7RPGTwe0J8vdy\ndcki0gQo4IiIW/D38OOWThMZec0QPj72KVtP78Qetp3WLSMoPR7Npt12tu5LY2zsNdxwXVt8vT1c\nXbKIuDEFHBFxK0FegdwRcytjrhnOR0fXk5i+B6N1Bte0iyL/UAc+2mZn0+5UJsS1Y/SANnh66B46\nInI+3QfnMuj+BO5JfXFfV9Kb1KI0Pjiyjn1ZB6rXZW5LZlI7SvP8CPL35KYhHRjSu5XuoXMZ9J1x\nX+pN/elGfw1IO557Ul/cV0P05mj+cdYc/oSUvMPV66QjZ75rQ0WxL5Ehvtw2rCMDYsJ1D51LoO+M\n+1Jv6k83+hORJq1DYDvm9PsFybmHWHP4E44XHsHa4ygt7Z05tS+KV94vof3/b+/eo6OuD7yPv+ea\nZGZyz0zuCRDCJSQh4aooIIqo7VHqDaxLdvv80ad73F2f7WUra7Xq0z7bg127e3b19OK2+yiePtLV\n1mKrXBRQLDdbAiThkkBCCLkwuSeT+0zm+SOQkqXYiAnzy/B5neNJMpn88p3z+Y358Pt9f99fSjQP\n3pbDvGkJoR6uiISYCo6ITBkmk4k5CbnMjp/J0ZYK3q7eTlNPJY6iM8QNzOJseQovvN5N3rR4HlyZ\nw/TUmFAPWURCRAVHRKYck8lEkTufwqQ8Pm4q5bc1O2gNniBm4WmcvlkcPznE8VfaWTTHw/3Lp5Oa\n6Az1kEXkOlPBEZEpy2wyszR1IQuT5/O7hkO8e/Y9OpwVxC0+g61tFr+vDHD4VDO3Fqay9tbpxEdr\nDR2RG4UKjohMeVazlZUZy7gpdREf1P2OHef24Is7RsISJ1zI5cNjAfZXNLF6YQb33JSNK0pr6IiE\nOxUcEQkbERY7a6at4tb0pbx37kN21+1l0H2EpORYButyePdggD1HGvjcTVmsXpRJhNbQEQlbk7pw\nRGVlJatXr+a1114bfezVV19l3rx59PT0jD72zjvv8NBDD7Fu3Tr+5V/+5YrtbNy4kXvvvZeSkhJK\nSkrYs2fPZA5bRKY4h83BfTl389yyjazMuIX+oI+h9MN4ln6MKbaJNz84w8Yf72d3aT3+wHCohysi\nk2DSjuD09vbyne98h5tvvnn0sbfeeovW1lY8Hs/oY319ffzzP/8zW7duxel0sm7dOu69915mzpw5\nZntf+9rXWLVq1WQNV0TCUIw9mnWz1nJH5nLeqXmPg01/IDjt9yRP89BeNY3N2wfZfugcD6yYwaI5\nHsxaQ0ckbEzaERy73c7LL788psysXr2ar371q2MW4oqKimLr1q24XC5MJhNxcXF0dHRM1rBE5AaU\nGJVASd46nlr6NYrcBXThxZJ7iJRFx2jzN/GjX1fwv//vx5RXtxKGa5+K3JAm7QiO1WrFah27eZfL\n9Sefe+nxU6dOUV9fz/z58694zmuvvcZ//ud/kpiYyNNPP01CghbyEpFPJ8WZzJcLSqjtquPt6u2c\naKvENreBRH8mdSey+MEvfMzJiuPB23LISYsN9XBF5DMwzCTjs2fP8o1vfIMXXngBm23sFQ5r164l\nLi6OuXPn8pOf/IQXX3yRb3/721fdVny8A6t1cicPXm1paAkt5WJcRsrG7c5jUU4eFd5K/t+xX1PZ\nWk1UwXliBqdz6kQG/+fVDm4uSKXknrlkJhtn3JPBSLnIWMrmszFEwWlqauJv/uZveP7555k7d+4V\n3798Hs/tt9/Os88++4nba2/vneghjqF7hBiTcjEuo2bjMaXyeOFXKG89wdvV26n3VeOYf5ZI33T2\nn+znQHkjtxSk8oVbp5MQExnq4U44o+YiyubTuFoRNMTtd7/1rW/x7LPPMm/evD/5/b/7u7+jrq4O\ngIMHD5Kbm3s9hyciYcxkMlGQlMfGxf+L/5H3RRKi4ul1ncG14CPicqv5qKKWjT8+wJZdVfj6hkI9\nXBEZp0m7m3h5eTmbNm2ivr4eq9VKcnIyy5YtY9++fRw5coSCggKKiop4+OGH+cIXvkBhYeHoz37p\nS18iLS2NnTt38vjjj3PgwAG+//3vExUVhcPh4Hvf+x6JiYlX/d26m/iNSbkY11TKJjAcYH/jx7x7\n9n06BjqxmSLAO52u2gyibBHctTiL2xdmhMVigVMplxuNshm/qx3BmbSCE0oqODcm5WJcUzGbwcAQ\nH9bvY0ftbnqGeokwRTFYP4Pe+nQirDaWF6ayZnE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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "wxzonAKFUzVs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Task 2" + ] + }, + { + "metadata": { + "id": "ey0Lr1Q-UYSX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 364 + }, + "outputId": "305b24ad-0191-4461-a9da-33d26e8c413f" + }, + "cell_type": "code", + "source": [ + "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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vDU/AH1S+5i5XreIQE6MIRRP4u13disehh+6BEXztnhrVcwaAaCyB7oER1e/Q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\nV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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "Xt3E-lkoU6lI", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n", + "#\n", + "def transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = transform_features(training_examples)\n", + "selected_validation_examples = transform_features(validation_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "aVA0cTegVCHe", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "f99c2367-497f-4fab-e3ae-07739d961626" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=2000,\n", + " batch_size=100,\n", + " training_examples=selected_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=selected_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 196.35\n", + " period 01 : 157.44\n", + " period 02 : 122.28\n", + " period 03 : 95.11\n", + " period 04 : 84.07\n", + " period 05 : 83.36\n", + " period 06 : 82.90\n", + " period 07 : 82.51\n", + " period 08 : 82.13\n", + " period 09 : 81.77\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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9sY/MjiQiItKg1WuBCQ0Nda1v2bt3L1FRUfTr148tW7ZQUVFBRkYGmZmZtGvX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\nDz/8EMXFxZg7dy62bduGqqoqyLI8gK+KiAKFZ2CIKGh27Ni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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "1IznvVy0VLwy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution" + ] + }, + { + "metadata": { + "id": "qvAGrcBAVFdo", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "5IGGFnDUV0e6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "b9f93703-6f20-4d13-c808-fe5f98c8a3cb" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=3,\n", + " training_examples=selected_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=selected_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 226.97\n", + " period 01 : 216.86\n", + " period 02 : 206.82\n", + " period 03 : 196.85\n", + " period 04 : 187.03\n", + " period 05 : 177.34\n", + " period 06 : 167.83\n", + " period 07 : 158.45\n", + " period 08 : 149.29\n", + " period 09 : 140.44\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Dv54byR1Xj8HDxY7Pdh3jvjf2caK4ngXhc7gz5s8EOgewq3AfD+x5kkPlmb1d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\nJcWcNKa/f6KQ88Y1soXkBpmK1SVt1CRd1OWJNlGhAayYZcV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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "4CA2f4e6V6CH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From a607a4c4cc84a588a2b0771f0a991b737eedf159 Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Thu, 14 Feb 2019 22:53:58 +0530 Subject: [PATCH 06/11] Feature Crosses Programming Exercise --- FeatureCrossesProgram.ipynb | 1598 +++++++++++++++++++++++++++++++++++ 1 file changed, 1598 insertions(+) create mode 100644 FeatureCrossesProgram.ipynb diff --git a/FeatureCrossesProgram.ipynb b/FeatureCrossesProgram.ipynb new file mode 100644 index 0000000..ac20873 --- /dev/null +++ b/FeatureCrossesProgram.ipynb @@ -0,0 +1,1598 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "FeatureCrossesProgram.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "p8W2hwjt5lMa", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "k9DV6A2i7BvD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Feature Crosses" + ] + }, + { + "metadata": { + "id": "NGofoZnj7Gc1", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Setup**" + ] + }, + { + "metadata": { + "id": "gradpjmR6Ql2", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DjtlN5Ht7MUR", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "klttOJTg7Ui4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "82796022-b66c-4d86-de91-8819743477f9" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 206.6\n", + "std 115.5\n", + "min 15.0\n", + "25% 119.4\n", + "50% 180.6\n", + "75% 264.2\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "Bw7h9vLe7ZJn", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bRIiUWfM7dRF", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "R7gWF7io7ks5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## FTRL Optimization Algorithm\n", + "\n", + "High dimensional linear models benefit from using a variant of gradient-based optimization called FTRL. This algorithm has the benefit of scaling the learning rate differently for different coefficients, which can be useful if some features rarely take non-zero values (it also is well suited to support L1 regularization). We can apply FTRL using the [FtrlOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer)." + ] + }, + { + "metadata": { + "id": "z4ggUWiz7frA", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VpZK5BsV7p_B", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 741 + }, + "outputId": "e668a854-3bb6-4b4c-adc3-0eeb9f8d7b7c" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 191.49\n", + " period 01 : 160.94\n", + " period 02 : 138.35\n", + " period 03 : 127.15\n", + " period 04 : 110.94\n", + " period 05 : 269.62\n", + " period 06 : 280.06\n", + " period 07 : 265.50\n", + " period 08 : 238.89\n", + " period 09 : 210.65\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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78ec/P0C7du0ZP34is2fP5L33FnL06BFmz34He/uax5phYeF07NiJJ598FIPB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\njTcSHKxhNW8zsEcUC6LO57HlURSEfMV20nlg3d+4buBVJAY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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "rJR0nl9p8bZv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## One-Hot Encoding for Discrete Features\n", + "\n", + "Discrete (i.e. strings, enumerations, integers) features are usually converted into families of binary features before training a logistic regression model.\n", + "\n", + "For example, suppose we created a synthetic feature that can take any of the values `0`, `1` or `2`, and that we have a few training points:\n", + "\n", + "| # | feature_value |\n", + "|---|---------------|\n", + "| 0 | 2 |\n", + "| 1 | 0 |\n", + "| 2 | 1 |\n", + "\n", + "For each possible categorical value, we make a new **binary** feature of **real values** that can take one of just two possible values: 1.0 if the example has that value, and 0.0 if not. In the example above, the categorical feature would be converted into three features, and the training points now look like:\n", + "\n", + "| # | feature_value_0 | feature_value_1 | feature_value_2 |\n", + "|---|-----------------|-----------------|-----------------|\n", + "| 0 | 0.0 | 0.0 | 1.0 |\n", + "| 1 | 1.0 | 0.0 | 0.0 |\n", + "| 2 | 0.0 | 1.0 | 0.0 |" + ] + }, + { + "metadata": { + "id": "GfLfHPGW8jsh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Bucketized (Binned) Features\n", + "\n", + "Bucketization is also known as binning.\n", + "\n", + "We can bucketize `population` into the following 3 buckets (for instance):\n", + "- `bucket_0` (`< 5000`): corresponding to less populated blocks\n", + "- `bucket_1` (`5000 - 25000`): corresponding to mid populated blocks\n", + "- `bucket_2` (`> 25000`): corresponding to highly populated blocks\n", + "\n", + "Given the preceding bucket definitions, the following `population` vector:\n", + "\n", + " [[10001], [42004], [2500], [18000]]\n", + "\n", + "becomes the following bucketized feature vector:\n", + "\n", + " [[1], [2], [0], [1]]\n", + "\n", + "The feature values are now the bucket indices. Note that these indices are considered to be discrete features. Typically, these will be further converted in one-hot representations as above, but this is done transparently.\n", + "\n", + "To define feature columns for bucketized features, instead of using `numeric_column`, we can use [`bucketized_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column), which takes a numeric column as input and transforms it to a bucketized feature using the bucket boundaries specified in the `boundaries` argument. The following code defines bucketized feature columns for `households` and `longitude`; the `get_quantile_based_boundaries` function calculates boundaries based on quantiles, so that each bucket contains an equal number of elements." + ] + }, + { + "metadata": { + "id": "nNWY8syk7t0e", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_quantile_based_boundaries(feature_values, num_buckets):\n", + " boundaries = np.arange(1.0, num_buckets) / num_buckets\n", + " quantiles = feature_values.quantile(boundaries)\n", + " return [quantiles[q] for q in quantiles.keys()]\n", + "\n", + "# Divide households into 7 buckets.\n", + "households = tf.feature_column.numeric_column(\"households\")\n", + "bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " california_housing_dataframe[\"households\"], 7))\n", + "\n", + "# Divide longitude into 10 buckets.\n", + "longitude = tf.feature_column.numeric_column(\"longitude\")\n", + "bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " california_housing_dataframe[\"longitude\"], 10))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "C1D3tjDa8u8p", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Train the Model on Bucketized Feature Columns\n", + "**Bucketize all the real valued features in our example, train the model and see if the results improve.**\n", + "\n", + "In the preceding code block, two real valued columns (namely `households` and `longitude`) have been transformed into bucketized feature columns. Your task is to bucketize the rest of the columns, then run the code to train the model. There are various heuristics to find the range of the buckets. This exercise uses a quantile-based technique, which chooses the bucket boundaries in such a way that each bucket has the same number of examples." + ] + }, + { + "metadata": { + "id": "U6j9PXh68paN", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + "\n", + " #\n", + " # YOUR CODE HERE: bucketize the following columns, following the example above:\n", + " #\n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "AL9LKz0V80Dd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "31dbe290-5baa-4627-aaff-12493584089a" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 169.96\n", + " period 01 : 143.69\n", + " period 02 : 127.19\n", + " period 03 : 116.01\n", + " period 04 : 108.00\n", + " period 05 : 102.08\n", + " period 06 : 97.50\n", + " period 07 : 93.89\n", + " period 08 : 90.88\n", + " period 09 : 88.42\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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J54xtk/piu9Qb26XelI3NXANTlbg5uBJerztZhdlUaxiL2d7Eks0nKCjURI8i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\nUZrrAkYnCPcPBqnx+jnqredMZyP+4RaCsQHa47x0dHk50vU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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "KxkXL29P9yr_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "###Solution" + ] + }, + { + "metadata": { + "id": "nsSOk4zU8376", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RnN2HkTC949T", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "0d2ddf19-ebaa-4b6b-8b91-e57259ee52dc" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 169.89\n", + " period 01 : 143.60\n", + " period 02 : 127.04\n", + " period 03 : 115.79\n", + " period 04 : 107.72\n", + " period 05 : 101.78\n", + " period 06 : 97.24\n", + " period 07 : 93.62\n", + " period 08 : 90.63\n", + " period 09 : 88.22\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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sm9QX26Xe2C71pmxs5hyYqsRsZ2ZYwzCKjCKoeRwHsx1f7TxLfkGRtUsTERGp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\nuSprGVdlLaOlr5V9/oMcbD5EffQIlEKRo4DBZh/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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "l3HPiuu7_SUL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Feature Crosses\n", + "\n", + "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n", + "\n", + "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n", + "\n", + "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model." + ] + }, + { + "metadata": { + "id": "wRhJz3NR_YJW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Train the Model Using Feature Crosses\n", + "\n", + "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n", + "\n", + "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`." + ] + }, + { + "metadata": { + "id": "la68fkJs98vU", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n", + " long_x_lat = tf.feature_column.crossed_column(set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person,\n", + " long_x_lat])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "CAW8SDjY_e5p", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "02d11895-07b6-4b58-d2ea-ca612f5d603a" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 163.91\n", + " period 01 : 135.64\n", + " period 02 : 118.49\n", + " period 03 : 107.13\n", + " period 04 : 99.04\n", + " period 05 : 92.98\n", + " period 06 : 88.42\n", + " period 07 : 84.83\n", + " period 08 : 81.93\n", + " period 09 : 79.51\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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bxsS+Y7nsO5br6LJERETqFQWYOmYymRjTKhEA1+aHAJi36jCGYTiyLBERkXpF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\nSHOgh0hmH0bHIFFnkIZoMw3BZgAMGCh2FMY7NK5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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "V8GV_DnbAz8J", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "###Solution" + ] + }, + { + "metadata": { + "id": "Aa0ixsny_jzb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person,\n", + " long_x_lat])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mBFIUfVQA8qT", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "6508fbe7-3c2d-4260-b296-95d36b868947" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=120,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 163.36\n", + " period 01 : 135.05\n", + " period 02 : 117.84\n", + " period 03 : 106.44\n", + " period 04 : 98.33\n", + " period 05 : 92.24\n", + " period 06 : 87.67\n", + " period 07 : 83.98\n", + " period 08 : 81.06\n", + " period 09 : 78.65\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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fx8KUtNqPxe7c2Xmra2qtnEpERKR5UIGxkrbugfT1jyS9NJ2IXlVkF5SzZV+G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\nhuP4tXLW8moREWmwemdghg0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Gg7lHsOuSRpuadpw52JZ3F5XQtV0r7ojpSGgbD2vHFRFp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\nvqWPhrM9jFl7MDnCmJxhgpN9BEfe4kDXWwA4rHb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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "XYEMf4OHBJDt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 61e2d64f4968a6101fc13fed6870bd842c96448d Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Thu, 14 Feb 2019 23:32:53 +0530 Subject: [PATCH 07/11] Logistic Regression Programming Exercise --- LogisticRegression.ipynb | 1703 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 1703 insertions(+) create mode 100644 LogisticRegression.ipynb diff --git a/LogisticRegression.ipynb b/LogisticRegression.ipynb new file mode 100644 index 0000000..888ffe4 --- /dev/null +++ b/LogisticRegression.ipynb @@ -0,0 +1,1703 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "LogisticRegression.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "NxO2_WI6DODz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "04BuEO81Debn", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Logistic Regression" + ] + }, + { + "metadata": { + "id": "QwRcOdwEDhOc", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Reframe the median house value predictor (from the preceding exercises) as a binary classification model\n", + " * Compare the effectiveness of logisitic regression vs linear regression for a binary classification problem" + ] + }, + { + "metadata": { + "id": "gh70qhTRDknj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Frame the Problem as Binary Classification\n", + "\n", + "The target of our dataset is `median_house_value` which is a numeric (continuous-valued) feature. We can create a boolean label by applying a threshold to this continuous value.\n", + "\n", + "Given features describing a city block, we wish to predict if it is a high-cost city block. To prepare the targets for train and eval data, we define a classification threshold of the 75%-ile for median house value (a value of approximately 265000). All house values above the threshold are labeled `1`, and all others are labeled `0`." + ] + }, + { + "metadata": { + "id": "ZSsNl5qlDoLH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "Run the cells below to load the data and prepare the input features and targets." + ] + }, + { + "metadata": { + "id": "r0vJWo6bDbLi", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "iCLUfDdVDsaK", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Create a boolean categorical feature representing whether the\n", + " # median_house_value is above a set threshold.\n", + " output_targets[\"median_house_value_is_high\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "oVZ7akz4D3DK", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "b645a917-d7ba-449f-fbcd-b6bc0c63c084" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value_is_high\n", + "count 5000.0\n", + "mean 0.3\n", + "std 0.4\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 1.0\n", + "max 1.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "VTYxdrlRD8gJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## How Would Linear Regression Fare?\n", + "To see why logistic regression is effective, let us first train a naive model that uses linear regression. This model will use labels with values in the set `{0, 1}` and will try to predict a continuous value that is as close as possible to `0` or `1`. Furthermore, we wish to interpret the output as a probability, so it would be ideal if the output will be within the range `(0, 1)`. We would then apply a threshold of `0.5` to determine the label.\n", + "\n", + "Run the cells below to train the linear regression model using [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)." + ] + }, + { + "metadata": { + "id": "QDLWqs4ND5fP", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "1OkZufdLD_BN", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4CMuBZonEBY5", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_regressor_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ihWi8ZoQEEHH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 741 + }, + "outputId": "c63d373d-125f-4f26-a8e7-72be98188718" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_linear_regressor_model(\n", + " learning_rate=0.000001,\n", + " steps=200,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 0.46\n", + " period 01 : 0.45\n", + " period 02 : 0.44\n", + " period 03 : 0.44\n", + " period 04 : 0.44\n", + " period 05 : 0.44\n", + " period 06 : 0.44\n", + " period 07 : 0.44\n", + " period 08 : 0.44\n", + " period 09 : 0.44\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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N7QdDbRMyzpZZrF0niQxRqhHQNxqQq79gsXaJiIj6IgacLkgaFwoA2J1eaNF2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\ny4V9+/bhySefxOjoqPjx/fv3Y+3atTCZTIv6PqfjdE6d32A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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "cJcOs3qSEoWF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Can We Calculate LogLoss for These Predictions?\n", + "\n", + "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n", + "\n", + "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability. For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n", + "\n", + "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily. Remember, `LogLoss` is defined as:\n", + "\n", + "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n", + "\n", + "\n", + "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n", + "\n", + "Given the predictions and the targets, can we calculate `LogLoss`?" + ] + }, + { + "metadata": { + "id": "yaDh4Ed1EGZr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "52a87e96-1126-418a-85b7-eb8f1af04a22" + }, + "cell_type": "code", + "source": [ + "predict_validation_input_fn = lambda: my_input_fn(validation_examples, validation_targets[\"median_house_value_is_high\"], num_epochs=1, shuffle=False)\n", + "\n", + "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + "\n", + "_ = plt.hist(validation_predictions)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "IjZnjf7VExfR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to display the solution." + ] + }, + { + "metadata": { + "id": "zVipFZptEuwv", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "4d614055-8ced-4300-f773-98f7d0d0309b" + }, + "cell_type": "code", + "source": [ + "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + "\n", + "_ = plt.hist(validation_predictions)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "MZxFFAefE48P", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Train a Logistic Regression Model and Calculate LogLoss on the Validation Set\n", + "\n", + "To use logistic regression, simply use [LinearClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier) instead of `LinearRegressor`. Complete the code below.\n", + "\n", + "**NOTE**: When running `train()` and `predict()` on a `LinearClassifier` model, you can access the real-valued predicted probabilities via the `\"probabilities\"` key in the returned dict—e.g., `predictions[\"probabilities\"]`. Sklearn's [log_loss](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) function is handy for calculating LogLoss using these probabilities.\n" + ] + }, + { + "metadata": { + "id": "13jAz5IXE0D1", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_classifier = tf.estimator.LinearClassifier(feature_columns=construct_feature_columns(training_examples), optimizer=my_optimizer)# YOUR CODE HERE: Construct the linear classifier.\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on training data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions. \n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "JQA_Q7S8E8YJ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "a37dd06d-12b5-44d0-97d4-4bb470fab29a" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.60\n", + " period 01 : 0.57\n", + " period 02 : 0.57\n", + " period 03 : 0.55\n", + " period 04 : 0.55\n", + " period 05 : 0.55\n", + " period 06 : 0.53\n", + " period 07 : 0.53\n", + " period 08 : 0.53\n", + " period 09 : 0.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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wBmazeONWR4cjIiLSalTMXCWMBiNTe08A4HD9JtKzyxwckYiISOtQMXMViQns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\nNOmC38ceQbfaSJlMWjQ0hH81T+o/PDbxicflfBq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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "20bI8H6vFvq0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see the solution.\n", + "\n" + ] + }, + { + "metadata": { + "id": "DoGKLKFNE-ga", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0) \n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on training data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions. \n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "EjwBZS1yFyzR", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "5d9cde4b-a3fa-4a8f-8c5b-0e299705f3bf" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.00005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.61\n", + " period 01 : 0.52\n", + " period 02 : 0.55\n", + " period 03 : 0.54\n", + " period 04 : 0.54\n", + " period 05 : 0.55\n", + " period 06 : 0.57\n", + " period 07 : 0.52\n", + " period 08 : 0.52\n", + " period 09 : 0.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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0VHjdIs5kIDuvksLSWhJjggnWGmRFkxCiS9TbGnhj179osDdwy+AbiPtJwW9b\n0oypfHf0RzJKM+kf0s+zQQrRTTxaM/Pggw+e9DglJcX1//PmzWPevHknva7T6Xj55Zc9GVK3Mrua\n59UwIDYEsyGG3WXZ1DRZMWj1ZzlaCCFOz+F08P6exRTXlTApbhzn9xnh9rEpYQPRqrVklO7m6sTL\nUBTFg5EK0T2kA7AHnbg8G3A1z8uTqSYhRCesyFlLhiWTpLABXJ14WbuO9VP7kRqeTGl9GUdriz0U\noRDdS5IZD+obEYRGrZBf0lIE7GqeVy1TTUKIjtltyeLrw98QHhDG7Wcp+G1LmrGlbcZOi+zVJHoH\nSWY8SKNWER2po6C0FrvDQZxrjyYZmRFCtF9xXSnvZn6ERqXmF0PnnrXgty1DIlJQKSoySnd3cYRC\neIckMx5mNhlotjkoKqsj1D/kWBGwJDNCiPZpsDXw5rGC3xtTZmE+Nm3dEUF+QSSFJpJXU0hFQ2UX\nRimEd0gy42Gn7qAdS2VjFdVN0iRJCOEeh9PBv7I+pqi2mIvjxvKzPiM7fc5hx6aaMmSqSfQCksx4\nmNnUkszkF7cmM9I8TwjRPt/kriWjdDcDQ/tzTeLlXXLO1rqZjFJJZkTPJ8mMh8WZjq1oOlYEfLx5\nnhQBCyHObrcli68OfUOYfyi3D7mpQwW/pxPqH0J8cBwHKg9R2+y5bWGE6A6SzHhYUIAGY2gAecVW\nnE6na547V+pmhBBnUVJXyj/3tBT83jn05i7vTzUsMhWH08FuS1aXnleI7ibJTDcwmwxY65uptDYR\n4h9MiNYgIzNCiDNqONbht97WwA3J17pGdbuS1M2I3kKSmW7gKgIuPj7VVNlYRVWjFAELIU7ldDp5\nP+sTimqLmRg7hgv6jvLIdfrooogKMrKnbC9N9iaPXEOI7iDJTDcwt9bNHEtmWjsBy6aTQojT+SZ3\nLTtKdzEwtD8zB1zh0WulRabS7Ggmu3y/R68jhCdJMtMNzKcsz5bmeUKI08ssy+Z/h1Z0ecFvW2Sq\nSfQGksx0gzCDP7oAzQnLs1v3aJK6GSHEcSV1Ft7N/Aj1sQ6/3bEhbXxwHCFaA7sse7A77B6/nhCe\nIMlMN1AUBXOUgZLKeuobbceKgIOl14wQwqXB1sibu96j3lbPDckziQ+O65brqhQVQ42p1DbXcagq\np1uuKURXk2Smm7RONeW3TjUFx1DVVC1FwEIInE4n/876mKO1xUyIHcOFfc/r1usPi5SpJtGzSTLT\nTVqLgPNLTp5qkiJgIcTK3HVsL93FgNAErvVwwe/pJIUlEqAOIKM0E6fT2e3XF6KzJJnpJqcsz5bm\neUIIILNsL18eWk6of0i3FPx0+ip9AAAgAElEQVSejkalYUhkCuUNFRRYj3b79YXoLElmukmf8CA0\nahV5x4qAZXm2EKK0rox3Mz9EfazDb7DW4LVY0o5NNe0s3e21GIToKElmuolGrSLGqKPQYsVmdxDi\nbyDUP4S8alnRJMS56MSC3zndWPDblsERyWgUtdTNiB5JkpluZDbpsdmdFJW1bOoWZ2gtAq72cmRC\niO7kdDr5IPsTjtQWMT7mIkZ3c8Hv6QRqAkgOH0ih9SiW+jJvhyNEu0gy043MUT/ZQVua5wlxTlqV\nt55tJTtJDElg1sArvR2OyzDXVJOMzoieRZKZbuTqBCzN84Q4Z2WV7eOLg8sI9Q/hjqHeKfhty1Dj\nYBQUdkgyI3oYt5MZq7XlH2CLxUJ6ejoOh8NjQfVWscaf9po5lsxI8zwhzgmW+jLeyfwAtaLijiFz\nvVrwezrBWgMJIfEcqsqhpsnq7XCEcJtbycwf//hHli1bRmVlJXPmzOH999/nySef9HBovU+gvwZT\nWCB5xTU4nU6CtS1FwLKiSYjer9HexJu7/kWdrZ7ZyTNJCDF7O6TTGmZMxYmTXZYsb4cihNvcSmb2\n7NnDddddx7Jly7jmmmt4+eWXyc3N9XRsvZLZpKe2wUZ5dWPLY0MsVU01VDZWeTkyIYSnOJ1OPsj6\nhELrUcbFjOai6PO9HVKbWpdoZ8gSbdGDuJXMtHaEXLduHZMmTQKgqanJc1H1YnFtFAHnS92MEL3W\n6vwNbC3JoH9IP58q+D0dU1Ak0bo+ZFfsp8HW6O1whHCLW8lMQkICl112GbW1tQwaNIjPP/+ckJAQ\nT8fWK5lNx+pmiqVuRohzQVb5Pj4/sJQQbTB3DJmLRqXxdkhnlWZMxeawkVW+z9uhCOEWt75Vf/rT\nn9i3bx+JiYkADBw40DVCI9rn+PLsn65okmRGiN7GUl/Ou7s/RK2o+MXQuYT4+1bBb1uGRaayPGc1\nGaW7GWEa6u1whDgrt0ZmsrKyKCoqQqvV8re//Y2//vWv7NsnGXtHhOq1GIL8XHs0GbT6lk7AMs0k\nRK/SZG/izV3vUWur4/rkq0kIifd2SG6LM8QQ5h/K7rIs7A67t8MR4qzcSmb+9Kc/kZCQQHp6Ort2\n7eLxxx/nH//4h6dj65UURcFs0mOpaqCuoRmAeEMs1VIELESv0dLh91MKrUcZG3MhY6Iv8Go8Nruj\nXbthK4pCmjGVelsD+ysPeTAyIbqGW8mMv78//fr1Y/Xq1Vx//fUMGDAAlUr67XVUaxFwa7+Z1k0n\npW5GiN5hdf4G0ot30D8knusGXuXVWCxV9Ty08Dte+XhHu44bJquaRA/iVkZSX1/PsmXLWLVqFWPH\njqWyspLqatlPqKNai4BdnYCDW7c1kKkmIXq67PL9xwp+DV4v+G222Xnts91UWZtYv72Qxib3p4wG\nhCYQpAlkp2UPDqc0SRW+za1k5v777+d///sf999/P3q9nvfff59bbrnFw6H1Xqcuz24ZmZHmeUL0\nbGX15byT+QEqRcUdQ28mxD/Yq/F8sHIfuUU16AP9aGq2s+uQ+xtIqlVqhkYOprKxSlpHCJ/nVjJz\n4YUX8sILL2A2m9mzZw933HEHV13l3aHTnqxPeCB+GpVrebZBqyfMP5TcmoJ2zWsLIXxH07EOv7XN\ndcxOupr+Xi743ZBxhA0ZR4mPMnDvtWkApO8tadc50oytU02yV5PwbW4lM6tWreLSSy9lwYIFPPbY\nY0ydOpX169d7OrZeS61SEWvUU2ipxWZvGb41G2KoabJS1STTd0L0NK0FvwXWI4yJvoAxMd4t+M0p\nqubf3+xDF6Dh7muGkBgTTJ+IIDIOltFsc3+qaVB4En4qjdTNCJ/nVjLz1ltv8eWXX/Lpp5+yZMkS\nPvnkExYuXOjp2Ho1c5Qeu8PJEUtty2NpnidEj7U2/1vSi3eQEBzPdUkzvBqLtb6Z15bsxm538Isr\nUzGGBqIoChcNjaaxyc7uw+Vun8tfrWVQeDJFdSUU17ZvVEeI7uRWMuPn50d4eLjrcVRUFH5+fh4L\n6lzg6gT80xVNUjcjRI+yt/wAnx1cSrDWwB1Db8LPiwW/DoeTN7/MpKy6gavGJpCWGOF6bcywaADS\ns0vbdc7Wqaadlj1dF6gQXcytZEan0/HOO++QnZ1NdnY2b731FjqdztOx9WqtRcC5xSfv0SQrmoTo\nOcrqK3g7898oKPxi6FxC/b27zcuXmw6z+3A5aYkRXDmm30mvDYwLJTzYnx0HLK7pbXcMjRiEgiJT\nTcKnuZXMPPPMM+Tk5PDwww/zyCOPUFhYyLPPPuvp2Hq1WKMOBU4pAs6TImAheoQmexOLdr1HbXMd\n1yXNoH9IP6/Gk3HAwpebcogMCeCOKwajUpSTXlcUhVFJJuobbezJqXD7vHqtjgGhCRyuzqOqUWr6\nhG9yazw0IiKCp59++qTnDh48eNLUk2ifAK0GU3gQeSVWnE5nS2fg4FgySndT2VhFWECot0MUQrTB\n6XTyYfZ/ybce4aK+P2Oslzv8llTWs+h/e/DTqJh/zVD0gacvAxiVbGRlej7pe0tOmoI6m2HGIeyv\nPMROyx7GxVzYVWEL0WU63Mb3qaee6so4zknxUXrqG22UVTUAMtUkRE+xtmAjPxZvp1+wmeuTr0b5\nyShId2pqtvP6kl3UNdqYe2ky8X3a3sxyQGwIIXot2/eVtmuqKS1yMCDdgIXv6nAyI1MhnRfX2glY\ndtAWosfYV3GAzw58TbDWwC+GzvVqwa/T6eT9FXvJK7EycXg0Y9P6nvHnVYrCyCQjtQ029uZXun2d\niMBw4vTR7Ks4SL2tvrNhC9HlOpzMePM3kd7C3NoJuPjkTsCSzAjhm8rqK3h79wcA3DHE+wW/63cc\nYdPuIhL6GrhhcpJbx5yXbAJg6972r2qyO+1klu1td5xCeNoZf6X49NNP23yttLR9XwRxqp8uz9Zr\ndYT5h5JfXeiqoxFC+IYmezOLdv8La3Mts5OuITG0n1fjOXSkmg9X7UMf6MfdVw/FT+Pe76ZJcSHo\nA/3YtreEm6YkoVK59/fMMOMQvj68kozS3ZwXNbwzoQvR5c6YzGzdurXN14YPlz/MnRWi9ydYp3WN\nzADEB8eyQ4qAhfApTqeTj/b+l/yaQi7qe77Xi2Cr65p4/fNd2B1OfjkjlYiQALePVatUjEwysiHj\nCPsLKkk2h7l1XLSuD5EB4WSWZdPssHl1ek2Inzrjn8bnnnuuu+I4Z5lNenYfLsda34w+0I84Q0sy\nk1dTIMmMED5iXcEmfijaRnxwHNcnebfg1+Fw8sYXmZRXNzJzfH9S+7V/Vel5yS3JTPreUreTGUVR\nSDOmsib/W/ZVHCA1IqXd1xXCU9xKrW+88cZTvrxqtZqEhATuvvtuoqKiPBLcuSAuqiWZyS+xMig+\njHhX3Uwhw4xDvBydEGJ/xUGWHPgKg1bPnUNvxk/t3e7nn317iKzcCoYPiOSy0R3bzDIlPgxdgIZt\n+0q5YfLAU3rStGWYcQhr8r8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h2iTJjI9pLQLOL2mZagryCyIyIJz8msJe/RvPmrwNNNgb\nmBI/kQBN963oSApL5Dcj70Lvp2Pxvs/5+tA3vfpz7knyqgt4cevrfJD9CU2OZmYkTufRn/2WQeFJ\n3g6tQ5xOJ+98nUVxeR3TLjC7RkB8TZjBnwExIezNr6S6tsnt49JcU027PRWaEG2SZMbHxLUWARf/\npAjY1nuLgK1Ntawt2IhBq2d8zOhuv36cIYb7R91NREA4S3NW8fG+z2X7Ay+qba7jo71L+Gv6K+RU\n5zHKNIwnLniQS+MvRtPDppROtPyHPLbuKyXFHMq1E/p7O5wzGpVsxOmEbfvdn2oaHJGMRlGzU3bR\nFl4gyYyPiYnUoVIU8kpOLALu3c3zVuWtp9HexNT4SV7rIGoKiuSBUXcTo+/LhsLNvJv5Ic0Om1di\nOVc5nA42Fm7hqS1/ZWPhFqJ0Ju4dfie3Dfk5YQHeX7bcGVk55Xy67iChei2/nDEEtY/3zxqVfGyq\nKdv9qaZATQDJ4QMptB7FUl/mqdCEOC3f/kadg7R+avpGBJFfYsVxbLqjNzfPq26qYX3BJkL9Qxgb\nfYFXYwnxD+Y3I+4iMSSBbSU7+b+Md2mwNXg1pnNFTnUez6e/ykd7l2B32Jk54AoePf83JId7Zo+i\n7lRe3cD/fZmJSlG4+5qhhOh8v+V/ZEgg/foYyMqtxFrf7PZxsqpJeIskMz4oLkpPY5Od0op6oHcX\nAX+Tu5YmRzNT4yf5RMfWIL9A7hl+B0MjB5NdsZ+Xt79JTZP17AeKDqlpsvJB1qe8kP4aeTUFnB81\ngicufIhLzON7RfO11sZ4NXXNzLlkIANiek7X6fNSTDicTra3Y6ppqHEwCookM6LbSTLjg8ymY52A\nTywCDoxwdQLuLSobq/i2cAth/qGMjj7f2+G4aNV+/GLIXEb3PZ+8mgJe2vY6ZfW9s17JWxxOBxsK\nvuPpLc/z3dEf6KuL4jcj7uKW1BsI8Q/2dnhd5j+r93PwSDUXpkYxaWTnOqp3N9dUUzsa6AVrDSSE\nxHOoKkd+CRDdSpIZH2SOOk0RsCGGOls9Zb2oCHhFzlpsDhvTEy7xuV4hapWan6fM4tL4iymps/Di\n1tc4Yi3ydli9wqGqXP764z9YvO9zHE4nswZexcPn38fAMN8uim2vzbuLWLOtkFijjnlTfacxnrui\nwoKIM+nJPFxOXYP79WPDjKk4cbLLsseD0QlxMklmfFDriqb80xQB95bmeeUNFXx35HsiA8K5sM95\n3g7ntBRFYUbidGYOuIKqpmpe2raQg5U53g6rx6ppsvL+no95cetr5FuPcEGfUTxx4UNcHDe2V0wp\nnSi/xMp7y7MJ9Fcz/5qh+Gt75vsblWzE7nCSccDi9jFpUjcjvECSGR9kCNISZvD/ycjMsWSml9TN\nLM9Zg81pZ3rCZJ//h+wS83huHjSbRnsjr+xYxG5LlrdD6lHsDjvr8jfx1Ja/sqUonVh9NPePvJub\nB88mxN/g7fC6XF1DM68t2UWTzcEdlw8mKrxr9xjrTuclt/TCSW9HAz1TUCTRuj5kV+ynweb+lghC\ndIYkMz7KbNJTaW1yNa3qTSuaLPVlbD76I6agSM6PGuHtcNxyQd9R/HJoyy7vb+x6j++PbvVyRD3D\ngcrD/CX9H3yy/wtA4fqkq/ndeb8mMbSft0PzCIfTyVtfZVFSWc/lo+MZcaybbk8VHamjb0QQuw+X\n09Dk/lRTmjEVm8NGVvk+D0YnxHGSzPiouKjWIuCW0Zkgv0CMvaQIeFnOahxOB5f1m+LzozInGhI5\niHtH/AJ/tT//ylrM6rwN3g7JZ1U1VvPPzP/wt20LKbQeZXTf81lw4UNMiL2oR93z9lq6OZcdBywM\n7hfGNeN6Rw3Qeckmmm0Odh50v3fMMOkGLLqZJDM+ytxaN1N8ct1MSxFwubfC6rSSulJ+KNpGH10U\no6KGeTucdusf0o/7R/6KEG0wSw58xecHlvb45LIr2Rx2Vudt4Oktz/Nj8TbiDDE8OGo+Nw26DoNW\n7+3wPCrzcDmfbThEeLA/d16VikrVswp+29K67UJ6O1Y1xRliCPMPZXdZFnaH3VOhCeEiyYyPcq1o\nOqEIuHWqKa8HTzUtPbwKh9PB5QlTUCk9849ftL4PD4yajykokpV56/gg+9Nz/i/s6qYavj+6ld+v\neIYlB75CraiZkzyT3533axJC4r0dnseVVTXwxpeZqNUKd189lOAg32+M565Yow5TWCC7DpbR2Oze\nn3NFUUgzplJva2B/5SEPRygE+NZ6WOESGRpIoL/6pCLg+ODjRcAjTWneCq3DimqLSS/eQYy+L8ON\nQ7wdTqdEBIZx/8i7eT3jHTYf/RFrcy23pf4crQ80/usONoeNQ1W5ZJXvI6tsL/nWIwAoKIyJvoCr\nEqeh9/O9HaE9odlm57XPdmGtb+bmqcn0j+49fXKgJTE5L9nE0i257D5U7uo/czbDIlNZX7CJjNLd\npIQP9HCU4lwnyYyPUikKcUY9+wuraGy24++nPmFkpmeuaPr68EqcOLk84dIeOypzIoNWz30j7mTR\nrvfZZdnDqzve4q60W/eJpNgAACAASURBVAjyC/R2aB5RWldGVvle9pTvY1/FARrtLcXpGkVNUtgA\nBocnMSHpfLSN50YS0+rDVfvJKaphzNA+TBge7e1wPGJUspGlW3LZuq/E7WRmQGgCQZpAdlr2cF3S\njF7xnRe+S5IZHxYXZWBfQRWFpbX0jw4mUNNaBFyI0+nsUU24Cq1H2VayE7MhhrTIwd4Op8sEaAK4\na9it/GvPf9hWspO/b/8/5g+7vVd0sW2wNbK/8iB7yloSmBM3DzQFRTIoPJnB4UkMDEvE/9gGocZg\nA6WlNW2dstf5ducR1u84gtmkZ+6lyT3qO9ke/foYiAgOIOOAhWabAz/N2RMTtUrN0MjBfF+0lfya\nQuKD47ohUnGukmTGh7UWAeeV1LiGrs2GWLaWZGCpL8cYFOHN8Nrl68MrAbg84dJO/YXvdDrZl19J\ncKjv9O7wU2m4NfVG9H46NhRu5sWtr3PP8DswBUV6O7R2cTgdFFqLyCrby57yvRyqysXubKmRCFD7\nMywylUERSQwKTyYyMNzL0XpfblEN//5mH0H+Gu6eORStX+9dpaUoCqOSjXzzYz57csoZNsC9P9tp\nxlS+L9rKjtLdkswIj5JkxoeZW5dnn7iiKbglmcmrKegxyUxeTQEZpbtJCDaTGpHSqXOt2lrAR6v2\nk7wxh/nXDEEf6Bs1KipFxfVJV6PX6ll6eCUvbX2d+cNvd00N+qqaJmtL3cux/07cT8dsiGkZfYlI\nJiHY3KuXVLeXtb6Z1z7bRbPNwfxrhmAK7Z1Tiyc6L8XENz/mk763xO1kZnB4En4qP3aWZjIjcbqH\nIxTnMklmfFh0ZBBqlUL+aToB59cU9pilzV8fahmVuaL/1E6NytQ1NPPlxsMowN68Cp7791YemD2c\n8OCALoq0cxRF4fKEKRj8dHy87wv+vu3/+GXaLSSFJXo7NBe7w+4q3N1TvvekJowGrZ4L+oxiUHgS\nKeEDe/1S6o5yOJ0s+t8eLFUNXDWmH2mJPWsErqP6RwcTqteyY78Fm92BRn32qSatWsug8CR2WjIp\nri0hSmfqhkjFuUiSGR/mp1HTNyKI/FIrDocTlUohztBSYJjbQ4qAD1flsbssi8SQBJLDBnTqXF9v\nzqW2wca1E/pjR+Hz9Qd55v2WhCY60neKTsfHXoTOT8d7e/7Dazve4tbUGxluGuq1eCz15S2Fu2Ut\nhbsN9pYW82pFTVJoIoMikhgcnkyMvm+vrfnoSv/blMOuQ2UM6R/OVWMSvB1Ot1EpCqOSTazeWvD/\n7d13fNRXfu//11S1GfUZdQl1IYEkJMA2GEw3Nm7gAus160d+ucluvFlf73qTOCQbJ7/k58S+u5u9\nW+Jt2Xv3YW/Bxl43bLBNscEGgwRIIFDvdWZUR11Tfn+oADaGQW2KPs+/QKP5zpFGGr3nnM/5HMob\nu1mS7NrMcK4hh1JLGSWWMrZImBFzRMKMh0uM0tNsHqCje5CYiCAC1AEYAyJp8pIi4P117wNwb8rM\namUsvUN8UNRMeLAfm5cnEBcbikYJrx6p4d9fLuaph/NIjQuZrWHPWGFUHkGaQH5x/rf8+sLLfCVz\nB6vjbpmXx54s3B3fNl2JaejyIYGGgAhWhheSHZFBemgq/mq/eRmTryit6eSt43VEhvjzl/f6TmM8\nVy3PNHCouJmicrPLYWZpxGIUKCg1l7Elaf0cj1AsVBJmPFyiUcenjJ/CGxMxPvuQGBxPUcc5jy8C\nru6p41JXJRlhaaTPcKnlTx/XYrM72LE2ZarQ8q5bktAFaPjtexX8rz+e5Zvbl7I0xXO+H1nh6Ty1\n7Ov8V8lv+H3Fa1jHBrgzaf2sB1Cn00lLf9vE0lEltT112CYKd/1UWnIjc1gcnkF2RAaRAZ7z/fE2\n5p4hfvV2GSqVkm9uX+ox9VrzKT0+lOBADWerzOy+MwOV8sZLTTptEGmhyVT11NI70ucTO/2E55Ew\n4+ESrigCXrk4avxj+jiKOs7RaG3y6DCzv/byrMxMNLRbOVHWQWKUjltzoq+6bU1uLPoALS++eYEf\n7yvl/9m2mNs+9znulBScwHcK/oqfnPs1b9ceoH+0nx3p98y450b/6ADlE+HlUlclfaOX66oS9HHj\n4SU8k5SQJCncnQWjY+ON8QaGbfzZXVkkRfvead+uUCoVFGQYOHqulcqmXhYnhbl0vzzDEqp6aim1\nlLEm7rY5HqVYiCTMeLiEK7ZnT5osAm60tlAYle+Wcd1IZXc1lT01E39QF037Ok6nk1eOVAPwyPo0\nlNeY1chPj+Tpnfn8732l/Orti1gHx9iywnO2gUYFGfnu8m/y03O/5kjzcaxj/exe/Ahqpeu/fnaH\nnbq+xolt05Xjy4yMnwml1+hYEVVAdkQGi8MzpHB3ljmdTl5+v5LGjn7W5sWyJs83G+O5qjDLyNFz\nrRRXmFwOM7mR2eyreosSs4QZMTckzHg4XYCGiGC/q7ZnT3UC7vPMImCn08k7E7My21I2z+ha52s7\nudTQzdKUCLIXfXlvk4yEUP7+qwX84JVz/PFQFX0Dozx4R4rH1BSF+oXw7YK/4uel/4eijnMMjA3y\nF0u/NtVs7lo6h7qmZl4quqoZtg8D44W7aaHJZIdnsjgigzhdjHRXnUMfl7Ry/HwbSdF6vrpZ2vJn\nJoQS5K+muNLMo5szrvkG4/MiAsJJ0MVS2V3DkG2IALXvb2UX80vCjBdIMOo5V22ht3+EEJ0fAWp/\njIGRNPV7ZhFweVcVNb31LI1czKLgxGlfx+Fw8uqRGhQKeHj9jWtu4o06/uGxQn6w9xzvnmygb3CU\nx7dmurSuPx+CNIF8K/8v+O8LL3Ohs5wfn/0lf5X3Z1NnGI3YR6nqrpnq+dIxePmU4siACFaGLyM7\nIpP00BT81Z6xHd3X1bX18bsPKgnyV/PN7UvQqGXJTq1SsizDwPHSNmpaekmPD3XpfrmGHJr6Wymz\nlLM8etkcj1IsNBJmvEBilI5z1RYaTf0s1Y3vPknUjxcBm4c6ParTrNPp5J2JHUzbkmdWK3P8fBst\nlgFuz40h3uDa0klkaAB//1gh//lqCcdL2xgYGuPr9+V4THdWrUrLXy59nJfLX+VU+xn+s/hFbo1Z\nTnlXFdU9tVOFu1qVlqWR2WSHj3fc9eTaKF9lHRzlv/50HrvdydcfyiEyRGYTJi3PNHK8tI2icrPL\nYSbPsIT9dR9QYimTMCNmnYQZL3C5E7B1arfOZJhptDZ7VJgp6yynvq+RfMOSGXW/HRm186djtWjV\nSravSbmp+wYHafnbryzjp6+f52yVhR/uPceTD+US6O8Zu09UShW7Fz+CThPE4aZjvFHzLgAJulgW\nR2SyODyDlJCkm6qpEbPLZnfwy7fK6OwbYfuaZJe3IS8U2YvCCPBTU1xpYtfGNJdmh2ODoon0D6es\ns5wxhw2N/HyLWSQ/TV5g8oymJtPVreZh/KiA5R5SBDw5K6NAMeNZmYOnG+ntH+WeVYsI0998L5QA\nPzVPPZzHr9+5yOlyE//xu7N8Z2ceoTrP6KuiVCjZkXYPi4ITsDnsLI7IIFi7MHfIeJJu6wgfnWvh\n6LlW+gZGyUuNYNuqRe4elsdRq5Tkp0VyoqydurbLZ8ddj0KhINeQw+GmY1R0VbEkcvE8jFQsFJ5R\nTCCuKyLEnwA/9VVFwPETYaapr+XL7jbvSi1lNFlbKDDmEqub/vbo3oFR3vuskeBADXfdMv2aG41a\nydfvy2FDQRzN5n6ee6mYjq7BaV9vtikUCgqj8rklplCCjBtNHl768zcv8Lcvfspbn9QzZnOwZUUC\nf3lfjksFrgvR8kwDAMUVJpfvk2dYAoy/Vggxm2RmxgsoFAoSjToqm3oYGbXjp1URoPYnKtBAo7UF\nh9Ph9t0sDqeDd2rHZ2XuTp7ZDqY3j9cxMmrnkXWpBPjN7EdUqVTw1c0ZBAdqeeN4Hc+9XMx3Hslf\nsH1CxGUjY3Y+u9jB4eJmGidmPeMMQWwsjOe27Gj8tJ5RZ+WpcpLD8dOqKKow8dC6VJeWmlJCktBp\ngig1X2RXpvtft4TvkDDjJRKidFQ09dBk7idtom1/gj6OjsFzWIY6MQYa3Dq+s6bztA60szK6gOgZ\nnL/S1jnAx+daiQ4PnLV+HgqFgvtuT0YfpOXlgxU8//szfGvHUhZfZ6u38F3mniGOnGnhWGkrA8M2\nlAoFyzMNbCyMJyMh1ON2B3oqrUZFXmoEpy6ZaDL1T9X2XY9SoSQ3MptP205T39c4ox5UQlxJYrGX\nSDSOv1Bc6wTtRqt7l5ocTgfv1n2AUqHkrkWbZnStV4/U4HA6eXhdqkun8t6M9cvi+KsHlmCzO/jP\nV0soKnd9elx4N4fTyYW6Tn68r5Rnfn6CA6caUSoV3LMqiRf+6jae2L6UzMQwCTI3aXnm+BuXoptY\naso15ABwznxhTsYkFiaZmfESiVGTnYCvLAKeCDN97i0CLuo4R/ugidtiVsxoZ1VFYzfnqi1kxIeQ\nnz43O7SWZxkJ8lfzk9fP8+IbF3jszkzWL5v+rivh2YZGbHxyvo3DZ1pon6iXSo4JZlNhPMuzjGjU\n8n5uJpamRKBVKykqN7N9jWtNKrPC0tGqtJSYy9ieuk0CpJgVEma8RGxkECql4nOdgGNRoKDR6r5O\nwHaHnffqPpyYldk47es4rjy2YEP6nL7ALV4Uzt89WsAPXznHSwcr6BsY5b7Vi+RF1Ye0WgY4fKaZ\nTy60MzJqR61SsGpJNBsK4l3aeSNc46dVsTQlguJKM62WAeJc6AelUWnICc/krPk8bQMdM9osIMQk\nCTNeQq1SEhcZRLO5H7vDgUqpxH+yE7C11W1FwKc6zmIasnB77C1EBEy/BqWo3ERdm5UVWcZ5+WOT\nFK1nz0S34DeP19E3OMpXN2WgVEqg8VYOh5OSagsfFjdzqaEbgDC9H9tuTWJtXizBQV9+dISYvsJM\nA8WVZooqzC6FGRhfajprPk+JuUzCjJgVEma8SEKUjkZTPx1dQ8RGjrfAT9THc3rwLOahTqLmuQh4\nclZGrVCxdQazMmM2B/uO1qBSKnhw3Y2PLZgtUeGB7NldyA/3lnDkTAv9g2P8j3uyZenBy/QPjfFx\nSStHzrTQ2Td+flVWYigbCuJZlhHpMcdZ+Kq8tEjUKgXFFSbuvz3ZpfssiViMUqGk1HKBu5Kn/9oh\nxCQJM14k0ajnE9ppNFmvCDNxnO44S1Nf87yHmZNtRXQOd3FH/GrC/F1raX4tR840Y+kdZvPyBIyh\n89syPlTnxzNfXcaP95VyutxE/9AYf71j6Yy3hIu519Bu5VBxM59d6mDM5kCrUbIuP5YNhfEuH38h\nZi7AT03OonBKajpp7xokOjzwhvcJ1ASQEZpKeXcV3cM9M3r9EJ7D6XQCuGXJXt6yeJGpIuAr6mYS\ngxPGPzbPO5rGHDbeqz+ERqnmzqT1077OwPAYb39aT4CfmntXL5q9Ad6EQH8N39mZz7L0SC41dPPC\nH87SNzDqlrGI67PZHZy82M5zLxXzL//3NMfPtxGm92PXxnR++M3VfG1rlgQZN1ieNb6r6eYa6I3v\naioxSwM9b2d32DnSdJxnjv+/vFv/oVvGIG8/vUjC5LEGV2zPjte5pwj409ZTdI/0sCFhDSF+069x\n2f9pAwPDNh5en4ouwH1nJ2k1Kp7YvoTfHqjgeGkb//5yMU/vzCdynmeKxLVNHjPw0blWeieCZm5q\nBBsK4lmSEi5det0sPz0SlVJBUbmZbbctcuk+uYYc9la+QYmljHUJq+d2gGLOXOys4LWqt2kfNBGg\n9p86ame+SZjxIoH+GiJD/Gk09eN0OlEoFPir/TAGGmiax07Ao/YxDtYfRqvUsGUGszKWniE+LG4i\nItifTYXxszjC6VEplfzZXVmEBGnZf6KB/+/lYp5+JJ94o7zTdwen00l1Sy+HipsprjBjdzgJ8FOz\nZUUC6wviiAq78XKGmB9B/hoWJ4Vxoa4Lc88QBhfeBIT6hZAUnEB1Ty0DY4MEaeT59CamQTOvVb3D\nhc5LKFBwe+wt3JNyJ3qte14vJcx4mcQoPWcqzfT0j04dwDheBGzCPGghagbdd111vPUkvaN9bE5c\nN6Mf3Nc/rsVmd7LjjhQ0as9oHa9QKHjwjlT0gVr+eKiK//jdGZ58KJeMBFnTny9fesxAQTy35kTh\nr5WXLU+0PMvIhbouiivMbHXxTLW8yBwa+pq4YLnELTGFczxCMRuGbEO8V3+Io02fYHfaSQ9N4cH0\n+0jQz07H9umSVwUvk2jUcabSTJPJejnMBMdxuuMMjdaWOQ8zI/ZR3q8/gp9Ky6akO6Z9nbq2Pk5e\n7CApSs8t2VGzOMLZsWVFAvpADb/Zf4kf7D3HN+7PYVm6e4+M8HXmniGOnG3hWMnlYwYKMw1sLIgn\nM1GOGfB0+emRKA6M1824HGYMObxVe4ASS5mEGQ/ncDo42VbEWzUHsI71E+4fxo60e8g3LPGI300J\nM14m4Yoi4NzU8S65l481aGZF9LI5ffyPmz/FOtbP1kUb0WmCpnUNp9PJq5MN8tanemy9w2050egC\nNPzsT+f52esXePyuTNbkuvfdh69xOp1crO/mUHEzJdUWnIAuQMO225JYvyyO8GB/dw9RuCg4UEtW\nYhiXGrrp6ht26bmLDooiKtDAxc4KRu2jaFXSC8gTVffUsa/qLZqsLWiVGu5NuZMNCWvRqtxX5/h5\nEma8zOQZTY1uKAIetg3zQeNRAtT+bExYM+3rlNZ0Ut7YQ25qhMcf9rg0JYK/+coyfvRKCf/n3XL6\nB8fYekuiR7wT8WbXPmZAz8bCeFZkGT1m2VHcnMJMA5cauimuNLN5eYJL98mNzOGDxqNc6qqa2uEk\nPEPXcDdvVL9LsakEgBVRBTyQdhehfiFuHtkXSZjxMuHBfgT5q686o8lf7UdUoIHmOe4EfLT5UwbG\nBrkneQuB0yzWszscvHq0BoUCHp7HBnkzkRobwt9PdAt+9WgNvQOjPLIhzWNnlDzZtY4ZuC0nmo2F\ncsyALyjIMPC79yspLje5HGbyDONhptRcJmHGQ4zaR/mg4SgfNH7EmGOMJH0CD2XcR0pIkruH9qUk\nzHgZhUJBglFHeWMPQyO2qeZuCfp42uewCHjINsShxo8IUgeyLuH2aV/neGkbrZYB1ubFuNz63BPE\nRgbxD7vHA837p5uwDo7yZ3cvnvWTvX3R5DEDh840c7H+8jEDd9+axB1yzIBPCdX5kRYfQlVzL739\nI4To/G54n6TgBEK0es53XsTusKNSyqycuzidTopNJbxR/S7dIz0Ea/V8JXUHK6KXueW4nJshYcYL\nJUbpKW/sodncT3r8+C6bpOB4TnecocHaPCdh5nDjMQZtQ9yfchcB6unVMQyP2njjWB1ajZIH1qTM\n8gjnXniwP3//WCE/erWEE2Ud9A/ZeOKBJfhp5cX3WvqHxjhW0srhK44ZyEwIZWOhHDPgy5ZnGqlq\n7uVMpZn1BTduuaBUKFlqyOF4y0lqeuvJCPOOGVtf09jXzKtVb1HbW49aoWJL0nruTFqP/zRf7+eb\nhBkvdGUn4MkwkzDRqKjJ2sLK6IJZfbyBsUEONx1HpwlibfyqaV/n4KkmeidOqA514R2bJ9IFaPib\nXcv42RvnOV/byff/eJb/+XCeWxv+eZovPWagIF569iwAhZkG/nCoiqIK18IMjG/RPt5yklJzmYSZ\nedY7YuXt2gOcbCvCiZM8wxJ2pG0jMiDC3UO7KRJmvNBkEXCTaX6KgA81fsywfZjtydvwV08vhPT2\nj3Dgs0aCg7Qub9v0VH5aFU8+mMtv3r3EybIO/uN3Z/jOI3kLeueNqXuQsvpuiirMXKrvAsAYGsCG\ngjhW58YQ5C9hb6EID/YnJTaYisYerIOj6ANvvIyYEZaKv8qfEksZD6bfKwX282DMYeNo03EO1B9i\n2D5CbFA0D6XfR2Z4mruHNi0SZrxQdEQgapXiqjOa/NV+RAUZZ70TcP/oAEeajxOs1bM27rZpX+eN\n43WMjNnZuSHNJ5qeqVVK/sc92QQHann/dBPPTRx/EBMxve3q3mZweIxLDT2U1XdRVteJuWd46ral\nKRFsLIxjSUqEFEkvUIWZBmpb+zhbZWFt3o3bGaiVapZEZlHUcY7m/tapmWYx+5xOJxc6L/Fa1duY\nhzoJUgeyM2M7q2NXenW9kvf/VVmA1ColcZE6ms0D2OyOqSLURH0c7QMdmAYtRM9S3cwHjUcZtY9y\nf8pd0+4B0WIZ4OOSVmIiAlmTFzMr4/IESoWCnRvSCA7Ssu9oDf/+8hn+58O5pMZ63rbFmbI7HNS1\nWrlQ18nF+m5qW/twTJyQG+CnYll6JEuSw7ljRRJKu93NoxXuVphp5NUjNRRVmFwKMzC+Rbuo4xwl\n5jIJM3OkbaCD16re5lJXJUqFknXxq7k7ebNPHCUxp2Hmueeeo6SkBIVCwZ49e8jNzZ26bcOGDURH\nR6NSjSfB73//+0RFjXeCHR4e5p577uGJJ55gx44dczlEr5UQpaOhw0p71+DUKcGJ+nhOtZ+h0do8\nK2Gmd8TKR82fEuoXwurYldO+zr4j1Tid8NC6VJ8r+lQoFNx9axK6AA2/PVDO//rDWf56+1KWpHjX\nevO1mHqGKKvroqyui0sNXQyNjIcUhQJSYoPJWRTOkuQIkmP1U8+rITwQs9l6vcuKBcAYGkBilI5L\n9d0MDI+5tMyYE5GJWqGi1FLGPSlb5mGUC8fA2CD76z7gWMsJHE4Hi8MzeDD9XmKCPK/7+nTNWZg5\ndeoUDQ0N7N27l5qaGvbs2cPevXuv+pxf/epXBAV9cVr+xRdfJCTE997dzqbEqRO0+68KMzDeCXg2\nioA/aDzCmGOMrYs2oJlmp8fyhm5KajrJSAglPy1yxmPyVGvzYtEHaPj5W2X8732l/Pm2xdyaE+3u\nYd2UwWEblxq6r7l0FBnizy3Z0eQsCmdxUiiBUgMjbmB5ppHGjlrOVVlYvfTGM7L+an8yw9Mp6yzH\nMtTpdQWonsjusPNJ62e8U/s+A7ZBDAERPJh+L0siFvtcXdKchZkTJ06wadMmAFJTU+nt7aW/vx+d\n7vq7GWpqaqiurmbdunVzNTSfkBg10QnYZOU2xv9oxusnioD7WmZ8/Z6RXo61nCTcP4zbYlZM6xoO\np5NXJo4t2Lkhzed+eT5vWYaB7zySx49fO88v376IdWjM5cZh7jC5dDQeXrq+dOkoJzkco5xQLW5S\nYaaB1z+upbjC7FKYgfFdTWWd5ZSYy9iYuHaOR+jbKrqq2Vf1Fq0D7fir/Nieto074lejUfpmdcmc\nfVUWi4WcnMvdHMPDwzGbzVeFmWeffZaWlhYKCwt5+umnUSgUPP/883zve9/jjTfecOlxwsICUc9h\n63ODQT9n156JIP34zpn27qGrxhgXHE3LQCsREUEoZ7Ck82bxO9gcNh5Zuo2YqLBpXeOjM83Ut1tZ\nmx/HytzZXwP3xOfGYNATFxPCs788wR8+rMLmhN13ec67oPbOAc5WmDhbaaa0yszAsA0ApQIyEsNY\nlmlkWYaRjMRQVNNsCOiJz4sYN5/PjcGgJylaT1l9F0F6f5dm89bpV/KHite51FvOLsO2eRilZ5jN\n56Wj38xL517nVMs5FCjYkLyKXbn3E+rv2x225y2iOSfe8U168sknWbNmDSEhIXzzm9/k4MGDDA8P\nk5+fT0KC6+9mu7sHZ3uoUwwGvUev/xtDA6hp7sVk6pv6YxkXGEtzXxtljbVET3M9tGu4m0M1nxAZ\nEEF2UM60vgdjNgf/950y1CoF225NnPXvoyc/NzqNkme+WjB+/MGhKjos/ey+M9Mt9UKTS0cXJ2Zf\nTD1DU7dFhvizIstITnI4i5PCrvpj09U1MK3H8+TnZaFzx3OTnxY53nfos3puzXZl2VVBckgS5eYa\nalva0Gt9vy/RbD0vw7ZhDjYc4XDjx9icdlJCFvFw+n0kBsczZgWz1ft/L68X+uYszBiNRiwWy9T/\nTSYTBoNh6v8PPPDA1L/Xrl1LZWUltbW1NDU1cfToUdrb29FqtURHR7Nq1fQbtfmyhCgdxRVmuq0j\nUz1OEvRxfNZeTKO1Zdph5kD9IexOO3cv2jTtrXqHipux9A6zZUUChtCAaV3DmxlCA9jzWCH/+UoJ\nH5e0YR0c4xv358z5AYp2h4O6NutU4a4sHQl3Ksw08ObxOoorzC6GmfGzmmp76zlvuciqGWw8WCgc\nTgen2s/wZs179I1aCfMLZXva3RQY8zxmRng+zFmYWb16NT/5yU/YtWsXZWVlGI3GqSUmq9XKU089\nxYsvvohWq+X06dPceeedPPnkk1P3/8lPfkJcXJwEmetINI6HmcaO/qkwkxQ8UQTcN70iYMtQJyfa\niogKNLAietm0xtU/NMY7n9YT6KfmnlWLpnUNXxAcpOVvH13GT18/z9kqCz/cW8K3Hswl0H92f+2u\n3nXUzdDI+NLRlbuOcpLDSY4JlrOkxLyKiwwiOjyQ8zWdjIzaXTr6Izcyhz9V76fEXCZh5gbqeht4\ntfItGqxNaJQa7k7ezObEO6bdRsObzVmYKSgoICcnh127dqFQKHj22Wd5/fXX0ev1bN68mbVr17Jz\n5078/PzIzs5m69atczUUn5VwRRFwfvr4TqGZdgJ+r+4QDqeDu5M3T7vx3juf1jM4YuOR9WkLvs1/\ngJ+apx7O41dvl1FUYeb535/h24/kzeg4h8FhG+WN3VMB5vNLR7csvvbSkRDzTaFQUJhpYP+JBs7X\ndrI868YtI4yBkcQGRVPeXcWwbWTaXcd9Wc9IL29Uv8fpjjMAFBrzeCDtbsL9p1ff6AvmtGbmu9/9\n7lX/z8rKmvr3448/zuOPP/6l9/3Wt741Z+PyFUkTYabpik7AWpWW6CAjTf2tN90JuGPQzGftxcQE\nRVFgzL3xHa7B3DPE4TPNRIb4s7HQtXNZfJ1GreQb9y/hdx9UcuRsC8+9VMzTu/KJcnGZR5aOhDdb\nnmlk/4kGiipMUWeFcQAAHvFJREFULoUZgFxDDgfqD3Gxq2Lar0W+aNQ+xqHGj3m/4TCjjjES9HE8\nlH4faaHJ7h6a2/nmHq0FIlSnRRegodF0dWFXoj6etoEOOgbNN9UU6b26D3HiZFvylmnPyrz2UQ02\nu5Mda1PQqGVJY5JSqeCxLRnoAzW89Uk9//5SMd9+JJ+k6GsXtJl6hrg4EV4uytKR8GKJUToiQ/wp\nqelkzGZ3qW4sL3I8zJSayyTMML6B5qz5PH+q3k/XcDd6jY6HMx7g1pjCWTu6xttJmPFiCoWCxCgd\nF+u7GRy2TdViJOrjx4uA+5pdDjNtAx0UdZwjXhdLniHnxne4hrq2Pk5dMpEUrWdltu90lpwtCoWC\nB9akEByk5XfvV/L878/wrQdzWZwUJktHwmcpFAqWZxk58FkjF+q6WJZuuOF9EvRxhPmFcqHzEnaH\n3avPDJqpZmsr+6reoqqnFpVCxabEO9i6aCMB6oV7sO21SJjxcolGPRfru2k295OREDr+seDxni5N\n1hZuiSl06Tr76z6YmJWZXq2M0+nklcMTDfLWp8kBg9exoSAeXYCGX719kf985RxJUXrq2qxTS0f+\n2stLR9nJ4RhDAxbUrgThewozDRz4rJHiCrNLYUahUJBryOGj5k+o7KlhcXjGPIzSs1hH+3mn9iCf\ntJ7CiZOlkYvZkXYPxsAbf/8WIgkzXi4hanyHWGOHdSrMTBYBN7hYBNzS38ZZUymJ+niWRmZPaxwl\n1Z1UNPWQlxpBVtLCLUJz1crFUegCNPz09fPUtvWREhNMTrIsHQnflBITTHiwH2erLFcdjns9eZHj\nYabUXLagwozNYePj5k95t/5DhmzDRAcaeSj9PhZHLJzvwXRImPFyk2c0NX6uCDgmKIpma4tLRcD7\na98H4J6ULdOaAbA7HLx6tBqFAh5an3bT91+osheF86Nv3Y7N7pClI+HTFAoFBRkGPixq5lJDN0td\nOIg1LTSZQHUApZaLPJxx/4KoDSnrLOe1qrfpGDQToA7g4fT7WRN364JeZnOV7/90+LjoiEA0auUX\nioAT9HGMOsboGDRf9/6Nfc2UWMpIDk4iOzxzWmM4VtJGW+cga/NiiYv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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "X446q8sUF-MK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n", + "\n", + "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n", + "\n", + "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC." + ] + }, + { + "metadata": { + "id": "7ZVoRKoSF2k9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "5bee465f-3634-4169-dcf6-88e88f625b96" + }, + "cell_type": "code", + "source": [ + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "AUC on the validation set: 0.74\n", + "Accuracy on the validation set: 0.76\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "MZM1aeqdGvMu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n", + "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n", + "obtain the true positive and false positive rates needed to plot a ROC curve." + ] + }, + { + "metadata": { + "id": "oRwl-l0lGht0", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "2821a876-9ae9-49d0-904e-07552a3695b3" + }, + "cell_type": "code", + "source": [ + "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + "# Get just the probabilities for the positive class.\n", + "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n", + "\n", + "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n", + " validation_targets, validation_probabilities)\n", + "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n", + "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n", + "_ = plt.legend(loc=2)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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ikqYyVqenkl9fiI+LN0uT5nNL0BC1yxK9REJYiG6064sCUj9pu2xgZLAXVkXh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\nzuwIYU8zDJxmGDjNMHDBmuFNfzDLMIyADtjfPxz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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "eHbGfD3eG3jo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n", + "\n", + "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n", + "\n", + "**Verify if all metrics improve at the same time.**" + ] + }, + { + "metadata": { + "id": "MlfaPq0DGzix", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "ce72df58-e0e6-4c6c-cb43-1f9c7d311b8a" + }, + "cell_type": "code", + "source": [ + "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n", + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=50000,\n", + " batch_size=500,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.47\n", + " period 01 : 0.46\n", + " period 02 : 0.46\n", + " period 03 : 0.46\n", + " period 04 : 0.46\n", + " period 05 : 0.46\n", + " period 06 : 0.46\n", + " period 07 : 0.46\n", + " period 08 : 0.46\n", + " period 09 : 0.46\n", + "Model training finished.\n", + "AUC on the validation set: 0.80\n", + "Accuracy on the validation set: 0.79\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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d+nK47CirD69j/ZHNLD74LUvTvqN3cA+Ghw0mLiBGl5yLiLQxCjjSLnTy7sjk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\nv/rVrxgcHMRut/O73/2OkBDttizyTYUEB3HNtES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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "doe4LBW3JFHL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "id": "yNf6N6I5JH-R", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "One possible solution that works is to just train for longer, as long as we don't overfit. \n", + "\n", + "We can do this by increasing the number the steps, the batch size, or both.\n", + "\n", + "All metrics improve at the same time, so our loss metric is a good proxy\n", + "for both AUC and accuracy.\n", + "\n", + "Notice how it takes many, many more iterations just to squeeze a few more \n", + "units of AUC. This commonly happens. But often even this small gain is worth \n", + "the costs." + ] + }, + { + "metadata": { + "id": "dnfVENqtG6Pr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "089b0e8c-0b34-4173-9f3c-0dbe53d1c20c" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000003,\n", + " steps=20000,\n", + " batch_size=500,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.49\n", + " period 01 : 0.48\n", + " period 02 : 0.47\n", + " period 03 : 0.47\n", + " period 04 : 0.47\n", + " period 05 : 0.47\n", + " period 06 : 0.47\n", + " period 07 : 0.46\n", + " period 08 : 0.46\n", + " period 09 : 0.46\n", + "Model training finished.\n", + "AUC on the validation set: 0.80\n", + "Accuracy on the validation set: 0.78\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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VBUBoaCjV1dXU1tZesc7LL7/M4sWLzY+zs7MJDw8HICgoiIKCAgwGAykpKcya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\nLRHR1eNvEiIKm3ijGrcVZcBq1aG9vRddvUOob+1DfVsf6tt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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ZQxUoCa1JKk-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 839fa27c50c6f0b518b9b03ed18d01980f78d628 Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Thu, 14 Feb 2019 23:53:08 +0530 Subject: [PATCH 08/11] Sparsity and L1 Regularization Programming Exercise. --- SparsityAndRegularization.ipynb | 1180 +++++++++++++++++++++++++++++++ 1 file changed, 1180 insertions(+) create mode 100644 SparsityAndRegularization.ipynb diff --git a/SparsityAndRegularization.ipynb b/SparsityAndRegularization.ipynb new file mode 100644 index 0000000..b71d822 --- /dev/null +++ b/SparsityAndRegularization.ipynb @@ -0,0 +1,1180 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "SparsityAndRegularization.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "VBqeHit0L8RH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9qYEQV0QMR-a", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Sparsity and L1 Regularization" + ] + }, + { + "metadata": { + "id": "XWFIFZR_MTch", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Calculate the size of a model\n", + " * Apply L1 regularization to reduce the size of a model by increasing sparsity" + ] + }, + { + "metadata": { + "id": "OYnM6psUMWsd", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "One way to reduce complexity is to use a regularization function that encourages weights to be exactly zero. For linear models such as regression, a zero weight is equivalent to not using the corresponding feature at all. In addition to avoiding overfitting, the resulting model will be more efficient.\n", + "\n", + "L1 regularization is a good way to increase sparsity.\n", + "\n" + ] + }, + { + "metadata": { + "id": "etFSyCxuMbQ0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "Run the cells below to load the data and create feature definitions." + ] + }, + { + "metadata": { + "id": "ZJkCY0XpMOa3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "k-5fihcCMf-1", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Create a boolean categorical feature representing whether the\n", + " # median_house_value is above a set threshold.\n", + " output_targets[\"median_house_value_is_high\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yZJIMchOMis4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "ebd8f1cb-4149-4632-98fd-ee8c62e19d09" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value_is_high\n", + "count 5000.0\n", + "mean 0.3\n", + "std 0.4\n", + "min 0.0\n", + "25% 0.0\n", + "50% 0.0\n", + "75% 1.0\n", + "max 1.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "Rzo8PLMGMliJ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "0y9DWm8LMpb2", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_quantile_based_buckets(feature_values, num_buckets):\n", + " quantiles = feature_values.quantile(\n", + " [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n", + " return [quantiles[q] for q in quantiles.keys()]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "MSEzTf_4Mr8k", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + "\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"households\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"longitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"latitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"housing_median_age\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"housing_median_age\"], 10))\n", + " bucketized_total_rooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_rooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n", + " bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_bedrooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n", + " bucketized_population = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"population\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"median_income\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"rooms_per_person\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"rooms_per_person\"], 10))\n", + "\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n", + "\n", + " feature_columns = set([\n", + " long_x_lat,\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_total_rooms,\n", + " bucketized_total_bedrooms,\n", + " bucketized_population,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ErsYQFUONOCC", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Calculate the Model Size\n", + "\n", + "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works." + ] + }, + { + "metadata": { + "id": "sjN_CKc2MuJx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def model_size(estimator):\n", + " variables = estimator.get_variable_names()\n", + " size = 0\n", + " for variable in variables:\n", + " if not any(x in variable \n", + " for x in ['global_step',\n", + " 'centered_bias_weight',\n", + " 'bias_weight',\n", + " 'Ftrl']\n", + " ):\n", + " size += np.count_nonzero(estimator.get_variable_value(variable))\n", + " return size" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "0CF5WE-FNSrn", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Reduce the Model Size\n", + "\n", + "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n", + "\n", + "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n", + "\n", + "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?" + ] + }, + { + "metadata": { + "id": "8VxMFwLyNVY5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Task 1: Find a good regularization coefficient.\n", + "\n", + "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n", + "\n", + "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n", + "\n", + "Again, the model will train on the entire data set, so expect it to run slower than normal." + ] + }, + { + "metadata": { + "id": "zUnLBuMNNQdo", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " regularization_strength,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " regularization_strength: A `float` that indicates the strength of the L1\n", + " regularization. A value of `0.0` means no regularization.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 7\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on validation data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " # Compute training and validation loss.\n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2kPIYHgFNYdl", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 707 + }, + "outputId": "f1c17a12-f1e7-446f-c1d6-2e4a08d738fe" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " # TWEAK THE REGULARIZATION VALUE BELOW\n", + " regularization_strength=0.2,\n", + " steps=300,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.32\n", + " period 01 : 0.28\n", + " period 02 : 0.27\n", + " period 03 : 0.26\n", + " period 04 : 0.26\n", + " period 05 : 0.25\n", + " period 06 : 0.25\n", + "Model training finished.\n", + "Model size: 725\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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YImlmLMTcuDCc7a34at95ilTYewbg1tBpeNl5sPPCXrKr1GmShBBCCEsjzYyF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\nsQPLF2Yg3ZoSkzFP4nWmXCxqNmcLgEVRvOqx/v5+PPHEE9i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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "0-zKf-ZzOlp_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "DSgN6N-BOoIk", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "A regularization strength of 0.1 should be sufficient. Note that there is a compromise to be struck:\n", + "stronger regularization gives us smaller models, but can affect the classification loss." + ] + }, + { + "metadata": { + "id": "6SFbqon3Na1P", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 588 + }, + "outputId": "8f4444b0-831d-4e04-b1d0-269685bc1c3a" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " regularization_strength=0.1,\n", + " steps=300,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.32\n", + " period 01 : 0.29\n", + " period 02 : 0.27\n", + " period 03 : 0.26\n", + " period 04 : 0.25\n", + " period 05 : 0.25\n", + " period 06 : 0.25\n", + "Model training finished.\n", + "Model size: 756\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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WPMWo19fx75VHSEkvYs74MPqHNP8U0K+qa6t5/cj7XCzJ4KHQSfT1br61OnJa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\n4vwKRG6OwoyIyDKRnprMxtX5bFydD8DE1AwXuoZpmV3y3do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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "vdxubtMqOrF0", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 0d9ac4570f5d2269379072a56186189b1a28a2fd Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Fri, 15 Feb 2019 00:08:27 +0530 Subject: [PATCH 09/11] Intro to Neural Networks Programming Exercise. --- IntroToNeuralNetwork.ipynb | 1227 ++++++++++++++++++++++++++++++++++++ 1 file changed, 1227 insertions(+) create mode 100644 IntroToNeuralNetwork.ipynb diff --git a/IntroToNeuralNetwork.ipynb b/IntroToNeuralNetwork.ipynb new file mode 100644 index 0000000..fcc37e0 --- /dev/null +++ b/IntroToNeuralNetwork.ipynb @@ -0,0 +1,1227 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "IntroToNeuralNetwork.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "8dFN230KQOhC", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "5gwfnWp5Q0Js", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Intro to Neural Networks" + ] + }, + { + "metadata": { + "id": "Q-C0CdVwQ2uX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Define a neural network (NN) and its hidden layers using the TensorFlow `DNNRegressor` class\n", + " * Train a neural network to learn nonlinearities in a dataset and achieve better performance than a linear regression model" + ] + }, + { + "metadata": { + "id": "54IO4oxKQ5R7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In the previous exercises, we used synthetic features to help our model incorporate nonlinearities.\n", + "\n", + "One important set of nonlinearities was around latitude and longitude, but there may be others.\n", + "\n", + "We'll also switch back, for now, to a standard regression task, rather than the logistic regression task from the previous exercise. That is, we'll be predicting `median_house_value` directly." + ] + }, + { + "metadata": { + "id": "NpZtfEwZQ7sc", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, let's load and prepare the data." + ] + }, + { + "metadata": { + "id": "BvtoPJJcQwgX", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "OkQoJPN4Q9_j", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "8t98k9XQRBi_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "651829cb-7571-4a34-f4db-1f2fb336272b" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 207.0\n", + "std 116.3\n", + "min 22.5\n", + "25% 121.0\n", + "50% 178.5\n", + "75% 265.1\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jsQOFB3DRI0j", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Building a Neural Network\n", + "\n", + "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n", + "\n", + "Use **`hidden_units`** to define the structure of the NN. The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it. For example, consider the following assignment:\n", + "\n", + "`hidden_units=[3,10]`\n", + "\n", + "The preceding assignment specifies a neural net with two hidden layers:\n", + "\n", + "* The first hidden layer contains 3 nodes.\n", + "* The second hidden layer contains 10 nodes.\n", + "\n", + "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n", + "\n", + "By default, all hidden layers will use ReLu activation and will be fully connected." + ] + }, + { + "metadata": { + "id": "3G85V_rwREEy", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RVsGiffqRLdm", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural net regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "eC0mC_0XRNyf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `DNNRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "BudBWu0ORTFD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Train a NN Model\n", + "\n", + "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n", + "\n", + "Run the following block to train a NN model. \n", + "\n", + "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good. We'll aim to beat that.\n", + "\n", + "Your task here is to modify various learning settings to improve accuracy on validation data.\n", + "\n", + "Overfitting is a real potential hazard for NNs. You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n", + "\n", + "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n", + "\n", + "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n" + ] + }, + { + "metadata": { + "id": "bdZdUQE9RQkd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 775 + }, + "outputId": "ef1b9f59-6293-46da-d189-9ec7f768e986" + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.005,\n", + " steps=3000,\n", + " batch_size=250,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 179.92\n", + " period 01 : 124.59\n", + " period 02 : 114.12\n", + " period 03 : 108.93\n", + " period 04 : 105.85\n", + " period 05 : 103.22\n", + " period 06 : 103.50\n", + " period 07 : 105.13\n", + " period 08 : 102.69\n", + " period 09 : 102.64\n", + "Model training finished.\n", + "Final RMSE (on training data): 102.64\n", + "Final RMSE (on validation data): 102.78\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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BhoiIiNodBhgiIiJqdxhgiIiIOphDh75t0XFvvfU6srOzmtz/7LNPtVVJbY4BhoiIqAPJ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\njijAiMhlm3srhEDARygUZyg2SdfAKGcGRzkzMMq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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "AIJ40ugcRYvv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution" + ] + }, + { + "metadata": { + "id": "8NuTQYAYRb5M", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search." + ] + }, + { + "metadata": { + "id": "A-mQRppKRVKr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "dde75a63-1b9e-42f5-919a-f1e2f7211941" + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.001,\n", + " steps=2000,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 173.19\n", + " period 01 : 170.32\n", + " period 02 : 170.78\n", + " period 03 : 167.74\n", + " period 04 : 165.46\n", + " period 05 : 163.97\n", + " period 06 : 159.89\n", + " period 07 : 155.59\n", + " period 08 : 149.92\n", + " period 09 : 146.35\n", + "Model training finished.\n", + "Final RMSE (on training data): 146.35\n", + "Final RMSE (on validation data): 144.19\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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4y2mlFpJjRr0kG/WSbMyjqlNIQlhbeJAHT98Sy3UTe9FoVFi+9jDPLU/g+Kky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\nxLRp03jjjTecFUlERKTd6BwQS1rCFH49/DF+1P0a/D39+fz4Zp7b8kf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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "Rn2pRMu_SYN-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Evaluate on Test Data\n", + "\n", + "**Confirm that your validation performance results hold up on test data.**\n", + "\n", + "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n", + "\n", + "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)." + ] + }, + { + "metadata": { + "id": "-PczimNHSQ2j", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "f1fa44b8-04fb-4540-f866-f056421a3e87" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "# YOUR CODE HERE\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, test_targets[\"median_house_value\"], num_epochs=1, shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 143.88\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "aMQ-LmOlS87O", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "ThF-Wr9dTB0p", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n", + "\n", + "Note that we don't have to randomize the test data, since we will use all records." + ] + }, + { + "metadata": { + "id": "LAmRcF9lS_Oa", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "47f5ac6e-ac2b-42de-ee42-d3f53d9871ce" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.002f\" % root_mean_squared_error)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 143.88\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "LxhAAuu4TEK9", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 60f6dcfc2057ba8f38c7d44f62aa4279b3048ca2 Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Fri, 15 Feb 2019 00:50:27 +0530 Subject: [PATCH 10/11] Improving Neural Net Performance --- ImprovingNeuralNet.ipynb | 1995 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 1995 insertions(+) create mode 100644 ImprovingNeuralNet.ipynb diff --git a/ImprovingNeuralNet.ipynb b/ImprovingNeuralNet.ipynb new file mode 100644 index 0000000..34d7194 --- /dev/null +++ b/ImprovingNeuralNet.ipynb @@ -0,0 +1,1995 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "ImprovingNeuralNet.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "fWR_gsmcUItH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "OxkV2ecuUdSd", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Improving Neural Net Performance" + ] + }, + { + "metadata": { + "id": "RrSWt0VXUgFM", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objective:** Improve the performance of a neural network by normalizing features and applying various optimization algorithms\n", + "\n", + "**NOTE:** The optimization methods described in this exercise are not specific to neural networks; they are effective means to improve most types of models." + ] + }, + { + "metadata": { + "id": "_Ij2A5guUiFm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, we'll load the data.\n" + ] + }, + { + "metadata": { + "id": "NsPKCT_cUaJc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "HFVoHOrTUkQf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2BLrYuTpUmpF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1205 + }, + "outputId": "8a55311e-5785-4132-d2e2-eb0ea1c0c97f" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.7 2658.1 542.6 \n", + "std 2.1 2.0 12.6 2160.7 419.5 \n", + "min 32.5 -124.3 1.0 2.0 2.0 \n", + "25% 33.9 -121.8 18.0 1474.0 299.0 \n", + "50% 34.2 -118.5 29.0 2142.0 438.0 \n", + "75% 37.7 -118.0 37.0 3186.2 652.0 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1435.4 503.8 3.9 2.0 \n", + "std 1151.9 382.1 1.9 1.2 \n", + "min 3.0 2.0 0.5 0.0 \n", + "25% 795.0 284.0 2.6 1.5 \n", + "50% 1171.0 412.0 3.5 1.9 \n", + "75% 1737.0 607.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Validation examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 204.8\n", + "std 116.7\n", + "min 15.0\n", + "25% 117.5\n", + "50% 173.6\n", + "75% 263.8\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "I02laRaOUse8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Train the Neural Network\n", + "\n", + "Next, we'll train the neural network." + ] + }, + { + "metadata": { + "id": "1zaE2zCKUpNh", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4WRKhb6FUwZf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural network model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "iuPiycKmUyUd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " my_optimizer,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A tuple `(estimator, training_losses, validation_losses)`:\n", + " estimator: the trained `DNNRegressor` object.\n", + " training_losses: a `list` containing the training loss values taken during training.\n", + " validation_losses: a `list` containing the validation loss values taken during training.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor, training_rmse, validation_rmse" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "YKd4Nz3JU0yz", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 775 + }, + "outputId": "6b8998f9-30b4-435a-f9fe-f5fbf681df94" + }, + "cell_type": "code", + "source": [ + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 164.32\n", + " period 01 : 152.67\n", + " period 02 : 143.09\n", + " period 03 : 124.80\n", + " period 04 : 110.28\n", + " period 05 : 106.10\n", + " period 06 : 105.60\n", + " period 07 : 104.25\n", + " period 08 : 104.24\n", + " period 09 : 104.62\n", + "Model training finished.\n", + "Final RMSE (on training data): 104.62\n", + "Final RMSE (on validation data): 105.76\n" + ], + "name": "stdout" + }, + { + "output_type": 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OAXD27Fnq16+Pr68vkZGRZGRkkJ2dTVhYGP7+/oYsS29cHax4Y4QfNpamLN1x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\nPcKE0w67ZIdNssMhOWDrfN6zreuxu09nALk+kPR83rWdrv6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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "CSpnMNatVxdF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Linear Scaling\n", + "It can be a good standard practice to normalize the inputs to fall within the range -1, 1. This helps SGD not get stuck taking steps that are too large in one dimension, or too small in another. Fans of numerical optimization may note that there's a connection to the idea of using a preconditioner here." + ] + }, + { + "metadata": { + "id": "InhmLO85U3Mv", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def linear_scale(series):\n", + " min_val = series.min()\n", + " max_val = series.max()\n", + " scale = (max_val - min_val) / 2.0\n", + " return series.apply(lambda x:((x - min_val) / scale) - 1.0)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "AU5rV8gLV3bZ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Normalize the Features Using Linear Scaling\n", + "\n", + "**Normalize the inputs to the scale -1, 1.**\n", + "\n", + "**Spend about 5 minutes training and evaluating on the newly normalized data. How well can you do?**\n", + "\n", + "As a rule of thumb, NN's train best when the input features are roughly on the same scale.\n", + "\n", + "Sanity check your normalized data. (What would happen if you forgot to normalize one feature?)\n" + ] + }, + { + "metadata": { + "id": "9jzB6nZgV1nf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "f138d7b1-198d-4b04-8453-4725a0afe574" + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " #\n", + " # Your code here: normalize the inputs.\n", + " #\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.007),\n", + " steps=1000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 206.83\n", + " period 01 : 117.49\n", + " period 02 : 110.53\n", + " period 03 : 101.98\n", + " period 04 : 91.32\n", + " period 05 : 80.45\n", + " period 06 : 76.61\n", + " period 07 : 74.72\n", + " period 08 : 73.59\n", + " period 09 : 72.48\n", + "Model training finished.\n", + "Final RMSE (on training data): 72.48\n", + "Final RMSE (on validation data): 74.68\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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y5EiWLVvG3//+96+9x/DhkvylpTX+KA9o3qEKCytb9Rmb4SQmOJriyGK27clj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\n4lQuOLpnjEu1yY0CzCjoojIu1caYxqouJ04N8uq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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "y58fgg4iWwsD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "###Solution" + ] + }, + { + "metadata": { + "id": "lBDaiRsVV523", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "8cb17a41-7de9-4a64-b0d1-12aa446349e9" + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n", + " steps=2000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 195.53\n", + " period 01 : 117.71\n", + " period 02 : 108.18\n", + " period 03 : 93.99\n", + " period 04 : 79.91\n", + " period 05 : 75.52\n", + " period 06 : 73.49\n", + " period 07 : 72.19\n", + " period 08 : 70.95\n", + " period 09 : 70.47\n", + "Model training finished.\n", + "Final RMSE (on training data): 70.47\n", + "Final RMSE (on validation data): 72.39\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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3MoOIiEiHowbmLFksluab2nk1UG0r4ECe1rYQERFxNzUwbWDQ9+NgrCG6K6+I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\nMHgQ/tgw2qqWQC/pCvbHumgXa6NdrE1xGGBKwIN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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "TdvhNC5FXrfz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Optimizer\n", + "\n", + "** Use the Adagrad and Adam optimizers and compare performance.**\n", + "\n", + "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate. This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training. You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`. Note that you may need to use a larger learning rate with Adagrad.\n", + "\n", + "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad. To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully." + ] + }, + { + "metadata": { + "id": "LrpTKozhW2yq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "ff7a3278-aebe-42cc-daaa-c802fdbb31a4" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n", + "#\n", + "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n", + " steps=5000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 97.45\n", + " period 01 : 74.93\n", + " period 02 : 71.07\n", + " period 03 : 70.37\n", + " period 04 : 70.15\n", + " period 05 : 69.63\n", + " period 06 : 69.34\n", + " period 07 : 69.14\n", + " period 08 : 68.91\n", + " period 09 : 68.73\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.73\n", + "Final RMSE (on validation data): 70.95\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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hgOkgw4D6jR0BoJ9aD40iAKnsgyEiIvIYBpgOGhAWAIVc4mrkrVsPxlRjRlHV\nGS9XR0RE1DcwwHSQTCpBQqQWeUUVqLBYAfB2aiIiIk9jgOmEuj6YtKbrwZRmeq0mIiKivoQBphMM\nUY37YCL8+yFA7o80I/tgiIiIPIEBphPiIwMhCGjSBxOH0mojii2lXq6OiIio92OA6QSVnwzRYRpk\nFphhtdkBcD0YIiIiT2KA6SRDlBY2u4isU2XOx64+mHRvlkVERNQnMMB0UtP1YCIDwqGWqbigHRER\nkQcwwHRSYv/aFXlznH0wEkGChKA4FFtKUcI+GCIiIrdigOkkncYP+iAl0vJMcNTeeTTQdRmJszBE\nRETuxADTBYaoIFRYbCg4UwEASNRxXyQiIiJPYIDpgqYbO0YFREIlU/JOJCIiIjdjgOmC+gXtGvTB\naGNRVFUMY7XJm6URERH1agwwXRARokaASu6agQEarAfDPhgiIiK3YYDpAkEQkNhfizMmC0rMFgCA\nQccF7YiIiNyNAaaLDANqN3bMc87CDAjoDz+pgo28REREbsQA00WuPpgcZ4CRSqRI0MahsLIIpuoy\nb5ZGRETUazHAdFFMPw3kMomrkReo31aAszBERETuwQDTRXKZBHERgcgpKkelxQaA68EQERG5GwNM\nNxg4QAtRBDLynZeRYjRRUEjkbOQlIiJyEwaYblDXB3Mit74PJl4bi4KKQpTVlHuzNCIiol6JAaYb\nJERqIQBIa9AHk+jqg8n0UlVERES9FwNMN1ArZYgKC0BGvhk2uwMA14MhIiJyJwaYbmKI0qLG5kB2\nofPW6ZjAAZBLZGzkJSIicgMGmG7SdD0YuUSGuMAY5JefQoW10pulERER9ToMMN2kfmfqBn0wuniI\nENkHQ0RE1M0YYLpJcKASIYFKpOaaIIoiAC5oR0RE5C4MMN3IMECL8iorTpU4LxnFBkZDJkiRWpru\n5cqIiIh6F5knT+ZwOHD//fcjNTUVcrkcDzzwANRqNe68807Y7Xbo9Xo8/fTTUCgUniyr2xiigvDj\n0UKk5poQEeIPhVSOmMBoZJiyUGmtglqu8naJREREvYJHZ2C+/PJLlJWV4Z133sGjjz6Kp556Chs2\nbMDixYvx1ltvISYmBtu2bfNkSd3K1QeT02BfpNo+mHQT+2CIiIi6i0cDTFZWFkaOHAkAiI6ORn5+\nPg4ePIhp06YBAKZMmYIffvjBkyV1q8hQf6j9ZEitXZEXqO+D4XowRERE3cejl5AGDhyI119/HcuX\nL0d2djZycnJQVVXlumQUEhKCoqKis76PTqeGTCZ1W516vabTrx0aH4LkY4WQ+skRHKhEoG44pL9J\nkVWe3aX3JSd+D30Tx8V3cWyuM9HcAAAgAElEQVR8F8emazwaYCZNmoRDhw5hyZIlGDRoEOLj43Hi\nxAnX83V375xNaan71lXR6zUoKirr9OtjwvyRfAw4eCQPYweHOY9popBZmoOTBUVQyZTdVWqf09Wx\nIffguPgujo3v4ti0T1shz6MBBgDWrFnj+nj69Ono168fLBYLlEolCgsLERYW5umSulX9xo5GV4BJ\nDIpHhikbGaYsDAsZ7M3yiIiIegWP9sCkpKTg7rvvBgB88803GDp0KMaPH489e/YAAPbu3YuJEyd6\nsqRuFxehgUwqtNwHU8o+GCIiou7g8R4YURSxcOFC+Pn54ZlnnoFUKsXatWvx7rvvIjIyEgsWLPBk\nSd1OLpMiNiIQ6XkmVFXboPKTIV4bA4kg4YJ2RERE3cSjAUYikeCJJ55odvy1117zZBluZ4jSIi3X\nhIwCM4bFBkMpUyJaE4XsslxU22vgJ+2Z69wQERH5Cq7E6wb1Gzs2WA8mKB4O0YEMU5aXqiIiIuo9\nGGDcILF/3caO9X0wiUFxAIA09sEQERF1GQOMGwSo5Ogf6o+MfDNsdgcAICEoDgIELmhHRETUDRhg\n3MQQpUW11Y6c0+UAAJVMiQGaSGSZc1Bjr/FydURERD0bA4ybuPpgGl1GioddtCPTdNJbZREREfUK\nDDBu4trYMbdxIy/AfZGIiIi6igHGTUK0Sug0fkjNNbm2SEis7YPhejBERERdwwDjJoIgwBClhbmi\nBqeNVQAAtVyN/gERyDSfhNVu9XKFREREPRcDjBvVrwfTeFsBm8OGLHOOt8oiIiLq8Rhg3KilPphE\nnbMPhpeRiIiIOo8Bxo2i9AFQKqSN70TSOhe0O8EAQ0RE1GkMMG4kkQhI7K/FqZJKmCuda78EKPwR\n6R+OTFM2bA6blyskIiLqmRhg3KzuMlJak/VgrA4rss253iqLiIioR2OAcbP6Be0arAej43owRERE\nXcEA42ZxkYGQSoRGfTB1C9qxkZeIiKhzGGDczE8uRUy4BtmnylBttQMANIoAhKvDkG7Kgt1h93KF\nREREPQ8DjAcYorSwO0Rk5ptdxxJ18aix1+BkWZ4XKyMiIuqZGGA8oMU+GNe+SOleqYmIiKgnY4Dx\ngETXgnbN+2DYyEtERNRxDDAeEKhWIDxYjbQ8ExwO58aOWr9AhKlCkWFkHwwREVFHMcB4iCFKC0uN\nHblF5fXHdPGw2KuRW57vxcqIiIh6HgYYD6nvg2m8oB3Ay0hEREQdxQDjIYYBzTd25HowREREncMA\n4yFhQSoE+itwIscIUXT2weiUQQhVBiPNmAmH6PByhURERD0HA4yHCIIAQ5QWxvIaFJssruOJunhU\n2SzIKy/wYnVEREQ9CwOMB7XUB8PbqYmIiDqOAcaDDFFt9MGUMsAQERG1FwOMB0X3C4CfXNpoBiZE\nFYxgpY59MERERB3AAONBUokE8ZGByDtTgfIqq+u4ISgeFbZKFFQUerE6IiKinoMBxsPqLiOl5XE9\nGCIios5igPEww4A2NnZkHwwREVG7MMB4WHxEICSC0KgPJlQVjCA/LdKMGa41YoiIiKh1DDAepvKT\nYUC/AGQVmGG1OTdxFAQBiUFxKLdW4FTlaS9XSERE5PsYYLzAEKWFzS4is6Cs/hgvIxEREbUbA4wX\nDIxqoQ9GlwCA+yIRERG1BwOMFyS6FrSr74MJU4UiUKFBKvtgiIiIzooBxguCAvwQFqRCWq4Jjtqw\nIggCDEHxMNeU4XTVGS9XSERE5NsYYLzEEKVFZbUN+WcqXMdc68GUpnurLCIioh6BAcZL6teDabCx\no44L2hEREbWHzJMnq6iowNq1a2EymWC1WrFy5Uq89NJLqKyshFqtBgCsXbsWw4cP92RZXtFwY8cp\nY/oDAMLVYQiQ+yPNmAlRFCEIgjdLJCIi8lmdDjBZWVmIjY3t0Gt27NiBuLg43HHHHSgsLMTy5cuh\n1+vx+OOPY+DAgZ0tpUcKD1YjQCVHak79DIxzPZh4/Fr0O85UlUCvDvFihURERL6rzUtI1113XaPH\nmzZtcn28bt26Dp9Mp9PBaHTeOmw2m6HT6Tr8Hr2FIAgwRGlRbLagxGxxHedlJCIiorNrM8DYbLZG\nj3/88UfXx5251XfOnDnIz8/HjBkzsHTpUqxduxYAsGHDBixZsgTr1q2DxWI5y7v0HoaoFvpgaht5\nuR4MERFR69q8hNS0B6NhaOlMf8bOnTsRGRmJV155BSkpKbjnnntw8803Y9CgQYiOjsb999+P//3v\nf1ixYkWb76PTqSGTSTt8/vbS6zVue++Gxo2IwHtfpSG3uBJza88ZEuqPgF/9kWHO9FgdPQm/J76J\n4+K7ODa+i2PTNR3qgelqU+mhQ4dw0UUXAQAGDx6M06dPY+rUqZBKnWFk6tSp+PTTT8/6PqWllV2q\noy16vQZFRWVn/8RuoPWTQi6T4LfUokbnTAiMxZEzR5FyMhshqmCP1NITeHJsqP04Lr6LY+O7ODbt\n01bIa/MSkslkwg8//OD6Yzab8eOPP7o+7qiYmBgcOXIEAJCXlwe1Wo0VK1a43uvgwYMwGAwdft+e\nSiaVID4iELmny1Fpqb9cl8g+GCIioja1OQMTGBjYqHFXo9Fg48aNro876sorr8Q999yDpUuXwmaz\n4cEHH0RpaSmuvfZaqFQq9OvXD7feemuH37cnMwzQ4niOEen5JoyId9515NrY0ZiBCyLGerM8IiIi\nn9RmgNm6dWu3nszf3x/r169vdnz27Nndep6exNnIm43UXKMrwPQPiIBKpkQad6YmIiJqUZuXkMrL\ny7FlyxbX43feeQfz58/HbbfdhjNnuF9Pd0iI1EIAGq0HIxEkSAyKwxlLCUotxtZfTERE1Ee1GWDW\nrVuH4uJiAEBmZiaee+45rF27FuPHj8ejjz7qkQJ7O7VShqiwAGQUmGGzO1zHE4PYB0NERNSaNgNM\nTk4O7rjjDgDAnj17kJSUhPHjx+Oqq67iDEw3MkRpYbU5kH2qviPd1QfDy0hERETNtBlg6vYnAoCf\nfvoJF1xwgesx9+npPi0taBcVEAml1I8L2hEREbWgzQBjt9tRXFyMkydP4vDhw5gwYQIA56aMVVVV\nHimwL2i4sWMdqUSK+KBYnK46A1N1x29ZJyIi6s3aDDA33HADZs+ejXnz5uGWW26BVquFxWLB4sWL\nsWDBAk/V2OsFByoREqhEaq6p0WrHBvbBEBERtajN26gnTZqEAwcOoLq6GgEBAQAApVKJf/zjH64V\ndal7GAZo8ePRQpwqqUREiL/zWFACAGeAGdtvtDfLIyIi8iltBpj8/HzXxw1X3o2Pj0d+fj4iIyPd\nV1kfY4gKwo9HC5Gaa3IFmGhNfyikCq4HQ0RE1ESbAWbq1KmIi4uDXq8H0HwzxzfeeMO91fUhrj6Y\nHCMuHuUMhlKJFAnaWBwrOYGymnJoFAHeLJGIiMhntBlgnnzySezcuRMVFRWYM2cO5s6di+Bgbi7o\nDpGh/lD7yRrdiQQ414M5VnICqcYMnBM20kvVERER+ZY2m3jnz5+PV199Fc8//zzKy8uxZMkSXH/9\n9di1axcsFounauwTJIKAxCgtThurYCyvdh3nejBERETNtRlg6kREROCWW27B7t27MWvWLDzyyCNs\n4nWDustIaQ1mYWICoyCXyLkeDBERUQNtXkKqYzab8dFHH2H79u2w2+3461//irlz57q7tj6nbkG7\nE7lGjB0cBgCQSWSI08bgRGkaymsqEKDw92aJREREPqHNAHPgwAF88MEH+OOPPzBz5kw88cQTGDhw\noKdq63PiIjSQSYVmfTADg+JxojQNaaZMjNYP91J1REREvqPNAHP99dcjNjYW55xzDkpKSvDaa681\nev7xxx93a3F9jVwmRVxEINLyTKiqtkHl5xyeuo0d00ozGGCIiIhwlgBTd5t0aWkpdDpdo+dyc3Pd\nV1UfZogKQmquCRkFZgyLdd7xFRs4ADKJjCvyEhER1WqziVcikeCOO+7Afffdh3Xr1qFfv34477zz\ncOLECTz//POeqrFPabgeTB25VI64wGjklReg0lrprdKIiIh8RpszMP/617+wZcsWJCQk4Msvv8S6\ndevgcDig1Wrx/vvve6rGPiXRtbFj8/VgUo0ZSDNmYqR+mDdKIyIi8hlnnYFJSHDuxzNt2jTk5eXh\nmmuuwYsvvoh+/fp5pMC+xl8pR3+9PzLyzbDZHa7j3NiRiIioXpsBRhCERo8jIiIwY8YMtxZEzj6Y\naqsdOafLXcfitNGQClKuB0NERIR2LmRXp2mgIfcwtHAZSSFVIDZwAHLK8lFlq/JWaURERD6hzR6Y\nw4cPY/Lkya7HxcXFmDx5MkRRhCAI2L9/v5vL65saNvLOHDeg/nhQPNJNWUg3ZmF46BBvlUdEROR1\nbQaYzz77zFN1UAMhgUroNH5IzTW6wiIAJOrigex9SDNmMsAQEVGf1maA6d+/v6fqoAYEQYAhSouf\njp3G6dIq9AtWAwDitbGQCBKcMKZ7uUIiIiLv6lAPDHlOw32R6vhJFYjRRCGnLA8WG3cDJyKivosB\nxke11MgLONeDcYgOZJiyvVEWERGRT2CA8VFR+gCo/KTNAoxBx/VgiIiIGGB8lEQiIKG/FoUllTBX\n1LiO1/XBcD0YIiLqyxhgfFhdH0zDWRiVTIkBAf2Rbc5Ftb2mtZcSERH1agwwPmygqw/G2Oh4oi4O\ndtGOTPbBEBFRH8UA48NiIwIhlQjN+2C4LxIREfVxDDA+zE8uRWy4BicLy1BdY3cdT9DGQYCA1FIG\nGCIi6psYYHycISoIdoeIjAKz65harkJUQASyzSdRY7d6sToiIiLvYIDxcYZW+2DiYRPtyDKf9EZZ\nREREXsUA4+MSWlnQzhCU4DzOPhgiIuqDGGB8XKBagYgQNdLyTLA7HK7jiUHOPpg09sEQEVEfxADT\nAxiitKiusSP3dIXrmL9cjciAcGSas2F12LxYHRERkecxwPQA9QvaNemDCYqH1WFDtjnHG2URERF5\nDQNMD9Daxo6u9WB4GYmIiPoYBpgeQB+kgtZfgdRcI0RRdB1PDIoDAO6LREREfY7MkyerqKjA2rVr\nYTKZYLVasXLlSuj1ejzwwAMAgEGDBuHBBx/0ZEk9giAIMERpkXy8CGdMFuiDVAAAjSIA4f79kGHK\ngt1hh1Qi9XKlREREnuHRGZgdO3YgLi4OW7duxfr16/Hoo4/i0UcfxT333IN33nkH5eXl+Prrrz1Z\nUo/RWh/MwKB41DisyC7L9UZZREREXuHRAKPT6WA0Ov8BNpvNCAoKQl5eHkaOHAkAmDJlCn744QdP\nltRjGAa03AeTWNsHw9upiYioL/HoJaQ5c+Zg+/btmDFjBsxmMzZv3oyHHnrI9XxISAiKiorO+j46\nnRoymfsul+j1Gre9d2cFB/tDqZAio6CsUX0XBIzAq0eBrMpsn6y7u/WFr7En4rj4Lo6N7+LYdI1H\nA8zOnTsRGRmJV155BSkpKVi5ciU0mvoBbNig2pbS0kp3lQi9XoOiojK3vX9XxEcG4s+sUmSeLEGA\nSl57VIJ+aj1SitJwqtDYq/tgfHls+jKOi+/i2Pgujk37tBXyPHoJ6dChQ7jooosAAIMHD0Z1dTVK\nS0tdzxcWFiIsLMyTJfUodX0waS1cRqq21yCnPM8bZREREXmcRwNMTEwMjhw5AgDIy8uDv78/EhIS\nkJycDADYu3cvJk6c6MmSepTWNnbkejBERNTXePQS0pVXXol77rkHS5cuhc1mwwMPPAC9Xo9169bB\n4XBg1KhRGD9+vCdL6lHiIwMhEYTmC9rpaht5jRmYETPZC5URERF5lkcDjL+/P9avX9/s+FtvveXJ\nMnospUKG6H4ByCwwo8Zqh0Lu7HcJ8tNCrwpBmjELDtEBicD1CYmIqHfjv3Q9jCEqCHaHiKxTjZu/\nDEHxsNgtyC3P91JlREREnsMA08O01geTyD4YIiLqQxhgephWN3as7YNJ5b5IRETUBzDA9DDaAD+E\n6VRIzTXB0WDdnGClDiFKHdKNmXCIDi9WSERE5H4MMD2QIUqLqmob8osqGh1PDIpHpa0K+eWnvFQZ\nERGRZzDA9ECtbexo0CU4j/MyEhER9XIMMD1Qq30wQfXrwRAREfVmDDA9UHiwGgEqebMZmBClDjq/\nIKQaM9gHQ0REvRoDTA8kCAIMUVoUm6tRbLI0Op4YFI8KayVOVZz2YoVERETuxQDTQ7n6YPKa9sHE\nOY/zMhIREfViDDA9lGFA230wDDBERNSbMcD0UDH9NFDIJEjNaRxg9KpQaBUapJVmQGywTgwREVFv\nwgDTQ8mkEsRHBiKvqByVFqvruCAIMOgSUGYtR2FlkRcrJCIich8GmB4sMSoIIoC0PHPj47yMRERE\nvRwDTA82sJWNHV19MKXpHq+JiIjIExhgerCE/loIQvNG3n5qPTSKAKQZ2QdDRES9EwNMD6byk2GA\nPgCZBWZYbfUL19WtB2OqKUNR1RkvVkhEROQeDDA9nCEqCFabA9mFZY2OD9IlAgC2/PkOSi3Gll5K\nRETUYzHA9HD168E0DikXRIzFeeHnINucgyd+Xo8T7IchIqJehAGmh0vsXxtgmqwHI5fIcM2QK3HF\nwPmotFXhhV9fxr6T37AnhoiIegUGmB4uOFCJUK0SaXkmOJqEE0EQMDlqAlaP+SsC5P74IO1jbPnz\nbVTba7xULRERUfdggOkFDFFalFdZcaq4ssXnE4PisHbcbYgLjEFy4a949peNKKos9nCVRERE3YcB\nphdwbeyY23qzbpCfFn8756+Y2P9C5JUX4MnkDThanOKpEomIiLoVA0wvYIhqeWPHpmQSGa4adCmW\nDr4CVocVm4+8ht2ZX8AhOtp8HRERka9hgOkFIkL94a+UtTkD09CFkeNwxzm3IMhPi48z9+Ll37ei\nylbl5iqJiIi6DwNMLyARBCT216LIaEFpWXW7XhMdGIW1427DIF0ifjtzFE8lv4CCikI3V0pERNQ9\nGGB6CcMAZx9MWl7bl5Ea0igCsHLUCkyPnoTTlWfwVPILOHT6N3eVSERE1G0YYHoJVx9MTsdW3ZVK\npLg0cQ5WDF8KAHjljzfxYdqnsDvs3V4jERFRd2GA6SViwwMhk0rO2sjbmnPCRuIf565CmCoUn5/c\nj01HXkV5TUU3V0lERNQ9GGB6CblMgrgIDU6eLkNVta1T7xEZEI5/jL0VI0KHIKU0FU8mb8DJstxu\nrpSIiKjrGGB6EUNUEEQRyMg3d/o91HIVbhyxHHPjZqLUYsRzv2zCjwXJ3VglERFR1zHA9CL168F0\nbfdpiSDBJXHTcdPIayGTyLD12Ht49/iHsDk6N7NDRETU3RhgepHEdi5o117DQ4fgzrG3IdI/HN/k\nfY/1h/8DY3X3vDcREVFXMMD0Iv5KOfrr/ZGeb4LN3j2r64apQ/H3satwbtgoZJiy8eTPG5BuzOqW\n9yYiIuosBphexhAVhBqrAzmny7vtPf2kClw3bDEuT5yLcmsFnj/8b3yd+z3EJrtfExEReQoDTC/T\n2fVgzkYQBEyNvhi3jr4eapkK7534EFuPvYcau7Vbz0NERNQeDDC9THs3duysgbpE3DVuNWI0A3Dw\n1C947peNKK4qccu5iIiIWsMA08uEBCqh0/ghNdfotks8OmUQ1pxzE8ZHjENOeT6eTN6AYyUn3HIu\nIiKiljDA9DKCIMAQpYW50orTpe7bYVoulWPJkCtw9aDLYLFVY+Ovr2Bv9lfsiyEiIo9ggOmFDFHO\njR1PdHE9mPa4qP8FWHPOTdD6BWJn+m789483YbFZ3H5eIiLq22SePNn777+Pjz76yPX4jz/+wPDh\nw1FZWQm1Wg0AWLt2LYYPH+7Jsnqdhn0wE0dGuv18cdoYrB13G1754038WvQ7TlUU4sYR16Cff5jb\nz01ERH2TRwPMFVdcgSuuuAIA8NNPP2H37t1IS0vD448/joEDB3qylF4tSh8AlZ/UbY28LQlUaHDb\n6BuxI/0TfJVzAE8lv4hrhl6JUfphHquBiIj6Dq9dQtq4cSNuueUWb52+V5NIBCT016KwpBLmihqP\nnVcqkWKh4f+wfOhVsIt2vPT769iVsQcOsXsW1SMiIqrj0RmYOr/99hsiIiKg1+sBABs2bEBpaSkS\nEhJwzz33QKlUtvl6nU4NmUzqtvr0eo3b3ttTRg8Kwx8ZJdiTnIsbFoyAXOa5rDpHPwnDohLwzHf/\nxmdZX6Kw+hRuveA6BCj8u/zevWFseiOOi+/i2Pgujk3XCKIXbhtZt24d5syZg/PPPx+ff/45Bg0a\nhOjoaNx///2Ijo7GihUr2nx9UVGZ22rT6zVufX9PKS2rxlNvHUJhaRViwzX46/xh6KdTe7SGCmsl\nthx9G3+WHEeoMhg3jlyO/gERnX6/3jI2vQ3HxXdxbHwXx6Z92gp5XrmEdPDgQYwZMwYAMGPGDERH\nRwMApk6dihMnuJ5Id9Bp/HD/deMwYXg4sk6V4YHXfsYPR095tAZ/uRo3j7oOSbHTcMZSgqeTX8TP\npw57tAYiIuqdPB5gCgsL4e/vD4VCAVEUce2118JsNgNwBhuDweDpknotpUKGFXOH4oa5QwEAL+/6\nE698/CcsNTaP1SARJJgXPws3jlgOqSDBlj/fxrbUj2B32D1WAxER9T4eDzBFRUUIDg4G4Fx0bdGi\nRbj22muxZMkSnDp1CkuWLPF0Sb3ehcPD8cC14xATrsF3f5zCg1uSkX3Ks1OXo/TDcOfYWxGuDsNX\nOQfwwq8vw1zD6VMiIuocr/TAdBV7YDrHZndg2/507P05BzKpgCsmJ2L62CgIguCxGiw2C7Yeex+/\nFv2OID8trh++DHHa6Ha9tjePTU/GcfFdHBvfxbFpH5/rgSHvkEkluGqaAX+7YhRUfjK8/WUqXvjg\nd5RVeu5Wa6VMieuHL8X8hEtgqjbj+UOb8V3eQY+dn4iIegcGmD5oZEIIHvzLeRgSo8OvaWdw/6s/\nISW71GPnFwQBM2OmYOXoFfCT+uGt4x/gf8e2werwXG8OERH1bAwwfVRQgB/uuHI0Lp8UD3OFFU+/\nfRg7vsmA3eG5ReeGBA/E2nG3YUBAJL4v+An/OrQZpRb3799EREQ9HwNMHyaRCJhzYSzuWnIOggOV\n2PV9Fp566zBKzJ7bjDFEFYzbz12J88PPRbY5B0/8vB4nStM9dn4iIuqZGGAIiVFaPPiXcRg7SI/U\nXBPuf/Un/HK8yGPnV0jlWDZkERYNXIBKWxVe+PVl7Dv5DXpgfzkREXkIAwwBANRKOW5eMBzXJA1C\njc2BjTt+x9a9x1Fj9cx6LYIgYFLUePxtzE0IkPvjg7SPseXPt1Ft91yDMRER9RwMMOQiCAImj+6P\ndcvHon+oP746lIdH3khG/pkKj9WQEBSLteNuQ7w2BsmFv+KZ5BdRVFnssfMTEVHPwABDzfTXB+C+\n5WMxeUx/5BZV4KEtP+ObI/keu6QT5KfF6jF/xcX9xyO/4hSeTN6AP84c88i5iYioZ+BCdk1wcaHG\nklNOY8vuFFRW23DekDBcM2sw1ErPbWL+Y0Ey3j6+HXaHHaPChyBIpoNeFYpQVTD06lCEKHWQSbyy\nqTrV4u+M7+LY+C6OTfu0tZAd/8tPbRo7OAyxERq89NGf+OnYaWTkm/HX+cOQEKn1yPkviBiLSP9w\nbPnzbfx66s9mzwsQEKzUQa8KQag6BHpV3R9nyFFIFR6pk4iIPIszME0wFbfM7nBg54FMfPJ9NiQS\nAZdeHI+k86Mh8eA2BGqtFCk52SiqOoOiqmLnn8pinKk6A1Mr+yoF+WmdszWqUGfIUYVAXxt0VDKV\nx2rvzfg747s4Nr6LY9M+nIGhLpNKJLjs4gQMidbhpY//xLb96TiWVYLr5w6FNsDPIzX4K9SIDoxC\ndGBUs+eq7TU44wo1Z+o/ripGujELacbMZq8JkPvXhppQV6ipm73xl6s9ukcUERF1DGdgmmAqPjtz\nZQ1e/eQYfksvRqBajuvnDsXw+BC3n7ezY2N12FBSVVI/a1M7g3OmshhnLCVwiM1XH1bJlM7ZGtfl\nqNqP1SHQKgIZbhrg74zv4tj4Lo5N+7Q1A8MA0wR/qNpHFEV8/nMO3t+fDrtDRNL50bjs4njIpO67\nsc0dY2N32FFabXKGmkpnuDlTVVL7d3GL+zPJJXLXbE19343zEpVOGQSJ0Ldu7uPvjO/i2Pgujk37\n8BISdTtBEDDzvGgMjA7Cv3cexWcHT+L4yVL8df5whAX1nN4SqUSKUFUwQlXBGBLc+DmH6IC5pgxF\nlQ16bqqKcab2cX7FqebvJ0gRotI17rlRhfCOKSKibsYZmCaYijuuqtqGN/eewA9HT0GpkOKapEG4\nYGh4t5/Hl8ZGFEWUWytcPTdFVcUN+m7OoMJa2ew1zjumgqBXhSJIqYVS6gc/qZ/zb1n9335SRf1z\ndcelfpBKpF74Ss/Ol8aFGuPY+C6OTftwBobcSuUnww3zhmJYnA5b95zASx/9iT8zS7FkxkD4KXzz\nH92uEgQBGkUANIoAxGtjmj1faa2qDTTN75hKKU3t1DllElmjYOMnVTQPQC0EIudxRY8JRERE7cEA\nQ91m/PAIJERq8e+dR3Hg9wKk5Zlw0/xhiO7XeoLurdRyFaLlrd8xZa4ug8VejWp7NSw2C6rtNc6P\n7dWottX+ba+BxVbd6HjdxyWWUlhs1RDR+QnU5oGowexPbdCpe76tQORfI4MoimxsJiKP4iWkJjit\n13VWmwPb9qfj8+QcyKQCrpxqwNRz+nf5HziOTWOiKMLqsNaGm5omgag2ADUIRHWf1zwo1b2u84FI\nLpFD6xeIIL9ABPlpaz/WQqtw/h3kFwitXyB7gDyMvzO+i2PTPryERB4ll0lw9XQDhsbq8Monx/C/\nz0/gz6wSXDd7CAJUcm+X12sIggCFVOFcbbgbFhw+WyCqmxVqHIiqYZfaUFRWAmO1CenGrDZDUIDc\nv1Gg0dZ+7DzmDD7+MuWsXvEAABR6SURBVK7BQ0RnxxmYJpiKu1dpWTVe3nUUKSeN0Gn8cOO8oRgU\nrevUe3FsfFPDcbE77DDXlMFYbYKx2gxjtQmm2r+N1SaYaswwVptRY69p9f1kElntzE3j2ZygBoFH\nqwiEXMowfDb8nfFdHJv24TowHcAfqu7ncIj45Mds7Pw2EyJEzBsfi3kTYiGVdGy9FI6Nb+rouIii\nCIvd4go4xmozTK6gUxd6TDDXlLc5m+MvV9cHHEXj2Zy6jwPk/n16Noe/M76LY9M+vIREXiWRCJg3\nPhZDonX4z0d/4KPvspCSXYob/28YggOV3i6PPEwQBKhkKqhkKkT492v18+pmc+pmbRrP5jhDz5mq\nYuSVF7T6HjJBisDa3pxGl6tqA0/d7I6CszlEPQ5nYJpgKnavCosVW3an4JfjRfBXyvCX2UMwZqC+\nXa/l2Pgmb4+LxWZpOeDU1B8z15S1uGVEHbVMVd+DI1fXBiwlVFIllDKl82NZ/cdqmQpKmRJ+UoVP\nr7zs7bGh1nFs2oczMOQz/JVy3LJgOPb/mo93vkzFC9t/x7RzorBoagLkMq5LQh2nlCkRLlMi3D+s\n1c+pW1XZ1CjoNA49JRZji6srt0WAULu2Tn3IqQ86LYcg1/Haz/P1EETkqxhgyOMEQcCUMf1hiHKu\nGfPloVycyDXipvnDEBHi7+3yqBeSCBLXnU4xGNDq51ls1aiyVaHKZqn9UwWLzYIqu8V1zNLgufrP\ns6C02oiCio7fit7eEKSSNwxDDEFEvITUBKf1PKvaasc7X6bi61/zoZBLsGT6QFw0MqLFxkuOjW/i\nuNRziI7aBQgt7QxBjYOQxW7p1Ho8rYWgIH8NRJukduFBhWuxwkZ/y5ofl0vkfbr52RP4e9M+vIRE\nPstPLsXypMEYGhuMLbtT8NruFBzNKsE1swZDreSPJ/UsEkHiCg+dWyygsyGoPgg1mgkq7lwNAgRX\n6FF0IPg0/Lil13H7CupO/BeCfMK4wWGIC9fgP7uO4qdjp5GRb8ZN84cjPjLQ26UReVR3hiB/rQwF\np0tcW1U0/rumlePOhQxrGjwuqylHtb2mS1tXAM41flqdCWorMMn84CdRQC6VQyGRQy6VQy5x/lFI\nZZBL5JAKUs4a9TG8hNQEp/W8y2Z3YOeBTHz6/+3da4xTVaMG4Hdf2047nQvOgByEj4FEDiCowHci\ngmhESfQEAqiDyOiPExND/KFBIhm5RqMZEhOjENSjJmSMMgJeMAqiUQyJA2g0oHNEhQ/5uM7Faefa\ndl/Pj+522mkZB2aGdg/vkzTde3Xv7Wo2xZe11l6r/jREUcCSeRVY8M+xEAWB9yZP8b7kr8G8N4mZ\nmi8ZhowsQSgjMPXM5qw5+4ZtDkr9BAhQ04JNz3Y8+MhQJDUegEQ5GYIyA1Hvcjm93HkfaGDi76Z/\n2IVEriFLIpbOm4D/HFeC//30/7Dzm5P49c8Q/ue/J6Osf09bE9EQSF26ohCBQbuuaZl/E3wSoUeD\nbunQLR2apUM3e94T5fEyA7qlo1uPQLPaoFtGn4/QXykBQk+LkKhAcVqC1GRLkZwMT70DkSqpKA0X\nIhaxoDqhSBXV+LszBim1jK1L2bEFphem4vzR3qXh7c9+xc//+gtBv4qFcyvgVyWUFftQVuxFwMeB\nhvmAv5n8xXsTZ1pmPOykBB7N0qGZvcuMLIEoW1AyktfLvIY+6IFJFMR48HECTaIlKTXkKMkAlBKG\nxNRje8p6QlLKcaKSl2OUuJTAZeAPPr9Yto0vvz+DXQdOwrTS/6h6k2EmHmiuK0rfVmQ+Vno18DeT\nv3hvcsO0TCfsGE7A0ZwQZCRDkNcvoyXcDt3UnDClQUu2KmnJgKWZWmaZpSWD02CSBSk9HCW63LIF\nppTtiqJ/YGLx+EGtSwK7kMi1REHAgn+OxaxJ5WiPmThxuhXN4SiawxG0tEXQFIrgTFNnxnkCgOJC\nTzLQJINOUXw/6FfZekNEQ0ISJUiihL4WSikrK0Szb2Dh0rItGE5ISoQazdKcFiIn5GQJPvFjepfp\nGWGqU++Gbmp/O07pev9IrP2vVQP6LleCAYZcoTToxY1lhfhHWfpEd7Zto6NbR3M4kvKKB5zmtgj+\nOBPG72cyr6cqohNmfLguEXCccHNdsQ8eJf+aUomIUomCmByXBAzdJKCJFqXUsUepLUYjC3IzQJEB\nhlxNEAQE/SqCfhUT/qMo43PdsNDaHs0MN07AOdfSlfW6RX41s/Wm2IfrirwoLvRAZOsNEV0jelqU\n8mvxXQYYGtYUWcTI0gKMLC3I+My2bXRFjeytN+E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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "KP_kZdUfXuY8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "6437c414-e746-4ea0-e50f-728acca9d782" + }, + "cell_type": "code", + "source": [ + "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(learning_rate=0.05),\n", + " steps=5000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 69.36\n", + " period 01 : 66.96\n", + " period 02 : 68.22\n", + " period 03 : 66.22\n", + " period 04 : 69.54\n", + " period 05 : 65.88\n", + " period 06 : 65.68\n", + " period 07 : 66.06\n", + " period 08 : 66.49\n", + " period 09 : 66.13\n", + "Model training finished.\n", + "Final RMSE (on training data): 66.13\n", + "Final RMSE (on validation data): 66.80\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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l50IIESyaJSo33ngjN954IwCffvopGzdu5Oc//zk//vGPmTVrFnfffTdbtmxh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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "LajU97W7YVqG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "###Solution" + ] + }, + { + "metadata": { + "id": "3ER95BhnZGHu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "First, let's try Adagrad." + ] + }, + { + "metadata": { + "id": "N8uno7FRYPzp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "ad83784c-e3cb-446e-9a03-b059f73a04a1" + }, + "cell_type": "code", + "source": [ + "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 81.30\n", + " period 01 : 74.73\n", + " period 02 : 72.48\n", + " period 03 : 70.17\n", + " period 04 : 69.87\n", + " period 05 : 69.64\n", + " period 06 : 69.98\n", + " period 07 : 70.29\n", + " period 08 : 69.45\n", + " period 09 : 68.94\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.94\n", + "Final RMSE (on validation data): 70.73\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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y9HTzR0F5IS5kXxShQiIiItPCoHIPM7kUfbu6I7+4AueuZul1jb4q7qlCRETU\nUBhU/iGonrd/PG080MJGhXN3ElBYzj1ViIiI6oNB5R9audmitbstzl7NQq4eBxVKJBIEugdAI2gQ\nlX5GhAqJiIhMB4NKFQb4qCoPKozX76DCXnf3VEnj7R8iIqL6YFCpQp8ubjCTS3H0rH4HFdoqbNDN\nuTNucU8VIiKiehEtqBQVFWHBggUIDw/HlClTcPToUSQmJmLKlCmYMmUK/vOf/4jVdb1Z/XVQYXp2\nMS7fztPrGoGqAABcVEtERFQfogWVnTt3ok2bNoiIiMDq1avx9ttv4+2338Yrr7yCb7/9FoWFhfjj\njz/E6r7egn08AABH9TyosKtzJ9iYWeNUGvdUISIi0pdoQcXR0RG5ubkAgPz8fDg4OCA5ORk+Pj4A\ngMGDB+PPP/8Uq/t669jKofKgwkT9DiqUS+Xo5eaPwooinM9KFKFCIiKi5k+0oBIWFoaUlBQMHz4c\n06dPx4svvgg7Ozvd687OzsjMzBSr+3qTSiQI9lGhvEKL03oeVNiHe6oQERHVi1ysC+/evRseHh7Y\nsmULEhMTMX/+fNja2uper80iVUdHK8jlMrFKhFJpW+PrYwa2x65j1/HnhXRMGNZRj+t3hNdlT8Rn\nJcDcFrCzqLk/+tvDxoYMh2NjnDguxotjUz+iBZWYmBgEBQUBADp16oSysjKo1X/fQklPT4erq2uN\n18jJKRarPCiVtsjMLHjo13Vt44T4a9k4k5CGFi7Wde6np7IHbuTexi8XjmFwyyB9SjU5tR0banwc\nG+PEcTFeHJvaqSnMiXbrp3Xr1oiLiwMAJCcnw9raGt7e3oiKqrwN8uuvvyI4OFis7hvMgL8W1R7T\nc1FtTze/yj1VePuHiIiozkSbUXn00UfxyiuvYPr06VCr1Xj99dehVCqxbNkyaLVa+Pr6ol+/fmJ1\n32B827nAxtIMJ+LTMGGgN+SyumU7W4UNujt3Rtyd87hVkIKWth4iVUpERNT8iBZUrK2tsXr16gee\n//rrr8XqUhR3Dyo8EHULcVfuIKBjzberqhKo6om4O+cRmRqFlrb/J0KVREREzRN3pq2FYN/6HVR4\nd0+V0+mxUGvr/lFnIiIiU8WgUgueShu0Udni3LUs5BTU/aBCmVSG3u49uKcKERFRHTGo1FKwjwcE\nATgRr9+sSqBuT5XohiyLiIioWWNQqaXend2gqMdBhS1sVGhp2wLxWQkoKC8UoUIiIqLmh0Gllqws\n5Ajo6IqMnBJcupWr1zUC3XtCK2hxOi2mgasjIiJqnhhU6mBAPRfV9nTzg0wiw5+pUXrNyhAREZka\nBpU66NDSAa4OlohKzEBxad3qvtyLAAAgAElEQVQ/vWOjsEZ3l85IKUrD7UL9NpAjIiIyJQwqdSCR\nSBDko0K5WotTCel6XePuoto/uVMtERHRQzGo1FH/7ipIJPrf/uni1BG2ChtEpcWignuqEBER1YhB\npY4cbc3Rva0zrqfm43Zm3T+9I5PK0NutB4rUxTh/J0GEComIiJoPBhU9BPv8tag2Tr9ZlT6qAADA\nyTTe/iEiIqoJg4oefNu5wNbKDH+eT4Nao61z+xY2KrSybYHzWReRX87jv4mIiKrDoKIHuazyoMLC\nkgqcuXxHr2sEqnpBK2hxinuqEBERVYtBRU+62z96LqoNcPOFXCJDZGo091QhIiKqBoOKnloobdDW\nww7x17OQnV9a5/Y2Ztbo7tIFKUVpuFWQLEKFRERETR+DSj0E+6ggCMDxc/U8qJCLaomIiKrEoFIP\nvTu7QWEmxbFzqdDqcfums1MH2ClsEZV2hnuqEBERVYFBpR4szeXo1dEVmbmluJhU94MKZVIZertX\n7qly7s4FESokIiJq2hhU6inY1wMAcPSsfmf39HGv3FMlklvqExERPYBBpZ7ae9rDzdES0RczUVxa\nUef2HjbuaG3bEheyLyGvLF+EComIiJouBpV6untQYYVai8gL+h5UGACtoMXp9NgGro6IiKhpY1Bp\nAP27qyCVSPTeU6Wnmx/kEhn+TI3inipERET3YFBpAA425vDxdsaNtALcyqj7QYVWZlbwUXZFWlE6\nkgpui1AhERFR08Sg0kCCdAcV6reoVrenChfVEhER6TCoNBAfb2fY/XVQYYW67gcVdnJsD3uFLaLS\nz6BCU/dFuURERM0Rg0oDkcuk6NdNhaJSNWIvZ9a5feWeKgEoVpfgLPdUISIiAsCg0qCCfet3UGGg\nqnJPFW6pT0REVIlBpQGpnK3RroU9LlzPRlZe3Q8qdLd2g5ddKyRkXUJuWZ4IFRIRETUtDCoNLMhH\nBQH1OagwAAIEnE7jnipEREQMKg2sVydXmJvJ9D6oMMDVF3KpnHuqEBERgUGlwVmay9Grsyvu5JUi\n8WZOndtbmVnB16Ur0oszcCP/lggVEhERNR0MKiII9qnvotq/9lTholoiIjJxcrEuvH37dvz444+6\nx/Hx8Vi+fDk+/fRTmJmZwc3NDe+++y4UCoVYJRhMuxb2cHeyQvTFTBSVVsDawqxO7Ts5tYe9wg7R\n6Wcwsd0YmMnq1p6IiKi5EG1GZdKkSYiIiEBERASeeeYZjBs3Dm+99RY++eQTbN26FVZWVjhw4IBY\n3RuURCJBsK8Kao0WJ8/X/aBCqUSKPqoAlKhLcfbOeREqJCIiahoa5dbPunXrMG/ePDg4OCA/Px8A\nkJ+fD0dHx8bo3iD6dXX/66BCPbfUd6/cU+VPbqlPREQmTLRbP3edPXsWKpUKSqUSS5cuxSOPPAJb\nW1t06dIF/fr1q7Gto6MV5HKZaLUplbaiXrtXFzdEnk9DfpkG3p4OdW7f4UpbJGZfhsxaAyerurVv\n6sQcG6ofjo1x4rgYL45N/YgeVL7//ns88sgj0Gq1eOutt/D999+jZcuWeO6553Dw4EEMHTq02rY5\nOcWi1aVU2iIzs0C06wNA705KRJ5Pw54/rmLaiA51bh/g4odLWdew7/wRjPAaLEKFxqkxxob0w7Ex\nThwX48WxqZ2awpzot34iIyPh7++P7OxsAECrVq0gkUjQt29fxMfHi929Qfl4O8PeWoGTF9JQodbU\nuX0PV1+YSeU4mcY9VYiIyDSJGlTS09NhbW0NhUIBR0dH5OXl6QLLuXPn0Lp1azG7NziZVIp+3d1R\nVKpGzKU7dW5vZWYJX2U3pBdn4np+kggVEhERGTdRg0pmZiacnJwAADKZDMuWLcPTTz+N6dOnQ6PR\nICwsTMzujUJQ97t7qui5qPbunipcVEtERCZI1DUq3bp1wyeffKJ7PGzYMAwbNkzMLo2Oytka7T3t\nkXAjB3dyS+DiYFmn9h0d28HB3B7R6XGY2P7/oOCeKkREZEK4M20jCPbxgADgmB4HFUolUvRxD0Cp\nphRnM5v3mh4iIqJ/YlBpBD07KWGukOH4uVRotXVfFNtHVbmnysm06IYujYiIyKgxqDQCC4UcfTq7\nIiu/DAl6HFToZqVEW3svJGZfRk5prggVEhERGScGlUYS5OMBoD6LagMgQEBkWkxDlkVERGTUGFQa\nibeHHVTOVoi5lInCkoo6t+/h6gMzqRkiU7mnChERmQ4GlUYikUgQ7OMBtUbAyfNpdW5vKbeEn7Ib\nMkru4Hr+TREqJCIiMj4MKo2oXzd3yKQSHD2bqtesyN09Vf5M4Z4qRERkGhhUGpGdtQK+7VxwK6MQ\nSemFdW7fwdEbjuYOiMmIQ7mmXIQKiYiIjAuDSiML9qncqfaIHotqpRIp+qgCUKopwxnuqUJERCaA\nQaWRdWvrBHsbBU6eT0d5Rd0PKuzjXrmnSmQq91QhIqLmj0GlkcmkUgR1V6GkTI2YS5l1bu9q5QJv\ney9czLmC7NK678lCRETUlDCoGECQz92DCuu+pT4ABKp6Ve6pkso9VYiIqHljUDEAN0crdGjpgISb\nOcjILalz+x6u3aGQmuFkGvdUISKi5o1BxUDuLqo9rsesioXcAn6u3XGnJAtX8240cGVERETGg0HF\nQHp2dIWFQoZjeh5UGOheuadKZCr3VCEiouaLQcVAzBUy9OnihpyCMpy/kV3n9u0d28LJwhHRGXEo\n454qRETUTDGoGFCw7qDCut/+kUqk6OMegDJNOc5knGvo0oiIiIyC3kHlxo0bDViGaWqjskULF2vE\nXspEQXHdZ0Xu7qlyMo17qhARUfNUY1CZNWvWfY/Xr1+v+/OyZcvEqciEVB5UqIJGK+Dk+fQ6t1da\nOaOdQxtcyrmCrBLuqUJERM1PjUFFrVbf9/jkyZO6P/NjsQ0jUHdQYYp+BxX+taj216TfOSZERNTs\n1BhUJBLJfY/v/UX4z9dIP3ZWCvi1d8HtzCLcSCuoc/sebr5wtXTBseST+PbiDmgFrQhVEhERGUad\n1qgwnIijPotqzWUKLAqYC08bDxxLicSW+K9QoVU/vCEREVETIK/pxby8PPz555+6x/n5+Th58iQE\nQUB+fr7oxZmKbm2c4GhrjsgLaXh0SDuYm8nq1N5OYYvnevwLm85+gTOZ57A+rgRPdZ8BS7mFSBUT\nERE1jhqDip2d3X0LaG1tbbFu3Trdn6lhSKUS9O/ujp9O3ET0xQz066aq8zUs5ZaY7zsbn1/4Bmcy\n47E6dhPm+T4BOwXHiYiImq4ag0pERERj1WHygrqr8NOJmzh2NlWvoAIAZjIzzO42Hd9e3InjKZFY\nGb0eC/zmwMXSqYGrJSIiahw1rlEpLCzE559/rnv87bffYuzYsXj22Wdx584dsWszKa6OVujUygGJ\nSbnIyCnW+zpSiRRTO45HqNdQZJZkYUX0OiQX6ndKMxERkaHVGFSWLVuGrKwsAMD169excuVKLFmy\nBP369cPbb7/dKAWakruLao+dq1+wkEgkGNM2BJPaj0V+eQFWxWzAldzrDVEiERFRo6oxqNy6dQuL\nFy8GAOzfvx+hoaHo168fpkyZwhkVEQR0VMLSXI7j59L0Oqjwnwa17I9ZXaaiTFOOtWc242zm+Qao\nkoiIqPHUGFSsrKx0fz516hQCAwN1j/lR5YanMPv7oML461kNcs2e7v6Y6zMLEokUm+Mj8GfK6Qa5\nLhERUWOoMahoNBpkZWUhKSkJsbGx6N+/PwCgqKgIJSUljVKgqQn2qVxIq8+eKtXp4twRz/o9BUu5\nBbYmbseBm4e5iy0RETUJNQaVOXPmYNSoURgzZgzmzZsHe3t7lJaW4rHHHsO4ceMaq0aT4uVuC0+l\nDc5cvoN8PQ4qrE4b+1Z4vsdcOJo7YNfVvdhx5SfuYktEREZPIjzkn9YVFRUoKyuDjY2N7rljx44h\nKCioxgtv374dP/74o+5xfHw8jhw5gkWLFiEvLw9ubm5YuXIlFApFtdfIzKz7lvK1pVTainr9+jhw\n+ha+OXgZjw5ph5DerRr02jmluVh75hOkFWegt3sPTO80CTJp3TaYE5sxj42p49gYJ46L8eLY1I5S\nWf2eXzUGlZSUlBov7OHhUasCTp06hX379sHS0hKurq54/PHHsXbtWgwYMAA+Pj7VtjPVoFJYUoHn\n1x6Dq6MV3pzdu8HXAxVWFGFj3Ge4np+Ers6d8GS36VDIqg+Mjc2Yx8bUcWyME8fFeHFsaqemoFLj\nhm9DhgxBmzZtoFQqATx4KOGXX35ZqwLWrVuHDz74ADNmzMDWrVsBAAsWLKhVW1NkY2kGv/ZKRCVm\n4FpqPrw97Bv2+mbWeMb/KXxyLgLnsxKxJnYz5vrOgrWZ1cMbExERNaIag8p7772H3bt3o6ioCGFh\nYRg9ejScnOq2y+nZs2ehUqmgVCpx584dfPPNNzhx4gTatWuHpUuX1njrx5QN8FEhKjEDx86mNnhQ\nASoPM3za53FEJGzD6fRYrIzZgAW+s+Fo4dDgfREREenroWtUACA1NRU7d+7Enj170KJFC4wdOxbD\nhw+HhcXDD71btmwZwsLC0KdPH/j4+OCLL76Av78/li5dis6dO2PatGnVtlWrNZDLjWv9RGPRaAU8\n+fYBFJVU4Mv/hMDCvMZMqTetoMWXZ37A3kuH4GLlhFcHPoMWdu6i9EVERFRXtQoq99q+fTs++OAD\naDQaREVFPfTrQ0JCsGfPHigUCoSEhGD//v0AgH379iEyMhKvv/56tW1NdY3KXTuPXMOeEzcwO6wz\n+nfX7/yf2hAEAQduHsbua/tgbWaFeb5PwMuuYRfx1kVTGBtTxbExThwX48WxqZ2a1qjU+PHku/Lz\n87F161aMHz8eW7duxb/+9S/s3bv3oe3S09NhbW2tu73Tp08fnDx5EgBw/vx5tGnTpjbdm6wgEfZU\nqYpEIsEIr8GY1mkiiitKsDr2YyRkXxK1TyIiotqo8X7CsWPH8MMPPyA+Ph4jRozA8uXL0aFDh1pf\nPDMz8741Lc899xxeeOEFrFmzBi4uLpg3b57+lZsApYMlOrd2RMLNHKRnF8PNSdzFrv08esPazAqf\nnv8aG+I+w4wuj6Knm5+ofRIREdWkxls/nTp1gpeXF3x9fSGVPjj58u6774panKnf+gGAk+fT8PGe\nCxgV2BoTB3k3Sp+Xc65i49kvUKYpw8QO/4dBnv0bpd+7msrYmCKOjXHiuBgvjk3t6P3x5LsfP87J\nyYGjo+N9r92+fbsBSqOH6dFBCStzOY7Hp+KRAW0gqyIwNrT2jt5Y1ONprI37BNsv7UZheSHC2ozg\n+U5ERNToavytJ5VKsXjxYrz22mtYtmwZ3Nzc0Lt3b1y6dAkffvhhY9Vo0hRmMgR2dUNeYTnOXctu\ntH49bT3wQsB8uFg6Y9+Ng/j24g5uuU9ERI2uxhmVVatW4fPPP4e3tzcOHjyIZcuWQavVwt7eHtu3\nb2+sGk1esI8HDsUk42hcCvzauTRavy6WzlgcMA/rzmzBsZRIFFYU4/GuU2EmFeej0kRERP/00BkV\nb+/KdRFDhw5FcnIyZsyYgbVr18LNza1RCiSgtbstWrna4OzVLOQVNdxBhbVhp7DFcz3+hfYObXEm\n8xzWn9mCEnVpo9ZARESmq8ag8s81CSqVCsOHDxe1IKpakI8KGq2AP+PTGr1vS7kl5vvOhp+yGy7l\nXsXqmI3IL+fiMCIiEl+dVmZyMaXhBHZ1h1wmxdGzKajjHn0NwkxmhtndpqO/Rx/cKkzByuj1uFPS\neGtmiIjINNW42CA2NhaDBg3SPc7KysKgQYMgCAIkEgkOHz4scnl0l42lGXp0cMGphAxcTclHuxYN\nf/7Pw0glUkztOB62Chv8cuMgVkSvwwK/J9HCRrxdc4mIyLTVGFR++eWXxqqDaiHYxwOnEjJwNC7F\nIEEFqJxVG9M2BLZmNth+eTdWxWzA0z6z0M6BuwwTEVHDqzGotGjRorHqoFro7OUIZzsLnErMwNRh\n7WGhMNynbwa17A8bMyt8kfAd1p7ZjCe6ToOPsqvB6iEiouZJ/N3DqMFIJRIE+ahQVq7B6cQMQ5eD\nnu7+mOszCxJI8PG5L3Ei5bShSyIiomaGQaWJ6d/dHRKIf1BhbXVx7ohn/f8FKzNLfJW4Hb/e/N0g\ni32JiKh5YlBpYlzsLdHFyxFXbuchNavI0OUAANrYt8LzPebC0dwBu6/uw44rP3EXWyIiahAMKk1Q\nkI8HAOCYkcyqAIC7tRsWB8yDu5UrDt06ioiEbdBoNYYui4iImjgGlSaoRwcXWFvIcTw+DWqN8cxc\nOFo4YFHAXLSxa4VTaTHYdO4LlGkadyddIiJqXhhUmiAzuQyBXd2RX1SOc9eyDF3OfWzMrPGM/1Po\n4tQR57MS8VHsZhRVFBu6LCIiaqIYVJqoYJ/KTdaOxhnP7Z+7zGUKPO3zOHq5+eN6/k2sjNmAnNJc\nQ5dFRERNEINKE9XKzRat3WwrDyosLDN0OQ+QSWWY0eVRDG4ZhLSidKyIXo+0IsN/pJqIiJoWBpUm\nLNhXBa0g4IQBDiqsDalEigntxmBs25HIKcvFypj1uJGfZOiyiIioCWFQacL6dHGDXCbFkbOpRrt3\niUQiwQivwXis0wQUV5RgdezHSMi6ZOiyiIioiWBQacKsLczQs6MS6dnFuJKcZ+hyatTfow/mdA+H\nVtBiw9nPEJV+xtAlERFRE8Cg0sTdXVT71YFLyCsy7o8C+yq7YYHvbJhJzfD5+W9w+NZxQ5dERERG\njkGlievU2hHBPiokpRfi7S+jkJZt3B8Fbu/ojUU9noaNwhrbL+/GT9f2G+1tKyIiMjwGlSZOIpHg\n8ZGd8H/9vXAnrxTvREQb/W0gT1sPvBAwHy6Wzth34yC+vbiDW+4TEVGVGFSaAYlEgnHBbTEztCOK\nS9V4/5tYxF7KNHRZNXKxdMbigHnwtPHAsZRIbIn/ChWaCkOXRURERoZBpRkZ6NcCz0zoDokEWLvz\nHH6PuW3okmpkp7DFcz3+hfYObXEm8xzWx32KEnWpocsiIiIjwqDSzPi2c8GSx3rAxtIMEb9ewveH\nrxr1GhBLuSXm+86Gn7IbLuVexeqYjcgtzTd0WUREZCQYVJqhNio7vBoeADdHS+w9eROf/HTBqA4v\n/CczmRlmd5uO/h59cKswBcsOfsAt94mICACDSrPl6miFl8MD0NbDDn+eT8eqbXEoLlUbuqxqSSVS\nTO04HiNaD0ZaYSZWxWzAnZJsQ5dFREQGxqDSjNlZKfDvqf7wa+eChJs5WP5VDHIKjO9coLskEgnG\neo/Eo93GIKs0B6tiNiCj2LgXBRMRkbgYVJo5czMZ5o/vhkH+LXA7sxBvR0QhObPQ0GXVaELXURjn\nPQq5ZXlYFbMRqUXphi6JiIgMhEHFBMikUoSP6IAJA9siO78M726NwcWkHEOXVaPhrQdhUvuxyC8v\nwIcxG3G7IMXQJRERkQEwqJgIiUSCsL5eeHJ0Z5RVaLDiuzM4lWDcMxWDWvbH1I7jUVRRjNWxm3Az\n/5ahSyIiokYmWlDZvn07wsPDdf/5+/vrXvv2228xZMgQsbqmGvTrpsJzk30hl0mxcfd5/HoqydAl\n1SioRSCmd56EEnUp1sRuxrW8m4YuiYiIGpFoQWXSpEmIiIhAREQEnnnmGYwbNw4AkJWVhQMHDojV\nLdVCVy8nvDStB+xtFPj20BV889tlaI14r5VAVU883nUqyrXlWHtmMy7nXDN0SURE1Ega5dbPunXr\nMG/ePADA+++/j2effbYxuqUatHKzxdLwnlA5W+FA1C1s3BWPCrXG0GVVq6ebH2Z3mw61VoN1cVuQ\nmH3Z0CUREVEjkIvdwdmzZ6FSqaBUKhEZGQlzc3P4+vrWqq2joxXkcplotSmVtqJduylQKm2x8rmB\neOuzU4i6mImSini8Oqs3bK0Uhi6tyrEZruwLZwcbrDj+MTae/QyL+/8LPTy6GaA602bqPzfGiuNi\nvDg29SMRRN5ffdmyZQgLC4O/vz9mzZqF9evXw97eHkOGDMGhQ4dqbJuZWSBaXUqlrajXb0oq1Bps\n/ikBUYkZUDlbYdFkX7jYWxqsnoeNTUL2JWw6+wW0ghazu02Dr5JhpbHw58Y4cVyMF8emdmoKc6Lf\n+omMjIS/vz8SEhJw584dzJkzB5MnT0ZGRgYWLVokdvdUC2ZyGZ4e2xUjerVEalYx3o6IRlK68f5g\ndXbqgPm+T0AmleGT+K2ITj9j6JKIiEgkogaV9PR0WFtbQ6FQwNfXF/v378e2bduwbds2uLq6YtWq\nVWJ2T3UglUgwZWh7TBnSDvmF5Vj+VQzOXzfeLezbO3rjGb8noZAq8Nn5bxCZGm3okoiISASiBpXM\nzEw4OTmJ2QU1sBG9W+Hpcd2g1mjx4fY4nIhPNXRJ1Wpr74Vn/efAUm6BiIRtOJ4caeiSiIiogYm+\nRqU+uEbFcC4m5eCjH86huEyNCQPbYlRga0gkkkbpu65jc6sgBWvPbEZhRREmdRiLQZ79RazOtPHn\nxjhxXIwXx6Z2DLpGhZqmjq0c8fL0HnCyM8cPf1xDxK+XoNFqDV1WlVraemCh/79gp7DF9ku78VvS\nH4YuiYiIGgiDClWrhdIGr4b3hKfSBodjk7FuRzzKKoxzrxUPG3c81+NpOJjbY+eVn/HLjYOGLomI\niBoAgwrVyNHWHC9P74HOrR1x5sodvP9NLPKLyw1dVpXcrJRY1ONpOFk4Ys+1/dhzbT+M+M4mERHV\nAoMKPZSluRyLJvuib1c3XEvJxzsR0cjIKTZ0WVVysXTGoh5Pw8XSGb/cOIidV39mWCEiasIYVKhW\n5DIpnhzdBWF9WyMjpwRvR0Tjemq+ocuqkpOFIxb1eBpuVq44mHQE2y/vhlYwzvU1RERUMwYVqjWJ\nRIIJA70RPqIDCksq8N7XMYi7csfQZVXJwdwei3o8DQ9rd/xx+wS+vbiDYYWIqAliUKE6G9zDEwse\n6Q4IwEc/nMORuBRDl1QlW4UNFvb4F1ratsDxlFPYmrAdGq1xLgYmIqKqMaiQXvw7KPHvqf6wspDj\n832J2HX0mlGuBbExs8azfk/By64VItOi8cWFbxlWiIiaEAYV0pt3C3u8Eh4AF3sL/Hj8Bj7dmwC1\nxvhur1iZWWKB35PwtvdCdEYctsRvRYVWbeiyiIioFhhUqF7cnazw6oye8HK3xfFzaVjz/VmUlBlf\nCLCUW2C+35Po4NgOcXfOY/O5L1GhqTB0WURE9BAMKlRv9tYKvPiYP3y8nRF/PRv/+zoWeYVlhi7r\nAeYyBeb6zEIXp444n5WIjWc/R7nGOPeEISKiSgwq1CAsFHI8M6E7BviqcDO9AG9HRCM1q8jQZT1A\nITPDUz4z0d2lCxJzLmNd3BaUqksNXRYREVWDQYUajEwqxczQThgX1AZ38krxTkQ0Lt/ONXRZDzCT\nyjGnWzj8XX1wJfc61p75BMUVJYYui4iIqsCgQg1KIpHg/4LaYNaoTigp0+CDb88g+mKmoct6gEwq\nw6wuU9HLzR/X85Pw0ZmPUVRhnLvtEhGZMgYVEkWwjwcWTvKBVCLB+p3ncDD6tqFLeoBMKsOMLo+i\nn6oXkgqSsTp2EwrKCw1dFhER3YNBhUTTva0zlkzzh621Al8duITtv1+B1sj2WpFKpJjaaQIGtOiL\n5MJUfBi7CXllxnk0ABGRKWJQIVF5udvh1fAAuDlZYV9kEjbvuYAKtXHttSKVSDG5wzgMaRmMtKJ0\nfBizETmlxre2hojIFDGokOiUDpZ4NTwA7VrYI/JCOlZtO4PiUuPaw0QikWB8u9EY0XowMkruYFXM\nBtwpyTZ0WUREJo9BhRqFjaUZXpjihx4dlEhMysW7X8UgO9+4PhYskUjwf21DEdZmOLJKc/BhzEZk\nFBvfQmAiIlPCoEKNRmEmw7xx3TC0hyeSM4vwdkQ0bmca1+JViUSCUW2GY5z3KOSU5eLDmI1IK0o3\ndFlERCaLQYUalVQqwWPD22PSIG/kFJTh3a0xSLiZY+iyHjC89SBMbP9/yCsvwKqYjUguTDV0SURE\nJolBhRqdRCLByMDWeGpMF5RXaLBq2xlEXjC+WYvBLYMwpeN4FFYUYXXMJiTlG99HrImImjsGFTKY\nwK7ueH6yL8zkUmz68Tx+iUyCYGQfXw5uEYjpnSejWF2CNWc+xvW8m4YuiYjIpDCokEF19nLCS9MC\n4Ghrjm2/X8E3v12GRmtcYaWvqidmdpmCMk05PjqzGVdyrxu6JCIik8GgQgbX0tUGr4YHoIWLNX6L\nvo3/RZxGhVpj6LLu08vdH090nQa1VoN1Zz5BYvZlQ5dERGQSGFTIKDjZWeDl6T3QsaUDTpxNxapt\ncSgpUxu6rPv4u3bHnO7h0ApabDj7Gc5nJRq6JCKiZo9BhYyGlYUZnn/UF327q5CYlIv3vo5BXlG5\nocu6T3eXLnjaZxYkADad/QJxmecNXRIRUbPGoEJGxUwuw5IZvTDQzwNJ6YV4NyIaGbklhi7rPp2d\nO2Ce72zIpDJ8Eh+B6PQ4Q5dERNRsMaiQ0ZFJJZgR0hGj+3khI7cE70ZE41aGcW0M18HRGwt8n4RC\naobPzn+NU2kxhi6JiKhZYlAhoySRSDB+QFtMHdYeeUXlWP5VDC7dMq6DAr0dvPCM/xxYyC3w5YXv\ncCLllKFLIiJqdhhUyKgN79lStzHciu/OIPaycZ2942XXCgv9/wUrM0t8lfg9/rh9wtAlERE1K3Kx\nLrx9+3b8+OOPusfx8fH45ptv8MYbb0AqlcLOzg4rVqyApaWlWCVQMxHY1R3WlmZYt/Mc1u2Ix8yR\nHRHs42HosnRa2nrgOf+nsebMx9h2aRfUWjWGthpg6LKIiJoF2euvv/66GBfu2rUrxo8fj/Hjx8PT\n0xNyuRy7du3CkiVLMHfuXMTHxyM5ORk+Pj7VXqO4WLxPfFhbm4t6fdJfVWPj5miFzl6OiL6YgVMJ\nGVDIpWjv6WCgCh9kq5r7Y6wAACAASURBVLBBd+fOOJMZjzOZ5yCTyNDOoY2hy2pw/LkxThwX48Wx\nqR1ra/NqX2uUWz/r1q3DvHnzsHHjRl0wcXJyQm6uca05IOPm7WGPl6dX7mK7/fBVbDt0BVoj2nLf\nzdoVi3rMhaO5A/Zc+wU/XdtvdEcCEBE1NaIHlbNnz0KlUkGpVMLGxgYAUFxcjN27dyM0NFTs7qmZ\n8XCxxivTA6BytsIvp5Lw6c8JUGu0hi5LR2nljEU95sLF0hn7bhzEjis/Iaskh4GFiEhPEkHk/4Mu\nW7YMYWFh6NOnD4DKkDJ37lyMHTsW48ePr7GtWq2BXC4TszxqovIKy/DGlpO4lJSLnp3dsGRGT1go\nRFtyVWfZxbl44/CHSCmoPBXa2swSrR08df95ObSAp70HFDIzA1dKRGTcRA8qISEh2LNnDxQKBdRq\nNZ588kmEhYVh0qRJD22bmVkgWl1Kpa2o1yf91XZsSsvVWL8zHvHXs9GuhT0WTvKBtYXx/OIvLC/C\n8ZRI3CpMQXJhCjKLsyDg7x83qUQKNyslWtio4GnjAU8bD7SwVcFOYWvAqmvGnxvjkVWSg4s5V3Ap\n5woyyzIhFeSwlFvAUm4BC7kFLGUW9z+WW8BSbln5WHb3eXNIJfzwp5j4M1M7SmX1/98T9Z+g6enp\nsLa2hkKhAABs3rwZvXv3rlVIIXoYC4Ucz070wZafExB5IR3Lv4rB85P94Ghb/aKsxmSjsEaI1xDd\n4zJNOVIKU3G7MBXJham4XZCC5KJUpBalIyr9jO7rbBU2fwcXGxVa2KjgZqWETMrZRVNWUP7/7d15\ncJvlvS/wr/ZdsiRbXmLHdmxix0lsEjunQ9jay9IzXeBACknTuMzce5lhuPS2JWWak7JOO/SEOdzT\nW2BouW3P4YRSDEmhQIFCD4RmSliy2YmJ7XiJE6+SbK3Wvtw/JCt2rBjFsazX9vczw8h+JUWP+L2v\n9PXzPO/zetHl6Eanoxudjh7Y/WOp++QSGcLRyLQgnCmlRDElyFwYclQMO5RzWQ0qNpsNJpMp9fvv\nf/97lJaW4tChQwCAL33pS7jvvvuy2QRa4qQSMe7+Zh20Khn+68gAHt97BDu3XYkikzrXTZtBIZGj\n0lCOSkN5alssHoPdP45B7zAGvUMY8A5hwDOMU+NdODXelXqcVCxFiaYQK5LhpVRbjBXaEqhlPL1/\nqfJHAuh29iaCyXg3hiZGUvcpJUqsz69DjbEaNcZq1FdUw2bzIBgNwh8JwB8JIBANpH72RwIIRC74\nPeqHL3z+cc6gGyMT1qyEnan3TQ05qdDDsEOzyPrQz+Xg0M/yNJfaxONxvHmoH6/+rRdalQw/vLMB\nlcX6LLUw+3xhX6LXxTuMAe8QBr2JnpdIbPoVpU1KYyq4JHpgSmBWGbP2oc/jJnvC0TB6Xf2pXpN+\nzwBi8cREcZlYilWGCtQYq7HaWI2VuhXTetjmqy7xePySw066+y4n7KilKlTnVWKjpQFVeRWLPsDw\nmMnMbEM/DCokOJdTmwPHB7H3L52QyyT43u3rUVdh+uInLRLRWBSjPlsquAwmQ4wnNP06SAqJPDXv\nZUWy52WFtghyifyy28DjZv5EY1Gc9Qym5pn0us4gnAyiYpEY5brSRI+JqRqV+nLIZpl4LaS6JMJO\nCIFoAL6w/5LDjic8gVA0se6IQa7DBks9GgsbUKFfuShDi5BqI2QMKmlw5xGuy63N4Q4rnnujHQBw\n9zfXYlOtZb6aJkiuoAeDyfAy4B3CgHcYVp8t9dc4AIgggkWdnwoupdpilOpKYJDrIRKJMn4tHjdz\nF4/HMTwxmpxjchqnHX0IRAOp+1doi7HaWIUaYzWq81ZBJVVm/G8vpbpEY1F0OXtwdLQVx20n4Ysk\nrp5uVORhYzK0rNSVXtJ+m0tLqTbZxKCSBnce4ZqP2pzqd+Cp/W0IhqLYcfNqfGVj6Ty1bnEIR8MY\nnhhNTtwdSvXC+COBaY/TyNTng0uyB6ZIY4FUnH76Go+bS2P3j6XmmHQ5euAJn+/9yleZU3NMVhur\noJNr5/w6S7Uu0VgUHY7TODLaijZ7e2r/NStNqdBSqi0RdGhZqrWZbwwqaXDnEa75qk3/iAf/5+Xj\n8PjC+KdrKvHNqysE/YGWbfF4HOMB54zel6lnjwCARCRBkcYy7ayjUm0JtHINj5sv4Ap60JUcyul0\ndGMs4EjdZ5DrsDoVTKphVhnn7XWXQ13CsQg6xrtSoSWYHB6yqPKx0VKPjYUNKNEUCe4YXw61mQ8M\nKmlw5xGu+azN6LgPT7Ych90VwH/buALbb1oNscA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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "rUm51HqZZMCA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Now let's try Adam." + ] + }, + { + "metadata": { + "id": "ej30K3R6Y9Hd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "f7b1324b-6d7a-46a6-bd0a-7b7402ef7b71" + }, + "cell_type": "code", + "source": [ + "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 205.21\n", + " period 01 : 119.10\n", + " period 02 : 109.79\n", + " period 03 : 100.57\n", + " period 04 : 86.98\n", + " period 05 : 75.22\n", + " period 06 : 71.70\n", + " period 07 : 70.49\n", + " period 08 : 71.54\n", + " period 09 : 70.08\n", + "Model training finished.\n", + "Final RMSE (on training data): 70.08\n", + "Final RMSE (on validation data): 71.98\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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RgBEREZEWRwFGREREWhwFGBERkVZm3bovGvW+p5/+G5mZGed8/f77f3OpSrrkFGBERERa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\nRIcBJgbsVPrF2ujTleoSbZBpcbfiwPkjONbwGQJ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9+vU4evRovJuU0KR4N0AvPvnkE5w/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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "VrBNlbkoZoV-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's print a graph of loss metrics side by side." + ] + }, + { + "metadata": { + "id": "H6nbVd-eZf3Q", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 376 + }, + "outputId": "ec86c7b5-6ccf-4498-a1ae-7f1a009ced60" + }, + "cell_type": "code", + "source": [ + "plt.ylabel(\"RMSE\")\n", + "plt.xlabel(\"Periods\")\n", + "plt.title(\"Root Mean Squared Error vs. Periods\")\n", + "plt.plot(adagrad_training_losses, label='Adagrad training')\n", + "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n", + "plt.plot(adam_training_losses, label='Adam training')\n", + "plt.plot(adam_validation_losses, label='Adam validation')\n", + "_ = plt.legend()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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b6dv37xOw0tJSmjULYOrUe3j++bkcvCZXJ09Gs3DhK7i4uDJ+/Ciys7P56KMP\nuO++Bxk4MJT585/Fzs6uTvNbV6SQV6FpC1eSErJJKW5CQHw8tn5+5g5JCCHqlbpoYwoQFNQBKC/o\nrVuXNxdxdXUlJyenyuNfeZ+LixvvvfcOhYUFpKQkM2zY9a2xr24fmptbcb9+fk1xc3MHwN3dg9zc\nHC5evECnTp0B6NdvAAcP/lHr/KhBCnkVmga6cui3i8ZlaFLIhRD1lceku6o9e65rddnG9OpGKbVp\n86nTlbc6feut15k27V569w7hs8/Wkp+fd922Ve332kYtiqJUaJNan2dk5WK3Knj6GLC21pJm70te\n9K03fxdCiIakrtqY1oZGo7muZShAZmYGfn7+FBUVsW/fb5SUlNzy5/Pz8ze2Mt23b+8t789UpJBX\nwcpKi3+gK/k2jqSevYRyg8EjhBCNVV21Ma2Nzp278uabr103zT1hwhTmzn2a+fPnMGHCFDZv/r7a\nafnq3HPPTJYvf5Mnn5yNi4tLhVmG+kTamFJ1i7xjh+PYtfU0bZN+p8+sSdi3bFWnx25spB2hOiTP\n6pA8q8NceY6KisTOzo5WrVqzdu1HKIrCPffcr3ocIG1Mb0nTv+67fmUZmhRyIYRoHGxsrFmyZBG2\ntrbY2trxwgsvmTukG5JCXg1HZ3scnWxJL/Mm5/hh3MbcYe6QhBBCqKB8Kdun5g6jWvVzwr+eadrS\njVKtDUlxGZQVFpo7HCGEEMJICnkNXJleT7H1Jv/MaTNHI4QQQvxNCnkN+DVzRqOR+64LIYSof6SQ\n14CNrQ4vXwNZtu5knJAzciGEEPWHFPIaatbSHTQaLqcUU3qLaxOFEKIhUbONaU1FRBxk3rxnAHj2\n2SdrHdPV7VKff34uhYUFpgm0DkghryFjW1N7X/JOnjBzNEIIUX+o2cb0ZixZsqzW77m6XerChYux\nta2fDVNAlp/VmLuXAVsbLal6X3Kjj2Po3sPcIQkhhNmp2cb09OlTvPPOMt5+eyUAa9Z8gMHgSEBA\nIKtXr8Ta2hqDwcCLLy6pEOPo0UP44YdfahyTt7dPhXapCxbM5dNP15OTk83ixS9SXFyMVqvl2Wfn\no9FobtgCVU1SyGtIq9XgH+jK2ZNlJJ88jLe5AxJCiKvs3X6WcyeS6nSfLdp5EjK4ZZXbqNnGtHXr\nNqSkJJOdnY3BYGDPnl0sXbqMyMijPP/8S/j6+rFo0QL27/8dvV5/Xaw1jWnNmnUV2qVesXr1SsaM\nGcuQIcPZsWMba9Z8wMyZD9+wBarBUPmd2OqaFPJaaNrCjbMnU0jMs6FdWhrWrq7mDkkIIcxK7Tam\nffsOYP/+vXTs2BlbWxs8PDwg4MogAAAgAElEQVRxdnZm6dKXKC0tJT4+ju7de96wkNc2pmudPBnN\nP/85G4Bu3Xrw8cergRu3QJVCXk8Zvyf/63atTn37mTkiIYQoFzK4ZbVnz3XNHG1MBw4M5auvNpCZ\nmcHAgYMBWLx4Ea+99iYBAYEsW7a00nhrG9P1NMb3FReXGFuc3qgFqppMerHbqVOnGDp0KOvWrQPg\nwIED3H333cyYMYOHH36YzMzyv3xWr17NxIkTmTRpEr/++qspQ7olDo52ODtZk27vTXZ0tLnDEUII\nszJHG9MOHYK5cOEce/f+xqBBQwHIzc3By8ub7OxsIiIOVbrf2sR0o3ap7dsHERFxEIA//zxEu3bt\nax2/KZjsjDwvL49FixbRp08f43OLFy/m9ddfp0WLFqxcuZL169czcuRIfvzxR7744gtycnKYOnUq\n/fr1u+4vnPqiaStPIg/FEX82ET9FqdfN5oUQwpS2bdvKvHkLjY9v1MbU29unQhvTZ599kuPHoxg9\n+o6bamOq0Wjo2LEzp0+fxNu7/GqlO++cxCOPzKRp02ZMm3YPa9Z8wEMPPXrde2sT05V2qVdP0T/w\nwD9ZvHgRmzZ9i05nzdy58+uk7/mtMlkb05KSEkpKSli1ahUuLi5Mnz6dmTNnMmvWLLp168bSpUtp\n0aIFGo2G2NhYnnjiCQBmzpzJM888Q9u2bSvdt5ptTK8Vcy6VHzZE0jw9kqH/noCNj2+dxtLQSdtH\ndUie1SF5Vofkueo2piabWtfpdNjZVVx399xzzzFr1ixGjBjBoUOHGD9+PCkpKbheddGYq6srycnJ\npgrrlvk0dUargVS5XasQQoh6QNWL3RYtWsS7775L9+7dWbp0KZ999tl129RkgsDFRY9OV7dT71X9\ntXOtps0cuXgRck6fpPWU8XUaR2NQm1yLmyd5VofkWR2S58qpWshPnjxJ9+7dAQgJCWHTpk307t2b\n8+fPG7dJTEzE09Ozyv2kp+fVaVy1nbbxaeHOxYtZnD+Xjl9iJhqt3CCvpmSKTB2SZ3VIntUheTbT\n1PqNuLu7c+bMGQAiIyNp3rw5vXv3ZufOnRQVFZGYmEhSUhKtWrVSM6xaaxrwV1tTK3cKL14wbzBC\nCCEaNZOdkUdFRbF06VLi4uLQ6XRs3bqVhQsXMm/ePKytrXFycuKVV17B0dGRyZMnM336dDQaDS+8\n8EKFtX71kZtnE+xsyteT5x4/jl1gC3OHJIQQopEy2VXrpmTOq9av+Pmbo5w5mcYAXSQdnn6sTuNp\nyGSKTB2SZ3VIntUhea5HU+sNSfPW5d/jxycXU1ZcZOZohBBCNFZSyG+Sf2D59+Sptt4U/PW9vxBC\nCKE2KeQ3Sd/EBhdHKzLsvcg6JuvJhRBCmIcU8lvQrLUnisaKS6cSzB2KEEKIRkoK+S1o9tf35Jez\nrCjNyzVzNEIIIRojKeS3wMffCSuNQpq9D/knT5o7HCGEEI2QFPJbYKXT4u1pR66tCymRUsiFEEKo\nTwr5LWoeVN79LPZ8qpkjEUII0RhJIb9FzVq6A5BU1ISSjAwzRyOEEKKxkUJ+i5zd9Oity0jT+5Bz\n/Ji5wxFCCNHISCG/RRqNBr9mjpRY2ZEQec7c4QghhGhkpJDXgeYdmgJwKS6nRv3UhRBCiLoihbwO\nNA10BRRSNC4UJyWaOxwhhBCNiBTyOmBnb42bA2TaeZJ5VG7XKoQQQj1SyOtI05YeKBotMdGx5g5F\nCCFEIyKFvI407+gPlLc1VcrKzByNEEKIxkIKeR3x8nVEpykj1dqTwtgYc4cjhBCikZBCXkesrLR4\nu1mRb+NI0p/R5g5HCCFEIyGFvA41b+8HQMzpJDNHIoQQorGQQl6HmgeVF/LLWVrKiovNHI0QQojG\nQAp5HXJysaeJroQ0Wy/yzpwxdzhCCCEaASnkdczPR0+plQ2XDp82dyhCCCEaASnkdSygU3MALl2U\nTmhCCCFMTwp5HWvaxguNopBYaE9pfr65wxFCCNHASSGvYza2Otz0JWTZupERJcvQhBBCmJYUchPw\nD3AGjZaLRy+YOxQhhBANnBRyEwjs2gKAuASZWhdCCGFaUshNwNPfFWtKSFGcKM6Qi96EEEKYjhRy\nE9BqNXg7QYG1A5cjpK2pEEII05FCbiLN2ngCEBMdb+ZIhBBCNGRSyE0ksHsrABLSSs0ciRBCiIbM\npIX81KlTDB06lHXr1gFQXFzMU089xcSJE7n33nvJzMwEYOPGjUyYMIFJkybx5ZdfmjIk1Ric9Tho\nC0i1ciX/8mVzhyOEEKKBMlkhz8vLY9GiRfTp08f43IYNG3BxcSE8PJxRo0Zx8OBB8vLyWL58OR9/\n/DFr167lk08+IaOBXCDm62lDmdaamD9OmDsUIYQQDZTJCrmNjQ2rVq3C09PT+NyOHTu44447AJgy\nZQpDhgzhyJEjBAcHYzAYsLOzo1u3bkRERJgqLFUFdGwKQOzZFDNHIoQQoqEyWSHX6XTY2dlVeC4u\nLo5du3YxY8YMnnjiCTIyMkhJScHV1dW4jaurK8nJyaYKS1VNgwPRKKVczrFCKSszdzhCCCEaIJ2a\nB1MUhcDAQGbPns2KFSt4//33CQoKum6b6ri46NHprOo0Ng8PQ53uz7hfuyKSCl3QpCTj0aGVSY5h\naUyVa1GR5Fkdkmd1SJ4rp2ohd3d3p2fPngD069ePd955h0GDBpGS8vfUc1JSEl26dKlyP+npeXUa\nl4eHgeTk7Drd5xW+vk1IOl/G4e1H6OrpZZJjWBJT5lr8TfKsDsmzOiTPVf8ho+ryswEDBrB7924A\njh07RmBgIJ07dyYyMpKsrCxyc3OJiIigR48eaoZlUoHdys/CL8U07kEohBDCNEx2Rh4VFcXSpUuJ\ni4tDp9OxdetWXn/9dV5++WXCw8PR6/UsXboUOzs7nnrqKWbOnIlGo2HWrFkYDA1nCsWrlQ82ZZEk\nleopKy5Ga21t7pCEEEI0IBqlJl9K1zN1PcVi6mmb79/+gdi8Jtwx1AO/Hh1MdhxLIFNk6pA8q0Py\nrA7Jcz2aWm+smrZwA+DC0RgzRyKEEKKhkUKugha3tQMgPqnQzJEIIYRoaKSQq8Dg6YxBySGtzEBh\ndq65wxFCCNGASCFXiY+rljKtFRcPRJs7FCGEEA2IFHKVNG/nA0DMyUQzRyKEEKIhkUKukmY926Mt\nKyEh3eIWCQghhKjHpJCrxEZvh5tVDjlaB7ISUs0djhBCiAZCCrmK/LzLm8ic++OkmSMRQgjRUEgh\nV1FApwAAYi+kmTcQIYQQDYYUchV5BbfCtiSPxFybGnV5E0IIIaojhVxFWp0OL7t8irU2JJyINXc4\nQgghGgAp5Crzb+YEwIXD58wciRBCiIZACrnKAnq0AUXhUnzd9lQXQgjROEkhV5ljYFMcSzJIK7aj\nsKDY3OEIIYSwcFLIVabRaPB2LEPRaIn5U6bXhRBC3Bop5GbQrJUHABePXTJzJEIIISydFHIzaHZb\nEFZlxcSnyNS6EEKIWyOF3AxsPdxxK00jV7EjM1XamgohhLh5UsjNxMddB8D5Q6fNHIkQQghLJoXc\nTAI6+AMQcybZzJEIIYSwZFLIzcSrWwfsi7NJzNJQVlZm7nCEEEJYKCnkZmLl4ICHNosSdFy+KE1U\nhBBC3Bwp5Gbk59sEgPOHz5o5EiGEEJZKCrkZNe8SiEYp41JMlrlDEUIIYaGkkJuRU1A7HAtTSMvX\nye1ahRBC3BQp5GaktbXFy74QNBpiTlw2dzhCCCEskBRyM2sa4ALAhciLZo5ECCGEJbrpQn7hwoU6\nDKPx8uvWBl1pIfGXC1AUxdzhCCGEsDBVFvJ//OMfFR6vWLHC+POCBQtME1Ejow9sgWthInmlOjLT\n880djhBCCAtTZSEvKSmp8Hjfvn3Gn+XssW5odDp8nMt/vhAVa95ghBBCWJwqC7lGo6nw+Orife1r\n4uY1a+sFwMUTCWaORAghhKWp1XfktS3ep06dYujQoaxbt67C87t376Zt27bGxxs3bmTChAlMmjSJ\nL7/8slbHaAg8OgehL8ogMa2M0lK5XasQQoia01X1YmZmJr///rvxcVZWFvv27UNRFLKyqr6JSV5e\nHosWLaJPnz4Vni8sLOSDDz7Aw8PDuN3y5csJDw/H2tqaiRMnMmzYMJydnW/2M1kcGz9/3Eu2EmPj\nTMKlTPybu5g7JCGEEBaiykLu6OhY4QI3g8HA8uXLjT9XxcbGhlWrVrFq1aoKz69cuZKpU6fy2muv\nAXDkyBGCg4ON++vWrRsREREMHjy49p/GQmk0Gnw9bYnJgYuRsVLIhRBC1FiVhXzt2rU3v2OdDp2u\n4u7Pnz/PiRMnePzxx42FPCUlBVdXV+M2rq6uJCdX3drTxUWPTmd107HdiIdH1X+YmFr7Xq3Zvy2X\n+ItpZo/F1Br656svJM/qkDyrQ/JcuSoLeU5ODuHh4dx3330AfPHFF3z++ec0b96cBQsW4O7uXquD\nLV68mHnz5lW5TU2uhk9Pz6vVcavj4WEgOTm7TvdZW1aBLXHO30aKxoeYi6nY623MGo+p1IdcNwaS\nZ3VIntUhea76D5kqL3ZbsGABqampQPnZ9LJly5gzZw4hISG8/PLLtQoiMTGRc+fO8fTTTzN58mSS\nkpKYPn06np6epKSkGLdLSkrC09OzVvtuCKzdPfDQZAAQey7VzNEIIYSwFFUW8tjYWJ566ikAtm7d\nSlhYGCEhIdx1110Vim9NeHl5sW3bNjZs2MCGDRvw9PRk3bp1dO7cmcjISLKyssjNzSUiIoIePXrc\n/CeyYP7NHAG4EHXJzJEIIYSwFFVOrev1euPPf/zxBxMnTjQ+rm4pWlRUFEuXLiUuLg6dTsfWrVt5\n5513rrsa3c7OjqeeeoqZM2ei0WiYNWtWtRfSNVQ+HVti/XM6cZfKUBRF1uoLIYSoVpWFvLS0lNTU\nVHJzczl8+DBvvPEGALm5ueTnV3070Y4dO1Z5sdz27duNP4eFhREWFlabuBskfVAQbhu/5LKuJWkp\nubh5OJg7JCGEEPVclYX8wQcfZNSoURQUFDB79mycnJwoKChg6tSpTJ48Wa0YGw2dwRFPuwIuAzGn\nk6WQCyGEqFaVhXzgwIHs2bOHwsJCHBzKi4qdnR3/+c9/6NevnyoBNjZNW7hzNBZiohPoGhJo7nCE\nEELUc1UW8vj4eOPPV9/JrUWLFsTHx+Pr62u6yBop1+B2OJw5y+UUF0qKS9FZ1+16eSGEEA1LlYV8\n8ODBBAYGGm+nem3TlE8//dS00TVC+jZtcc3fQ46tKwmXMmka6Fr9m4QQQjRaVRbypUuX8t1335Gb\nm8vo0aMZM2ZMhbuwibqntbPDxxligJhTiVLIhRBCVKnKdeRjx45lzZo1vPnmm+Tk5DBt2jQeeOAB\nNm3aREFBgVoxNjp+bX3RlpUQc7rqW9UKIYQQNWpj6uPjw6OPPsrmzZsZMWIEL730klzsZkKGDkE4\n5yeSkVNGbnahucMRQghRj1U5tX5FVlYWGzdu5Ouvv6a0tJSHH36YMWPGmDq2Rsu+RUvcir4nrYkf\nsRfSaRfsbe6QhBBC1FNVFvI9e/bw1VdfERUVxfDhw1myZAlt2rRRK7ZGS6PT4edlx+kiiDl5WQq5\nEEKISlVZyB944AECAgLo1q0baWlpfPTRRxVeX7x4sUmDa8w8g1pgezCXSxeR27UKIYSoVJWF/Mry\nsvT0dFxcXCq8dumSNPYwpSZBQbju+okEXWtSEnPw8G6c958XQghRtSoLuVar5YknnqCwsBBXV1fe\nf/99mjdvzrp16/jggw+488471Yqz0bH1b4p7WRoJQMy5NCnkQgghbqjKQv7GG2/w8ccf07JlS375\n5RcWLFhAWVkZTk5OfPnll2rF2ChptFr8mjkRmakQeyqR7iHNzR2SEEKIeqjK5WdarZaWLVsCMGTI\nEOLi4rjnnnt499138fLyUiXAxsylQ1sMhakkJuZSXFRi7nCEEELUQ1UW8msvsPLx8WHYsGEmDUj8\nTd++A255cZQpGuJiMswdjhBCiHqoRjeEuUKunFaXtYcHHtY5AMSeSzNzNEIIIeqjKr8jP3z4MIMG\nDTI+Tk1NZdCgQcblUDt37jRxeI2bRqPBt5U3VgnFxJxOguGyhl8IIURFVRbyLVu2qBWHqIRDUBAu\n506Tom1GVkY+js725g5JCCFEPVJlIffz81MrDlEJfbv2uG74lZQmzbh0IZ2gLlLIhRBC/K1W35EL\n9emcnPE2lAIQczbVzNEIIYSob6SQWwD3tgHYFWcTdyGNsrIyc4cjhBCiHpFCbgGatA/CLS+eomKF\npIRsc4cjhBCiHpFCbgHs27bDNT8BgNjz6WaORgghRH0ihdwCWNnb4+Nli0YpI/ZsirnDEUIIUY9I\nIbcQTu3b4FiQTNLlHAoLis0djhBCiHpCCrmF0LcLwjUvHkWBuItyu1YhhBDlpJBbCLuWrXAvTgIg\n9rzcrlUIIUQ5KeQWQmttjWdTV3SlhcScTUVRFHOHJIQQoh6QQm5BHII64JKfQE52EZnp+eYORwgh\nRD0ghdyC6NsH4ZYXB8AlWYYmhBACKeQWxbZZM9yV8gvdYuR7ciGEEJi4kJ86dYqhQ4eybt06ABIS\nErjvvvuYPn069913H8nJyQBs3LiRCRMmMGnSJL788ktThmTRNFotbq2boS/KJP5COqWlcrtWIYRo\n7ExWyPPy8li0aBF9+vQxPvfmm28yefJk1q1bx7Bhw/joo4/Iy8tj+fLlfPzxx6xdu5ZPPvmEjAxZ\nXlUZffsgXPPiKC4pY9eWU6Sn5Jo7JCGEEGZkskJuY2PDqlWr8PT0ND73/PPPM2LECABcXFzIyMjg\nyJEjBAcHYzAYsLOzo1u3bkRERJgqLIunb98B/8wT2GuKOBF5mS9WH2DTF0c4fzqFsjK5kl0IIRqb\nKvuR39KOdTp0uoq71+v1AJSWlvLZZ58xa9YsUlJScHV1NW7j6upqnHKvjIuLHp3Oqk7j9fAw1On+\nTEVxdyDe0Zp+l7/H6emXOLD3IhfPpnLpQjrOrnp6hATQtVdT7PU25g61UpaSa0sneVaH5FkdkufK\nmayQV6a0tJRnnnmG3r1706dPHzZt2lTh9Zqsj05Pz6vTmDw8DCQnW05XMbu27cja+xvWJ/8gbNwA\n0tOLiDwUx+ljiWz7/jg7t5ygTUcvOnbzw83TwdzhVmBpubZUkmd1SJ7VIXmu+g8Z1Qv53Llzad68\nObNnzwbA09OTlJS/G4EkJSXRpUsXtcOyKI4h/cj6fS9Jn60jZeO3OPXtT8jAUHoPasGJowlERcRz\n/M8Ejv+ZgG8zZ4K7+xHQ2g2tVhYpCCFEQ6Pqv+wbN27E2tqaf/3rX8bnOnfuTGRkJFlZWeTm5hIR\nEUGPHj3UDMvi6Nu1J/CVV3EJG4UGDelbN3PhuWdIef9tWtmmcveDPQmb0BH/ABfiYzLY+s0x/m/l\nfiJ+v0h+XpG5wxdCCFGHNIqJ7vUZFRXF0qVLiYuLQ6fT4eXlRWpqKra2tjg4lE/3tmzZkhdeeIEt\nW7bw4YcfotFomD59OnfccUeV+67rKRZLnrYpKy4m59ABMnbuoODMaQB0rm44DRiIU/+BZBfriIyI\n42TkZUqKy7Cy0tA6yIuO3f3w8Fb/OydLzrUlkTyrQ/KsDslz1VPrJivkpiSF/MYKY2PJ2LmdrH17\nUQoLwcoKQ7fuOA0ajLZ5S05FJhIVEWe8vau3vyPB3f0JbOOOlZU6kzMNJdf1neRZHZJndUiepZBX\nq6ENktL8fLL37SVjx3aK4stv6Wrj64vToMEYevUh7nIBUYfiiDlXfne4Jg42BHX1JaiLL/ompr3a\nvaHlur6SPKtD8qwOybMU8mo11EGiKAr5p0+RuXM72YcOQmkpGltbHHv3wXnQYPKbuBP117R7UWEp\nWisNrdp50rG7H16+jiaJqaHmur6RPKtD8qwOybMU8mo1hkFSkplJ5p5dZP66k5K0VKC8x7nzoMHY\ndOrKmZNpRB6KIyO1fGmfp4+B4O5+tGzniZWu7qbdG0Ou6wPJszokz+qQPEshr1ZjGiRKWRm5R4+Q\nsXMHecciQVHQOjjg1G8AjgMGkpRrTeShOC6eKS/29k2sCersS4euvjQx2N7y8RtTrs1J8qwOybM6\nJM9SyKvVWAdJUXISmb/uJHPPLspyckCjQd8hGOdBoZQ2a8OxPxOIPnKZosIStFoNLdq6E9zdHy8/\nRzQazU0ds7HmWm2SZ3VIntUheZZCXq3GPkjKiovIOXiQjJ3bKTh7BgCdmxtOAwah79WX85cKiDwU\nR1pyeYMWdy8Hgrv70SrIs9a3ym3suVaL5Fkdkmd1SJ6lkFdLBsnfCmIukrlzB1n7f/97CVv3HjgO\nDCXdzouoQ3FcOJ2CooCdvTXtu/jQsasvDo52Ndq/5Fodkmd1SJ7VIXmWQl4tGSTXK83LK1/CtnM7\nRfHxANj4+uE8KBRNhx5ER6cRfSSegvwSNBoIbFM+7e7T1KnKaXfJtTokz+qQPKtD8iyFvFoySCpn\nXMK24xeyIw5VWMLm0G8Qsdm2RB6KIyUxBwBXjyYEd/ejdQcvrK2vn3aXXKtD8qwOybM6JM9SyKsl\ng6RmSjIzyNy9i8xdOylJK7+ZjF3LVjgNCiXXpx3Hjlzm3Mnyvui2djradfKhYzdfHJ3tjfuQXKtD\n8qwOybM6JM9SyKslg6R2/l7Ctp28qEgArBwMOPbrj3WPfpyKKeT4n/Hk5xUDENDKjeAefvg1d8HT\n01FyrQIZ0+qQPKtD8iyFvFoySG5eUVISmb/uKF/ClpsLGg1NOgbj0D+Uy9beREXEk5RQnlsXNz09\n+wXi29wJe71pbwXb2MmYVofkWR2SZynk1ZJBcuvKl7AdIGPHdgrOnQXKl7A5DwyloHV3jp9I52x0\nMmVlClorDYGt3Wnf2Qf/AJebXpMuKidjWh2SZ3VInqWQV0sGSd0qX8K2nax9v6MUFf21hK0ntn0G\nkGblzsG9F0hPKb8VrMHRlnadfGjXybvGS9hE9WRMq0PyrA7JsxTyaskgMY3SvDyyfv+NzJ07KEoo\nX8Kmb96MJiEDyA8I5uTJdM5EJ1FSXAZA0xautO/kTUBr9dqqNlQyptUheVaH5FkKebVkkJiWoijk\nnzxBxs4d5B4+hHJlCVuv3uhDBnIp157oIwkkxmcBYKe3pm1HL9p38sHFvYmZo7dMMqbVIXlWh+RZ\nCnm1ZJCox0lXwrnvNpcvYUstb8xiGxCI86DBFAd25OSJVE5FXaYgvwQAbz9H2nXyoVV7D6xtdOYM\n3aLImFaH5Fkdkmcp5NWSQaKeK7lWysrIjYokc+d2ciOPlndhs7fHMaQfDv0GkpBrQ/SRBGLPpwNg\nbWNFq/aetO/sg6ePQS6Qq4aMaXVIntUheZZCXi0ZJOq5Ua6LU1PI3P0rmbt3UZqZCYB9m7Y4DxqM\n0rIDJ6NTOHE0gZysQgBc3PW07+xDmw5esoytEjKm1SF5VofkWQp5tWSQqKeqXCslJeT8eZjMX3eQ\nF30cACuDI079B2DoO4DEXB0njiZw/lSKLGOrhoxpdUie1SF5lkJeLRkk6qlprosuJ5T3Sv9tD2V5\nuRV6pWtbtef08WSijybIMrZKyJhWh+RZHZJnKeTVkkGintrmuqzorxvN/Lrj717prq449R+IY78B\npOZpiT6SIMvYriFjWh2SZ3VInqWQV0sGiXpuJdeFsTFk7NxRfqOZwgKwssKhS1ecBoaiC2zNuVOp\nRB9NIDHuqmVsHbxo19kH10a2jE3GtDokz+qQPEshr5YMEvXURa7LCvLJ2vc7GTt3UHQpFgBrLy+c\nBgzCqW9/Mgs0nDiSwMmoRAryyxu3ePk50r4RLWOTMa0OybM6JM9SyKslg0Q9dZlrRVEoOHeWzJ07\nyD6wH6WkBI1Oh0PP23AeNBjr5oFcPJN6w2Vs7Tp54+Xr2GAvkJMxrQ7Jszokz1LIqyWDRD2mynVp\nTg5Ze38j49cdFCdeBsDGvynOA0Nx7NOH3EINJyIvN5plbDKm1SF5VofkWQp5tWSQqMfUuVYUhfwT\n0WTs3E7On4ehtBSNrR2OvXvjNDAUG/9mxF1MJ/pIw17GJmNaHZJndUiepZBXSwaJetTMdUlGBpl7\ndpXfDjYtDQC7Fi1xGhiKoedtFJbAqajE65axte3kQ7tgbwxOlruMTca0OiTP6pA8SyGvlgwS9Zgj\n10pZGbmRR8tvBxsVWX47WH0THPv2w3ngIKy9vEmMz+LE0cucPp749zK2QBfad/YhsI07Wq1lLWOT\nMa0OybM6JM9SyKslg0Q95s51cUoymbv+uh1sdvkyNft27XEeFIpDl26UlMGZ6OQKy9hc3PX0G9oK\n/wBXs8VdW+bOc2MheVaH5FkKebVkkKinvuRaKSkh53AEGb/uIP9ENABWTk449RuA04CBWLu5k5aS\ny9EDl4g+kgBAQGs3Qga3wsnF3pyh10h9yXNDJ3lWh+S56kJu9cILL7xgqgOfOnWKKVOmoNVq6dSp\nEwkJCTz66KOEh4eza9cuhgwZgpWVFRs3buS5554jPDwcjUZDhw4dqtxvXl5RncbZpIltne9T3Fh9\nybVGq8XWzw+nkH4Yet4GVlYUXrxA3vFjZPzyMwUXztPEzZHWIUEEtHYnPSWPSxfSOfZnPCUlZXj5\nGur1XePqS54bOsmzOiTP5TmojMnOyPPy8nj44YcJCAigbdu2TJ8+nblz5zJgwABGjhzJsmXL8Pb2\nZty4cYwfP57w8HCsra2ZOHEi69atw9nZudJ9yxm55arPuS4rLCT74B9k/rqDgnPnANC5ueE25g4M\nIf04dyqV33ecJSerEL2DDb0HtaBNB696eZV7fc5zQyJ5VofkueozcpOdUtjY2LBq1So8PT2Nz+3f\nv58hQ4YAEBoayu+//86RI0cIDg7GYDBgZ2dHt27diIiIMFVYQlRKa2uLU9/+NHtuAc0WLMRpwCBK\ns7NJ/OQjLi19GX99HoUCYbcAACAASURBVHc9eBs9+jansKCE7d+f4Ju1h0mMzzJ36EKIRsxk96rU\n6XTodBV3n5+fj41N+Y033NzcSE5OJiUlBVfXvy8icnV1JTk5ucp9u7jo0ems6jTeqv7aEXXLInLt\n0ZGm3TtSmDKVCx99Qsqe34h5aSHeYcMZNu1uQga1Ytv30Rw/Es/Xn0bQuWdTBo9qh6EedV6ziDw3\nAJJndUieK2e2m05XNqNfk5n+9PS8Oo1Fpm3UY3m5tsH1vgex69WXpP9by+XNW0nevRf3iZMYMKIf\nrTt4smfbaY4ciOX4kXi6hzSnUw9/rHTm/f7c8vJsmSTP6pA8m2lq/Ub0ej0FBQUAJCYm4unpiaen\nJykpKcZtkpKSKkzHC1Ef6NsH0fyFRbhPnExZcRGJH68hdsnLuCoZTLyvBwNGtMHKSsO+nedY/+EB\nLpxOqdEfpUIIcatULeQhISFs3boVgJ9++on+/fvTuXNnIiMjycrKIjc3l4iICHr06KFmWELUiEan\nwzVsFAGLFuPQ4zYKzp0l5qWFJH++jnZtnJj6cC+Ce/iRlZHP5q+i+GHDUdJTcs0dthCigTPZVetR\nUVEsXbqUuLg4dDodXl5evP766zz77LMUFhbi6+vL4sWLsba2ZsuWLXz44YdoNBqmT5/OHXfcUeW+\n5ap1y9WQcp17/BhJn62l+PJlrAwG3CdMxjGkL+lp+fy27QyXLqSj1Wro2M2PHv2aY2tnrVpsDSnP\n9ZnkWR2SZ7khTLVkkKinoeVaKSkh/eetpG76DqWoCLuWrfCcNgPbps24cCaVvb+cISujADt7a3oN\nDKRdJx+0WtMvV2toea6vJM/qkDxLIa+WDBL1NNRcF6elkrzhC3IOHgCNBufQwfx/e+ceHGd13v/P\ne92VtJJWWkm+X2XANrYhYCdgG0hjCG0uECCJCcHJH5nOdJLOtB2aQmkTSNPpDPlNZjppMrSdttMM\nNOAUAoQQIBBiaoJN7JjgCzbgu2zrupJWl728198f72q1q6tt5Fe35zPzzjnnfc959+jRs+/3nPOe\nPSfxuTvBLGP/3jP8/s1T2JZLXUOMTbesYP6i0ddJmAhmqp2nGmLncBA7i5CPizhJeMx0W/cfOkjb\nTx7Hbs0Pt3/+i1Rdv4l0v83u14/z/sFWAFasque6jzdesh3WZrqdpwpi53AQO4uQj4s4SXjMBlt7\ntk33Ky+T/MXPS4bbo4uX0Hquhzde+YC25l50XeXq6xZz9ccWYRgTvy7CTLfzVEDsHA5iZxHycREn\nCY/ZZGs7maT9p0/Q9/u9+eH2LSQ+dwdqWTnvH2xl947jpPstYlURrv+jRhpX1k/Ycq+zyc6Tidg5\nHMTOIuTjIk4SHrPR1qXD7VXUf2ErlddvxLZc9u06xTt7zuC5PvMXVbPp5suomxP70J85G+08GYid\nw0HsLEI+LuIk4TFbbT10uL3ssstpuOdeIosWk+pK8+avj3HyaBJFgVVXzeOjNy6jrNy86M+brXYO\nG7FzOIidRcjHRZwkPGa7re1kkvbtP6Fv3++D4fZP3Ezi9jvQystpOtHJb189SlcyjRnR2bB5KVde\nM/+itkud7XYOC7FzOIidRcjHRZwkPMTWAf0HD9D2xOPYra1oVfnh9us24nk+h/adY88bJ7ByLjV1\n5WzasoJFy2rHv2kRYudwEDuHg9hZhHxcxEnCQ2w9iGfbdP3qJTpfeL5ouH0bkUWLyKQtfrfzJIf/\ncA7fh6UrEmzcsoLqmrLzurfYORzEzuEgdhYhHxdxkvAQWw/HTnbQ/uQT9L39e1BV4p/YQuK2YLi9\no7WXN149SnNTClVTuGrDIq65fjFmZOyNC8XO4SB2Dgexswj5uIiThIfYenT6D+yn7Yn/wW4bGG6/\nm8rrrgfg+HvtvPnaMfp6cpTHTK67aTmXr5kz6s/VxM7hIHYOB7GzCPm4iJOEh9h6bDzbouvll+j8\n5S8Gh9u/vI3IwkXYtss7bzXx9u7TOI5Hw/xKNt98GXPmVw27j9g5HMTO4SB2FiEfF3GS8BBbnx92\nRztt25+g/+19wXD7lltI3PY5tLIyelNZdu84xtHD7QBcsWYOH/v4cipikUJ5sXM4iJ3DQewsQj4u\n4iThIba+MPr2v0P7E/+D3d6GVl0dzG7/2PUoisK5pm5++8pROtr6MEyNazcuYd36hWi6KnYOCbFz\nOIidRcjHRZwkPMTWF45nW3S99GIw3G7blF1+RTDcvmAhnudzZH8zb71+gmzGpioeZeOWFWy4fikd\nHX2TXfUZj/hzOIidRcjHRZwkPMTWF4/d3k7b9p/Q/4e3QVWp2XILtfnh9lzWZu8bpzi47yye57N4\neS1Xf2wRC5bUTHa1ZzTiz+EgdhYhHxdxkvAQW394+vb/IT/c3o5WHaf+i1up/Oh1KIpCV0c/u35z\nnFPHkgDMXxxnw+alzF98afc/n62IP4eD2FmEfFzEScJDbD0xjDzc/hUiCxYAYGUcXvnFu5w+1gmI\noF8qxJ/DQewsQj4u4iThIbaeWEqG2zWNmi23kLjtduYsaqC9vZfWcz3sfeMkp48Hgr5gSZz1m5cy\nf5EI+kQg/hwOYmcR8nERJwkPsfWloe+d/HB7RztaPM6yr3wZ5cprUDQNgJazKfb+9hRNRYK+YfNS\n5omgfyjEn8NB7CxCPi7iJOEhtr50eJZF10u/pPPFF/BtG2PuXOpuv5PYtetR1GAHtZazKfa+cZKm\nE10ALFxaw/rNS5m3sHoyqz5tEX8OB7GzCPm4iJOEh9j60mN3Jkm/+iKtr74Gnkdk0WISd9xJxdqr\nCku6tpxJsfe3pYK+YfNS5oqgXxDiz+EgdhYhHxdxkvAQW4dDfX0lZw8eJfnzZ+n93W7wfaKNK6i7\n4y7KV64q5Gs+E/TQz5wMBH3RsqCHPneBCPr5IP4cDmJnEfJxEScJD7F1OBTbOXf2DB3P/ixY7hUo\nX7WaxB13Uba8sZC/uambPW+c5OypbkAE/XwRfw4HsbMI+biIk4SH2DocRrJz9sRxOp79GelDBwGo\nuPoj1N1+J5FFiwp5zjV1s7dY0JfXsmHz0hE3ZhHEn8NC7CxCPi7iJOEhtg6Hseycfu8IHc88Tfbo\nB6AoVG74KInb7sCcO7eQ59zpoId+7nQg6IuX17JeBH0Y4s/hIHYWIR+T1nQ7e5J7mWvOY3Xt5ZQb\n5RN2b2E48oUMh/Hs7Ps+6YMH6HjmaXKnT4GqUrVxM4nP3o6RSBTyDRP0xqCH3jBPBB3En8NC7CxC\nPiZ7Wt7mv999AgBVUWmsXsqaulWsTayioby+MMtXmBjkCxkO52tn3/fp27eX5LPPYDWfQ9F1qm/8\nOLWf/gx69eBvzM+e6mLPGydpbkoBsKQx6KHPdkEXfw4HsbMI+bj06yl2Ht3LwY7DnOxpwicwSX1Z\ngjV1q1iTWMWK+DJ0VZ/Qz52NyBcyHC7Uzr7n0fvWLpI/fxa7vR3FNIl/4mZq//hTaLFYkMf3gx76\nzpM0n8kL+ooEGzYvpX7u6A+ZmYz4cziInUXIx6XYSXqsXg4l3+Ngx2EOd75HzrUAiGpRViUuZ21i\nFasTV1Bpxia0DrMF+UKGw8Xa2XccUr/dSfL553C7u1HLyqj55B9Tc8snUaNlQR7f5+ypYMi9JS/o\nS1ckWD8LBV38ORzEziLkY2I7Lp1ph6qIRlmktMdtew5Hu49zsOMwBzoOk8wGy1sqKCyrXsyaxCrW\n1K1ifsVcGYI/T+QLGQ4f1s6eZZHa8Rqdv3wBt68XNRaj9k8+TfyPtqCaJlAs6CdoOdMDwNLLEqzf\nNHsEXfw5HMTOU0jI+/v7uf/++0mlUti2zTe+8Q3q6+t5+OGHAbjiiiv4zne+M+59JvIf+pt9Z3js\nV++jqQqXL4pzVWOCtY0J5taWl4iz7/u0pts4kBf146mThSH4mkictXWrWFO3msvjyzE0Y8LqN9OQ\nL2Q4TJSdvWyGrldfoevlF/EyGbR4nMSnb6P6hhtR9KDhGwh6F3t2nqTlbCDoyy6rY/3mJdTNmdmC\nLv4cDmLnKSTkjz/+OK2trdx33320trby1a9+lfr6er75zW+ybt067rvvPm677TZuuummMe8zkf/Q\ndNZh56EWdh9o5lTL4H3r41HWNdaxrjHBFYvimIZWUq7fTvNu8j0OdLzLu53vk3EyAJiqwcray1lT\nt5I1iVVUR2b3ZKChyBcyHCbazm5fH50vv0j3r1/BtyyMunpqP3s7VddvLKzj7vs+Z04Gk+JaBwT9\n8jrWb1pK3ZyZ+SpK/DkcxM5jC7n28EB3OASampo4evQon/jEJ2hubmbXrl20tLTwN3/zNwDYts2+\nffvYvHnzmPdJp60Jq5Ohq1x/1QI2XF7HTVfPZ35dBbqqcLajn/ebUuw+1Mqv9jRx7GyKTM6hstyg\nPGpgagYLYvP4SMM6tiy6kStqVlBhVtBr9XE8dZIDHYf5ddP/cbDjMCmrh6gWocqsnPVD8BUVkQn9\n/wkjM9F2Vk2TitVXUr35BnzXJfPeYfr27aVv7x60qirMufNQVJXqmjJWrpvLnAXVpLrSnD3Zzbt/\nOEeyrY+aRDnlFeaE1WkqIP4cDmLnwAajEfo78q997WucPn2anp4eHn30Uf7hH/6BZ599FoBdu3bx\n1FNP8f3vf3/MeziOi65rY+b5sDiux+ETnew53Mrew600tQ62BhfPrWT9yjmsXz2HVUtr0TW1pGxz\nbxv7zh1gX/MB3m37ANf3AKgpq+aaeWu5dv4a1sxZSVQf/R8jCFOZXHs7TdufovXXwcYsFcuXsfjL\nX6Lm2msKjVXf9zn2Xjuvv/weZ/O/Q1+1bh43fvJy5szyn60JwkQSqpA/99xz7N27l+9+97scOXKE\nb3zjG1RWVhaE/M033+Tpp58eV8gnY2W3ju4M+48n2X8syZFTXVhOIM5lEY0rl9ayrrGOtctrqY6V\ninPGyXC48wMOdhzmUPIIfXY/ALqqc3lNI2sTq1lTt5LaaM2E/k1TFRkiC4ew7Gy1tpB87ll697wV\nbMyy4rJgY5YrVhby+L7P6eOd7H3jJG3NQZ2WX1HP+s1LSNRP7yF38edwEDtPoXfkDz30EBs3buTW\nW28FYPPmzWiaxuuvvw7AM888w/vvv8/9998/5n0me4lWy3Y5crqbA8eSvHOsg45UtnBtydzKwoS5\nZfOqUIuG0j3f42TPaQ50HOZgx2HO9bcUri2IzSvMgl9atQhVKe3lzxTkCxkOYds5d6Yp2JjlD28D\nUL76SuruuIvosuWFPCMJeuPKetZvWkptfUVodZ1IxJ/DQew8hd6RnzhxghMnTrBp0ybOnj3Liy++\nyJIlS1iwYAHz58/nBz/4AZ/97GdZVLSJw0hM9LuSC33/omkqc2rLWdeY4Ob1C/nY6jnUx8vwPJ8T\nzT0cOd3Nznea+c3bZznT1o/jetRURogYOjXROCtrL+PGhddz3dz11JfXBQ+4niY+6D7OruY97Dy7\ni+b+VlzfIx6pxphBC9HIu65wCNvOelU1VR+9jvI1a3E6kqQPHyK183Wyp08Rmb8AvaoaRVGI15az\n6qp5NMyrorszw5mTXRx6+xxdHf3ohoZuaBimNm3mkog/h4PYeQq9I+/v7+fBBx8kmUziOA5/8Rd/\nQX19Pd/+9rfxPI+rrrqKv/3bvx33PpPdIx+LTM7h3ZOd7D+WZP/xJKm+wPkUBVYsqGZdY4J1jXUs\nrK8oeVhlnRzvdR3lYMe7HEweoccK6qMpGiviy1hbt5o1iVXUlydG/NzpgrSsw2Gy7Zw+cjjYmOXY\n0fzGLB8jcfvnMOcMbszi+z6njiXZ+8ZJ2lv6CucjUZ1EQ4xEfUUQNlRQU1eBYVzaeTEXw2TbebYg\ndp5CQ+sTxVQW8mJ83+d0a1/+3XoHx8/2MGDsmsoIa5cnuKoxwaqlNUTNwV6353s09Z4NFqJJHqap\n92zh2pzyBtbUreTK2pXUlyeoMiun1dKx8oUMh6lgZ9/36T+wn+QzT5NrOh1szLJpM4nPlG7MMvA7\n9JYzPSTb+0i29ZPqypTcS1GguqasIPC1+bCyOjqpvfepYOfZgNhZhHxcwnKS3rTFoRNBb/3A8ST9\nWQcAXVO4YlGctfnfrc+tLd2BrTuX4lDHEQ4kD3Ok8wNszy65XmnEqI5UBYdZVYjHC+eqqTQrpsR7\nd/lChsNUsrPveYMbs7Q0Bxuz3PRH1H7qM+jV1SOWsS2Hzo40ybZA2AcE3so5JfnMiEZtfdBrT+TD\n2roKzEg4jdupZOeZjNhZhHxcJsNJPM/n+Lke9h/vYP+xJKdbB4cWG2rKWLc8wboVwWI0RtFP7SzX\n5v2uo3zQfZzuXIpUrodUrofuXApriMAXoyoqVWZlIPBFYj8g/PFINdWRKsr1skvaw5EvZDhMRTv7\nrkvP7l0kn38Wp6Mj2Jhlyy3U3vonhY1Zxizv+/T35kqEPdnWR3dnmqFPsap4lER9jNq8wNfNqaAq\nPvG+PRXtPBMRO4uQj8tUcJKu3hwHjic5cCzJwZOd5CwXANNQWb2klnWNCdYuT5Cojo5Y3vd9sm6u\nIOwpq6dE6FNWTyHu+O6o9dBVfXiv3hwej+oj12M8poKtZwNT2c6+45Da+TrJXzyPm8pvzHLrn1C1\ncRN6Te0Fi63juHQN9N7b+wu9+GymtGGrGyq19YM994EwEr34JZWnsp1nEmJnEfJxmWpO4rgeHzR1\n805+CL45mS5cW1BfwbrGBGuWJaiPR6muiGDo5z9k7vs+/XY6L/Q9owp/j9VbWEt+JKJaZNhQfqFn\nP3DOrBy27vxUs/VMZTrY2cvl6N7xGp0vvoDXF4xIKZEI5tx5+WMu5rwgbsyZg2qc/6pwvu+T6bdK\nhD3Z1kdXMo3nlfp1rCpSeO9el3/3Xl1bhqqO/72aDnaeCYidRcjHZao7SVt3hgPH8ovRnO7Czi9G\nM0CszCAeM6mORYhXmMQrI1RXmMRjEeKxCNUxk3jMLBmiHw/P9+i1+gbFvahH310UH1jgZjQq9PIS\noZ9fU4/pllETqSaePyqM8mnzc6PpwlT36WLcTIbU/+0ge+IEVkszdkszvlP6LhxFwairK4i8kRd4\nc948tNj5L33suh7dyfSgwLf309nWR39f6U+bNE2hpq5i2Oz5svLSxsR0svN0RuwsQj4u08lJcrbL\nkVNdvN/UTVdfju7eHKl+i+6+HJnc6EPmABVRvSDs1RUR4pUm8YpIkfAHjYHIBfzMx/YcenK9BaEf\nSfhTVg8ZJzvqPXRVJx6ppib/nr4mEg9EPlpNPN/LrzIrp8RkvenCdPLpofieh5NMYrU0YzU3B2E+\n7vb2DMuvVlQURL3Qm583D6OuHkU7P1/OpC062/vpaOujM/8OvrMjjTuk0VweMweFvb6ChYtrSWdy\nmBEdw9QwIzqaJn460Uxnf54oRMjHYaY4Sc52SfXl6O4LhL27zypJp/qD9MBs+dEoi+jEY2ZRb37k\nnn7EPH/Bzzo5UlYPRG1OtbXQnU3RlUvRXXT0Wn2jDuerikq1WZXvxVflRX5A/AcbAdPpp3iXkpni\n00Nx+/tLhH0gbre1gVcqumga5pw5RUP18zDyQ/ZaefnIH1CE53mkujIlk+s62/ro7cmNWU7TFIyI\nTqRI3M18aEQ0TFPHzIcl6SH5NV2d0SNVnufh2B6O4+E6Ho7j4tgD8SDtOkGequoysjmbSGTQVpGo\njmHqqOrMtVExIuTjMFMfeqNh2W5e1AcEv0j08737VJ9FX2b0WfAAUVOjOhahZmBYf6CnP6QRUFb0\nU6CxbO14Dqlcb4m4d+cCwU/lUnRlU6SsHjzfG7E8QKUZKwzZFw/fD/bwq4loM2sHrpGYbT7tOw52\ne9uIvXgvkxmWX6uOD+vBm3PnodfUFLZlHY1c1s4PyQevlbo701iWi5VzsPOhZbnYA6E19kjZaKiq\nUiLsRmSwAWBGNAxzlHRxfvP8VsrzfR/X9QrCOVRIA7F1g/N2kfDmRde1B9ODZdwiUfZw7aL8jjds\nrsLFEtgo/3fnj8iQdPH1yJC0GdHOaz7EZCNCPg6z7aF3vtiOR6o/VyT4Fqn+HN29Ft1F53vTYwt+\nxNAKoj43UUHUUInHItRU5kW/Mt/DP48h/eJ39wMi351NDRN/2xt91KFcLxtB4KuIR+IF8S/TJ3eh\nkQ+L+HSA7/u4Palh4m61NOMkk8PyK6Y5TNwLk+3M4Q3A87Gz5/nYlottOVg5FysfFtJDhH/EdC7I\ne7EUC72iKCP2gC8FigK6EYwu6IVDQzMG05quBXFDzecL0oN5NGKxCMmO/iJbOORyRfYrstHFNBB0\nQ80L/AjCH50ajQER8nGQh96Hw3E9evqtoqH8ItHvG+zh9/RbY8yDD4b0C+KeH76Px8z8ucGJe0O3\njR2K7/v0O2lSuR66st1FAt9TIv5Zd/T39qZqBAJvVmNqBpqqoykqmqKhqVoQKhp6Ia6iqhr6kOua\nGpTRFQ1VHV6mNG9pOV3RC+U1RUNVzn+oVXx6ZDzfw/FcHM/GymbItZwLRL2lFbetDa+1Hb+9A8Uu\nbQj6gBuPYSWqydXGyNTGyNSUo9THyHgKfsTE11UUVUUl8E9FUVBR8v8zpZAuiSug5NMlYcm5IA+A\n76j4to/nKHgWeA54Nni2j2sHcdf28awg7dp+cFhB6Fge+KDp6qC4Ghq6rmEYwTGSkA7mLRLeEdJa\n4X5BfKLmC5yvP/u+j+N4JcJeIvzZ4cKfK0lPXGOgYX4lm2++7GL+3BERIR8HeeiFg+t5GFGTY6c6\n6e4NBD+YsDc4xN/VO/47/Mpyg5pYJN+TLxb9wV5+Zbk57ruzrJMdJu7dVmkPf7xZ+WEz2AAYbFgM\nNAwKDQklGG51HR9VUVBRC40AVVEL55SBuKKiMBhXFSW4VnROKbrPqNeLzilFn1P62Wpe3Io+j+B6\nILIOtufg5A/bL4oPCYeeL8T9kfLYOJ6LO8YaCgV8n8q0R03KobbHpabHpaYniFdkR++5ugrYhoI1\ncOjqYNxQsHQF21DIGWqQT1dKrxtq4ZynTc5okIKCoRmYqoGhGpiaianl4/m0oeqF86Y6mB4oZ6oG\nhmZiKjq642O6Pobto9keuu2i2S6q7eDncvi5HF4uh5fL4mWDcPBcDi8bpDVNxY9E0crLUcvLUcsr\nCnGtOF1RgVZWjlpRjhotG/c1yUh8+MZAcL26powvfm3DhL3DH0vIZWaQEBqaqpKoLsObVwXzRs9n\nO27JhL2u3tzgu/ze4Fxrd4bTbX2j3kNVlMLP7uIF0c/38IvSc8rrmVvRMOp9BsTB9V1cz8P1nXzo\n5s/lw7wIub6H57sF0QiuDylXKFOUzx9yrSjPQDnHc/FKyg2Wsb0crjNYR6/fx/O8MdcCmImoioqh\n6uiqjqEa6IpG1IxiKBq6aqCrWnBe1Qv5dHXINUXH0PQgLOTRUVUdJ+egJ1No7V0oHZ2YVo5cqg8/\nl0PJ5tCzWaI5C7I56MnCRb4H9jUNIiZ+xAh6+4XDwIsY+KaZD4O0FzHwzOJQx40YeIaOryn4vo+P\nj+f7uL6L7dpYno3tWliuhWvlcPOC6mdz+DkLrDTkulEtB8Wy0R0fI3+YTiDOQdrDcMBwfHTHQ3d8\ndNtHc4ORjFz+uBhcXcUzdBRFQctaKBdgTx/woiZ+1MSLmnjRCF5ZJLBjWQQ/GgnCsghEo3j50C+P\nokSjKGawBoaqKijlCkpF0ChQUYgoCtHCqImGougloyj4UFdWG9pEPBFyYcph6Br18TLq42Vj5svk\nnILYl/TwBxoBvTma2vo40Tz6aIuuqYX39DUDPfvK0qF9Q1dRFQ1N1dHVCBFVCXqThZB8L3TqvFMf\nGGUafIB7eH4Q+gzGPd/Pp4uu+x5eUZkgPeS67xfOBenBMn7RvTyKrufL+IXPzsfzn68qWpEIDwpo\nIV4ksMXnS8Q2jJ8oLhxu55HwfR/ftvEyGbxsBi+bzcezQTqTLcTdbAY3nSm9ng2u+91BQ2HYOrTn\nia8b+GYkOIwIqAqqbaHYFoqVA9saPuP/Qj9DVcA08c0oXkzHMzQyho5raDiGimOo2Hp+FEIHS4Oc\n7pPTfLKaT0ZzyageGdUlq3nYhoKtKcF9Bw2K4fhEbJ+I5ROxvEIYtQbOD56L2D5RyyVipYn09RN1\nLsx+jgo5UyVnKuQMpRDPDpwz1fz5IJ41B/NYukLEKOP/3fhwKD4pQi5MW8oiOmURnXmJilHz+L5P\nf9YZFPrewXf4xeljZ1MX+5wsoCig5cVdURW0AbFXFdT8NSV/ThupMZCPD+TT1OLrwT3UYeeG3Cvf\nqIjFImQydlAmX59CXAniykC5oriSzzNYRkVV9CFliu5JvsdSKFNcfoTPHlZm8LN938fzfFwvHxan\n7Xxv0vOxveJ8Nq5nDabHuoc35NqF5B+aLx+qmoplOSV5PB9cz8cvLusP3i+Ig+up+H4ZrhcFakqd\nSQdi+SNwZAzfIeLZmPkjMiQsxP0Rztk2Zi5NxEsBkFV1bNXAVsqwzEosJUhbqo6lGtiKjqMZeLqB\nZ0TwDRPfMMGMoJgmRKKokQhqNIoWiWCYZjBDXlcxDQ2zKDR0jXJDLblmGBqR/DXTUEvmvQQjTAMj\nBkFYWR2hs6uv0DD1ffLhQNrDz3/fS84XxTOOC9kMZLL4mSxksij5MDhyqNkcZILRFSWTJZLNUZbN\nofRZKGNMoB0Jd+FclBuAENr3IuTCjEZRFGJlBrEyg4UNo2/M4Xk+Pelicbfyi+3ksJ3gYVDysM4/\nsD3PKzy4PX/Iw7uQZ/AB7no+tusV5aPo4R4cs2swfHpT3BArNOKGNMJ0TUFVtUJDTBto6A1ppGmq\nUmjUlDbOSkd+BvJpw64PNvYGyvuqgqUoOKpCNn/d88G2XSzHw3JcLNvDcjxs2yWXD4ddK8RdrJ6B\n3ruVPybAjoqCaaiFBoChq5h5kTd1lVhFBNdx0TQVXVWCUFPQNRVNU9DVID329Xy8TEGLBelCmSH3\n0NTBsgONTN+y7qLfKQAACI1JREFUcNNpvHQ/XjqN29+Pl0nj9qfzYf58/rpR3xC07kNAhFwQCB5+\nA8PpS+dObl0GRL3QePAoahgU9RRHaSx4nk9VdRldXenCOd8vvi/5c4Nlfb8o35hlSuvn+RTKet7I\n8fP77KIRjSKRKgjiEOHT1CHhaPmLhXHE/Ooon8Pw60PuoyjQ0FA16ybK+r6P43rkbA/b8bAGhD8f\nloh+viFQnMcuaiTYjkdu4FxRA6I3bWM5WWzbm/SGraYqwxoLA0KvazqaFkfXaoLr5QpaZXB9yZxK\nbhMhF4TZiaooqPlZyxe7L1d9fSXtsZm/8I0QPoqiYOjaBe3dcLEEjQafqng5rW09uG7QiHBcD9fz\nS9KOFyxq4+TPua6P43lFeXxc70KuB5/hDLsexC3bLrnuDpmId7Kll89sXBrKhDcRckEQBGFKEjQa\ngtdjmfKp3TAdGEEbEPqoqcmsdUEQBEGYLij5+RDjLVh1KZj6C8wKgiAIgjAqIuSCIAiCMI0RIRcE\nQRCEaYwIuSAIgiBMY0TIBUEQBGEaI0IuCIIgCNMYEXJBEARBmMaIkAuCIAjCNEaEXBAEQRCmMSLk\ngiAIgjCNESEXBEEQhGmM4vv+ZO8SJwiCIAjCRSI9ckEQBEGYxoiQC4IgCMI0RoRcEARBEKYxIuSC\nIAiCMI0RIRcEQRCEaYwIuSAIgiBMY2a9kP/TP/0TW7du5e6772b//v2TXZ0Zy/e+9z22bt3KXXfd\nxa9+9avJrs6MJpvNcvPNN/Ozn/1ssqsyo/n5z3/Obbfdxp133smOHTsmuzozkv7+fv78z/+cbdu2\ncffdd7Nz587JrtKURJ/sCkwmv/vd7zh16hTbt2/n2LFjPPjgg2zfvn2yqzXj2L17Nx988AHbt2+n\nq6uLO+64g09+8pOTXa0Zy6OPPkp1dfVkV2NG09XVxY9+9COefvpp0uk0//Iv/8LHP/7xya7WjOOZ\nZ55h2bJl3HfffbS2tvLVr36Vl156abKrNeWY1UK+a9cubr75ZgAaGxtJpVL09fURi8UmuWYziw0b\nNrBu3ToAqqqqyGQyuK6LpmmTXLOZx7Fjxzh69KiIyiVm165dXH/99cR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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "mhcox-wnaJdu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Explore Alternate Normalization Methods\n", + "\n", + "**Try alternate normalizations for various features to further improve performance.**\n", + "\n", + "If you look closely at summary stats for your transformed data, you may notice that linear scaling some features leaves them clumped close to `-1`.\n", + "\n", + "For example, many features have a median of `-0.8` or so, rather than `0.0`." + ] + }, + { + "metadata": { + "id": "U27iEDjmaEo_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 715 + }, + "outputId": "adccc27e-2bb0-4dfc-c3f6-95f1246794a5" + }, + "cell_type": "code", + "source": [ + "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "8TIg3pcGaRt9", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We might be able to do better by choosing additional ways to transform these features.\n", + "\n", + "For example, a log scaling might help some features. Or clipping extreme values may make the remainder of the scale more informative." + ] + }, + { + "metadata": { + "id": "BIEzqTKNaPn9", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def log_normalize(series):\n", + " return series.apply(lambda x:math.log(x+1.0))\n", + "\n", + "def clip(series, clip_to_min, clip_to_max):\n", + " return series.apply(lambda x:(\n", + " min(max(x, clip_to_min), clip_to_max)))\n", + "\n", + "def z_score_normalize(series):\n", + " mean = series.mean()\n", + " std_dv = series.std()\n", + " return series.apply(lambda x:(x - mean) / std_dv)\n", + "\n", + "def binary_threshold(series, threshold):\n", + " return series.apply(lambda x:(1 if x > threshold else 0))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ze0mQfnBaWcB", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The block above contains a few additional possible normalization functions. Try some of these, or add your own.\n", + "\n", + "Note that if you normalize the target, you'll need to un-normalize the predictions for loss metrics to be comparable." + ] + }, + { + "metadata": { + "id": "THWVyuUoaT8L", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "aab74ae4-4f0b-4f7c-8771-b9838858d6df" + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n", + " #\n", + " # YOUR CODE HERE: Normalize the inputs.\n", + " #\n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n", + " processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n", + " \n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + "\n", + " processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n", + " processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n", + " processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n", + "\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n", + " steps=1000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 93.66\n", + " period 01 : 77.32\n", + " period 02 : 72.79\n", + " period 03 : 75.01\n", + " period 04 : 70.75\n", + " period 05 : 70.20\n", + " period 06 : 69.30\n", + " period 07 : 68.81\n", + " period 08 : 68.66\n", + " period 09 : 68.55\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.55\n", + "Final RMSE (on validation data): 70.40\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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5Zue/zznnHGJiYmhsbMTPz4/S0lKio6M7fX9FRb0ryyMqKpiysprjvi5pSAjr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\nRsrdIpN6bDGyef9sJwoi9Cod9KNcyFGWZUQTsSHBJhTLDjX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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "X7BnaPlGabvO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "SKYv0mn5amYj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "These are only a few ways in which we could think about the data. Other transformations may work even better!\n", + "\n", + "`households`, `median_income` and `total_bedrooms` all appear normally-distributed in a log space.\n", + "\n", + "`latitude`, `longitude` and `housing_median_age` would probably be better off just scaled linearly, as before.\n", + "\n", + "`population`, `totalRooms` and `rooms_per_person` have a few extreme outliers. They seem too extreme for log normalization to help. So let's clip them instead." + ] + }, + { + "metadata": { + "id": "64BbaHwRaY6C", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "dbde7a90-dcb0-44c1-86db-230fd410fdd4" + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n", + " processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n", + " \n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + "\n", + " processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n", + " processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n", + " processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n", + "\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n", + " steps=1000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 98.11\n", + " period 01 : 78.23\n", + " period 02 : 73.39\n", + " period 03 : 71.58\n", + " period 04 : 73.31\n", + " period 05 : 70.46\n", + " period 06 : 69.83\n", + " period 07 : 70.11\n", + " period 08 : 68.59\n", + " period 09 : 68.74\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.74\n", + "Final RMSE (on validation data): 70.49\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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zRQFGRETEQxRgjlGAn4W+PcLI3VlBbS30C+/Djj07Kdtb7u3SREREOj0FmOOQ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\n2sRHAkwCZKPSrnhqEwordPQOsfeok0+PufD4QwD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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "0yZznyAka_5Y", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Use only Latitude and Longitude Features\n", + "\n", + "**Train a NN model that uses only latitude and longitude as features.**\n", + "\n", + "Real estate people are fond of saying that location is the only important feature in housing price.\n", + "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n", + "\n", + "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n", + "\n", + "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise." + ] + }, + { + "metadata": { + "id": "ocVzRJYAa7O3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "1aee4098-7a6c-4a24-902c-e824b182ec5c" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train the network using only latitude and longitude\n", + "#\n", + "def location_location_location(examples_dataframe):\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " return processed_features\n", + "\n", + "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n", + "lll_training_examples = lll_dataframe.head(12000)\n", + "lll_validation_examples = lll_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n", + " steps=500,\n", + " batch_size=50,\n", + " hidden_units=[10, 10, 5, 5, 5],\n", + " training_examples=lll_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=lll_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 173.73\n", + " period 01 : 108.09\n", + " period 02 : 105.79\n", + " period 03 : 104.58\n", + " period 04 : 102.94\n", + " period 05 : 102.39\n", + " period 06 : 102.02\n", + " period 07 : 101.08\n", + " period 08 : 100.81\n", + " period 09 : 100.72\n", + "Model training finished.\n", + "Final RMSE (on training data): 100.72\n", + "Final RMSE (on validation data): 102.38\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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CuaYoV0RERDSwRCzAXHjhhdi0aVO75atXr263LD8/H/n5+ZEqpc8ZValQi2o4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\nZ4tbgOnr62Pz5s0sW7Ys0rZ161ZKSkq46667eOO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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "VB3wXBYDb7Av", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "id": "S6xKrMxEcCHe", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "It's a good idea to keep latitude and longitude normalized:" + ] + }, + { + "metadata": { + "id": "jNuy2yecb8MP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "5574ad39-0e47-429b-ce52-9da4b4315026" + }, + "cell_type": "code", + "source": [ + "def location_location_location(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " return processed_features\n", + "\n", + "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n", + "lll_training_examples = lll_dataframe.head(12000)\n", + "lll_validation_examples = lll_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n", + " steps=500,\n", + " batch_size=50,\n", + " hidden_units=[10, 10, 5, 5, 5],\n", + " training_examples=lll_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=lll_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 114.79\n", + " period 01 : 105.07\n", + " period 02 : 102.13\n", + " period 03 : 100.61\n", + " period 04 : 100.21\n", + " period 05 : 100.08\n", + " period 06 : 99.25\n", + " period 07 : 99.64\n", + " period 08 : 98.70\n", + " period 09 : 98.45\n", + "Model training finished.\n", + "Final RMSE (on training data): 98.45\n", + "Final RMSE (on validation data): 100.13\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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LJ2n/5XjVUeLbRju0vstVQ/oizqHeuCb1xXWpN03jsIDTWAcOHOCRRx7hxRdf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\n1Ml/4rxCU6zHTCQRQaevG+2eLrR5OzEwYYC2SW3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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "XJIT6YSDcE70", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 68ed5dd3f9389f54cf5aa049920543917e754c03 Mon Sep 17 00:00:00 2001 From: Zaurezzh <43146651+Zaurezzh@users.noreply.github.com> Date: Fri, 15 Feb 2019 20:44:42 +0530 Subject: [PATCH 11/11] MNIST Digit Classification Programming Exercise. --- MNISTDigitClassification.ipynb | 3366 ++++++++++++++++++++++++++++++++ 1 file changed, 3366 insertions(+) create mode 100644 MNISTDigitClassification.ipynb diff --git a/MNISTDigitClassification.ipynb b/MNISTDigitClassification.ipynb new file mode 100644 index 0000000..160774b --- /dev/null +++ b/MNISTDigitClassification.ipynb @@ -0,0 +1,3366 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "MNISTDigitClassification.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "VT8-Kpt0dc_t", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "EKarAIoJeH_P", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Classifying Handwritten Digits with Neural Networks" + ] + }, + { + "metadata": { + "id": "BuNoils9eLD4", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST.png)" + ] + }, + { + "metadata": { + "id": "uz9mUjAneQ9z", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Train both a linear model and a neural network to classify handwritten digits from the classic [MNIST](http://yann.lecun.com/exdb/mnist/) data set\n", + " * Compare the performance of the linear and neural network classification models\n", + " * Visualize the weights of a neural-network hidden layer" + ] + }, + { + "metadata": { + "id": "JTHFrMM2eTZN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our goal is to map each input image to the correct numeric digit. We will create a NN with a few hidden layers and a Softmax layer at the top to select the winning class." + ] + }, + { + "metadata": { + "id": "kTrVpUGleVk3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, let's download the data set, import TensorFlow and other utilities, and load the data into a *pandas* `DataFrame`. Note that this data is a sample of the original MNIST training data; we've taken 20000 rows at random." + ] + }, + { + "metadata": { + "id": "4DBE7mMyePFx", + "colab_type": "code", + "outputId": "df415842-0239-49a7-b268-185b9cbc8498", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 233 + } + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import glob\n", + "import math\n", + "import os\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "mnist_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "# Use just the first 10,000 records for training/validation.\n", + "mnist_dataframe = mnist_dataframe.head(10000)\n", + "\n", + "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n", + "mnist_dataframe.head()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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"outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ictF_JYUe9Kx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Build a Linear Model for MNIST\n", + "\n", + "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n", + "\n", + "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix). The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n", + "\n", + "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training." + ] + }, + { + "metadata": { + "id": "JiJKwXWGe6_G", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " \n", + " # There are 784 pixels in each image.\n", + " return set([tf.feature_column.numeric_column('pixels', shape=784)])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FayaMoTefBcq", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls." + ] + }, + { + "metadata": { + "id": "cE-ZEfzYe_Tb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n", + " \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n", + "\n", + " Args:\n", + " features: The training features.\n", + " labels: The training labels.\n", + " batch_size: Batch size to use during training.\n", + "\n", + " Returns:\n", + " A function that returns batches of training features and labels during\n", + " training.\n", + " \"\"\"\n", + " def _input_fn(num_epochs=None, shuffle=True):\n", + " # Input pipelines are reset with each call to .train(). To ensure model\n", + " # gets a good sampling of data, even when number of steps is small, we \n", + " # shuffle all the data before creating the Dataset object\n", + " idx = np.random.permutation(features.index)\n", + " raw_features = {\"pixels\":features.reindex(idx)}\n", + " raw_targets = np.array(labels[idx])\n", + " \n", + " ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n", + " return feature_batch, label_batch\n", + "\n", + " return _input_fn" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2IYo28U_fDvv", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def create_predict_input_fn(features, labels, batch_size):\n", + " \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n", + "\n", + " Args:\n", + " features: The features to base predictions on.\n", + " labels: The labels of the prediction examples.\n", + "\n", + " Returns:\n", + " A function that returns features and labels for predictions.\n", + " \"\"\"\n", + " def _input_fn():\n", + " raw_features = {\"pixels\": features.values}\n", + " raw_targets = np.array(labels)\n", + " \n", + " ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size)\n", + " \n", + " \n", + " # Return the next batch of data.\n", + " feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n", + " return feature_batch, label_batch\n", + "\n", + " return _input_fn" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "awcqHujHfGrb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model for the MNIST digits dataset.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " a plot of the training and validation loss over time, and a confusion\n", + " matrix.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `LinearClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + "\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create a LinearClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(),\n", + " n_classes=10,\n", + " optimizer=my_optimizer,\n", + " config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + " \n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bu2hYG0ZfQKK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n", + "\n", + "Stop if you get anything above about 0.9 accuracy." + ] + }, + { + "metadata": { + "id": "lN8IdTlUfS1N", + "colab_type": "code", + "outputId": "2012575b-3855-447c-ddff-3b6ec01c4077", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1092 + } + }, + "cell_type": "code", + "source": [ + "classifier = train_linear_classification_model(\n", + " learning_rate=0.02,\n", + " steps=1000,\n", + " batch_size=100,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 4.19\n", + " period 01 : 4.02\n", + " period 02 : 3.69\n", + " period 03 : 3.54\n", + " period 04 : 3.47\n", + " period 05 : 3.45\n", + " period 06 : 3.45\n", + " period 07 : 3.50\n", + " period 08 : 3.25\n", + " period 09 : 3.30\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.90\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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btqFVr3/99Xn07z+QTp068/zzz1JaWlq1SAnAL79sYtWqlWi1GgICWvD//t/zLFjwJjEx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\nHIMOB9VNnZysaKO04gIf1nVQ2dDBjn+dJzjQREb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SMEiIiEgSBgkREUny/yWXJdUuFuvnAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "MpLrJ5uEg-TH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "VkvT9L-phByS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Here is a set of parameters that should attain roughly 0.9 accuracy." + ] + }, + { + "metadata": { + "id": "yJbAJbKWg40V", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "4515ecd2-3d44-45a6-acb1-260e265b6c71" + }, + "cell_type": "code", + "source": [ + "_ = train_linear_classification_model(\n", + " learning_rate=0.03,\n", + " steps=1000,\n", + " batch_size=30,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 4.48\n", + " period 01 : 4.16\n", + " period 02 : 3.87\n", + " period 03 : 3.56\n", + " period 04 : 3.63\n", + " period 05 : 3.45\n", + " period 06 : 3.47\n", + " period 07 : 3.47\n", + " period 08 : 3.40\n", + " period 09 : 3.40\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.90\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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JbcehVt34xRlz1lrUTupsHVJn65A6V6vtA4xFLpu/8847fPPNN6xZs4axY8cy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\n1nO1WieezePNrw6i1aiZd/fNNAu4/ME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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "1esR8qufnr4R", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Replace the Linear Classifier with a Neural Network\n", + "\n", + "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n", + "\n", + "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class." + ] + }, + { + "metadata": { + "id": "vy905JkrmZfI", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n", + "#\n", + "def train_nn_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " a plot of the training and validation loss over time, as well as a confusion\n", + " matrix.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `DNNClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " # Caution: input pipelines are reset with each call to train. \n", + " # If the number of steps is small, your model may never see most of the data. \n", + " # So with multiple `.train` calls like this you may want to control the length \n", + " # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n", + " # or since it's in-memory data, shuffle all the data in the `input_fn`.\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n", + "\n", + " # Create a DNNClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.DNNClassifier(\n", + " feature_columns=feature_columns,\n", + " n_classes=10,\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "joTxq6qEnyPm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n" + ] + }, + { + "metadata": { + "id": "_2BdYXj9nvxp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 346 + }, + "outputId": "767b785f-dc7f-49bb-a5a9-38f018ab508d" + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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10000.0 10000.0 \n", + "mean 0.0 0.0 0.0 \n", + "std 0.0 0.0 0.0 \n", + "min 0.0 0.0 0.0 \n", + "25% 0.0 0.0 0.0 \n", + "50% 0.0 0.0 0.0 \n", + "75% 0.0 0.0 0.0 \n", + "max 0.0 0.0 0.0 \n", + "\n", + "[8 rows x 784 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "id": "Qk59e3B5n1BO", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "efdc4c19-c0ab-47f4-c88b-2df8a4df2455" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Calculate accuracy on the test set.\n", + "#\n", + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 6.51\n", + " period 01 : 3.16\n", + " period 02 : 3.09\n", + " period 03 : 2.22\n", + " period 04 : 2.58\n", + " period 05 : 2.07\n", + " period 06 : 1.99\n", + " period 07 : 1.85\n", + " period 08 : 1.69\n", + " period 09 : 1.81\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.95\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Fs2bNQqUavOG9ZcsWKisr2bJlCyaTCZ1OR0JCAvPnzx/y85uaOs8++mHExhpo\naHDfEpphWoWIUB17iuuoq29FpSgDzsmOnMymtk/ZcWQ/OdGT3RaLN7k7z6KX5NlzJNeeIXke/peT\nERXnxx57jOzsbBYvXszKlSvJzc1l7dq1PProo4Oe/8wzz/T//fnnnyc5OXnYwuyPFEUhLyOa7YUm\nKuvamZAwMMm5xmw2VX5KkbkkYIuzEEKIsTeiMecDBw7wzW9+k3Xr1nHNNdfw7LPPcvSoLLCRnzX8\nlKqsyAyC1DrZQlIIIcRZGVFxdrl6V8LasmULl156KQA9PYM/pXy673//+1x77bXnGJ5vm5oejaIM\nXZy1Kg3ZUZOotzZS39ng4eiEEEL4qxEV54yMDK644go6OjrIycnh3XffDcj5y2crTK8lMzGc0upW\nOrtsg55zYrUw2aVKCCHEyIxozPkXv/gFhw4dIisrC4BJkyb1t6DHu7xMI6U1rRyoaOK87LgBx3Nj\nsuFg73znS1IXeCFCIYQQ/mZELefi4uL+p65/97vf8etf/5pDhw65Oza/kH+GXaoigyJIDkvkcHMZ\n3Y6RDQUIIYQY30ZUnH/xi1+QkZHB7t27KSgo4OGHH+a5555zd2x+IT3BQJheS0GZpX9s/nS5xmzs\nTjuHmo54ODohhBD+aETFOSgoiPT0dD7++GNWrFjBxIkTh5zjPN6oVAq5GdE0tXVT3dgx6Dm5slqY\nEEKIszCiCmu1Wlm3bh0bN25kwYIFNDc309o6+HaJ41FeRjQAhUNshJERnkaIRk9RY8mQrWshhBDi\nuBEV5/vuu4/33nuP++67j7CwMP76179y0003uTk0/5GXOfx8Z7VKTU70ZJq6m6ntqPNkaEIIIfzQ\niJ7Wnjt3LtOmTaO8vJwDBw5w6623otfr3R2b34gI1TEh3sDhqma6euwE6wamNdeYzZ76rygyl5AU\nluCFKIUQQviLEbWcN27cyJIlS/j5z3/OQw89xNKlS9m6dau7Y/MreZnR2B0uSo4Ovn/zVOMUFBTZ\nQlIIIcQZjajl/Morr7B27Vqio3vHVuvq6rj33ntZuHChW4PzJ/mZRt7fcZSCcjMzJsUMOG7QhZEW\nnkJpSwVWuxW9RnoehBBCDG5ELWetVttfmAHi4+PRagN3C8RzkZUcjj5IQ0GpeciHvvKM2ThdToot\nhz0cnRBCCH8youIcGhrKn//8Z0pKSigpKeGVV14hNDTU3bH5FbVKxdT0KBpbuqhvsg56Tv9SnrIR\nhhBCiGGMqDj/8pe/pKKiggceeIAHH3yQ6upqHn/8cXfH5neOrxa2f4intlMNyRi0YRRZSnC6nJ4M\nTQghhB8Z0Ziz0WgcsHdzaWnpjzj6AAAgAElEQVTpKV3d4tT5zovPSx1wXKWomGqcwk7THqraakgL\nT/F0iEIIIfzAOS/z9cgjj4xlHAEhOjyY5JhQDh5rosfmGPScvJgcAHlqWwghxJDOuTjLSleDy880\n0mN3cqhq8ClV2VGTUCkqWcpTCCHEkM65OCuKMpZxBIy8zN6u7YLSwZfyDNHqyYyYwNHWStp62j0Z\nmhBCCD8x7Jjz22+/PeSxhoaGMQ8mEExKiUSnVfVtITlp0HNyjdkcaS6n2HKI8xNmeTZAIYQQPm/Y\n4rxnz54hj82YMWPMgwkEWo2KnLQovio109hsJSZy4GIjucZs/lW6jiJziRRnIYQQAwxbnH/1q195\nKo6Akp9l5KtSM4XlFhbNTB5wPCk0gaigSA6YD+JwOlCr1F6IUgghhK8a0VSqVatWDRhjVqvVZGRk\ncNdddxEfH++W4PzVybtUDVacFUUh1ziFbTU7qWitJCsy3cMRCiGE8GUjeiBs/vz5JCQk8J3vfIeb\nb76Z1NRUZs+eTUZGBg8++KC7Y/Q7cZF64qP0HDjahN0x+GIj/auFyVPbQgghTjOi4rxnzx6eeuop\nlixZwuWXX84TTzxBUVERN910Ezabzd0x+qW8TCPdPQ6OVLUMenxy1EQ0ilqKsxBCiAFGVJzNZjMW\ny4mpQW1tbdTU1NDa2kpbW5vbgvNnx5fyLCgffCnPYE0Qk6KyqGqvobl78AIuhBBifBrRmPONN97I\n8uXLSU5ORlEUqqqquP3229m8eTM33HCDu2P0S1PSItGoVRSUWvjmosHPyTVmU2w5RJG5hAuTLvBo\nfEIIIXzXiIrz9ddfz7Jly6ioqMDpdJKWlkZkZKS7Y/NrQVo1U9IiKSq30NTWTZQhaMA5ucYpvH0Y\niswHpTgLIYToN6Li3NHRweuvv05BQQGKojBjxgy+853vEBwc7O74/Fp+RjRF5RYKy81cNC1pwPG4\nkFhi9UZKLIewO+1oVCP6v0MIIUSAG9GY88MPP0x7ezsrV65kxYoVNDY28tBDD7k7Nr+Xn9U77lxY\nNvhSngB5xhy6HT0caS73VFhCCCF83Iiaao2NjTz99NP9/77kkktYvXq124IKFAnRIRjDgykqt+Bw\nOlGrBv4ulGvMZnPVNorMJWRHD77cpxBCiPFlRC1nq9WK1Wrt/3dnZyfd3d1uCypQKIpCfmY0nd12\nymsGf6p9YmQGOpWWIvNBD0cnhBDCV42o5XzDDTewfPly8vLyACgqKuLee+91a2CBIi/TyJZ9NRSU\nmZmYEjHguFatZUr0RAoai2m0monRG70QpRBCCF8yopbz9ddfz5tvvsk3vvENrrnmGv7v//6PI0eO\nuDu2gJAzIQq1SunbpWpwJ1YLk9azEEKIEbacARITE0lMTOz/9/79+90SUKDRB2mYlBLBwWPNtHb2\nEB6iG3DO8eJcaC5mYcp8T4cohBDCx5zz3B2XyzWWcQS0vEwjJceaKSq3MC83YcDx6OAokkITKDYf\n4qHtjxOqDSFEG0KoRn/i79oQQjS9f/b+/cQxrUzBEkKIgHLO3+qn71IlhpaXEc3bW0opLDMPWpwB\nlqVfykdHt9Bht9JgbaS7vWfEn69T6wjVhBCi1RPaV8BDTinioYRq9acWdynqQgjhs4b9dl64cOGg\nRdjlctHU1OS2oAJNalwYEWE6CsstOF0uVIPkdHb8DGbHz+j/t91pp9NupdPWSbutk05bJx19/+6w\nddJh732t02alw977mtlqodpRO+K4dCptfxHvLe4hgxbx0L4Cf7z4a9XaMcmLEEKIwQ1bnN944w1P\nxRHQFEUhP8PItoJajtW1kZ4Qfsb3aFQawnUGwnWGs7qWw+mg026lw9ZBh81KZ1/h7himwJutTWdV\n1LUqbX/xTolM4OsTriAyaOCT6EIIIc7NsMU5OTnZU3EEvLzMaLYV1FJQah5RcT5XapUagy4Mgy7s\nrN53oqh3Dl/Q+49be4t6ey2HGsq5Z8atJITGuemnEkKI8cVtg45Wq5UHHngAs9lMd3c3d911F5dc\ncom7LufzpqZHoyhQUG7hqgszvB3OAOdS1F0uF9sat/N/BWt5es8fuGP6zWRGTHBjlEIIMT6MaJ7z\nudi8eTN5eXn8z//8D8888wxPPPGEuy7lF8L0WrKSIiitbqGjy+btcMaEoihcO3U5/5H9TayOLp77\n8iUKGg94OywhhPB7bivOV1xxBd/73vcAqK2tJT4+3l2X8ht5mdG4XFBcEVgP081PmsPt+d8B4E/7\nX2d7zU4vRySEEP7NbcX5uJUrV/Jf//Vf/OQnP3H3pXxefmbv0pz7y4ZeLcxf5cXkcO/M2wnR6nmj\n5B+sK98oc+GFEOIcKS4PfIMWFxfz4x//mLVr1w45P9pud6DRqN0dilc5nS5Wr1mPRq3itZ8tCci5\n4jWtJn75ye9p6DCzOOsibpm1EtUgu3EJIYQYmtseCCssLMRoNJKYmEhOTg4OhwOLxYLROPjGDk1N\nnWN6/dhYAw0Ng+8E5U1T06P4vKiOfQdMpMSd3RPVvuj0PGsJ5Ycz7uSFr17lo9JPqW+xcFPuKnQy\nN3pUfPV+DkSSa8+QPPfmYChua9Ls3r2bP//5z0DvftCdnZ1ERUW563J+Iz+j95eTgmE2wvB3EUHh\n/HDWHUyOmshXjUU8v+9lOmxj+8uXEEIEMrcV55UrV2KxWFi1ahW33XYbP/vZz6R7E8jNiAagoDRw\nizOAXqPnrunfZXbcdMpaKnh674s0dTV7OywhhPALbuvWDg4O5qmnnnLXx/ut8FAdExIMHK5qwdpt\nRx8UuOtba1Uabsr9FuFBBjZXbuO3e17g7um3kBQ2+PriQggheklT1gvyM404nC5KjgXWlKrBqBQV\n1028imsmXklzdwtP732RI83l3g5LCCF8mhRnL8jP7O3aLiyzeDkSz1AUhcvTFvKdqSvpdnTz/L6X\n2Vdf4O2whBDCZ0lx9oLMpHD0QRoKyszjai7w+QmzuGvad1EpKl4p/B8+qdrh7ZCEEMInSXH2ArVK\nRW56FI0tXZgs4+sp5hzjZH4w83ZCtSG8deifvFe2YVz9giKEECMhxdlL8vpWCxsvXdsnmxCeyv2z\n7yZGb2R9xcf8b8nbOJwOb4clhBA+Q4qzlxxfyjOQ5zsPJy4khvtn30WaIZkdtV/wUsF/0+Po8XZY\nQgjhE6Q4e0mUIYiU2FAOHmumxzY+W43hOgP3zrydnOjJFJqLee7Ll2jv6fB2WEII4XVSnL0oL9OI\nze7kYOX4XZwjWBPMHdNuYk78LMpbj/H03j9gto6/rn4hhDiZFGcvyj++WlgA7lJ1NjQqDTdOXcHi\ntEXUdTbw1J4XqGqr8XZYQgjhNVKcvWhSaiRBWvW4fCjsdCpFxTcmXsF1k66ipaeN3+39I4eajng7\nLCGE8Aopzl6kUavImRCFydJJQ7PV2+H4hEtTL+Lm3FXYnDZe2Pcqe+q+8nZIQgjhcVKcvezEamHj\nu2v7ZOfFz+Du6begUWn4S9EbbK7c5u2QhBDCo6Q4e9nx+c4F0rV9iinRE/nBrDsx6MJ4+/Ba3j3y\ngSxWIoQYNwJ3SyQ/ERupJyE6hOKjTdgdTjRq+X3puFRDEvfPvpsXvnqFj45tobWnjf/Ivh61Su3t\n0DzC7rRTZD7ITtMejrVVMiM2n6UTLsWgC/N2aEIIN5Pi7APyMqPZuLuKw5XN5KRHezscnxKjj+b+\nWXfz4v6/sNO0h9aeNm7NW02wJsjbobmFy+Wisq2anaY97K7bR7utd953kCaIzZXb+KxmF5emXsxl\naRej1wR7OVohhLuo16xZs8bbQQB0do7t6lChoUFj/pnu9HlRHeGhOnIz/Ks4eyLPOrWO8+JnUNVe\nwwHLQUosh5kem0uQWufW63pSS3crn1bv4M2D77D+6CYqWivRqXVcmHQBK6dcw21zb0Bt11Heeowi\ncwnba3aiKAopYcnjpifBU/ztu8NfSZ57czAUxeUjA3kNDW1j+nmxsYYx/0x36bE5+P6znxITEcyP\nvzWTiDD/aRV6Ms8Op4M3Dv6Dz2t3E6s3cvf0W4kNMXrk2u7Q47Cxv7GInbV7KLYcwoULjaImL2Yq\ncxNnMzV6Sn/hPZ7nbkcPmyu3sfHYFqz2LiKDIrgi/XLmJp4nRXqM+NN3hz+TPPfmYChSnH3EM3//\niv2lvU9sRxmCSE8wkJ4YTkaCgQkJBgwhvtlK9HSeXS4X/y7bwPqjmzBow7hr+ndJC0/x2PVHy+Vy\nUdpSwc7aPeyt30+Xowvo3QxkbsJsZsfPIFQbMuB9p+e5w9bJR0e3sKVqOzanjTh9DF/LXMLMuGmo\nFHluYTT87bvDX0mepTj7BXNLF9sLaqkwtVFe20pLx6ndPTERwf3FOj3BwISEcEKCvf/IgLfy/EnV\nZ/zt0L/QqbV8L/9GcqInezyGs2G2Wthp2sNO014arb2/hEUGRXB+wiwuSJhNQmjcsO8fKs/N3S2s\nr9jE9pqdOF1OUsOSuCprGVOjp6Aoilt+lkDnb98d/kryLMXZLzW1dVNhaqW8to0KUysVtW20W22n\nnBMfHdJbrBPDewt2vIEgnWe7Nr2Z5y/rC3jtwJs4XU5W56zg/IRZXoljKF32LvbWF7DLtIfDzWUA\n6FRapsfmMzdxNpOjskbcyj1Tnhs6zbxf/iG76/bhwkVWRAZXZy0nKzJ9LH6UccXfvzv8heRZinNA\ncLlcmFu6elvWfcW6wtSGtdvef46iQFJMaG+XeEI46YkG0uLC0GrcV7C9necjzeX8cf9rWO1Wrpl4\nJZenLfRaLABOl5ODTUfYWbuHfQ2F2Jy9v1BNiszkgoTZzIzLJ/gcnrIeaZ6r22t5r2w9BY3FAOQZ\ns7kqcxkphqSzvuZ45e17eryQPEtxDlhOl4uGJuuJYl3bytG6drpP2oJSrVJIjg0lPSGcjMTeop0c\nGzpm86l9Ic817SZe+OpVmrtbuDT1Iq6ZeKXHx11NHXXsNO1ll2kvzd0tAMTojcxNmM35CbMw6kf3\nFP7Z5rmspYK1pev7W+znxc/gyowlxIXEjCqO8cAX7unxQPIsxXlccTpd1Fo6qag93rruLdh2h7P/\nHI1aRVp82Ckt7CRjKCrV2Y9R+kqem7qa+f2+VzB11nNe/Ay+nbMCrcq9Y/Lttg721H3Fzto9HG2r\nBCBYHczs+GlckHAemRETxmzc91zy7HK5KLYcYm3ZeirbqlEpKuYnzmF5xuVEBkWMSVyByFfu6UAn\neZbiPO7ZHU5qGjuoMPW2rstr26hqaMfhPPF/vU6rYkK84UQLOzGcuCg9qjMUF1/Kc4etkz/uf42y\nlgqmRE3ke/k3jvlCHQ6ngyJzCTtNeyhoLMbhcqCgkGOczNyE2eTH5KJTa8f0mjC6PDtdTvY1FPJe\n2XrqOxvRqjQsTLmQxRMWEaYNHeNI/Z8v3dOBTPIsxVkMwmZ3UNXQ0V+sK0ytVDd2cPLdoA/S9LWu\nTzx0FhMRfEpr0Nfy3OOw8ZeiN9jfWERKWBJ3Tb+FiKCh/wMYCZfLRWV7NTtrT121Kyk0gQsSZzMn\nfiYRQeFjEf6QxiLPDqeDnaY9vF/+Ec3dLQSrg7k8bSGXpC4I2BXXzoWv3dOBSvIsxVmMUHePg2P1\nbf3d4RWmNkzmTk6+QcL02r5ibSAjIZw505Kwd9mG/ExvcDgdvHXoXbbX7MQYHMXdM24lPiT2rD+n\npbuVL+q+ZGftHmo6TACEaUOZEz+T8xNnkRqW7LHpSmN5P9scNj6t3sGGo5tpt3Vg0IaxNP1SFiTP\ndftQgD+Q7w7PkDxLcRajYO22c9TU1j//usLUSkNzV/9xtUphxaUTuXx2ik/Nq3W5XKyr2Mj75R8R\npg3ljmk3kxGRdsb39a/aZdpDsbl31S61oiY/JocLEmaTa8z2ykpc7rifrfYuNlV+yqZjn9Dl6CYq\nKJIrM5dwQcKscb2QiXx3eIbkWYqzGGPtVhtH+4r1pr3VNLd3c35OHDctzyZY51str+01O3mz5B20\nKg235H2bvJicAee4XC7KWo6y07SbvfX7sdpPrNp1QcJsZsdP9/rYrDvv5/aeDjYc3cQn1TuwO+0k\nhMTxtcylzIjN86lfuDxFvjs8Q/IsxVm4kUqn4Rev7uRIdQuJxhDuuTafRKNvPWS0v6GIPxf9Lw6X\nk1XZ1zMv8Tygd9WuXaa97DTtoWHAql2zSAiN92bYp/DE/dzU1cwH5Rv53LQbp8tJmiGFq7OWkx09\nya3X9TXy3eEZkmcpzsKNYmMN1Jpa+PvmUj7aXUmQTs3Ny7M5P8d3Chv0zvv941ev0WHvZEHyXOo6\n6vvnAGtVWmbE5nFB4mymRE30yS5dT97PdZ0N/LtsA3vr9wMwOWoiX89cNqJhgUAg3x2eIXmW4izc\n6OQ87yqu4y8flNBtc7D4vFS+eUnWmC12MhZMHXX8ft+rNHU3AydW7ZoRl+/zeyN7434+1lbFe6Ub\nOGA5CMC0mFyuylxKUliCR+PwNPnu8AzJsxRn4Uan57mmsYMX/llArbmTiSkR3Hl1HlEG35mm09Ld\nylcNRUw1TiFmlKt2eZI37+fDTWWsLVtHWctRFBTmJMzkyowlfpW/syHfHZ4heZbiLNxosDx39dh5\nbV0Ju4rrCQ/RcsfVeWRPiPJShIHB2/ezy+Wi0FzMe2UbqG6vRa2ouTDpApalXzbqeeS+xtu5Hi8k\nz8MXZ/WaNWvWeC6UoXV29pz5pLMQGho05p8pBhoszxq1itlTYgnVa9l3uJHthbXoNComJkeMy6d/\nx4K372dFUYgPieXCpAuID4mlsr2aYsshPq3eQbejhzRDClo3rIzmDd7O9Xghee7NwVCkOItRGSrP\niqKQlRRBzoQoCsrM7D3USGV9O/mZRrQa3xmH9he+cj8rikJyWCIXJ88jIiicitZjHLAcZFvNTnBB\nqiHZK/PAx5Kv5DrQSZ6lOAs3OlOejeHBzM1N4KiplcJyC7sP1jMlLYqIUJ0Ho/R/vnY/qxQVE8JT\nuSh5HnpNMGUtFRSai9lR+wU6lZbksESffOp9JHwt14FK8izFWbjRSPIcrFMzNzceh8PFviONfFZQ\nS3R4EKlxgTVW6U6+ej+rVWqyItNZkDQXtaLicEs5+xuL2G36khCNnmBNECpFjVpR+82Qhq/mOtBI\nnocvzm59IOzXv/41e/bswW63c/vtt7NkyZIhz5UHwvzT2eb5y0MNvPL+AazdDhbNTOZbl02Sbu4R\n8Jf7ubWnjfUVm9hW/TkO14l9xbUqLQZdGAZtGAZdKGH9fw8jTBvae0x34t8aL67x7S+59neS5+Ef\nCHPbfwGff/45hw8f5q233qKpqYlrrrlm2OIsxoeZk2P5WewcXninkC1fVnPU1Mqd38gjJkLv7dDE\nGAjXGVgx+WouS72Iz2p20dTdQputnfaedtp6Oqhur8F+UtEeil6jx6ALHVDATy7qx4t9iFbvt13o\nQgzFbS1nh8NBd3c3ISEhOBwO5s+fz2effYZaPfjDItJy9k/nmudum4O/bjjIZ4UmQoM13P71XPIy\njW6IMDAEyv3scrnocnTT1tNOu62dtp72vr939P+9zdbRV8x7X3cx/FeUgkJYXyHvLd6hpxTvsJP+\nbtCFEqQOGraLPVBy7eskz15qOavVakJCQgB4++23ufjii4cszGL8CdKqueXKHCamRPDGR4f43d++\n4uoFGXztwnRUfjI2Kc6eoijoNcHoNcHEEXPG850uJx22ztOKd3t/8W7re73d1k5Td0v/1p7D0ao0\nhA3SvX68gGe6ktH1hBCuM/jNOLkIPG5fhGTjxo386U9/4s9//jMGw9C/JdjtDjQaKd7j0eHKJp54\n/Qvqm6zMzo7jvlWzCZenucU5sDlstHV30NLdRktXG60n/9ndRmvXiT+bu9uwOYbeizxIE0RCWCwJ\nYbEkGuL6/h5HgiGWqGCZsy/cy63F+dNPP+XZZ5/llVdeITIycthzpVvbP41VntutNl5aW0RhuQVj\neDB3XZNHRmL4GEQYGOR+Hnsul4tuR09f93oH7bZ2Wrvb6FS1c7SxhnprIw1WMz2OgU8U61RaYkNi\niNUbidXHEBtiJE4fQ2xIDBG6cCncIyD3tJeW72xra2PVqlW89tprGI1nHkuU4uyfxjLPTqeL9z6r\nYO22ctRqhVWLJ7NwepJ80SH3syednGuXy0VrTxsNVjP1nY00WBtp6Owt2vXWxiELd4zeSFxITH/h\njtXHEBcSQ7jOIA+v9ZF72ktjzh988AFNTU384Ac/6H/tySefJCkpyV2XFH5OpVK4ekEGmUnhvLS2\niP9ef5DS6ha+vWQKQVoZ8hCepygKEUHhRASFMzEy45RjvYW7vb9gH29pN/QV8cHGv7UqbW9rOySm\nt6Xd9/dYvZGIoHAp3KKfbHwhRsVdeW5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A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `DNNClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " # Caution: input pipelines are reset with each call to train. \n", + " # If the number of steps is small, your model may never see most of the data. \n", + " # So with multiple `.train` calls like this you may want to control the length \n", + " # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n", + " # or since it's in-memory data, shuffle all the data in the `input_fn`.\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n", + "\n", + " # Create a DNNClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.DNNClassifier(\n", + " feature_columns=feature_columns,\n", + " n_classes=10,\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "C-0js9rQpaJ5", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "77a52498-3049-4bd2-e76f-9fd7af64d45c" + }, + "cell_type": "code", + "source": [ + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 5.06\n", + " period 01 : 4.10\n", + " period 02 : 3.43\n", + " period 03 : 2.72\n", + " period 04 : 2.56\n", + " period 05 : 2.00\n", + " period 06 : 2.02\n", + " period 07 : 1.69\n", + " period 08 : 1.88\n", + " period 09 : 1.81\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.95\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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p1FnnwCuVouKeTpdMVnJkCSmlqZYuSwgh2hUJZzMK83dlTJ8gcour2Xoo09Ll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\nYf7X/odg7ay5z22NsXptMBhILU9nR8Z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Enn76afTv3x+DBw/GgQMH5ChJREQKkaUjSUxMxOeff46ioiKEhYVh9erV6Nat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\nRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "lnwcHt2rq1Q1", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we verify the accuracy on the test set." + ] + }, + { + "metadata": { + "id": "m-TBTzSNpcX5", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 346 + }, + "outputId": "4ea97029-e54a-40fd-c8df-c3c05a8c21b2" + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n", + "\n", + "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained." + ] + }, + { + "metadata": { + "id": "I9pNHKrJq6Oe", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1172 + }, + "outputId": "d78d0c9d-1d3f-4b6c-be00-ceaecb4a4b97" + }, + "cell_type": "code", + "source": [ + "print(classifier.get_variable_names())\n", + "\n", + "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n", + "\n", + "print(\"weights0 shape:\", weights0.shape)\n", + "\n", + "num_nodes = weights0.shape[1]\n", + "num_rows = int(math.ceil(num_nodes / 10.0))\n", + "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n", + "for coef, ax in zip(weights0.T, axes.ravel()):\n", + " # Weights in coef is reshaped from 1x784 to 28x28.\n", + " ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n", + " ax.set_xticks(())\n", + " ax.set_yticks(())\n", + "\n", + "plt.show()" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n", + "weights0 shape: (784, 100)\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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tmImI4qfjedHVFetE0S9SFzF2CuZm1U7MOa4n4hUiP5vbJPMaYGtueYbgnNqq\n8GzCtamIiDpT8W+HVVM44mZCt6ZsHa6RrZHCa0faaHI6Oltw3f1ipA5TUxGe/XzY8w6v/0REnux+\neXlhPSndLu9P+FCsd1W7Mf+49hcRUfNJfC/XbPWJx/FFTUwW7wkKxz6y/PA2HJulvcf1jLhmYN2B\nSpHnHY3nLG4j3tMuz92bae+VrIDFevxsqZFlawnZ0M4ZhUKhUCgUCoVCoVAoFIo+hP44o1AoFAqF\nQqFQKBQKhULRh3DptT3zGHa8/5yJ086dLF5rroWFVdES0AmqKqQtWfJY2Ev6MBqEw7LE5a3YYSNh\nt2XbE7szS8rGE2hV7WBWw+6WDTS3nty/BxZdWSNlO2ots1Br7wLtirdLEhGd89i5yGO2iYc+2CHy\nEiej7ZsfX4Bl18vb4KKiziZn48UrYP0XwChERETZE0FP4G17HkEyL3wE7gl3uK61rGT5/TqxAi3C\nkx9YZB0VPiRv5WoTc0vlwMER4h1rvwFdLWcU7CFXbpDXPS4U7ccz74CVZdmaPJHnGYrWO27nXbtT\ntmTm56LlPzUTbawhQyU9bf9XaPlf8Pzz5ExseuJfJo6aliJe47aebdWgLjgKZQtl6HDY4tWwcwy0\nLGyPLwd1MDiI22XL9s/iXFhUxmfgs31Y+19Ptywvkaxt343Zl9fsLxF5NVvwb/69TSflOSXOHYDj\n/u6giW2aFB8T3DY9fJi0CuR2fokZF5Czwe/jLztkC/MVD8Jmu3Y3ru0vK34XeXd8+qGJm5rQiu7m\nJi1rX//rQyaeMBItnu98L1vlu7rRlvnQq38zcfygM02844XXxHuWbsBcvO2d6/CCtZr4+KGN2tUV\n7aSlu6T1K28TbTwBekhbpaTiePijtvOVq+mwpHrsyYOF6I0ffkjORP4+UMT+wx4xD8fOLYSrN1tU\noYvQlu0oxZgOSRkg8mqOYS6GpmG9amuWNFFuuR2ejWtefxzjyMPPsoZk/3ZjdZvTfYiIPDxQH+qL\nUEM5TYaIqOLXArzG2oMD02UdL16Kc+IWpBUbCkRe4EB8b+asv5Kzse3Vp0wcNEgeox+jbXczG/TO\nZkkZ4xbpnF5kg9u9BiahxpSsPSzyXBkVmlurxk3FOl2+9Zh4D7d4bSnBWOpslMcqWs3DEJf9fELk\nxbGa2tuF86vaYtE+pmN/48Ior7wtnkiOp4Q059r3lpf+aOLStfI8Emah5h37CG3tNo23fr9sX/9v\nFBfKvc2oRaB/1e7F/ON7ByKisExQ3zrbsHes2Aw6QnertIl35OO+vb36F5xDuFybJw3EOOCSAR6W\nPXgIW7eJrcFuFlVw5z5QJbMDped4AAAgAElEQVRTsa/wjpNriXcEKMhD5t9AzsarV15p4sPFkp72\nyNePm/j9G18y8ZghslYGpGON92d7bDfrGeLAh6AUFVTi3k+9VFK+QjIxTwu+BBVq0KK5Jj7xwxrx\nHkchnmvc2FpVWiDH2Mhr8V0FjGa1OTdX5F3yzwUmbmfUqKA0OS5c3bG2dndgXBR+c1DkJZ0PmYiY\nuHnkTBxa9TaOx7rmfI/Ka2tDrtynddSD6tHLxq1fgqRB8387GM3FO8xX5HGKoaMU94bTabssiY3I\nMdjj8/fbtNseVhv5scaMl/SV5jKszVVsH+AVLo81djKeaTpaUA/a6yT9xVGK8x0851pyNorzYDXP\n7aOJiIpXoF5wOhBf74mI4qZgnHV1gPJl25a7+aBuuXkitmlSnKbJacahSajx9SVyLeXrDrc3t6no\n3BK9ie09+VpMRJQyZ5yJ21qwr2qtlJbo9Qcx1zsbMc7CRksb8R5m854+4UqyoZ0zCoVCoVAoFAqF\nQqFQKBR9CP1xRqFQKBQKhUKhUCgUCoWiD3FKt6Z+50wwccHKTeK12KlwP2lvRBudv0WbIeZSwdug\nc3+XLi7B/mj7LSwATeqDdetE3oOs7Z63HYVmgWJS9M0h8Z4TBWhHGz4Z7VaFOwtFXsYcvFa3B22r\nNvOrbANa5lsZPetwiaRmpAagBdWDKURXWy46x74FNeGsp5xPa5p+EVoofWNku2o9cx6JncHacS3H\nj7dv+cjECx++0MS8rZuIyD8e7YYzH7vHxCVHJJUish8cCbiifGcb2tdsdfSp58KVydUN4+rsebId\ntbUEbWbdlpI2R3sFKBNhjN5SekK2M3P3AN5S9/3L0k0qZ5Bss3UmOMWLq/ETSfVxN18cX3u1bIfk\njjGcetRsfV5oNNq0ORUqfIikU4UcR4umTwTmb0A46BcuLrI10NECWoSfH66X+zDZGu7HxhEfixHj\npDuCbxRaZDMuhatMwzuSNpM0AcfuydT5u6z28kbmekMZ5HQknIuaMCdazsX1r643ceZwzMVjZWUi\nb+39D5l43P1wfNrzyqci7y//hqsTp5Bd5ZDnXF37xw4Ct83GPL/Fcqy7+xq4oLVUo56FxY8SeVdP\nATXsnbWfmDhh1AyR99a195r40EmMq+sWnyvydqyCK1gHo55e9967Im+iy+n7u4MXo3x2NEpnEd4K\nX8conwHpYSKvdA1zxvPCHPGLk7WHO7yEhKBmOrzk2hU7MxnH1IHW3LAxaJ1ubZUOMT09aLmtK8aa\n6eoltwVdXXBK4O49Nq0pac5IE7c1ol295GdJw4mfj4nFKTA2lTYgSdJ/nY0o5lzSZc0JRxnOs7kA\n9dHPorAEpoBKwZ1+bEc9/wTQOV1cUK9jpkj3p6YifFcXuzZ5X4K6a9fAhmO41nz8NRXIuh4/Cvu5\n6ny4ZoTlyHZrDt4CHjNdHitv6687iP1SW7ls8+buFQlp5FT8+hQoQGOunyBe+/lBtOePX4zXbCpi\nzTbs27iT09j5cgHg7jE+bB8VNTRb5C27F/SOC194FO/Phkvellc2ivdkXwx3oYfP/buJ17+2XuT9\n9SlQ8R67/noTR4bJcZlXgvvhzWp/b43cU40YgrXaNxFrLncNIyKq3iH3ts7G/AdAsRlvfVdXF/bY\nfl6YO6u37RZ5F4/B3nnPh9tNbF+bAeeBUlr3Iei5YUOlG9n+F/HMk7EI9+eTmyH38Mn69eI9ny/D\n/bl94dMmnj9a2uqULAc9ZOnvoC3f+c71Iu+b+zCGtzK3voVTpoi8IFYrw3Nw77iTIhFR6l7U4uve\ncy6tqa0C896mePon4x6UroK0hL0u8noTkIr31O2X66JHEMZB/T68FpwlnY18oyClwT+b75PtdaeF\n0Z+4M5C93nlH4pr7R2KffPxzec0j2ToTNhJjLLSflNWoP4nnyg5GZbKfP33jpIOSs9HK7qOLVSu5\nyxVfn7gTJxFR4fI9JvZk90poYpCkhHLadWu1pLOHpoNC66hBbWtpxPOETYXqaGC/SzAanE1r4lS6\ngP4Yj7Zrb2NZgYl9I3Dvq7YeEXmcIt3K9hHcBYyIyCtI7v9taOeMQqFQKBQKhUKhUCgUCkUfQn+c\nUSgUCoVCoVAoFAqFQqHoQ+iPMwqFQqFQKBQKhUKhUCgUfYhTas50dYF7Fjdt4J/mxZ4BLvLWz7eJ\n10I7wAH0DgX/PWFUosjjdr7e4eAoDyuU3HrOF0sYOQ3/74JTib9T6hRE7fjMxBvfBtd31j1nibyX\nb4RuQZAfjmH8AKklsnMN9HLSY8A1vPjx80VewVfQkvFjnMuk8zJF3gBfacnsbLh5Q9OgYn2BeM2d\n8eqKvocVWdlxyfG8+CbwebldZ7tldbtlFbQLfL3A+82+ZozIy18P++xBl0GXorEemhKcT05EVFwL\nLYULHjzHxLV7pCZHxl8nmXjTE7DazFoo9TA4J/HEx7A1PlYubWonjc0ycRizFD93kLTk3PeJtPR2\nJgL7gQvJOaFERF7MPpDbLfa7YqjIK/wW94ZrInRZ/OC6CtzfmDDM7crdx0VeRHayiT09cS04R9z2\nVvbzB8+2rgK8VK8AS3MmHPOqOwTn1FImLZOPvY1rHj4eWgxx4ZLLXLcT9zR0NHi/tqVpcKbkLDsb\ncf3B86479I54bc4jqFul68Av//fSf4u8qkOYpx/fCE2Dha8+JvLeue5uEwezepYcI8ftsKsxN5++\nBXoJT3z7gomfWfhP8Z7FT11m4pPf4ng+2fO1yPtg4w8m5rbfdcekrkCDA/d4dH+MkczzLxV5/eeh\nBrS3o0btfF9a12//Fd91/fvvkzPRVoOaZ9HBqYJpCXDLXtvik1vT1jErXzc3qVXiGQguN7cN9vaV\n+giuruDQNzWhhvr7Y61pqresJpkWRWAMePEFP28VedzS2TcWfHdunU1E5B0N7ROPAPDM28vlGsE1\nB7idaKdlGSq0tU6D/lP5OnD8Oxss2+kQ6Av4xEPXytPSJzjxEWpYymVYJ2p2yvHt7w+b1N5eaCU5\nHPLaRGVA26JsP7Qogodij1Bl2bL7JuD4qrbhNd94aT/bWIP6H5KI46nplPp/XFPJywdjuLVJrrNN\n+ZiLXFcgLDtG5DUX1tPpwtibJpuYW+oSEc2490wTH3kD19LbsrDleoDuPphHXKONSGrg+UZjHuT/\n/KvI65cOzY+9H2BPmXQOtE7CAwPpz9DG9BZSo+R6dMcVV5g4cyL2pT7RUoci9zOMvw17MEZnDBki\n8rjOTHs1arCtExU++s91iZyBTmZtzC1wiYiailEfDzGb7d4eqXPRy3Qvsq/AXm/vR9tFXu3X2Otl\nz8Ee6ehbMq/fxbhWXLuwthn7r3lj5L525zvQsOlkmmjxYXI/su8gNMeuu+ciE/sGJIu8SedAq2ZG\nCMb65s9kjf71CHQv2n7EtbzoIqntlnDmaSik/xsx07BXrN0n99Bc47CbWQjbc4xrrbSzNcQrzEfk\n+UYyLRmmi1JvadNUri0wcdQM6A76M11FRxnfrxL5sOdPrm3ZcFTuPbllcvAQzFlbR6eNPSM152FN\nc/fzEnm9TMOL16TGo9JunOuYkJS7cgr48wW3HCci8mHrP9d85TqORESBzMqe69HYGqVcF62F6be6\nesq+kQZX6OVx62s+L30snbfwDNRHRz10Z728pZZdbzeurx9b6+sOVZJMROgTjvHjY+m4+kTi31zf\nrNGyjXf3xz1OkO7r//Xe//wvhUKhUCgUCoVCoVAoFArF/yvojzMKhUKhUCgUCoVCoVAoFH2IU9Ka\nqnaj9c5umwxOgLXV5s+WmNjXS7ZqcZvf8l/RRrx15R6RN9Z1mIlXvQw6y7gJsg2T23f5+qIV++DK\nt0zM7S2JiLa8D1vdzKFovbPbYFOj0Tq8ZDPeExUk24MDfNDCdaICbXQprF2PiMid2Y7t+QVt9uMT\n5ed1BqKlOiREWu45A9yWeONnW8RrIycx+3DWpjzkshEij9uA5X+KFkpPDw+RN+mO6Sbe+xq+i9vT\nERHV7UCLdPgwtFX/8hjsqZtbZZt7Ujiob+31oCRFTZQWz25uuD8zH73VxIe+lpSLqAkYP7xtN9ii\nNcXPQc/Zssd+MrFtcZc9VFrjORPF3+OaR81IFa9V/45WX35OjZZFNm8p5Ieed1Rau2eMw/kWf4fv\n9U+X7YDdnbgH7b1oG+xqw3gOCJGUQN7Sz1tf2yokZSqEtfEf/Aq1YqhFTUu+lNliMutizzBJPyjL\nQ4uiP6MwuLrLttqSFWjnThpETkdlGcZ34lR5Li8uetLE93yOenb0569EXvwkzM2cWaCgPb/wZpHH\nLVTPeQqvvXj1/SJvcjcGQ0467v3L1/zLxAvvk5TNyKSJJn5jy8cmnm61zZ83eqaJ7z5vgYlbHHJu\nX3Ib6F7Vm0DN+Pjv94q80XOwTmz4DhTaKefliLyr33iaThd4m271Fjl3/JJRa9sYTcDLavvtasU8\n6GBtuo0nS2Ues3jmbbG+8bJ9u60S9p3CstEF88ovQa47kYNwLbu6MI48LMtQX9a26x+PtZW3FBMR\ndbKaXLwOFpe+3nJP0N2Mcwo/C3W38jdJYW5gbeMkGchOQcAAtJ83H6sVr4Wx9aCJtaK7WTbjvP2c\n07x6JeOC8n5ZgddY7Q0fIekijS2ot3V7UB+5FW1HjbRv5+MxbBQ+r2J1nsjrHg+6TXcSxl8bG39E\nctwGpSPPw1eOCz42o8fCI7uxUI5Nr1A59p2J+iNVJrYpEntehhWyD6uF/S4dKfLyl8CSmdMq6g9X\nibxG9u+eNoz91IWSW+Dth33KsS8wL3PfBBUlMFWupU3HQJlwZ3SOAdfJNSKuBPV5/VsbTBzmL1vr\n61tApbj7iUUm7rCog5VbUb/6s/PY/tomkZcw9/TRYYiIXvgHqKf3vP938doPD3xv4tueu9rEflGS\nPtfRinn678Wvm/jMbHl/hlyPtWLl45iXNoXMUYLngxpW5+eci7Xvnfd+EO+JC0V9fOUrrLNv/ONj\nkXfJYhS0TZ9inzzUogD+uhqW92v3ga66aIakK9Wx+z08Bfvh5DlyrDeWMLqlZDf/j1Gfi/nBLZeJ\n5N7Tm9E+uD09EZE/o8O0VaEO8bWFiMjVHXMkII2vSfIZLGEOxi1//nRhls4B1vNi/hd4HmmqwhgI\n6x8u8rpZ/ePnEZQm80pWgOrMKVitFfL5k1Ng3Bi90qb58etyOhA5GvIArVVSQoFbYTvacK0brVrp\nFwd6EF8X7XPhYyE8G+tTzX5JC64/hM9vzkWtbGTPiKHRUhqBH2tgIgZ72ZaDIi2ESxmwxdmmMDed\nQH0pWoYxEjxYTiR+H1sZpS1ksKwvlZuK6FTQzhmFQqFQKBQKhUKhUCgUij6E/jijUCgUCoVCoVAo\nFAqFQtGHOCWtiava73lfujAF+YGmM2waqDEhWbLVsGIDWskC+qN9LD1Wuk18+gHa/Tu70QI3wlVy\nC1y9oBDd2IhjiBmFY6gvKBDv4ar9n98NisCZ/WU72+D0ZBMX16B1asSZ0vUmYlQ8/REaj0s177QL\n0P7I2/W4Kj4R0aGvoB6f+MwF5GzsfAMULU5jIpL0kaCBESZ2cZMtwsufW2niLqaSP+OSCSLvwzs/\nN3EDa7V0d5W/A/qFQVn7/Vs/MfHJarTuX3XRmeI9kYyyExCN9u2y7bJNrW77ThMHD8P5Hd+RL/JC\ns/EadwEbd45sBd39OtpOHR1oX1xw19ki7+DHu+h0IYg5vzRaqvFubE50NKL9k7sXEBH5sVbq4AFo\nvUwple2V3pG+fxg3HZWt/80n0YIblg43Nx8ftCq2tsprfnIFHGN6GC2it1tSxDyYkvnA+aDK2M4f\nEawFM3kK3NsOf/q9yHNj4y94EBvnViv86UblVlB2+p0pHfDu/gyt2B///R4TT79xush75srHTXze\nhTjnOz/7QOQ1NGA8trWhTXTWLOkwMfBiOJ8Nagfd8MCLcFTzDJDUlOoS1JRrHoLbRPm6ApH3xaYv\nTfzVbc+ZOMVqIY8dOt7EQWm5Jl56taRhjg9ES3p/RkP94t2fRV7Kjzj3a95+m5wJRwkomhETpOsg\nb4ut3AiaDp9vRESOYtCIvIRzk6SEFO5G62tiFsa63R6cuxX0JXc31IM7XnrJxE/9XdIFUgeDNhOR\ng8/mjkxElmsSQ9466ejCHbd4e39gpjx33tpdsxvjLXJ8ksir+l1SxpwNr2DQbTosN5CKNahbMWeB\nslO2+oTICx+H68Yp3PZ9bDyCml1fg3rrKGoQebweBQ/5Y+e4uibZap6UmWziPV9i7cs8a/Afvp9I\n1iEXqwT6JaE9nNPvipftFXm+SaDJFS1Hm7d9H9uqpCOVM7HzR1CS2rskpWHqNXBt5BS55lLpmsFd\nOA5vBAVh+gPS9ZPTAre9DdpPZJFck/b+gNoYOxJ7RQdbZ6MmJYv3cJcoDy9c/4IfpIPQwIvOM7Gb\nK1yi8iqls0gaq43L34BMwIi0fiIvhLXkf/8o3OBmXjtN5FX8VmDimIvI6eC1vLVaju9jpaB6Ft//\nBd4TI581xi/GXvSCM7D3vvYJ6eR35UmM/dlXTTXx56/+JPIun4VrlfH3cSbOfR30tHNGSxmCUXdi\nLW0oKTCxp7us1/EThpvY1R312jtSOs7sehPUxCmMMjzm9ikiL4ft1z09sb95/8aXRd45t50Gfuj/\nhm8U9n3tFn3Og7nCuvsibsyXe0pONwofiWfEhiOSNlOXizHRwCgvETny2aylFPU1MgdrdXgsakNz\n8yHxnujpoIVFsj1qQLJ8XqzcivWdu4HW75G135OtLW3FrPb7S+qXL6MCBaYi5tfrv/Kks6Cz0d2B\nOuruJ7/bweQp+LN4W6mcs/xZOIStY9zJiIiohTlPlW3AHsamTB/dhnWXy0lw57SkCVLeImoA9rnc\nzbnfdGmN1FCHda2cUbD8kyRNKmoi1rWKX3Hva36XFKzQERi3vZ04j5YSKe3xf4J2zigUCoVCoVAo\nFAqFQqFQ9CH0xxmFQqFQKBQKhUKhUCgUij6E/jijUCgUCoVCoVAoFAqFQtGHOKXmTPkv4F0nZieI\n1+LOgG1wzV7wxj9gnFAioukjoNey+1vwg4fOyRJ5RatWmbg/06MJGy55pYc+xWckTQKfkOtrNB6U\nnOKRd1xn4tEZeP+6z6Rd4Ny755j46BJY5B3faHHrGacweDi4ssFMs4WIqKUWvEjvCHBJS5YdFXlt\nHdJOztlIYFoF/L4REVVug6ZBaBbOpXqntHSdcfVkE3Ne/Jp3N4i8H7ZBm+j99x8w8etPfinyLpwG\nfvDEodDBefrLb0z84VcrxXuS14MfPbIf+MABadKWMjgbHMfln6w3cXiA1FLobMJ13/A1tC24XgIR\nUeJwcFXTE3Gs3uGSH3zcsuB2JrzCoEvBualERG6ejPvJbFFtC1PPYFjDcc2KzGvOEXlHv4a9ZOgw\nzL+AM+R15raydYUY0+GpmNuN+XIu+qfi2oYPgpYDt/IlIurpAleTW+wmL5BWzW21jOvag2NIOFta\neIeW4jzqmf5AT6fU5Qkd+sc6D85CRy242M8uvEe8xu1eOa9213tbRd6Vd0N3gGuX7Hj1RZEXz67B\noXegXTD+/sUir3Qf7F6vvvxhE7/6zG0mLv4hV7xnzO3/MPEPd9xt4u+3S42EriXLTPz0l3eZeO+r\nUkvm05ufMPFVrz9r4rNHSK2zDZ+gZnMe/+U3zhV5u7/bTacLfK1psnTGuAVmbw/u4b735XUJ47aP\nfAhaWl9RkZhz2zbAStXtV/l3lYhA1IQXfoR2xGVzcV0KqyRvPzMGem68HrhYIiScN13yI9bC1Clp\nIo/b9IYzXTZbf6CWrS38GnENK/szTgf4OduW1sS+u/EE7jGfl0SSbx41CZx0mzPPNbVSZ4HzfnT5\nYZGXfhZ0qAL7oVYW/QCL7coGWStDDuC+pmbjGJotraD2cmi/dDPduPJ6ed1TG3DunuHQBPAIltai\nhRuhh5FsjQUOW7fAmTjzYYzvkz8dEa/xddGd6TtE9Bsh8kqXQ+tg2j/xec1lliV4CNbgxASsE3V7\n5bp/qBhaSckzsd8aez80n06slZpoadMXmLi1FXuy6ClSR+HEKuiiDMpKNfGR/VLbbcK980w8YB/0\nGrpa5F7Th+mERAWjJhX+KK8l19rLPg2aMzVMO6K9RtaLez99yMQHX4e2WMZfp4i8o+9tNHH/q6AR\nQ5bmzLm3zzZx3hLoFV5x73kir4HZAz9w66sm/ssUfK9dDzY9vsTER5ht9fwL5bGufRjajEG+GFc/\n7ZK6hdfNh0bMS+yZJPihH0Ue/67LFuH8Ln78fJFXzrQyaDg5FcIa2NLyCxvBNUYxlvwTpa5HF7On\n9g3D81Szj6xRJ5k9tX8U9vV8fSIiCp+IZ5+ogVjvig9gHIWmymcibzbPfQJRC+uL5GcnTYVuTVkI\n1veqjdIi2YNpkXX6QxPS3p931GPccwtrW78sytL0cjYq2H0MHij14vh6zecpv85E8tmohq337RVS\nf8w9CNeG751qj8q9igfT0VuxG3u71Rsx58+66Qzxnu5uPAv19LSy/5eaPY5yrHF8b9djaXby+xPA\n9Du5zicRkbsfzoM/Y4YMiRZ5bZWn1mLTzhmFQqFQKBQKhUKhUCgUij6E/jijUCgUCoVCoVAoFAqF\nQtGHOCWtKeHcDBP/9uJ68VrqPFiftpai5f3SO+aLvDVv430ZcWgd5naaRERPvIMWem5Het+1slW/\nH7fcY62WY64HTYZbHhIR7XoJVqpe0Wi3Sm6MFHmurA329mtgae2fKukcGz6DVeL02WjnbbXalKp+\nRXuYRzCzBr5+ssiLK5IUImejJhc0DlcPed1DstCe25ALCkrFLmkPVlqHFumcC0aZuLWzU+YVom3y\ntcfRujlnhGwlPngMeYOS0RJ3+3yMH48gad/L8cM60CLO6JFW51m3gPpyUb8wE7dYbfNNJ2DjN/Ec\nWCKGD5c27/XMxi+Qfd5PD/wg8jLjT18bPrfvtcd37R60VfunoE00Mk1aJre0YJ76+8NmtbW1QOQ1\n5eM6+cSCLtFgWXg7CtFe75uIPA8/tETb1tcJ04eZOH8p6DpZf7lG5JXmou20p4NRnH4vFHle4WhB\n5dSbyMmy9bO1Em2iHawdk9sBEhHVH2Y0LCe3/RIRZVwC+/XYmZIKwC0137nlIxOf+dhNIm/PS7Ce\nX7IBteiSc6X9qXcQaBFLtmC+fDRjvch76KW/mfjnfWtMfO4otEf7esm5mLER927RfReaeNpDV4m8\nBy4E/cnTF2Pzw3XrRN7dt19u4p4e1HUvZuVORFR1CPPgni9wjfZ+9qbIu/hluW44E77xmH+8hZVI\nUv0CUrBu+J+wbOhPoJ5WlmNeNbbKlv6SWrwvMwF1sr5FrjV8LTx7FOozr9vN1mdXM0pOzDis9T09\nsu03bBjqYUcdWngdxdIasodRebiV5n9Yi4ajnZvTlrn1OBGRZ7Bs+3Y2uttgGVq15aR4LSQb+wxe\n5zg9iYgoYgruSe6ne0wcO1ZarHN71uYC3BM/a17xNv/6XKw7XQ24J5OunCDeU8Ho544CHKu7ZdXq\nHQcKy74daNEfPmmQyPNidr7dzEq7wbqP0QNxjTqbcHyNx+Q6EZQuW+OdiROf4JrHzZYWqSU/YT1I\nuQRU2+Jtv4q80FEYg76+ySZubJf21J3NmGO+SVjvdv6yX+SVsznXXo3W+qamAybOnH2teE9d3Q4T\nr//XpyZus/ZXft6glvUwalrOJXKtrz2KuVS5HvFAZglNRLT2EdCkRl+VY+LN70jKf0qSlBdwNs65\nBJbW+T9LCi2nRTY1oO61VJSJvIQFoARW7wXlbsU2uTZU70Dd23IU9JjMmByRV7cbn3/ThaCJpV8J\nOkvRip3iPUGD8EzRv0XOK4706aAc+ydhnahjNA0ioihWNx7IBh05JFPSr+Nexj6A21YXL5cSCr0W\nVcOZiJyYzL5I1sn6Q5hLfsyiuDVProud9Vhf2hi1p61CWjUnzsH162DvCRoga03LSdTDjg4cQ3By\nEvt/SaEJCMVnN9ViLCZkymfbdlYfggeAgsXXFSKicrauRbN9adNRee78eae3G/cpZkqqyKvajrUq\nTrIenQNWV+oPWdcmDXtKf7a/OfzlHpEXGoE9Ej+vgIwwkbfiS9TiaTNGmtjHS65dHV24pr8x6t+T\nf8PetXq7fGb1DsM65sr2Zc1Fku7bWo6x1cnW2bYqucfqZpQ7n2ispfU7Ja2VSxf4JeE6NOTKa8lp\n1X8E7ZxRKBQKhUKhUCgUCoVCoehD6I8zCoVCoVAoFAqFQqFQKBR9iFP21VT8BlrOuL9JKk7RGqhT\nJ83LNnFvr2zpmnAe2i3feP5rE9/GWhCJiGr3oIXQkQcqxJMf3C7yDr6P9s/Bi9C+7R+BNuLSPOnU\nETAQrVSclhIzVbaLFX6JttPQ0WjlXvH+epHXLwothWvewmteHrLFPSUSLY7tLWiXaiqRbVBVm9Gm\nlnoaqBSHmZI7b3MnIvLfjpbodtZCa9MY+L85laSuWbYb3nHFFSbOq0Ab9LDbpoi8uO1o9XvwgbdM\nPDINVI+sJElNSZkBVfXaH1krWpccc42F+F7uWOEZItvkD21Fa3fOFWhpbbXa2biq9vdfLDXx1IUT\nRR5JgXqnoo2pnLv7ynHGWyD9E1hbfOVekRcShbbB9nZco9JNB0ReAKNGcQcv202lfDfGVegozBde\nN0KHSYXyhpN4LXk+jqe29jeRFxSPfs2OkZg7vH3ehos7bgB3oSAiamDUtB7mPmO3oHI1+tOB4m1o\nFz/800HxWtYlmPx3f/6uiTs65JyNPgN162/TcJ1ih0wSeT/c9YyJ73x6kYmPfyPvd3stWu89PdGq\n+uxTN5p47wrZus/dNY5+h8/L+/6QyOM1sHI35ttHv/0s8pbe8ZCJ929APObS0SKvcTmOlbcjH98h\n3Uraq54y8bjb7yNngvYHtT8AACAASURBVFOZPAO8/zSvahtcW9otyiunnNQxipK3tYbsykN7fnMb\n2renDR4s8riDz8lqUPMunzvdxIUnJA3A3QPLf9kmuAYFD5J038KvME5jzkJ9rraoQA5WN4My0ebt\nHmC1KFej7ZfThGLPlK4ZpcsxXtJGkdPhE4kaET1V9oe7++KYXRkF26Za7X0T9L6wOMwdm3rawdql\nOVU0xHJ14rS4ng7UdT7nuzvkezo7UcMc7fge93ZZK4M8cUx8r3Jk63GRF8vcCn3j4YQSNVPul3g7\neOtJUNzsse6fJB1ZnIkg5ibCaUxERNXlWK9CWEt57XZJI+d0mKo80FQ8rHW2dCXGY2M5zrfbonAM\nT8V1ChmMvaKXF9bCnh55D3t6MLcn/xMOO5ufWCryAn0w/grZPLf3NtxwrYK5cbm+Jl3yRlyGicXH\n6MgLRoq8wDRJR3A2fluGff3QNDkXAxmVImoc9oQbHl8l8k4wt8zzb4bz6h2XPCXyhrH7M3kQqEcF\nS+S66M2oC8f2FpjY8RzWrmjLOeeX19aa+JLnbzXxz/e/JfK+3Yq68czncDF88qu7RF7VDqwhnLKz\n9P7vRN55T4FafIw9I2VeO1vkle+Re0Jnom4f1hfuykkk6yGn6Nu1gVcOTpPtsWpe7Q7M4YT5oORW\n/V4s8vrPwTjo6MB88fLCvGxvl2t4QIB8Nv1v1NfvEP92c8P72mq5k488d77GHV+JGpU8Xo7zhn2o\nUcGZWIOLvpeOfoEDTu9cdPEAvd4zUD4HejDnvZZSSWvm8O/HqHp78azBnx2J5P7wl9X4TcF+9mti\nlOzF80AxHDqfSVpYW3dOT6svxx4mpJ+87i6uBSbmzzuuHlLyoHILnl162DNX4kVyL+bugzWcP19w\n2ZT/+jy5f7KhnTMKhUKhUCgUCoVCoVAoFH0I/XFGoVAoFAqFQqFQKBQKhaIPcUpaU+FetPGUHZSt\noFmLQFeq+P2EicNHxIm8xiNoJeOOPTU75ee1FrG2WEavcXGVXJGMy+D2UvAZWu1j56Il0zNUtnj6\nsTbiugNosbKVvfcfKzDxlBw470yaNkzktZY0mXjYYtBhAqNl229zDag7zUVoLa38VbpS+PeXbXDO\nxtT5uFdNR6STggtzbwoajBYzL+safv8yWjk73gIFJSFcXsOh2Wh7H97Uz8TH3t8u8vIL0QJ50Xg4\nf+1lbk9Jk+T17OlEK9n11y0w8f4Nsu2v6WN8F28DbrHavGfce6aJj76FlkWvMHnuO3bBfWjGVaD3\n2deogLX/D5AMk/8xOBXHbmFuKQKloWgpoydkSXpCT+c2xKzV0m7f80tGq2nxCqj9h2ZLilLUEDg4\nHP4RLcFjb5+CY7NaH3uYW4CLC763uVBSdzqC0MbYzNyjCnYUiLw25lIz+sqxJnaUNYm86MloZWxk\nDgENB6QjR9SkZDqdaGSOVydr5Fwcy+rU78+9ZOLwHOkCFpaJeRUUBErpbw8/LfI4DWb6GNANV659\nV+T98NZqE8eORpvo0g/h3HTB32R7dORQtIz6+SG+/9wrRN4DXz5n4o9ufNLESeNmiDx+Lbgyf2a+\nHBfnzQKVsKUFNIPYCFlDy05KZXxngrf/N+XJ4+tqwdoVPgJUv6YCSQn88u0VJh7ZD/ezw6JoDk1O\nNrGHG+ZLwlmSAuSzDRTDED+4FHBKkbur/FtMwgK0g3e343u5Ox0RUeQ0HAOn3djuOOXrQS0rXYc4\nYY7M82LUoNp9oCLw60VEFJYj9xLORsVm7G+8wmVNrduO9SlyarKJuxwdIi8kFLQfTuWyqUe+scjz\nCvZlsWyp72zG2OIuF77RqA2HXt8m3nO0FHspNzZGGh0OkRfdhJrY1Y3jy5qVJfJc3bDn8mbUL3us\nc+fHQEYv8mV1jIiolTut/LmBzf8V6lnLfElZtXyNnX82OybvObK1nu8xi78H7eB4saQBBvnifalj\nsTeJ6JDrYuwM7IE8PXFd9jzzFY4hVtJu4xilj1NrQ6MkPS5yItr9Qyqw/nbUSSc2vq9LG8UowrVt\nIm/Du3BL4VS37LOGiLxjb4PuFfsv6VrjDPC92bo9kkJ7bjYoKJxKN+RseYwtS3Buj94JGtHiM84Q\neekLsZ9/5K+vmHjsgAEib9IErDUjYkHzCmS0lfdv+1S8Z2ImBrifH+p6vTUXH3wSbl03X/i4if+x\n8HyR99NazPVFj15i4gkLJN23aBnoxPHzUNcfuOBukffQl/+i04WAfrgutssPrxXimc6iBHqxvW0N\ncxNsr5PjNmgQ5tWvz4JK1n+UfGbw9sYawl0IuSNkZ6d0TSrO+9bEnJYSFCvdNUu24JnBhdVM/pxC\nROTqiXNPHJ30p3mRU/Ea/17u+ENE1HScHa/cRjkFXcyVjtMyiYhc3HAu3uHYZ6TNllSwZnaMtYwC\n798knY9ThyWbOKkV94o7QRERddSjvo1krwWmMDfeErnH6urCeucdjLHZVCqpb9yFr24/nge8I+Q6\nUb8Pr6VegX2yi4v8jaLuIPK6WvDZ4SPlfibMcgW2oZ0zCoVCoVAoFAqFQqFQKBR9CP1xRqFQKBQK\nhUKhUCgUCoWiD6E/zigUCoVCoVAoFAqFQqFQ9CFOqTkz+oYJJi7fWCBea60An6t6KzhcxRvzRJ6f\nPziE8cwGr3yrtJEauBiWfnteh91fe73k0gYmM40Ttz/2Lo7LkrbfpQfBqw0eCB2OcakLRN4bd95p\n4h1fg0+YNVNaZXU1gUfGrb0jBhSJPG6dFT0xGf/vJTU+PPylXZmzwa1RI0ZL/YqKTdB46WCWuodW\nSpvfix8FF7Z6O+435ycSEX2+BDoV1/wD79mxZKfIG78YY4tblU6LwXtOfLFZvCflQlgN5y+BXTrX\nWCAiGrIQ/GAXd1zrNc+vFnmbnwFXlfMGK4slJ3HifPB7OxvAfbXPPWG+5Cw7E76J4J1y3RYiosAM\nzAnOR+X8UCIiNx9wyt0Z77f5hOTcBg6CdoJvHLj6tk7UycP4d2Qwju/ER7Br9GFWrEREjkLo45Qw\nq9yBf88Redx2tD0VNWBglLzXNb/jGGrZ8XHtDyIi3wR2HnuhcxFocVu55sPpwJArwRvvf3GJeK32\nBLS7ophF9q6Pfxd5zDyQSuug9bP2gLQCve+LN0w899FzTLzyoWV/enzFv+4y8eJXrjSxu7vUkTj4\nKuZSQyPsXh9e8qbIW/oP6Mxc8+azJs5d/oXImz4PulhRzJ70iateEXnX3QnL0JAQaAyNuWuEyGtt\nlXPYqWCaEFxLhIjIOwzjs3w91kI3y5b33POnmNhRgDnR0ChtiKeOhB5IxMREE3sGSq2SkKHghsfH\nQ+PlyIe4n9Ex0oLTLxr/rjmA9Tg6R3LrGwqg68G1r+oPS443t/8MHoLjcfeR517F1n6fOFy/2v0V\nIo9blp8O+MRA96O3W9bUkBHQ8/CNwTHa/HJu9+pg2l8hQ6UOSVMB9Fo8s3DvQhIzRV7dSWhHxGZB\ni63iCKx3YyYni/d4bsY2rrkV61P83OEi78dP15vYzxvHcHTDUZE3YCqzIGX8ea4pQUTk/ydrUluN\n1NdoOMz0J6aSU5F+zTgT+/4i9yz7N0Irzp1ZwHKtQiIiv37QWIs5E2M/tCpG5DUwHY2CrdBUGnql\n1P/gOgOOUtT04OEYE99+9It4T89K7Hmzmc5UxllSpCdyIPRSyjqw9wzqL7X/6g/jvrXXYP3kenJE\nRJPHQFclIBn3t7tNrp/lm+Xe1tkIZnu4mbdIjZjnbn3HxH+9+TwTH10jrdNn3g0NwVH7ocW24Vup\n0RRfjnvcwzRPpvxtishb9SLu0cA46EW4z0NduuKJi8V7Otm9Lz7yg4mzhvQTeQUrMeeunTnTxMeP\nyOcirr9ZyDQNa5ukpl7/6ZizXLeFX6/TjQamL2rvPfk+0lGKY3fzljW+munMBDINr7INBSLv8G+4\nflXsWqRZe+PK8pU4Bn/sKyoPQZey6bjU/utsxD3k+8aqbXJPwXVmAtOxltq23+GZGG+OGqxxti4Z\n10ONzEkwMa9dRKff1t7dn32fpQnkKMEa58Ke2/wTpC5OVxP20VF1qCs+1v6dW6mXrkatbKuS+6CY\nKdgPd7ViTjQV4tklbsg08Z72dtTAo+9tMnHEhESR18R0ICPZc7qjTOplBg3GeGythI5Oe7Vc7yLH\n4N5xi/WmPPmcFZwhNUFtaOeMQqFQKBQKhUKhUCgUCkUfQn+cUSgUCoVCoVAoFAqFQqHoQ5yS1sTb\njPIOy3a7qWfDri06G432JdskfSWwH1qwIuLRptvb843I43aL3J4y77tDIi/jSrTq8lbxxKy5Jm5u\nltbK3JZs2aNo6V+3W9rgvfMAWu3PnQcv5A6rTdc7Gq1ZkxdfZuKeHmn3VrhxnYl7WXtYYIpsD7bb\n4JyN45+CZpJ6kaRocTsvV0YBOrTlmMhrq0abmS9rYVvz9nqRd8n50028+XO0Yk9cOF7k1TEL1Rpm\n3TrmbrTxBmXKtq9dz4IylTIH4y+K2UsSEb1298cmzkrCa4OHy3Z90TbP2tU990q6ScwE2Fzmvg6q\nFbe+I5LtpE4Ha7tv49akJC3heYue3ZJYfwRtftw+u+6ktEgNHYkxUcOs5bhdKhFRfBc+f9MWtIpz\n+/KafVb7bQxaxQfMhP1e2XpJhwzoB8rEwa/3mDhlbIrI4xTBembH3dQq6ZAprJ3Sk7XShls0P0Hd\nkkwrp6CzE22hHe3S+vX1B1CPHl/6kYljn5wo8op3rzcxp1zc9PbfRJ7Dgfrt749rPfMBixYXhPqd\nt3a5iVc8+KOJxy8cJ97z4S+obefl4EJxe2siorQszJHC7fi8Hz5bL/L++upiHHcl7uMFk2TdSJ8K\nWtiGB2FBetiiIi5+6xk6XeBUD7t2c9oApyI6iuU8CGYt25wGkTRM0mE45ZDneVitzs3M5pi3lKfM\nx31vOiHnee6bqGUJC5BXvuW4yDu4Gmswt/rm1sJERIMvxdrM19zao7JtPGggzt2NzV+PFEnvbbVa\nm50Nfj1b8iVFKyQb96HxBI6fnxcRkaMN7duR6ahNNTskBTRuFtYQ3i7d4XdE5HUw2mxTPWgMof3w\n/tKaveI9mTedZeKDL/9s4s5GSdHkNr9lNRhXPdY60cFasUsO4Ty8j0l73PhJsK31ZRQx7zA5Ljpq\nZC12LnDs+b8XiFeGz0Fdq96JNZ1bwxMRBQ/AeOT0M9HeT0TRjGo6bOAiE+f+uETkccvU8rWgPx05\nCmrQyH6S5vL6StAvOMWnn0Wdri/D3ranA2PxxPu7RJ4Pqz3HcrF3z/9V7s8vvAFjp53R2rndPRFR\n7BS57jobEy4CrbVmp9x/cfvn+gLsE3JumyLy+Lz6fRlo72csknn33/6aiV/85j4Tj006V+Q9c9NN\nJs6vRM13+Qn1tbtL1v+jZbBfX/jqUybual0h8g68ifM48x7cg2q2xyUimpwJ2mPG33CNHrns+T/9\n3lFbsc91c5V/f89fj9o+95mx5Ez4s7XPL07SXJqLse/h+9LudllP+Z68tQRrpm+wrCmdtahfz32M\n/X5MsKTtrVgOuuD0HFDdPIJB67Trk1c4xj6nWflblEDfeHaOrIRy6i8RUcVO1HgudWHLIvhEoYby\n5y1bToDTnBIzyOnose6JeI3td1oLsWZ2t8r3hGXjGUJQ2kokVajkZ4xHTnEOSJVyA3xP016H+xU5\nEPfUyytCvKehHjUxhFFKa621OWw06jWXa+HULCKiyLGgQ/H7EzZ+iMjr7sbzWVsVe1azVFhK1+Dc\nYxbSf0A7ZxQKhUKhUCgUCoVCoVAo+hD644xCoVAoFAqFQqFQKBQKRR/ilLSmSO4OESbbHLc/s97E\nWVfBaWnvD7LlNmMs2nGD5qGNf/D8a61vQ6t96mj0iB3bKKlHEYloxWvoj8/b9SZaFYdcfYl4j2cQ\nc81gbX473tsq8q5/Dr1FrqKNStKavELQYrft8bfwvbdMF3ncTWrXK1CLHnyFdBYJjEug04nEuVBy\nr9lZJl4LHADaWTtrI++fKSk7XQ4oZHuylsC5988Vea2sjSuRqaAf+EaOiyEXwXUg7gyMkYAA9OnV\nBEv3mZV78RmDWVtjgI8cm4Gs3b68Hq1343IkhWXr27gnIy6Ew1PGAEnfOfLKbybeX4TW5FFRstWy\nl7m4kOxa/h+jYT9ayiMmSbXxllK0CnYyJzFXTzm9C35Ce2VhNebO2HlyPLZVYhx4MTrQgWXS5YJT\nHMaORMu8mw++98R+6fLg64mWTMdJtLoGDZItiS2sZTIsEO2OPd2yBb+rAa2HARFoCw0NkZ/HnVn8\nWcvkySWSNhk3L51OJ7q6cF6v3fCeeO0vV8028a8Po2053HJ+iRiNesGd7SrqfxV5v+zbZ+LXV6JO\n2XQRVzdcg1rmfnWAjfW56ZeJ94xNx3XKuBStpV/c8aHIW74TbfTnjEFbNh87RER/m4P28kXToLrv\n7iad7b6+5W4TtzEni2DLsW33m3D4yLnpHnImPIJQ/2z3AU576enAmhYyRFI0HWVonw0dBqpfrzW+\n3X3helTDXBF950pnOE4BbSnCGAsfgZZdXsOJiDrrQaHZ+TbGUVuHbLfemYf1s5Pdt7OGSzeg5nxO\nrcLaZ7cHuzM6chmjTnPXOSKigFRJ/3U22lmd6+mUVL+mY1i7+LXlFBgiorrtWE/bynFPA601pPBr\nUJS8YzBmQofFijze2n7kDTiKxMwAhSjEcnmoyYVzSewcUBq622V7ffQkUFMCOXWkR4453r7fXoW9\nD3cLIyLqZPe1i7W1V6wvEHnh4+S660ycXIXa1dktzzdkMOYOP9Zm5qpFRHTwFewDQ9gY/H2dXO9m\n3QwXoc2PwkUufeEwkbftpY0m9vUCVW/YNFDKC7bli/ecPQJr8JjpoGO1npQ0gPSz4DDacvInE/sP\nkA4u3FQsMwf7q/1fFoq8fd9jjciaB2e4CsudVdA3Z5HTUbEBx5V9u9xTdndjDB74DHSl7GvGiLxv\nnwcldxhzvNr9taR8Pf3u7Sa+//LnTHzHFVeIPO5axuvZ5qVwyRozc6h4z7wb4NyVv/l7E4dmSuev\ni5+/y8QnVoHSlj0/W+S1FKCW738R+9WHvnhA5NXnY63mdM0gy9nHdghyJvjaVXugXLzGn6d4/bdp\nwbxG1R/FntfFcub1L8Ua/MA115j4njelW+Q5bC+xbCNcL6MY/WniGXL+8skTxlz7XFzlMdTvhrtS\n1HQcd0+jXEvixmBuOxpAMeTPSkREHgGoFb3sI+Jm9hd59rVwNiK421C13CuGj0Atb6vBazbNuptR\nLgMSMQb9YiXdzZPRJzuYM3NAkqQ1cbppQCJeCwpCzSrYLx1A+fNYC3OJtem+NYy6xqmrnoGSZs2P\nLzQFz6m1+ZKa7B+H42tnFOFei+7mHnBql2btnFEoFAqFQqFQKBQKhUKh6EPojzMKhUKhUCgUCoVC\noVAoFH0I/XFGoVAoFAqFQqFQKBQKhaIPcUrNGc7Z6qiWuitj7pxp4g2PgfualCj1EeqPQNvCMQ08\n22NLV4u8HsbVT7tgqom5vgkRUVURuPHc8mvwVRea2MXF4r8x7vWAOHC8g4fLY/ULBT+/9hgsYctX\nS5vfzQfBMZs4BjZaVftkHtdvGH/v+SbO/UDa5dVFgrsYdvkEcjYOfAmebuYFktO68s21Jp44G9xI\nN2+p9eBgtnYezGKyZKW0zuW8TG5/Fxwmua/9c8DvbW8Ht/TgsndNfHzdUfGe8yYwvaFGpoviLofx\n+AHgCiedBy2UA59I7nFyEvik3DaTazYQEcXNx+cF5UFzYMuK3SIvqxm6ADSFnIqUyxkffJPUcelg\nugABA6DTYHNkXRmXtpvx83f8tEfkDcnB+fpEQwMhLs7SW6gGH94vCVzS3i4QZnMWSyvk4qW5OAY2\n571CpX5PLdNGCh4G7YCQIXLO1jBLdk938Fe3bdgn8uLZ+IvLRA3wS5PcVs8gWW+cjc4OjK1b35Na\nKHue/cHEaw9Ab2nRgkEizzcYdaqnB9f6khcfFHkXdIHTnLf6FxMPmnu5yDu87BMTf7Rhg4nvegp2\nsUvu/EC8J68CNWumDzjkMxZOEnlJERgzXK/kyteeFnlf3nK/ifudC/vQoFSpr3HngkdN/OiHt5l4\n7TNyPbHHvjMRmoXx2FIsNSFay3HNI8eCu33kY1krwgfivBoPoP759ZPjMW02BB48Q6B10HBE2hrH\nTsac5Zzq+sOwgA3KkPPXzRt1M5Rx66s3n6Q/A7ddrmqU5x7LXvNl3PLggfIeNuVBLyxqKjje7t6n\n3I44Hb4JOEY3P2mn6mAcdX9mmVp3qFLkuQdi3XD3B4f86E9Sy6qUaaRlNGH+lu2XGnCxw8Hpb26D\nJhC3VLdtS1uZ5lgEs/us3SPXibAMrE+ewdDUaTggzyl4EO4XHyNcB4xIzjFuncvvKdF/cvediaZc\nnMfwS0eK1zz9oFW29QXUtfF3Sm3A3NWwp67dgfk7NCtN5FVtxbzIvg32xyv++ZnIGzwZegRuTF/j\n+FqmDZQm17Hwaly/gH5Yw6PnjxJ5BVugq9Jeg3U/jGlLEREd/wD1pv9V0NSYlSv3f1xPanwKak/J\nWrmXDe4vNZScjaCBqE3/XviYeG3xI9CQ/O0w7lVWp7zfM87OMfGu9Vg/xy6Q17D4e+zfR6fhHk9b\nNFnk1e3G3Mw9CE2cadcgr3pbsXjPyofxLFTdhD3zmEypERaWg2eD6PHQd9z8pHw2GMPswnc+yDRs\nfpZ72ehJySZ+6QborQ1LTRV5OddPpNMFvm92szRIgtj4YcsE9VpW5NW7cV28wrAndHGXzyPhgbBn\nPlmDGvDwokUir6YZ89mP6T8VMc1FsuzGPVhN52tz6Bg5x0QdZicVminndmsLNE06me5bw2G5hnMt\nO79E1IOaXdL6OXyk1ClzNhqZVmhgf/ncVr0T492b6aO5ustr6B0CTZ+yTah7th157PBxJu7sxD0J\nCBgs8tzdof/V0Yq1sK5uG47NssjO211gYr5vSRsu1yd3X6xx9eyecM0yImmD3tmJY/CNCRR5LWWY\nB1FsPe5obBN5dQflumtDO2cUCoVCoVAoFAqFQqFQKPoQ+uOMQqFQKBQKhUKhUCgUCkUf4pR9xNwS\nMeXCIeK1+uNo+YsORzukZ4ikBew+dNzEQzzQDhhk2WZyO6vcj9CinndUtg3OffJGE5/4GNbKuw59\nZOKMG2RrffwQWCBGZaDFsbZI0jnaW9Fm1MOoGb290mrygrvnmbibUavC+svWxa5mWNo1FBfg+K6a\nIfKqc3PpdCIyEvfHbvfnFrSuXhgOATEBIq+FWTquehWtl1kZsm0yilmRhfZD3NlZJ/L2fPkS/sHo\nNtxmLyZFtsMXsTE3cDpahz0tSsz6D2ApvPFRtMEOTpCW5UeOo+07eibOY9/qgyJv6FkY+37JuJYD\n46VFaPjY02eJ3lqJ9syQLNluV8VoTsGZeK1yk7TNDGSt5/3ZNQ/PljaPDmY1WnYQrYLBAf4iz4PZ\nHHuGgFLkYFSPrlZp3+sTj8+oZ636hz7cSX+GqCnJJq7eLuvBkRLUjZx+uE9DEqXtq0co6lJAGtrG\n3Rglh4iocjOuZYJ0MHQKuDVhZKZsMZ/0MKhry84+18Rr3lgv8gYlw344cRzm2Fe3PiLyznnqBhM3\nMiv2JWvvE3ljrgZd8PJJqJ1FP6IuDR0sveHHnoVW+T1vwYq2skHa1Hp64Pqe/yy+94ub5THMemCO\niX38cO8Ovv2jyLvmTNTywAjYeV/wnLRV9fQ8fTbM3PLSL0FaQ3pHoBbx9mD/IFmjju8sMHHqYNQN\nr3CZV5ELO2VBI0mU3xsQgLETPAPXoqIIdLaa3bLt1zMUc7ZyPWpF0GBJf4qvB02qlrWJJwyV9Y/X\nAG7bzemLREQejObCW+E76qXFZWD/02ulzSmMnL5JJFuaHRU458b/xd57RsdZXW3/x+qj3nuXLVu2\n5SL3hgvGNr2YHiAFSAIPpJD2JiR50suTQBqEFBIgAQKhJAaMTbFx70VusiRLsnpvo1GXRvp/eNZ7\nX9c+Aa//Woxefdm/T8eeM6O7nLPPuWf2ta/z7aLfQBfkUHxHMhbItSCmDOus14u9RT2l5BtjzLkt\nkM5EunA9976Odcy2jV+zBHa+Q50kcZ0qr5+fH657ShFij3+QtIxmq9auRtyf1FR5jQZbEMt6LuC6\neErlOcUspPVFhpGPTWgmUso9lXKPwVKrok9jTrQdlbK9/LWIIyyb7WC7cSPP95Wv/dVpb/75HaLf\nvp9AenS2Dn9r0ybIbvbuknvPJbNwDOdewWvjls35/n8g1saG434kkTTGGGOmfRo29711uIe5t0i5\nwPg/8flNH6DsQP/wsOgX5ZX2wL6Gnwce/r2UpgySHe39377NabcflffnjTf3Oe1NyyDR770ox8WJ\nC5BszaF9QliqlCcUv4w9yaJbFn5ov++8+G/xnp8+TVLb32GffKpcysTufgiW6Lw3tu3gR/txH4ZI\nFtxb3in67T+KezeLzilnpozRYQkytvsSjvkRloX3CNl7+wVi3xhi7d0jckkam4TrHFcgnzNG12Gf\nkePGvq/y+VOiH8tZ5qwpcNqFVAogbb2UL470Qn4SlY9xyZJlY4xJvxHPe71keR6aLMfRMMmM/dgG\ne4p8FuutwjgY6sCYZ8maMXI+G/nI6RPGyPK51SqhEJqOc+PnW3vuNB+AlIklT50npIx3qAPPaq5E\nxLOuoYuiX9xUSPuHPfiMvkb8Hb5+xhgTROUuqlrxbD8/r0j0Yxn4OMW5nkq5jnHpleAEjL/42XL8\nRFNpl64aHJ8tC47IlRJ2G82cURRFURRFURRFURRFmUT0yxlFURRFURRFURRFUZRJ5JKypk5KaR39\n0zHxWvIm5KcmXY7U+j3P7BP9Fi6A/GSwC6lADW9Jl5+EVUgDHmhElXN/f1ml2+tFOloIpWVzSnRg\noEz5Pv3cc047gN67XAAAIABJREFUmN6TuX6x6Nddi9RDltfMfuga0a/xCCqlDzQi1e3Ca2dFv/lf\nRmX0oQ6kxLackrKZKVMmzlnEGGOCKWWbU9GMMSYnFenb+7cghX7ZlVbqlxupfhGUbh05Q6YvBpO7\n1p4fvea0l351reg380Y4xgwOIt0+MBCfd7L0L+I9Cz6J1OS/fP9lp33fD24X/W75xX1Oe/t34EQT\nEiQryE9diXS0bb9+12mvukGOi6pdkOYteBDuQ0GxUsLXvh8pzPnSpOhj01eHucPOUsYYM9SOFMiu\ns3DRsZ3OwjJRKf1CCdIVPQelU8nL+/c77Y3zIV9JzJZSxKqzcEpKJjc3li3se2a/eE9aLFLtU5Zi\nzrvPyMr1cctQGZ/Tl+2pMi0FKfOBJKuItK4RvzGUJXsya9wkLJ04aZoxxsRlQ05w8u9PiNdCEiBX\n+PqzDzntniqZXvn9R55y2ksuIB3+mkc2iX53X3a30/7iNYhh5U0ytfTM9zCXbr51ndN+ewvu3Zq5\nMh0+cTnS6Gdcd6fTHhuT8eXvD3/Hab/whe857et/cqvo96t7f+e0U2KQ7mlLOPaXwmnj8Uc+7bS3\nf+uXop8fOTBc/9hjxpdw+vZonzzfngu4V+wmGGjJfZMHMBfHRpBKO2ZJgEamYF0bI2eL+LlynPr5\nYby73UjHD4nCnM28XKa4M4Mk3bFlSC6SjsRQlvNwl3QfSKX08NotkOGkbZL6wCGSAk2hlGdbWmRL\nIn1NbzWlkbdJN8qRHkrDp2NMXJst+gWQW9yUAJIDnZPxbJTkCnHkXvXEU0+Jfj0kc3ru23AwiyEJ\nS3a+dOuIJEnIuBcBbWxYSiSY1nPYq/hbLlnD3bivuVdj/+apkFKKiHys1TyGYxfJ4+s+jTXJXGF8\nSux8xP+BJo94bffPIY9f+lksyMfeltKH/GxyYaF1wnZjZPemeXMxpltPyBT8AZIErVsEyVlgBObo\n8zt3ivdcfT8cpIIrEfPaD0kZ7+p7sacMIEnu69/+l+h34w9u+NB+bZZMIY/KFTS9C7ksO+sZY4y3\nb2Ln4tkXsacu+vwy8drbj2932guKoOPoa5Eyk889fo/5MGxp2MaZkPaEkyz1zR9KCW3RYoz9rmNY\nMxt34DkhMUo+a2zeBFnTyllwHVw8TcbAnnbIHQLDsF/yjkn5WPlzGIPsNsROUMYYk0TH8fYJej6x\n5Gkvb/nAaT+2bY2ZKOzyCSHkvMTr51C33Hsm5EI+NjqKc3Q3VYp+adMggx6KRAz2v1fGsrQG7JtZ\nehSSij1gb72UmwSEYp72N0GiH2VJtRrfxzHx2tW8V8YDfpZMXk7jYFzKK1nSzI5tTTvkuccv+eh1\n3BdE5OE8bRcmvh58zt1lcr1j6RrHZXZ4MsaYKf74fL5OAZbbV0AA7tdACz6P1xbbPZLHPssXPRfk\nfjo8F9d6xCOl1Qy7NXlpDA90SdclP3IWG3Hj88KzYz6y34f+vUu+qiiKoiiKoiiKoiiKokwo+uWM\noiiKoiiKoiiKoijKJKJfziiKoiiKoiiKoiiKokwil6w5M+M26GWjsqSO2NMInd8g1ZvgGhDGGFNW\nAovOiGnQsuXePUf0O/s06p2wPntoRGpdS56GTWFrIzTQrMGMeGuveA/r99r2QHPbNVVq+d75LTTK\n1zyKGg0dF86LfjGkGa96D9rRqVcViH5cKyJmGmoENGyT9XbyP7XSTCQ9ZFmcsjZHvMb1BYqoLkLp\n3nLRL6cAOsf4CNL/NUndbzBpS49UoFbLatdtop/XC61p63noapNmwuo8daO0KKt6GTr5ZfmotdF2\nQGo3B1tR36eDrF9r2qUNatoCnNPGB6D5ZotVY4wpzEZ9iLN/OozjWybtK6OmSU2qLwmKRj2VqOmy\n9ksw2RF2ncK8tGvTeCpQY4E1ymOWVfyDm1C7JDQL9SamBEqN5LzLoal2UR0XtqbLz5X62MAo6KbP\n70D9ELZGN0bWRuL5232mRfRj68nuYryWe89c0S84EsfX14x5OdQpNc9sl5g5ATaFgYHQnbaVyHNZ\n8n82O+3mE6dxTNYc+8mz0LVHJEJL21Isa1n95X3UWgkKQsxqffRPot/1//N1p/3rT33VaS/IhX3l\njM9fJt7z/JdhJXvPbzDun/zsr0S/+TmIN2wb/KO7ZI2Yx7aiPlVLHeJwf7PU1nf/AXO79DW8p6FT\n1sO49w8/MxOFdxBrUvthWRPClQJNtYt07bbOOWltttP2C8a84pohxhjjLoGeOTwHY6fjtPy7YzM/\nvCZE875qp+3vkst94mKsSdGzMD6G3VJ3Xb4FcXfWJ1CLrO5fpaIf2zgnrUJsDAp3iX78b/d5aNXt\n+j0TTX8dNOqZN84Ur/E9HqD1pGl7heiXsglr1DDV0glNkLWSekknP0jz+ZHNm0W/zHjE9kCK38F9\ndO+swlvuc3wNcdwFn1sn+vV2YJ1MnIX9l7tBrvVco4NrAkVY61tkDmn1ezFmPDXS0pRr4vgarg+R\nsEiuNbPJFjU8Bccws1DugVI34B627MN+dcPD60U/3h9yLcTZo7JOyNRCjP3SU2RPXYb27774gHhP\nG9WrS7sGe5t//kRaNW/KwrodQDVsrntU1kVsO4Y6bUFUAy77lkLRr2kX9sD1DRhHhdfIfnZdIl8z\n55OoNdJdIms4LF2HtbyuGNdpxTevEv2aD2Ffvec17NPsZ5Llj8Kqe2AA92TTIxtFP7aH76LaklPv\nxNz5yl3zxHvSf/C6077r59jzdhTLOm91byB2zvw07h1brxtjzOqZiEvLbsLeeN+rh0W/pY+scdoP\n9uHeCdtlY8x6Ky75Eq5PYu+huQaNOCbLoT0mA/WuBvsxhgOstWtwEK+Nj2MPGBQeIfp1duC6R1L8\n8lRhvzBo1S6Km4dnXe8A1qTql2RN0aA4nCPXVeHaMcYYEzUDNY5qt+Ez/qPUKP0H15iMXSCfvSd6\nLvY3YF0c6ZF15fjceskamutMGmOMK4lrxGD9tI+94T3En9jZ2IMExco9w2D7IbxGf4vXpDnzksV7\nuEYMrxN2XRleZ3mfFpwg72Mo2YUPUw3WHqsWW2gazn2wFWMr2Dqn7lLE2yy5/fjf4//P/1IURVEU\nRVEURVEURVH+X6FfziiKoiiKoiiKoiiKokwil8yP4lS0Nx99Sbx25feQiheVjtT66HyZwjr+R1hw\nxxfBsrDkdwdFv9Ql+IzsyyHzGRiQaX4tB5B2mkbpTQEXkMIUMTVWvOf1X0MKFUeSnKxgaQ97++P/\n5bTP/ga2elPvXyD6sf3XIMmukubJ3CRPC1LP/f2RIlVR2SD6medgWRv/lTXG1ySvRpqtnV7ZegTH\nGENymdmb5LWJnQ3L7abf7HHawYkyTZItNZm2EpkCX/kG0j+jEpEuFp2HY634x2nxnuhpOL4T72Fc\nFX5qkehX/kKx077sGqTL+lkpdWe2I8Uwoxyph22dbtGPJRM3fvc6/J1npNVmDEkDfA2n/NtpeZyG\nyNaifbUypfXseaTwrr4b1qKcQmmMlEOFpuHetLxfJfrFr8Sc7TwGO/Sqcoypxi6Z4h5A6eALC2Ar\neOQteS2L1mH8tR7BfMm5ZZboF9mGMTHiwTXitENjjBmg1FWWX9gW3ikbcs1E8rM7kc5+5Tpp2R4Y\niLjVW4nr9sqWXaLfT++AnMftxnU7+sox0W8lpVF6hyF3u/5/vib6XTUXkr7XDj3jtEt+/77Tbjoo\n5+/m717vtKu2IMV6VYGUds59BP2O/PxVp22PiwM/+B+nveTRLzrtoy9Iu/GjJJVcUIjxc9OPbhL9\nOpqwvqTnytc+LhzjElZkitcG2/uoI+QhcQvTRD+2Ex1oREr0FH+Z65y0kuRBJG30XJSptBf+Avvs\n1Csh04ibh3jQfqJRvKdpN+JBFElPhvgcjDH512HOjZDcMHahlAtw6vBAG+bbSL+ciwEu9PMnSZcr\nWa7bEy1z4rTlca9ct1iylXETxnTGjXJ8t+yudtquVEja+lplqrwrCvcu/TpIOIMPyFTnOJbmkNw0\nmmK8LWvtICmrXyD2Jl6vlAT2U+p9fyPWSI6b//tvrC8jFEczr5PnXvUi1ueU9bn0Hrk+sdzL19S9\nVoJjuFLKoHm9Hx1CzGebdGOM6WvEtT24G+fUvGWP6PfJr93otE+9grgbv0TObX6thGQqbIfe0ynH\nR3QS1tkQitvr1sm9ZyzNZ3cZZDcdJ+W+rvoQ5va09dDnDrbLv+spRxxZ9JmlTjs0ScpDzj0BWcF0\nqXD1CSwvLtkh15rZV2IvMJf2em3F0rL4hT9uddoP/Pxupx0UJefY/h897bTX/uAbTrt227OiHz9H\nBEVjL9X8Af7u/M9+Xrznytuxnyj9E9bjzOulbDuG9tNl/4BV+LglMZ//CLzn3VXYB7H80RhjPNVY\nT9myfXxUft6u3+9y2rl/utP4khgam9ZpiP1YcAzuh39IoOh34dV3nHbKujynHRgmZVLuNuzdI+Nx\nbYd65F42kuyvg+PwrMLPGUO9UnI8xmsBPQO7MuScCIzA82f0TOz92w7LZ9aq5xFTIqZCmhxuWXP3\n1yMOxZOUabjHkuFYNtO+hvf/thQnguyg+1vk+sK44hDruJRGeIzcX/uTfXZfLe7dsFVuwH0W8yog\nDO+JyMc17KuRzzuDTdjH5HwCUsT2I1ISPtKNsZl9C2JN895q0Y/HAt97W8LHskweF8ZP5sKMXsK2\n2xjNnFEURVEURVEURVEURZlU9MsZRVEURVEURVEURVGUSeSSsqbdf9jttK/98SfEa6V/2uW0Zz94\ntdN+/yfviH7zr0I6UeNOpKRzapsxxmSuXeK0BweRFtZTIR12Ism5Zcu/kGq5dBEkRXb17WOVqAj9\n0JVXOu1eKw3KFY+/lXkb0ptKfi8ro0dROlp0KFKjt3/nedFvzpWomh6ZjNSuOWtkenDKmjwzkbgS\nkWJmV8uOL0L6XDOluRdvPSX6RX2A83T3I0W4er+UuqTNRopvdiJSujosVxN2CMq6CffOXYN7n3/P\nfPGepvdxH187cMBplzwoP/uh+5F+PNyFlLUyKw12xiKkQQfHYTwmRkkXphUzcB6VzyMdPPVymaJ3\n8e9IX8z4wc3Gl8TOQSXyjpNSnuBKRrplf72b2jLtsGg5xh1LmWwZIKcXdpIUwpUZJfpx+iy7hGTn\nIL31aKV0RLtuIWRmIeRsMz9Lyui8g6jA3zeE9D/bzWYKV7ivQmovOwcYI+MGp/7/x+f5T+z31bfc\nDycs28Hn9a/BXendYoyzn70gZUh7/vtHTnvxNz/jtHPSZbX6sRFcw5KX8XnvdewQ/WZnYbz3d0Ai\nkUCytS4rbZ7vN5NyhZwT37v9Uaf9xZ/e47S3f6tY9Cv8MuSCx5/4g9PmdFRjjAndiTWJ5Qm1/yoR\n/ZY8/A0zUXTStfALlvE0rghjv5dkhX6W01nyPLgeddXCDZDT040xpm5LmdNOWAF3pWHLRSHtWji8\nsINUQDjSb8NzosV7+Jh4zrMrgTHGBEYibbf7LMYHS7P+91hxHpwabUsM2eXHO4wUcna8MMaYuCLp\nUuFr2EWj/biUGgfG4pxbycHHlSavTdZmSL74Mwo+K6W2btrHxKQhDk+xjBrTpl7rtC+egAwwaTGk\nKQOd1p6I4ndwDNbppn1ybWZXCpYr9ZTKOJRzJ/YtjbTmslTNGGOSLyfXI4rDtmtSzetybvqS1g7M\nsawI6RjC467lAFw6A6Nlv/p3sS/1jmE83vfD20W/tx/H3nZ6KsYmu4IYY0wiOSHO4dg6jHi14Ctr\nxXsqnoUEJiAY63nalfmi39nfY89b1oi1ec3mpaJf5nzEipSlWFvHxqSkK+tWrHedJI879tdDot/U\nJRO7R33/GcT1tXeuEK/FzoIEaHQAMadpV7Xod+d9cG8aJRfSui1yrVn6Tch56s6g5MHBHbLfxnzI\nfVPI0Yulqw2l28V7XBQ7QxMQU3urpAy1nRxGg8gB5+ZbpcPatu++4rRZypR7tZRJVW+FFMyf5BNz\nviQ1aMuC5Vj1JZ4KxBGXtYZwbI8uQNxlp19jjJB+DHVD2jI+JmWnrYewxrUYzG17jQuk9c/lwn5m\nfBzjw91eK97TW42Yws9LnWelixiXtJhCe8qBRhkno2bivg3U4TokrpDPGf60l2DpNJcXMUbKWFNk\nqPUJLipVERIjpVzN+7EexBRiv2m7OhmDcdZLzxohEXKNj56Kuc3PaizHNkaurSwV6inHWhhJ67kx\nxoRlYizwXoxlUcZIZ9iG97AW2Nf94utwQ826Guuxu0yWRgij5yR2Gx3qkFItdvP8MDRzRlEURVEU\nRVEURVEUZRLRL2cURVEURVEURVEURVEmEf1yRlEURVEURVEURVEUZRK5ZM2ZTd9FLZkdP3hVvJY3\nD3q56ndgBV1o1VPpLm5x2vmfXea0x7zSRqr8Bdi29jdDsxc9W9oT79561GlftgG6/fIj0FfnWPUm\nwoKhO+TaIqlL54p+fV3QtTV/gM+LzpeWZ9M3w0Z826NPOu2iW6Tt4fgodIPl/0Cdh6RVUmvYXQ7N\nWlKS8TmuBNT2YB2nMdKmrZ/0kIvvWiL6hWdAv9dP1pNc18IYYzyV0NZyPR53S4/oV3A7rn1wBOzZ\nkjOhGy55+6/yGKhuyLN//57T7j7TIvr9+Neo/fPdb37aaa/43CrR763HoRdeVAQN7/lKWV/jsm9t\ncNoNtdCdHjp5XvS742e3molC1KKYIrWQvfTaSDfuZ/LabNGPa0cMkb1pbJ7Uk8dOwz0texZa8LAM\nWXOm7B+oSzTnAWjeD/0a78mzBnR4NsZRJM0r76CsX5FcNM9pR81AzYIuywqea8aEJEEra+tFu87h\nvrGFHdfGMMaY5p2oS5QrSx75hJQlqKEyNiLtw9uPQNN6+0oUo7BrdgSQNvnE439z2tHz5LXe8muM\n7+WLUBujpk1qZO+7H/Vetvz4Taf9madQ28ZT+bJ4j/scPiOSbJj/9NN/in6/ePMfTvvoY7DF/v4/\nfyn6tZw74bTzP4l5OtAtj3XJNNhns+35D772R9GPLa7zFsh6aR8Xjt9NO2Rdj45jWEPil6Dug79V\nl6KnDdrm8BTctwHLgjlxDf5W+0Ho7HmsG2NM3xg01embUKdihOrCDLtl7GfLXi/1m2Ktn1UvYJ4n\nrsJ1tbX1cUsggGeLbLsmR3cp7ml4FmKKXX+gk+J61izjc9i+N5jqPhgjzy1ijtyDMOOjiJVxc6HB\nt+dsNNUt83pRsyJz+i2iX805zLPUWauddtUHqI0RNU3uRwbb8HneIRwPW4QaI2uExc5HzRQv1ecw\nRtZOS70G843rEhljTOxc1AXgenW2DWpIory2vmQZ1W6p314uXsu4CnUBhj24Fq0HpNVtJNUmWEU2\n5X11cs8yIw319EKolsWR52R9FlcQrvPOs7D8vYNi+oGfvSuPdR5ixY/u+jmOzSVrM95813qnnboy\n22m7T8s9UEga6hl0lKNOhHdA3utQqnswPoa1NMBfztmYwgnYmBJcwyc4Vo6XLrKmDaOaT/86LGtB\nfuFq2GePDWMeROTLmnqnf/Wa0w7NQfy57Ea553XTnuEPf8O6+K3ffM5pX/znWfGetI2oTZN3Jz7P\n09As+nFtuz1v4pnmqoevEP02bUA9xqqXjjvtV373tuj32SfvddqBgRjDQwNyvxRT8NGx7OPC9uB2\njTWOPW0HMf+CE+U6xmtFcDTGfm+9jD1h6bKmzf/FtoBvO4o1M4hqUvE6G5Ym97XROdlOu/kI9p5R\neXIchVPc9JSh3k5Ypjw2rpEySLV4eIwaIy2Z26huXNQMaZvu7Zd1o3wN12vsqZH1zcJz8KzWW4Pn\njqBoGaf8/fHvALLLbj93QfRjS+pQqkHZ12BZok/DNWjeiT1XzHysQSFxMm40Hq8wH0bENHkf/V04\nvn5a4+yaMDk3YRPCa6a9fvLzaATVp3WlyM+LmX7pmKqZM4qiKIqiKIqiKIqiKJOIfjmjKIqiKIqi\nKIqiKIoyiVxS1tRTiVStlV+W1n+NZDk10AA5TKhltxs5C+lI7iqk9tm2mf4uHMqsL8D+rfp1aW+X\nnQC7rJR1kGMkr4at41+/8oJ4z5zsbKfd24K/29MsLdTY3nvUDenDcKtMt6567z2nvfpRWHN3nJaW\nzpF5SGkaYbuut2T6rSEJxnSpvPEJI5QG13pAnvNgE9L7ut1oj22XFshsLTrqxuddbJbptDMXIw06\nMw8pZ2NDMoXv7IuQdBQ9iOt+4KmfOu2UDVJus/VJXPeFBUgframXx7B8BiRKbJd6+E/7Rb9QSj8e\nIXnXqFceq5tkZ2y1ufqKItFvqIvGiY8t7ropbTlxdbZ4jW2xoym11N+y+Y2Ix/uGuiDJCgqSafKe\nbryWfStsOGPipWwveUm10+4sR6rh6m9f77SbDpbyW0xAGK55RhFStD2ec6JfTAwst0fyIJXrrekW\n/Thl1E1yCZZTGmOMXwjSZVkSEmtZ9n2URbSvYOtv92lpzbhqDaRcxw8gnXZtvrRJzX70Nqd99CnI\ng4baZJzi8Z19C+7j3M/eI/od/SkkQRsfgH3o2BikAHYK7uwvwvLz27f+0GlPS5HX871v/wzn8b2H\nnHb5W1tEv/qjiEu7z73ktJdPny76pcciJZXXkNtWSPvVmJxsM1F0lyHVN9xKdR7uxD1oIote23I7\nYRkChOci+vVb0pHas5BJzbwOFsfRlm3kEP1dlskGkZQxPF2uzT0XkZYcRWnDpU8dEf2CWQ5JYyxq\nljyGtn1kaZoLqYhfiDx3Tu/lFOqBYpn6H5rx4anrvoKt7F1WmnzUbJxbSALiRXiqvN+dJbAzjsji\nlG8Zp8KgGDHBLvzjwqG/iX6cej88jPvI1qK27TfLUTwXECvtMdd5FBIHF6X/B8XIlPSYm/C3giJx\nPO5SmeLeRNJvtg+10/B7Lkir7okiqkCOx2O/2uO0U+dhvvkFSMlr+kZI8T21mBMnXjwm+k1bhD3m\n1OshP3FteU/06ynHPdgwF/LtIxWY57d/6wbxHpaBfGEW4rMt0whyYYz5+2NcRs+4KPodfWKf084m\nm9/D750S/ViClZ2ItXT2Zin5HxuWqfu+Jo7WcVsmEEOvteyHrf36OXNEv3/9dpvT/uTjkLIee0HG\ns1RaQ9iy2I6p7SSJ+c5T/+W03/8V7ndkqJRSJNI+8vzvdjntUzU1ot/8aZDkNnVhzIWlyhhd+9YZ\np93Xgv35DZ9aL/pxHAqKxHw79qyUfp2pRYz+4b+vNb5kzItryTJyY2RMiFsICVtfg5QOsuy98zTi\nFUsojTEmLBryWq8Xa1JPg4yNISRXbdqFOeJP+8H/kI2H4X4MkW26GR8X/TKuwd6EY/AAyUyNkaUj\nuum6pF+VL/rxNeP91kCzJXVeLsti+Jpmuk623XfbYUjSQqnMgSsxXPTzNGE8DtK4dSXLfk3v4jkz\nZSOe90LipdxttA/PnCmXox/b2vtZcmy+hlk3QR5or5/htHYNkbTalneHkNzSj2LqYLP8LoN3ylwK\nwh7rPdVYTxM/RG2omTOKoiiKoiiKoiiKoiiTiH45oyiKoiiKoiiKoiiKMolcUtbEldEDw2TqayK5\nYYQlIeWsrVhKdvoplSdhEVJLD/1NVrifPjfbafv7I30o0kqRrTiD9MCmnUiJYneFB/7wBfGeylcP\nOm1OH3XFyRTCrd9BFffrf4rU0pYT0pWH0y53/OAtp33lj+4V/WreQ2oppxgHW6ld026SFdp9DbtG\nsVzCGFklPzoKx1XTJNMSC+ehUvXhtyFJ2vBFmV7Zuh9pk8mXI3XzrV/I6vL59HePPwm5URzdkxDL\nQYOlTC5KRVt7zzzRb/fPkHa6+4kPnPbAsKxyPisHY/hoGVKOb/lvmXJc+ypVbKc01rQN00S/noud\nZqKIXw43B8usyQSS4xCn//XVytR6TtPjiuLukrdEv9FeSHs4BbWj+E3Rz1CWJ1fPbzmCGJC0JFu8\nxV2FVP3Gc7g3nPppjDHefMheKp+FtDE0J1r043RKrgrPbWOkrDCU4lrT+9Jtx996n685+xvMg4Do\nYPHagT1wSbn5Bzc67a3f/LXot/Y7cFea8xnEqe9ulvHnvm/ACcY/ENKUYz//k+i34BtwNPvxHV90\n2jdehxgQWyTTiqvfgLvSQ1+BzCpv3dWin8cDN4spUxB7vQNSPjbv83Dym+nGfH7868+Ifr98C5LV\nz6/f7LT/5/Xvin4DbopfUonyseGxbjvF+YVg/MTS3BmjGGyMdApidxvvoJSPFVxNNkU07xvela4H\nHONT1yNOdp6BVCh++mzxnpYmxOqIbMglEijWGGOMHzkv8Xxj1z5jpARylNbIxnfksfZdRFwKCMP1\nSiWnE2P+Mw3Y18QuwP1xW2n4QVG4HoEkxazbLvcCLKVhh4kQy4WEP8NdX+207ZR1TwXWEE81rlMv\n/X/0XOnywBKl8CzEx67TUibGrjXth5CeHmG5PzW8Uea0s27F+Au1pF/sqhNAsnRbTttkSaR9ydbv\nY00as2QHq++G1LGGnJxi86V8peT3kH5kXgupQlKU3B/ydXr7UUhBZ66bIfrFLcK4Klyx0Wmvp/g3\nMiKlXiyF4D0qp88bY8zeH//Lac/91GKnfeZvUoIVQS5Ptccwz694cJ3ox/cmfgXm/alXpJPgkgdX\nmomE3cOe/750hr3qetzHHnLFaXFLCajY31GsXP6ArBXAaz7L8YY9cg/C+7sGKuNQ1gjJxsO/+Yx4\nT1AE1ll2b4s+liz6/eUp7G+uKYI8fv//7BD90vOx7ubeBlnrqOXYw3IMllfacqpPfuMmM1EEhmE/\nY7tlcvzjuB6RIxfnDpKcsAS+3yqD0XkKczac1i7b6YtlopHknOMhSe9ov9yLeIewdkWS1K3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DEh2/iUsRGsaRwPjJHZMjGF9Ku3TDAU2Q/xS5C5MOyRvxq3U2Z0KGVttB2R88WP9gh592BPONCK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d0hjJc+DuULHimHgtaxmo6M1HIY9JnivdQGo3lVlxeBrWdvQIOf+CifLPTi32ectU1RBac0wv\n5/cbY0zSPPyO8g/wO9IvKxR5NSQJbCOq8+GPZNf+zHyMV3c9akC8jSLcuh/3hR1xmndLl4u0RfI6\n/InAYBSSAJuTVmTBl0uU7PTgyELkNe+G/CLQRt92k2QliaRM/0vqRu8rugrrqpncTbw2SQ67s/Dv\nsMs9WcYVQdKq1CI5Nj3VoP06M5HH+48xxnhIgualsfbY6rid9u1vpMVhDFjGZIwxIUTT9tHc55pn\njDEOkny1HsTctMt9Ey/kPQ7jEJEjnQZZipO5DDKLPtrv2I3FGGNcOV/ugDdsoZRPsxSHKd+dZZI2\nH0rSU54LaVFyvD0ksXERVbzzjNyfWGbsb2z/F86Rvn4pg5tx+wwrdjdB+nDqTVl7XPHYG9wFyPPZ\n1lgMyfO6a/DbvUekNC1uGs4Ppavh2DmK5Iu9tjHcV4o62U5yu/haKZuZd+sFVnx24z4rXnrPxSJv\n43KctWdfDxl05izpLnR8Dc4zDnLRSZiRIfIatkrps78Rmc7nB3lvfCQZ4fYK7TZnQJbrcl7+vGUi\nj6VmzfW4T93dZ0ReWBieB8LDEXs8eJ6IjJQuQoODmINRuZD0BoVIiVx7OfarSK4BNicZXs9tJ/Ds\n4uuUzxpJM3F9rcWQkUQXSMlO26km838Bu+tlOO0B3CbALgtmGfOB7XAgG5Ypz+Sh1DKBpX5xU6Sb\n3qo3UR8uWjzViptJflx2Wp4dMlLxrNLcAEndlOsmizx2/akkx6c6m8vbpKsgJy1Zh3qQXCilg8nz\nsTZPv4c2ARlz5XNq1SYpHfc3Sj9GTbBLqApvwdkinlwme+qlrLztMOYqnxXtkumcm/C8y65lXZXy\nHvLzWftJzOH2Y/ieIwfl+h2ejzXBe/iKXbtEHrdlcYRg/qWNsc05ctwMIKct+zmli+Yju60Vf3hE\n5GVPyzbngjJnFAqFQqFQKBQKhUKhUCjOI/SPMwqFQqFQKBQKhUKhUCgU5xH6xxmFQqFQKBQKhUKh\nUCgUivOIc4qBPQ3QTr38y3fEaz945ddW3FYP67GeRqk9u+9+6D1feOYDK/7ho7eKPO4HMjCAnhCr\nH/9CpI2/EH1mnn/oP1Z877N3WnHlJ8XiPUvugOavfju0s59/tE3kcd+MDgsqoAAAIABJREFUY3vW\n4XoGpcUn29IW3Y+eF/390lIrYSes0dg6Nma81Bq++RJ+49irv2X8jUjSxHXYdLrVG6CBDE9Hn4DO\nEqkbdyZBM5qxBFr4lmN1Iq9xJ/rstB6A9tXnk7ZhJ6uh88z1jLHiftLFd5ZLLby7CFrQhu3QgxdX\nyWt1R0NDHjsVGtTyt6S1aE83xquULMBLT1WJvDDSIU759mwrdrhiRN6Zt2BtmSHdNb82/vn421Z8\n++1Sk516ETSTIaTvL/5E/t7cmdCunngfa3HGI9eKvHnO71vxhkeftOKZP7tD5IWGQs+88ke/teLw\nZMyjwUHZpyb9AqzFTb/Bb7Lblu4pgYZ3Tibe8/Plj4m88lXQj77y7aeteNlPrxB58ZnTrHhgAOMe\nN0z2QvriqdVWfO+LNxh/Y8CH+R1k69dU/h56t2QuxRprOy1tKZPnZFtx2ZvQsSbMkn0C6jeV47uc\n+C7uRWOMrAmJ0/AZ3kbUrJBo2YekqwJrk+uG/Vq5Pwv3zSi6RPbD4D4VDtLZhyXIeZF8KeZwF9WH\nmInJIq93CO17WWveckjWv4RpsFisXQdtePYNo0Rewy70LYgagXXkrZdrm3vBtJNVKfcFMUbOpU4a\nG9ZJs721MbK3VkAweqyEuuRYR0ajf09d8R4rjpskNdkD1LMsOAKf0WuzTXdlo262UL8dT528vvZi\n/N5s/7diM/1kR9vbJ+uUj/7dWQyN+9kGOb+PrMAaC6WeHdOunCTy1r2EHmvjx+F+vvzhapHHZ5BL\nHBiT9nqcxbp7Zf+nvBmo/x7q+xM9Wta2wBB8Xt0azE23be3w/Ob5FxAi7VJDqUfQ/s/Rx2Xysoki\nr+Pk0PW5GLcEPfOCnLJHHdv0th9Fb4LmTtn3IHkKat5R6sEy4Xo5hgzuexASKb93oHfAnv7f/07z\nratE9lS4+Br0xwmm9cc9FYwxJpDW6dypODdxbTXGmMYO6gdBvaY2fb5H5IU78F25iZgvNZ/J3oSO\nJFmH/Q1vK64xIPir/58x90oKtNkwtxxBLQ6n3lhdXbIXRXtVmRVHpWVbcUSEPLT19OAc2NKy3Yqd\nTvTQ6O+X9Zr71jgj0POCe9EYY4wzkfpSzMd5tWGPPHty/Y7KxfjYn7N4P+H7EmZrgxhsWyP+hHs0\nnmvaS+XZ3UM2xJXFqPlpBbL2xIzFv4dTLXMNixV5XeVYP3UVqMmxE2XPmat/gnMg9yHqbcaelH/j\nGPGeqs/wTBSfju/d9Jp8Xhw/BdbI/IxQWJQp8va/j76XIy6g9yTKNdVHVvBp1IfTbpueMl1+vr/h\nof0lJV8+q4proedi7l9njDHt3N+M+paFxYWLPO6ByhbUUbb+ieUbsa566PpiIulZL1Lab4dyP7gm\n1MMxmfL+xbnwbOsKw+/os1lkBwShLvHYcQ85Y4xp3ov5HUTviXXJ6+uu6jDngjJnFAqFQqFQKBQK\nhUKhUCjOI/SPMwqFQqFQKBQKhUKhUCgU5xHnlDW5UkCtuubOS8RrB/8KC9qi++dZ8bonXhd56XGg\nbP/l0w+tuK1tr8gLCgK16PRnn1jxzJuni7zYEaCg3kw2o037IZNZvWq3eE/5659a8cVjQGEbmSFl\nAHm3wUb3Xzf9yYqXTJF23rH5sP39xbU/t+Ien6SW/ugPd1nxs49AgvWdp+8SeQ+/+pgZSoQ4QcGK\nGS1pat3VoFYx1a+7TNJuG8naN24E6JV229WQMND7QuNBK3PaLGJj2kHrLFsOSdGY78N6udNdLt4T\nGonPy74a8jbHxjKZRxIMXyvkDe5x8rcP9IGix/TRdBudjX9j7WZ8V0CgvL6YsfLz/YmE6K+2lXVF\ngfMflgRJ36atO0Re+ghQPpe/D3v4b6RJa+WkcZjHSeMhXXj3B0+KvOufesyKWfo30AdK4+4nnxHv\nYTvX+Hj8psZGOd+uIjpqOUl3Xv3O30Xe/OtmWvHVjy214ojYLJHX1gJqaRvJQ9yjJPU/Zr+kHvob\n3TVYb3YbemcqamDpcsgEgl1ShsT03OSLIWnY/Yoc75xCjF1dCejxPTZ5i4ttnQ/BujWR7FQHbJaX\nMRmQJQ0OYr54OqX9bJMX481yUK47xhjTTfbKaRdDuuSIlTRYppCyzWh4qlwfZ8nyvmCa8Svaj2L+\nJM2X1rT1myGbjciGRIXvqzHGxIwGfbu7GrUw1GYn3bwD+xpbYdtpxGxXXboeVGG2x41Kl7bNZz9A\nrYgiK2RXnqSQ9zQfQF4W8gID5dg0HQP1mOWp4cnSSpvnb+JcrNOaVdLOOn56uhlKRA2n33JCWoa2\nkp1xWDquf9RYWS9Yjrf3MOjwjTukPKGkjuRvuJ0mNEhKMy67CGcNB1Gno0eB5h1gszftLMU1BJPE\nJjBI7rlVn+D60i6HhKN5f43IG6R9sd8Dqnnl4UqRl5KO+zdqOqRabUfqRV5d5dDJmjpOQ5LJtubG\nGBNAcu64qaiFsTa73ZZ9+P0tJHna96aUAI1ehH02mWzt2eLYGGNC6CxRdDMkuSwrcyTJtRORibV5\n5m2ch9ja1RhjVr+4AZ9N59eadSdF3tgsrKsBL8YwP1nKSNgyPu86/L6yd6Qk+sSRMiueavyPIJJj\n8xnBGCmL6CNbbV+HPKelz8ee1FGFMWksPiHyEosgMWW5UUPVRpEXEYM5Ex6OPYmlTKGhsh4M+HCv\n+4JR1/v65PkmJgGSuaZqSLPDU2Wt5H2Xf/tgv2y1wO8LdWMd9LbZ5L22Fg3+BEt8HRFffWaZeDc2\nZK/t+qo+xFixNK/vkJSdOlNwTqunZ4l0m1W4g6S7LCmPKsSzY4CtTjpJpp0+D3PKsUauWR6DKKrP\nITbZeEIVxr6nFvUl1PbsVLYB++eo2zE/ztBZ0Bhjeuk5c7RU7/sFmRNRO/q7pIS2+DWcoxNHoZa4\nR8p1wM/S3SRp6+uUn5dzLfJqt0Fqa7enHnMP9kUvPaeyVHR0gazrXANc+TjTuLfLfcxLv/HoWZxf\nJyQOE3mxJLnjlht22ZmXxqeX9k+WkBpjTHjMVz/TGaPMGYVCoVAoFAqFQqFQKBSK8wr944xCoVAo\nFAqFQqFQKBQKxXnEOWVNzadBg2V6qzHGuMeAxnR2LXi6U66RHe49RF8/vfUNK247Lqmg7BzhoS7G\ncRMkVentH75mxXVtoIs98M+7Ec/8hXjP2S1brLh5F2jimdeOFHkv/BCSrG/fAHlN4gWyu7PLBVrk\noolwJhhx6wSRx7KZ3CRIXtps7gWR07PNUKK9AjRjpkAbI6UG/d2gY6UustkNES3x6HJQ29LGSep5\nSRXo+8PcoMeVHJNUshnfgusRO414vaAYu+LzxXsCA0ELK9myxnwV6sjJiV03WKJjjBEUzxZyp2KZ\nhzHGlO0ss+LsFMgYInOkW5OdsudPXHLhZCsOT5PU164uyBNylmH9/frGK0Xeml88a8UNtHbCk+xS\nHtDmmaI5aKPE+nyglBddirUU4cZ6qapaL94z7jqslzf+8pEVTyuQ841pzq9tgtPJnz/+p8hrqQIF\nvG4LZGZHtksXlOm3QB7ZUw+a5ZrnpRRo6fcWmqEES3HcRbIjfeMOrBHXcNBuPTXSXaR1P+jDYakY\nu4xUSS1leu6068dbsa9Luud01YAWHBwOGn1kAmpvw2FJm293gH7M9F5HjKT+sqtQGzmPpM7PE3kO\nyms9inoVniEldw7q9l9P9ytmlE1SODB09O0UkoR0nJa1nGsoy9HsLgUdJVg7teQgGFMo50RJDepp\nXibGI/nCXJHHDjtJlRjPfTtRG4rCJa3W0Lj5SMp56i1Jo47JQT0MvpBkUkmynkbnQ+aSlHK5FbPT\niTHGnHoD+3FjOe5f8qgUkWd3s/A3Bkl+yTImY4xJysE4sMvV8b3S+SWCqMrZCXhPa5eUDibHYK8Y\nkY49c+NRKR9p4PtB0oDjm7DeMpLlHOntwZw7VYP9M/NkvMgrb8D6C1qLcSw5K2VN7DpVOAXzLG+G\nXLPtR/B57jFYf4EOeawsGC6vw6+gdR49XN6XJpIrndlfZsVT7pZS+c4G1NepF0H2bv8dIxd8w4rL\njrxlxXZ5TdtxyB7ZZYvPH946Od/CSL6ZMivbio+vOibyqltwTrnwekh6S9+XUv5py3BeYLesLcel\na+bSi/EZp97Eus+6tFDkOXZLGaW/wffQ2yL3J5YxOOmsYpfUOxz0rJAGuVa3zWGtox7yiT5yiAkM\nknLBLgNpoq8DkkuWx7AzpTHGhIRhnQ8MYF2yE81/fwdqQJADLR6cifIsFpuMcezqQg3whkmZVDs5\nLkYXYL1FJNnsmoYStBZbW+SZZYDkvoZUII2t8ne4nDgHJCbgXgbYxoZlvWPH4TkhZ4kU3dXtxXxP\nnACZii8R6ygsQu474fNx/3xePIvanSNZUslSUJYRGiNlqGu34tlp3sA4kTcwgM+oWY19JiRE1qGU\ni6SU2t+oP4RnZF+/zWUsNNSebowxptXmKpdz+SwrDgrD5/XaWkZ012H8i9fhrJJ2Qs7bgjswrp56\n7K0Vn2BNtGZK58ysKyFJ66rA9/i6ZfuRDg/qy1v0t4IW2x6+mM6iLNOzn8/dmTFf+lpnj5TwxQTK\nOW2HMmcUCoVCoVAoFAqFQqFQKM4j9I8zCoVCoVAoFAqFQqFQKBTnEfrHGYVCoVAoFAqFQqFQKBSK\n84hz9pxJLoLO65ePfVe8FutC34sH//0bKz7+5ociL3PxcCvuqID91AefbBF5N+cstuLAMOhFI2Oz\nRd6sReipkbsAFt7NlbDbDYiTdq712/DvjMXQHa59SvYt+c6/f2LFZ1bAVu+5X7wh8r79LLSHHaQj\nc8RJTWJEBDTa1/8F2m27HfB98641QwkHWeuFjpU63YYduDeuAvS5OPHGQZHn6UU/lbQCWIrZtbSj\nZmG8Y0mHnku9RowxxtdDdoROaPmcTugpT3/xiXhPZBbsJvd+jusblict0etaMc9CSD/vsFl8pl0K\nrWr9QejT66uaRR5b+o0kizf7bw9Pkb1g/InMJSOs+PG7nxWv/eKNX1nx4CB0qxXbZb+XYQuhwbyJ\nLDqj02S/l8NPYQ07MzE2zZ1SW1m6Cp+/7RPopi+lnjhzfrpAvOfx22GtPZks6Q9XVIi8GYm3W/E9\nNy+y4o2/+rfI4z44qWOg6b/qiYdE3o7fvWTFXupDdPmdF4m8yLRz29t9XQSForZVrZR9XCJy8N3B\n1OsnfaHU/zfTXA1Px3uSZkr78PqdWNsOB3TVDft3irwQ6m2RmI/6OjCANe/KlX2YnJH4vL4+9FUI\nCJDWwF6yvma7xaYDss8F28zyb7dbePvaoFnOvxk2jJ1npXY96/pRZqjAvaWCbZahkfnQG7dRT47e\nVNlLgMeNNem9DbIXRUY89O+su28rlhpvTx300SHRqPETZ2LNd5fJe7T6EHpMxJdgnc+/aZbIiylC\nHY9PQq+wmtOyr1P6MOzh9fWfWjH3NDJG1s2CpehVxRp+Y2TvISO3D7+g/RS+r8VW2/pPQ2vP2vOC\nVNmfwNsL/XoE9Yvg+WyMMdHh6CnC9/2HD1wv8kr2oW9WG/UzOlEN3X5Vs7xPFy7Amp1EFrGtJ2Sv\njX7qaRDooDpk+7zZE7B2tq1BP8HYSDmHCydgr67dhOu29yarbMLvGOnnll5hSThz9TTIHgGx43FO\nCaIz5TGygzXGmIQ86jHRTjWvUP7eyjPv47sa8V0dxfL+eb34jNgcjEd3OdZf/CzZq69q9Wkr3rcd\nfTJmLJU9HA0dWRvoXDvjhmkijfeZ0Fj057j9oWUir2EL9t1c6tHgqekQef2dsk+DvxEcTv25EuR9\n99RT3w/qzdNv6+3R14dzX2cNakdPg1zbIVG4H410D8NS5Pf2Uq+byFzU9fZirKuRd8i+frz/eb3o\nF+aKHS7yKk68Y8XuFJztgoPl+cPjwboKCcFc8hpZy7nnCaPhgOyRxT1tEvzcjsaRgLEpKCoSr3Gt\nZRvrgS/k9YW6sXedPEnPJtSLxhhjIlqw/obdjH569QeKRR73iWktwb1s2oN6Wn1qg3gPn5P5/b2t\nsmeIk865zbtxnokZJ+3quXfV/CS8p6NW7ov5l2KO1G/CuvR4ZZ+WCFuPE38jZTKep5oO1IrXIqnv\nCp9h2o7KZ6vOhjLEZ7EufR2yL2fDbvR1Ss/COSMy1y3yjj292YpddMbinjihsXKOeKi3ZFcpriHY\nGSLyDB25fnv3rVZ85GSZSOuhs1l/F+phaLzsJ/jBuxusePFlM6w4IUs+p7buk/fWDmXOKBQKhUKh\nUCgUCoVCoVCcR+gfZxQKhUKhUCgUCoVCoVAoziPOKWtqbwG98sZbpDyh9Sho1Y9e820rvvVOyVtl\nSnN8AehiozIkxYcpze+uhKTom+MljfiNlz+34ndugnTh9sWgVH/j73eI9zDJlm2b7XKYo/8AFXvS\n92GbaKeDv/fzFVZcT5bElzjl54WGgjfY0QLLr1ueul/k1ZWBq5pReLXxN5j+2VUlf0t0EaQGbBEY\nkyZpZW6yDtuyFbTsixZL67qu07CoG+wH1TJompxq3jZQBMPyQGdrqduH/26zn/3gLxif8nrQ6N7Z\ntk3k/ehKUE17SI7F8iRjjIkmmvEAUbFjo6Q8KWM8xrW3HdftrZc06oFeotlKFuvXRu1m2D/+8Ll7\nxWtBQaBefn8x5tb3fnSjyGNr5UCSUtQfOyzy9pXgu2akjrbim/4kKfhMG7z1mZ/jWo9jPJ7/1svi\nPd9/FusqMhayv48ekRbZVXvkmP4PHnjiCfHvx76Bz9vwxiorvsUhZQWjvgX71A2/R97b//hM5H3j\nb7d96ff6C417QOMMz5DzLDAYfyt3D0PtqFlfIvIyLiOpSh3omrx+jTEmYTKo8+Xrt1pxRIakTkdn\nwYLU58PneTpB1W0+JCmYnppTVpxIcqrKjySt2D0Oa9tTh/lSsaVU5CUNR16QE7XCmSyp5k27cP86\nyT40c+kIkVe3FRTmjHzjVzQQ5ThmktyfnGm0xojO3Guzhw0MwViHu1Hn2ELdGGNiSOLlcIOO76mV\n1ObT+3A/MzNwL4+dwn349QsviPdcPBsSpQ/IQnLBHXNFXko69tb2dsiH2f7cGGN8Puz1PBfrNpSJ\nPJZI7HoNErvJN0wReWHxQ2ulzfvB+AWjxWu9raCSe/aQrWmMtBTeuRPr4IJ0yOwGbXI83l+WXTXX\nittPSit2lg/HjsLeXETXGuey1Q2aZ8c345zhCJH07TayC1+xFvUgN0na0K/eCdlPClmAJ7nlmaDf\ng/0uaQb2yNL1p0VebrLN5t6PEHbXNmdSljkFBGG9hQbJvSGIaO4sWQyJlBLwky/hbLLtBO4zy7aM\nMWZcdrYVTyZ774hMkqCOl7JLRwzm2HSiyQ/YpNMsTYvMxueFp0hL51CXvPb/QXe1PAMlkS3vsbch\nFW+zWcuPmu3nA40Nvk6st47yFvFazHCsAw/JySJTZK0s/2IP8qpRH2srpbyP1w/Lvw4el7b2J8mW\nftZw/P7EkZCtdHWcEu8ZoDNvVAxLe+TkjE2DjXJ7C+zS4xKlpLTlNOo6SxE7SuQ9CiBb3vYS7Itx\nY1JFXutJKYf1J1jKeeQ92RYhhNYc29rnjskUebz+uGZmT5B5CVNRbzqpXQZLcI2Rz5+VH0NGHkrS\n39R8KUP6ycNoG3D3/PlWHB0l96OuTuzpRysrrfiSZnkm4HYMXWcwblE2CX3TDpxt+kiuw9JIY4w5\nuw/njwk3Gb+j7RCerbKulOeqM+9i//fWYC3m3yN1xy1HYWv99j9xxi6pk3bXtS24HwvGQ542J2aC\nyOO5wPtOdALWMsvljDGm9Rh+R2gczirhmbJWhrVgXNtpXdllvA76DB/VjYFeee5mKZOhddmwRbZb\nCXac888vypxRKBQKhUKhUCgUCoVCoTif0D/OKBQKhUKhUCgUCoVCoVCcR5yTV8N0+vqjktYemwX3\njvuXglv14VOfizymeK6pXWfFBcmSSlZ4w1wrfmgp6IB/vFM6G/3ijV9a8T0lN1gx0xPrd5SL9zBV\n+KXH3rbih//zuMgr34Tra2sDRXLjsWMib/E1oIOzQ4rXWy3yQkNBs9r5JKRa426X9O166pifIY1Z\n/AJvK+h38WOlS0DZ+5C0sBPKYL90XNh/ArTbRd8A1a/L5pKSc9MYK2aaY0+zpMnGF8Cl4/QKuP40\nnQAVjV1MjDHmb8uXW/GcGaCOsROGMZKSy2Nvd+QYRd35XaUkLRgmnWlix4KmyK4C3fGSIsxuKv5G\n+nzcL6czW7z286u/ZcUXjwW13t69fP0zuM/X/Pn7VvzNi28Xeb97Hs5stetAq93y+FqRlzUC7kjL\nf/cjK55CLkzzZ0l64oF/bLfi6T8GXfnKx78n8por4RLy4hPvWfGqTS+JvBCSfVyVh99evFw6fXXX\nYayyclB7dp6StGSHM9EMJZIvyLbi3g7ZhX+gD/RIH3WDj5uYJvICA0HJZbo+d8U3xpicmVdYcZBj\nhxVXfSFdogxRRp3klOFtQ90o3yhdFVInoI7UkNOIq1CuneAwSAZaybFh9F2TRV4HSZRCSL7jyooR\nefHD4bZXswPU6arV8vqExNDPSFsMd7M+j/weds0IicI41e2QlNYeot1HFZEjU6CsIR0ke3FMwTw4\n/NkRkcdzepAkqOyw9qtvflO855+fY6/+yz33WLHdeaFrMs4BTpLuBgdLeU1dCfa4IKLs2utQiAtr\nNsEDmWi/RzrCVH4AWXWWZFf7BXmTsq3Ya3PJCiT5c2YaqPKfbtgl8hKiQJFetxGylzybVChnFO5b\nGTkyBdr2uKIpmFsspx5/A1x79i7fLd6TQo6WuYVYl/VnpdwmihxPWPIUHyVp3skkX6oj2TbLrIyR\n8sh+onZnzswReb52Wef8ieBwqi8HJWU+ajjWFTuY5V0nJWwlb2Mtpc7FtdulI3x+aCMHr4vHjBF5\n3//rX614xUVP4hqornENN0bKknjt1G+1uRg+gLOnKwlnz1NvbxB5feSKwu4xOQvmirzaAzjnttO5\nacKisSLPLofyN9gJJ360lLC0noG8iM9fTUdtMgGWgMZjrn/vB0+JvOsWovXC+ByMN7s4GmNMbiLm\nuzMUn817bp9Hus8EOTAfvV7Mx8FB+dkd1XieSsyD01bZjo9EHst6e2kdJU2X98hL96+zDPPWfu4O\njZayTH+CpbZBgfL/+ydk4lnI14Lf0XhKyqziSMKz9A93WnHtXim9Z0e4+LHZVtx6Rj6D1a3B+fVM\nLe55Rh9qQ9bCYeI9Xp/vS+POTilN5hq69HuXWXFft9zHOstxLkuah/kWFCZlpzWrcI5KnY68AZsT\nV3bi0Mp9gyJwXSXvynNGwQ2oCy3kGlr5mTxTDnixH1w2H60v3vtkk8hbOAHPB53kfHxgu5THrzmI\ns97kAuyRbnr2szvKhVIN8JFMOSj8q//sEUpjkp0pn5W7qA0G7zveernG9p/CeWnhty+x4vjJ8hxf\n/q78u4IdypxRKBQKhUKhUCgUCoVCoTiP0D/OKBQKhUKhUCgUCoVCoVCcR+gfZxQKhUKhUCgUCoVC\noVAoziPO2XPmlf/AunhcjtQRnyX7wFvuQM+ZO56WTVPWPPa6FS/6xSIrrvpc9np4aMkPrfgP7/7M\nirnHjDHG1O6D9jBtMrSaKx/5hxWfsdl1ZSXAzvD+Z6FjbCrfJ/IOfgKL6EunQVt34yNXirxg0qO/\n+9P3rZh1qcYYM3YqrPmiSBvXRHa6xhiTdqmfvV5tcOejZ8qZ5XvEa64CaDy7DTR1gTabr4vGocdL\neAp6Ddh7zrSQfRn3EIgeFi/ySj+D9vAkWZUWzYb+88x2aSH84k9+YsXVZME2YrK8f55K2OeFZ0EX\nn3axzGPLwYQ50G+37K8ReR1ufBf30WnaXiny3BNkHyV/4vHb/mLFUbYeO4+8iv4xa38FzXJcoVyL\ns+/B32IbS2CXeuucOSJv+79gqzv1Low763yNMeY3/8DaDndAQ3/pZViXvS094j3Tf4y1VLICdq4t\np2V/hESygf7xfzDuDUektrVqJf4dtAxjk7FI6ohjE2bidwx8YcW3Z0t72FWP4jdd//Q042+ULkeN\ncWbIXg+B1Der4wTuR0S2tFxs8KAPgSMBcyHEpidvaUKfmXAX6ndonJy3bfRdrP1v3ot1UNMi+y90\nbIX+2kW9LAZ6pK2gYxquL4h0upUr5ThGkL0h92YwtjnX1wf9tiMOn22vLwM2K2N/wkf9HLiXhTHG\ntBzC3hM/CRpjV7ocw7QFqEVN+6CTjx4ue/Z0V6GWHXptrxWPnC+bsJRtRq3kPiGs42Z7TmOMefhK\nrMWE8bBc5d9gjDGnP8E5wD0SvVRCIuR+19ME7XVUDn5H7uIZIu/486utOH4E9XVIlj1sIpbKtelv\ntNK8b+qQPTVCg7H/tVJ/keGp0pqW7TYjXZiPvJaNkf3IUrPxm4PC5D47SBbk3i7Ms9bDGBPuL2SM\nMe89h3p20UT0P7HX64njURObqrCeB2x54XSOySIL286Tsgb0UV+szjKsS7td6vg5I81QgS3qw231\ntOM09ve2aqyJmBy5xvg+bXxjmxWPGy3PC5uOowdSA62xF9etE3mv/xZn1pjRWC/1m9FrqNFmq+oa\njnOYg3o0OVPlmujrwT1vrynD+wukjaynBv0/MqahT83xN2VPE1c+7sWwcdgj2LbZGGP6u2Q/B3+D\n+611VMo+JNG5WC/cu6V2jewz1k97T109xv6Gyy8XedOH0Tpgi3qbdS73kwqmOt9Htt+Hn5M9qLIX\n0JlrEGdhu3U6P0Oc3Y35Ez8qV+R1N+Je8F4waOtD0ttOvbu8uEe9bbJPSm8z/Vu2Svra4J6V9r0m\nlfa7stfxDJc6VfbO4foXHIz6H5Un53dkLD6vqw3zoO24tE0PdOI+j7sAvUy7qX9IkK1W/+Tqq6w4\ngF6LyJX7UW8T7mUIWdeH2Gzs+d/cDyhmtKzjcVNx5uVnp5OfHxcFjxnmAAAgAElEQVR5IbQ3jV1m\n/I4w6sXJsTHGnH4DvV9SqN/q0VWyfwpbp/97LXpVzhkp94IDpegJdLCszIpjbGuxiv7ewJ8RTN8T\nHCXPI5lLcUbaRP0yc0bKXjK+ZqydsFR8L/cMNMaY+GnoG1e5Aj1xHEnyeWxCCOZm1Ycn8Hlu+XmR\nubKfoh3KnFEoFAqFQqFQKBQKhUKhOI/QP84oFAqFQqFQKBQKhUKhUJxHnFPWdP9PYVW975294rUL\nvneRFQ8MgH575oMtIm/UEnDn2skuteima0Ted8kerORNfJedTulIQl5592YrnvkApBkX2uj9O56A\nhfBvboHN4R23LBR5YWyXF4Bbw1IWY4yJSs22YrYqvfa+y0ReH9GSd2wGle/KuyaKPCNZxX5HzUZQ\nq4KccsgjM0G3d+WAZhVkkzWVvYHrZ9pkaIy0SU2fyTbhoF6WfbFV5K39eCeujyQTLGsqnCelKYHB\nGAfHXowVU3ONMSaDrPG6a0EFDQ6RVDlnEsanrxtxxhXDRR5bGDaSJW729dKSs3qtpNn6E4+9A0v5\n2uPyXm7+HWQHbCPeePyEyKvfCFp1Llmej31Qyg7qd0H2suIJWFLf+tQdIi/+fVjxpsRg7pQehOym\nqrlZvCepNNuK0y/DOL18619F3pRW0OSb9kFes/v0aZHHVpiRyZAcuN3Srt5DUqCVf4UMoKJBUqh/\n+NIDZiiReTWotWdXSLvAmEmQH3YcAz03IkvSaVky0U5Wy3HjpeTCS5T/9hLUVLYfN8aY3Z9A4jb5\n8vFfet0zb5ASry6SMezeDLvFcJu0k78rIJj+X4BNStFxAvMkjOQtQaGSCtp4AOsvIgP3pbNCyivZ\nmjtTLuevjbbjmDPeOmmj6MzAtVd+jPXnGiZp2Y4o1N3QONxLtkQ1xphIqsmZJAvrOC0lJsOuRC06\n/gzW5bzR+O9/+0Tay2fEQwr2wnMfWvGtV18i8mr3QYbL9d5uyxo3AjTn2h347YEOKXNJuijbitmC\nmSnTxkj6dtav5XnBH3AQ3TzHRjH2nP1y6+DYKCkzCY3DPQijs8nRDXJtT5iMPb+R5LCDvVKeED0W\nEo5gshzvJep1zVlJ3WeJzRe7sJYTbRbZvDZTx0Jy563vEnkhNMbddB+Co+Xari2GNW13L/bPtFi5\nHx/egnsx4WbjVwRHYgyrN5WK1yITsN+HOzFOdkvTmDGQF6z+DWSnpWulpfySyZOtmOUwYSHSErel\nGWeOTDpH9TZhDCOHyXvEduMsaxrwSXmIMx5zrGZjmRXHT5K13wygvvb2ohYGOeW1sv14JJ2j9r4n\nJf9xLjnv/Q223o3KSJGvDeC+ddfivjuS5HlugGpJYj/W1ZX5s77ye931uJ+F35Dn8gA6b3ZWoN7y\nnutySUlDN8n8Wymvq6Jd5MWMgdwtjMb07JpDIi9hSjrl4bvYntkYY0JjML8j0r/clt0Ym6zJz4gu\nQvuIukpZo6q/wLktbiZ+E1vIGyMlRkFBWAfh7gyRV7YWbREy5mLc8pbKdeDz4T511WI8Mi7FoeD0\nq/LZ1jUCe/WaDyENv2rRFSKvYTvOlINUxpNzLxR53d14LghPQk3xdXlFXsJIXFPpx5BXTrp/pshr\nPlhrhhIsLbPbeKfOZitwzC23rdVCeATm48Vj0SLkyddeE3k3X4F7mkXW9VxrjTEmfQbOFr0knwtP\nQ12yy8nW/34V8qjtwsrPt4u8KfmQISXG4rqjCuWZLYikntFjMNdb98vzjSMR98IRzxJVOdd5zX4Z\nlDmjUCgUCoVCoVAoFAqFQnEeoX+cUSgUCoVCoVAoFAqFQqE4jzinrGmQqJFLHn9IvjYI6lNTJWhh\n2zdIajJTaS8jt6btv31O5H2wC13Pb1l2MfIOS3rw2tdB++sfAOXqx8vQtto9XDp31LeDUjiVHGz6\nPX0ib8Tl6ALdXguKbErhRSJv668hwbjlZ+jsvflfm0TeyKn4rtueftiKy9ZuEHlps/3cNt2GngZQ\n7+3d/1uOgmYXRtKyHhvVOWE2uqo7E6mjtU0iUb4OdDwHU+Bt8qeLFkJ20lUK6mFkLqi1HTYHH+7G\nn3U9ut83768WeVGTMH/cblzf4ddftX0exr/gesjiyj7dIfLYFaa7ErTa5iOSXtjbICUO/kRvL8bp\nmZ9JauAd94IaePjVNVZs73DvygZ1v2YD5vdH720UeRNz4RhQ0Qh66o4/fi7y2FXtgct/asXX3DTf\nige2SFe2337vX1Y8azhonMPSJNU8gmiI0QWYE5mtcm3nXYU1W7Md7mgtSeUi76O/4tqzyb2tvk3K\nYYKCzk01/LroacQcCYmV38XuN5Hk3tFxSq4Ddj1yUjf9vc9IuVtcAqQzMeQkFhwu1+zUJSS52AkJ\nS/pi1K+SFbIbv9OBz5i5BBTU6EI5PlwfhKOSzV3JUw95aGAI/p9Bn0fKfILptzPlv7dN5rFzhL8R\nRxICdroyRlJfQ0meEDdOUvV7SLbHtPYgm0tKTwPqMDuyRNnuczs508yahNrYT85SLD00RjoS3X4T\nJL6ROVJG5x4FujFTh92ZeSJvkLjdydMgWQwLk+4IpWvh1tRPcoaYwgSRF23bx/0NdlBqL5EysViS\nHRxfBRnSsUrpdDa1oMCKGyswBrmF8jdXrgatv6QetXz8NJvmrh9nLr7XGz+Fy6JdrsROmuycFhkm\n60tgINZV1xnMP6bxG2NMMNUhHvumnfK3J+ZgvPo6Mc+6WuU+OOHysWao0EN1I6ZAzpeYsah5XCtq\n10n5U3kZ9vFrroQkgeuQHV2HUW/YmcQYY5InYC+r21RmxSwp37nqgHjP7BumW7GL3KTsrpnsiOYe\niftvd6fjOnziVbgB9XfLM28o7UEsyRlziXQX2v+5lNv4Gyyf6O+X86f1JGSkMcOwLnnPMEY6Q7rH\nIo/bCxhjTOpFqFt95IoTFSPP4c1VJHcJQK1gl5XOk1K23V2Ke8gtD/ImSQl8BzmFuujMG5Yg5SG+\nDuxjnlp8nn2fiBuD/aWNzs0O27l7oH/oXAxZplG0VN7L7mo8gzVuRR1JXyJbFwTTGunsxHkuJETK\nABOnQOYkZG9NUqYelYSxbm7G93bXkGyVxtYYYzpoTJc9vMh8FcLTUId9HbiGM5tWiDxuixFB0hZu\nD2GMbEMQPRxru5bki//9vCHmVNA+ERgi7w3Pz85S7JkRkXKesQNeNEme/vTggyJv1GKsi/5urMVQ\nt/y8pLGYTy0VcPpsP4Hnk45Tci2y49Oqg/i7BO/Zxsg9s53kgqFuuX/ys+nx9Rgru/tTYCjmcMIU\n7AUe2zN1T52sX3Yoc0ahUCgUCoVCoVAoFAqF4jxC/zijUCgUCoVCoVAoFAqFQnEecU5Z08EVoF4m\nj5fU1IbjoJwxbWvyREnTHXEb6NLtdZA49PXLLvTf/f3tVnzsLXzvdb+6SuTdHQ95VW8v6Hu7n4Cc\nY9xC6Syy6QvQE3OpI7RwDzHGuEfgtU8f+xi/YZh0vRnz3XlWvOcJOL/MuE263nz4NKQUqRdBKhKR\nKWnjwcHRZiiRczXGztMgZRxdVaAbslwpdmSSyGOqZMX7x62Y3SqMMWbULddZcXAwKHxer3Q+KN+0\nwYqjR4DCx3Kq/EWXivdU7YVsgymtLL8zxpjWZox3PXVUT5gq6Wf9LIvoAR2S6e7GGNO8C7KptEWg\nxDEl3xhjcm4cOnla4wnMwQQbrX0Xue3c+Xs4rEVFyeupPgrZXfIsdD+/Z8YtIu/wPyHr6vCgu3+X\nV0pF5hXdaMUbT660Yk8b3JWuuuUH4j2frYec0UuSDTu1lCngTHE8s1J2Wj/xu/et+MaHlljx/td2\ni7wFt8y24nefw5pdMFbWNU8TyRukysIv4Lk10CNprSyfq1mFDv893fK+h5GkiGuv3TXER1KDlS+C\n2s6yF2OMqSxHt/m8SZBIHFyOdWR3Tuupw9gNEiWd6cv/vUD83hqSEyROl+4L7PzjTIDMp7v+q6mf\nbcVYs3aqL1Nk/Y3GXZB+sRTUGOm6Ep6BdRrmkpKLwEDM6daz2Bft7hoxw0CLbSuB/KLRJjEJIQpu\nGLkFHD4Fd6vbb5Rugh0s5SEass8mEUuZOcKKu+pQx1srpDtdGM1flgu0NstrdZHckh0f7PKDmtX4\n/ILpxu/oasIcjoiXzi89NZh37D40drykRB87VGLFvP7qj8l9NouklGPHwR3CTp0++jnOVewwMWkE\nvrenU45PuBv3/VDxl1+PMcaU1GGdxxLlO9UmdYggWdsgveZMl/sOS5mOHIBsa/zsIvN/hRO7MEcC\nbHtIViP2LqbZV1dLJ5n0JKzNcJJ9289pJ17DPps9B3KJD0k2ZIwxY+hsy9KyxGk4f1xMjmXGGFP5\nEaj63iZcd9XesyKvtw97RtZ4SM1T5uSIPKcL0ktHHOpud7d0IfPWYg3ETkWtKf5Cylhn3z/XDCWS\npmVbcV+P3O/YBa6vF+uS5V/GGNMVjDUXQZITdngyxpiuKuTF5uJ5patLnvOdsZgX7afxGhsNssOi\nMcaUbMQ6KJiJdd5yRDq6RGTgzM9S59hR8vOCgjAfeb+LKpCHE5b1sjSIP9sYI1y8/I3QaOxph1/c\nJV5LGY35mHszzqUR8fJMXr0d8pPBPuw1CVPkWSnclW3FDcV4T/rYBSKvrhSS/dQJaKVw/D9wLowe\nYZMIF6M+eMndKtD2vFizFdL5eHJ8Y1moMcZEpGGsy9+m5+ZQKU1LnIN5XvIB1l+izYWz2+ZM6W+E\np2POddrkvkfeQA1MKcAzYmeHnGcecu9jWVPhfPn3gSCSAEWS+6bdIdnTiTNXzhg849QlYRyjosaJ\n95xe/ZEV301Sxq3vyLk58mLsVyy9t0vleb8LpL3GmSzPDmHkIjdAMuWatSUiL26iXOt2KHNGoVAo\nFAqFQqFQKBQKheI8Qv84o1AoFAqFQqFQKBQKhUJxHqF/nFEoFAqFQqFQKBQKhUKhOI84Z88ZttEt\nfukL8drO/bC4zoqHTqu0QVqZZS2D1W1vOzRcGXOlDWfTbmjK1pDt1YQA2cflP9/5mxV/sAO9Md7b\nudyKWZ9mjDF3PIO+F0dfQG+MAVvPELYXvuRhaBfbS2wWXTHQLo57EBrYI3+X/TDYosudALva8hX/\nEXk7X4T99I1//7vxN3xd0E22nZR6a7aNaz0MjWfezVK/x/0E4qZAA9l6RPaSqS3eYsWR6dDFdtfJ\nexg/kSzG6qAJdpOFnNcrdbodJ9H3JmEG9NZn9kl9dPo82LM54nHdQWFSg882lfW7oO226x1dw6Ft\n3ku9TNISpQVp4Hz6W2eW8StiC9CzaM5EacsYT1r24y+hT8isn08UedyHo+xd0r7atLRZl6C/wfdm\nkA52k+wxce8i2AyWUi+Y+qPojbFm18viPZv/ib43rNucuGyCyPvgedjt3vX07Va87J5LRF4sWUiy\nDXZL12aRt/cDaGUvmYnvGv2N60Sez9dqhhLd1OPJYbPNZHDfhxDqQWCMMTET8Ju9ZK3KfRWMMea2\n3/zGiq9biN5fUcPkvM2gVg3cg6BoMXrTRNr6LwRQTyrWDXvqpb5/z8s7rTiYLGdDdleLvLQrYNsd\n5GC7bNlzJiQKfTgyi6Bf7u2VdS1+ktSy+xOJ1K+p5VCteK2nGtebSDWqr0/24gkIwH12JkCj3F0n\n719UHH5jkwc1KnG2LDA1n6HXQexk1Gful9J2Rtbg1AvRp4LrZF+X7NfTcADrPn4svvfsKll3ua8a\nz4mOUptufRPODsMn4D12G9mIrKHtxcbgvkvGGJORjfkd7cb47Nh5VOTNnIueVVxfexvlmm3vxr+j\nqCdE3V55Vll35IgVX0L9sCprcK7KykkW76mrxL7Ilswte2pE3gDVh4QU2AHnXG+zED6E9wWRdb19\nnwik/iyphzHP6o/INZEyhGtx+jdnWfHBl2WfsbA06h9D/XLqP5DrwJmK8eWeA322vlXhYag9lVvK\nrHhynjzL7jiFHlJ3/hF92binUrttLYZn4vreem2VFY/KzBR5yW7UYe6B4OuUfVpiEtHTK+0S1Nba\nrWUir/0w5lXLXox73vRckVe/Bf01coagtV6/j3pUFctniLBE7HGuNPSFNLbt0z0MZ8fAYMzbzkrZ\no4Mt6stX4+wdO1b2gGg7WWbFbGPN9vKdttqWkIR1xb1H7L3JPNQHh/MGbf2fqjbhWaifetS12M7d\n6RdjjL2tqDUx+bK3W6BD1gR/om4jnp+KbhovX6ReN037sffXdVeINLbjdlLvDraQN8YYdwzqc8Jw\n1MnAQPlIW7sOfT6qfViX4VQPPHXS4tjXirXEVuSn3z4s8iqbUHcPfoz1MXVEocjzUt+f1Mtxtu4s\nl2fNHa/gDF1QhHVfuVveI3u/Vn+jej3GMeNS2WMtfgpqeXgy6mvo5jKRx3WU7eAbtsoeWnyOYXvz\ntEmyyRyfy6vPfohrcOE+BQY6xHtiqQ8Qn0Hm33+hyHNno35HR2MudXXJHjFVuzE+Y6/DMwT/PmNk\nrYjMQr3OvUE+t7WekGdWO5Q5o1AoFAqFQqFQKBQKhUJxHqF/nFEoFAqFQqFQKBQKhUKhOI84p6zp\nxt9eY8VVn54Ur10wH7S15W9CgnDPw9eIvFPP7bHiuJmgRL3yzIci76fL/2DFk4+AItbvlRZqS39+\nhRWPfxt0pOfue8KKWRZljDGvbfgzfkcF6IAtXZLONm0RqErdRE3qLrfZT3eB5n3iX6DSzvz5fSJv\nTMchKy7d+JkVd9qo65f95lYzlKiksYvMjRGvsbSCabIlb8h7yFIf/ozGSknPTQrItuL2ctzrqCwp\npUhMhK3r4V3/sGK2ou3vkTKaTrKQCwgCPS5tuqT49/eDRihsYTOk7KPiHVDUA8iKt6lW0g1z54Gm\nOHoRqGlMuzTGRjWVbu5fGy888IwVXzhXSoCiic5b9ipkZdP6pCSEKeqxJI1JmTBZ5LVWQq6w7R+Q\nB322f7/I+9snT1vxwACotH/918NW/E23pPOyNffxSljsjukYKfLmz5uE6zmF+/rGs5+IvO+88B0r\nLnkPa9EVJi1qR1yAMcy/HBbtPT2SZvnE7agVf1i50vgb3gbMzXCbbIMlT7FjQcms21Qu8prIRtnX\njjXLNFtjjLn32mvxeWSda6frZ12Nex+TMNWKe3tx3ys2bRPv8bViXbWQ9WRMUaLIm/7tOVZc/G/s\nBYkXZou87mrURGcS6LLdZ2XtTbsIUqvWctBOG7bJcQyOxFxPvc34Fd1Ek+d6ZYwxSfMhFWo/jfEI\niZZ7zbALbrfizk7Qrc1gscirPwGZYkgk6O9dNjvN0DjQr3mdR9K9bKqSFPyDH6LGF82DXTZbuhsj\nZQCtZ0CLD4mSlqHNJGfpPou5fGpfqciLd+G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VebU7UI+au0CXzhufbcXb9x3n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ynroV/sYnFZl0P2M9KPtOeQWN4fQ50YT8ERSDMeqOFWjrZsbqvoSxZvwX0u/4RLiDKJNejE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pXjj3iInJ+Py1JyISEoH/J1T5KuTeE2doqebEyboHRLARxX3GevS4D9G/eyqwF+Mn654N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\nvd/RoVMsaXkBuvGgEx9CC3BfA2SdnuzodMeGoYvt3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9X1K0qux/p39Ge7vHbN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RkUmKR+MUs9x9NJeq4edPmabPPTkJe4MMqv/E319E17WdrMHnDlO9r1TnN4oY7SO5Hmjjk8+p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vYup938HqSIafE4H33sUZk2dO0I5A5ztmLsGg7KM9bsh3Y6bZYIX/gPKcvJfxifcfInR5z2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\nl+nnFC6WOWMYhmEYhmEYhmEYhjGP2MMZwzAMwzAMwzAMw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Zk4vpLatarfyBDmXwZpjCcc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q+o2RxHeaHJBdx7FcOi/db2INKdyipaLnfgRpe8MW+RCWOWMYhmEYhmEYhmEYhjGP2MMZ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2JKupQz4f5gupFNKWJ3pOvmg5bvGZLn9yPvY08qSkGOnn0T3g3i52SLawZbMOf4XD9quTVFFiBX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\nceomvNLyM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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ra6VYUA9rMq-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the weights will probably just show some fuzzy blobs or possibly a few parts of digits. You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n", + "\n", + "It can be interesting to stop training at different numbers of iterations and see the effect.\n", + "\n", + "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n", + "\n", + "What differences do you see visually for the different levels of convergence?" + ] + }, + { + "metadata": { + "id": "5VwPglSIrup4", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Already done with 1000 steps**" + ] + }, + { + "metadata": { + "id": "We4u3oaDrZYf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# For 10 steps" + ] + }, + { + "metadata": { + "id": "H9bf7QyBrB7T", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "35a63479-c584-4fe4-e868-e3489ac56903" + }, + "cell_type": "code", + "source": [ + "classifier = train_nn_classification_model(\n", + " learning_rate=0.03,\n", + " steps=10,\n", + " batch_size=50,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 26.07\n", + " period 01 : 26.59\n", + " period 02 : 21.87\n", + " period 03 : 19.91\n", + " period 04 : 24.33\n", + " period 05 : 20.43\n", + " period 06 : 14.89\n", + " period 07 : 12.27\n", + " period 08 : 16.51\n", + " period 09 : 9.48\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.73\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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wpgv2tjqrH1dRFGaODcHd2Y7sE12w09rzzYU1ZJRnWf3YQog7I0VdNBiNRsPj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\nSTgqEwlw2VhbvMGvJ8yp1EVETJIa05XfDrwfG22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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "1Js0eCOGs0Cm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# For 100 steps" + ] + }, + { + "metadata": { + "id": "ZJMZefpVrcVU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 973 + }, + "outputId": "e57be541-8b27-49b6-b241-3af16a86de54" + }, + "cell_type": "code", + "source": [ + "classifier = train_nn_classification_model(\n", + " learning_rate=0.03,\n", + " steps=100,\n", + " batch_size=50,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 13.57\n", + " period 01 : 8.43\n", + " period 02 : 8.86\n", + " period 03 : 6.58\n", + " period 04 : 5.65\n", + " period 05 : 4.92\n", + " period 06 : 6.24\n", + " period 07 : 6.11\n", + " period 08 : 4.48\n", + " period 09 : 4.41\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.87\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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5LfJjVRk2rkwIIcRg0OPQbmpqAqCqqork5GSMRnkwyOXo3CJP8ktAgULOawsh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\ncDQPjR6k+qQvrtif680W1qWd5swlE+EhRp5PGMM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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "30Xl0CCAs9h4", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**It is observed that the higher no. of steps, the higher is the accuracy of the model.**" + ] + }, + { + "metadata": { + "id": "_plydcvIsvN6", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file