From eaf042c52b56f9e8f48116f1ca722646baeadc98 Mon Sep 17 00:00:00 2001 From: AGCreates <43198265+AGCreates@users.noreply.github.com> Date: Sat, 26 Jan 2019 01:15:49 +0530 Subject: [PATCH 1/3] Assignment 4 Completed by AGCreates. --- First_Date_with_TensorFlow.ipynb | 298 ++++++++++++------------------- 1 file changed, 115 insertions(+), 183 deletions(-) diff --git a/First_Date_with_TensorFlow.ipynb b/First_Date_with_TensorFlow.ipynb index a5b7fc0..a30a52c 100644 --- a/First_Date_with_TensorFlow.ipynb +++ b/First_Date_with_TensorFlow.ipynb @@ -21,7 +21,7 @@ "colab_type": "text" }, "source": [ - "[View in Colaboratory](https://colab.research.google.com/github/abhiWriteCode/Assignment-4/blob/AGCreates/First_Date_with_TensorFlow.ipynb)" + "\"Open" ] }, { @@ -99,10 +99,10 @@ "metadata": { "id": "vmMcjzTxbWzw", "colab_type": "code", - "outputId": "c2901160-d845-41c1-d060-964bff742ab3", + "outputId": "8291527d-4aa9-4d4c-f944-dd2aaf5e5af3", "colab": { "base_uri": "https://localhost:8080/", - "height": 67 + "height": 68 } }, "cell_type": "code", @@ -112,7 +112,7 @@ "print (t2)\n", "print (t3)" ], - "execution_count": 3, + "execution_count": 4, "outputs": [ { "output_type": "stream", @@ -143,10 +143,10 @@ "metadata": { "id": "ol6O5I7Tb2nb", "colab_type": "code", - "outputId": "382424df-d94d-4fe8-d347-67ea7b51570d", + "outputId": "2d9d1191-8b41-4134-a138-d4be2a9721a8", "colab": { "base_uri": "https://localhost:8080/", - "height": 202 + "height": 204 } }, "cell_type": "code", @@ -159,7 +159,7 @@ "print (sess.run(t3))\n", "sess.close()" ], - "execution_count": 4, + "execution_count": 5, "outputs": [ { "output_type": "stream", @@ -224,10 +224,10 @@ "metadata": { "id": "FyVz0GNqgreZ", "colab_type": "code", - "outputId": "9d119dd6-c652-4e97-ea4a-0e8519f6906b", + "outputId": "b1f861fc-981e-4df4-b8c5-0c18a0a0b558", "colab": { "base_uri": "https://localhost:8080/", - "height": 50 + "height": 51 } }, "cell_type": "code", @@ -244,7 +244,7 @@ "print ('Comp Graph 1 Alt: ', sess.run(comp_graph_1_alt))\n", "sess.close()" ], - "execution_count": 5, + "execution_count": 6, "outputs": [ { "output_type": "stream", @@ -272,10 +272,10 @@ "metadata": { "id": "4856BTvRhiBb", "colab_type": "code", - "outputId": "2d7c918b-d32d-4351-91e4-4ee318989111", + "outputId": "35451a1d-7286-4225-a8ab-c3f5655da3e6", "colab": { "base_uri": "https://localhost:8080/", - "height": 67 + "height": 68 } }, "cell_type": "code", @@ -295,7 +295,7 @@ "print ('Part 2 Result: ', part2_res)\n", "sess.close()" ], - "execution_count": 6, + "execution_count": 7, "outputs": [ { "output_type": "stream", @@ -331,11 +331,11 @@ "metadata": { "id": "-uHNe1BolJY0", "colab_type": "code", + "outputId": "4039c026-a7f0-47d0-99a0-d411ae6a720b", "colab": { "base_uri": "https://localhost:8080/", - "height": 67 - }, - "outputId": "ca3885b4-428f-407d-e804-c33f2edda1ca" + "height": 68 + } }, "cell_type": "code", "source": [ @@ -355,7 +355,7 @@ "# Execute \n", "# YOUR CODE HERE" ], - "execution_count": 7, + "execution_count": 8, "outputs": [ { "output_type": "stream", @@ -387,11 +387,11 @@ "metadata": { "id": "0ZhYwAlLmEvB", "colab_type": "code", + "outputId": "cef4f995-b664-4b1f-9b24-cf1d3f44e638", "colab": { "base_uri": "https://localhost:8080/", - "height": 151 - }, - "outputId": "6fc0f7a8-0539-40f4-cb1e-b8e730ad7662" + "height": 153 + } }, "cell_type": "code", "source": [ @@ -402,7 +402,7 @@ "comp_graph_part_1 = tf.reduce_mean(comp_graph_part_1, 1)\n", "comp_graph_part_2 = tf.constant(([7, 9],\n", " [8, 6]), dtype=tf.float32)\n", - "lelf_graph = comp_graph_part_1 * comp_graph_part_2\n", + "p1_graph = comp_graph_part_1 * comp_graph_part_2\n", "\n", "comp_graph_part_3 = tf.constant(([2.79, 3.81, 5.6],\n", " [7.3, 5.67, 8.9]), dtype=tf.float32)\n", @@ -410,19 +410,19 @@ " [7.86, 9.81],\n", " [9.36, 10.11]), dtype=tf.float32)\n", "comp_graph_part_4 = tf.transpose(comp_graph_part_4)\n", - "rigth_graph = tf.reduce_sum(comp_graph_part_3 * comp_graph_part_4)\n", + "p2_graph = tf.reduce_sum(comp_graph_part_3 * comp_graph_part_4)\n", "\n", - "comp_graph_complete = lelf_graph + rigth_graph\n", + "comp_graph_complete = p1_graph + p2_graph\n", "\n", "with tf.Session() as sess:\n", - " part1_res, part2_res, total_res = sess.run([lelf_graph, rigth_graph, comp_graph_complete])\n", + " part1_res, part2_res, total_res = sess.run([p1_graph, p2_graph, comp_graph_complete])\n", " print ('Complete Result: \\n', total_res)\n", - " print ('Left Result: \\n', part1_res)\n", - " print ('Rigth Result: \\n', part2_res)\n", + " print ('Part 1 Result: \\n', part1_res)\n", + " print ('Part 2 Result: \\n', part2_res)\n", "# Execute \n", "# YOUR CODE HERE" ], - "execution_count": 8, + "execution_count": 10, "outputs": [ { "output_type": "stream", @@ -430,10 +430,10 @@ "Complete Result: \n", " [[404.4483 439.7983 ]\n", " [409.7483 415.64832]]\n", - "Left Result: \n", + "Part 1 Result: \n", " [[37.100002 72.450005]\n", " [42.4 48.300003]]\n", - "Rigth Result: \n", + "Part 2 Result: \n", " 367.3483\n" ], "name": "stdout" @@ -456,40 +456,40 @@ "metadata": { "id": "GQWyCvsQmMcL", "colab_type": "code", + "outputId": "c841d00d-a993-4140-b5b7-61ab3bddb87b", "colab": { "base_uri": "https://localhost:8080/", - "height": 168 - }, - "outputId": "860c214c-c0b6-467c-c55c-11f5f3b33e06" + "height": 170 + } }, "cell_type": "code", "source": [ "# Build the graph\n", "# YOUR CODE HERE\n", - "tensor1 = tf.constant(([7.36, 8.93, 10.41],\n", + "t1 = tf.constant(([7.36, 8.93, 10.41],\n", " [5.31, 9.38, 7.99]), dtype=tf.float32)\n", - "tensor2 = tf.constant(([7.99, 10.36],\n", + "t2 = tf.constant(([7.99, 10.36],\n", " [5.36, 7.98],\n", " [8.91, 5.67]), dtype=tf.float32)\n", - "tensor2 = tf.transpose(tensor2)\n", - "comp_graph_part_1 = tf.reduce_sum(tensor1 * tensor2)\n", + "t2 = tf.transpose(t2)\n", + "comp_graph_part_1 = tf.reduce_sum(t1 * t2)\n", "\n", "comp_graph_part_2 = (tf.constant(7.0) + comp_graph_part_1) / 19.6\n", "\n", - "comp_graph_complete = comp_graph_part_2 / tf.constant(([1, 5.6, 6.1, 8],\n", + "comp_graph_tot = comp_graph_part_2 / tf.constant(([1, 5.6, 6.1, 8],\n", " [0, 0, 7.98, 9],\n", " [0, 0, 7.6, 7],\n", " [0, 0, 0, 8.98]), dtype=tf.float32)\n", "\n", "with tf.Session() as sess:\n", - " part1_res, part2_res, total_result = sess.run([comp_graph_part_1, comp_graph_part_2, comp_graph_complete])\n", + " part1_res, part2_res, total_result = sess.run([comp_graph_part_1, comp_graph_part_2, comp_graph_tot])\n", " print ('Total Result: \\n', total_result)\n", " print ('Part 1 Result: \\n', part1_res)\n", " print ('Part 2 Result: \\n', part2_res)\n", "# Execute \n", "# YOUR CODE HERE" ], - "execution_count": 9, + "execution_count": 11, "outputs": [ { "output_type": "stream", @@ -569,7 +569,7 @@ "metadata": { "id": "1h1-D8K1uT48", "colab_type": "code", - "outputId": "497154fb-a2eb-450d-878d-484f29e9a2e9", + "outputId": "bf8df7f9-8ef1-4f92-e635-0c30075c120b", "colab": { "base_uri": "https://localhost:8080/", "height": 347 @@ -581,14 +581,14 @@ "plt.plot(train_X[:10], train_Y[:10], 'g')\n", "plt.show()" ], - "execution_count": 28, + "execution_count": 14, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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kruZfwaz9M2Fv5YAVg9bCUs6bSoioevyfgsgIVOgqMHn3m8jX5iEmYCVaOrcS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\nHz58eOW6/fv3x9mzZ0UP8rru+5/rmtu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Cu3J2AjCo9RAmhUymc5Pgv9zG0We2lSvOrZmdm833yEXEfIZhsPVwKnPSE0g/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\n98gHDRrEtm3biI6OxtPTk8T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"text/plain": [ - "" + "
" ] }, "metadata": { @@ -606,7 +606,7 @@ "source": [ "** Question **
\n", "Why did I created a session to plot the graph?
\n", - "Ans) For visualizing characteristic (or tendency) of data" + "Ans) tf.session.run is used to evaluate a tensor. So in order to visualize the characteristics of the plot and to evaluate and find the values of the tensors we need to use session." ] }, { @@ -688,10 +688,10 @@ "metadata": { "id": "ttI7ZT-ozAm1", "colab_type": "code", - "outputId": "a147d429-a076-44a1-f1cc-b48cba3461e9", + "outputId": "14fb80da-8552-4dad-bd0b-46ef731fcff4", "colab": { "base_uri": "https://localhost:8080/", - "height": 431 + "height": 432 } }, "cell_type": "code", @@ -727,25 +727,25 @@ " plt.legend()\n", " plt.show()" ], - "execution_count": 33, + "execution_count": 19, "outputs": [ { "output_type": "stream", "text": [ - "Loss after epoch 0 is 0.06481158\n", - "Loss after epoch 20 is 0.06480222\n", - "Loss after epoch 40 is 0.06479287\n", + "Loss after epoch 0 is 0.6203117\n", + "Loss after epoch 20 is 0.61980206\n", + "Loss after epoch 40 is 0.61929274\n", "Now testing the model in the test set\n", - "The final loss is: 0.33398113\n" + "The final loss is: 0.14766909\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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Cle5K/pOxi3V7P2HrqU1sObWJvMo87/wAvwAuix7FmJixjHGNZWzMOAaED8LP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\nRwAAAABJRU5ErkJggg==\n", 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REZMohEVEREyiEBYRETGJQlhERMQkCmERERGTKIRFRERMohAWERExiUJYRETEJAphERER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\nJ09mz549REVFERg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"text/plain": [ - "" + "
" ] }, "metadata": { @@ -776,23 +776,23 @@ }, "cell_type": "code", "source": [ - "def linear_regression(learning_rate=0.000005, n_epochs=100, interval=50):\n", + "def linear_regression(learning_rate=0.0000001, n_epochs=1000, interval=100):\n", " # YOUR CODE HERE\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " \n", " for epoch in range(n_epochs):\n", - " _, curr_loss = sess.run([optimizer, loss], feed_dict={x:train_X, y:train_Y})\n", + " pred_train, current_loss = sess.run([optimizer, loss], feed_dict={x:train_X, y:train_Y})\n", "\n", " if epoch % interval == 0:\n", - " print ('Loss after epoch', epoch, ' is ', curr_loss)\n", + " print ('Loss after epoch', epoch, ' is ', current_loss)\n", "\n", " print ('Now testing the model in the test set')\n", - " final_preds, final_loss = sess.run([pred_y, loss], feed_dict={x:test_X, y:test_Y})\n", + " final_pred, final_loss = sess.run([pred_y, loss], feed_dict={x:test_X, y:test_Y})\n", " \n", " print ('The final loss is: ', final_loss)\n", " \n", - " return final_preds, final_loss" + " return final_pred, final_loss" ], "execution_count": 0, "outputs": [] @@ -801,34 +801,29 @@ "metadata": { "id": "A6MaclhK4rc6", "colab_type": "code", + "outputId": "9161a302-2016-458b-c42b-34b2204611f0", "colab": { "base_uri": "https://localhost:8080/", - "height": 218 - }, - "outputId": "3bb819ff-24c9-4ece-9e8a-377471bba55d" + "height": 136 + } }, "cell_type": "code", "source": [ "# Okay! Now let's tweak!\n", "_ = linear_regression(learning_rate=0.000034, n_epochs=500)" ], - "execution_count": 35, + "execution_count": 21, "outputs": [ { "output_type": "stream", "text": [ - "Loss after epoch 0 is 0.06481158\n", - "Loss after epoch 50 is 0.0647882\n", - "Loss after epoch 100 is 0.06476484\n", - "Loss after epoch 150 is 0.064741485\n", - "Loss after epoch 200 is 0.064718165\n", - "Loss after epoch 250 is 0.06469486\n", - "Loss after epoch 300 is 0.064671576\n", - "Loss after epoch 350 is 0.06464831\n", - "Loss after epoch 400 is 0.06462506\n", - "Loss after epoch 450 is 0.064601846\n", + "Loss after epoch 0 is 0.6203117\n", + "Loss after epoch 100 is 0.6177678\n", + "Loss after epoch 200 is 0.61523753\n", + "Loss after epoch 300 is 0.61271757\n", + "Loss after epoch 400 is 0.6102085\n", "Now testing the model in the test set\n", - "The final loss is: 0.3325342\n" + "The final loss is: 0.14125724\n" ], "name": "stdout" } @@ -838,43 +833,33 @@ "metadata": { "id": "peoHmV2M40uU", "colab_type": "code", + "outputId": "85832b32-46e3-4612-c232-b822c3fae1c6", "colab": { "base_uri": "https://localhost:8080/", - "height": 386 - }, - "outputId": "46114ad0-17f2-4cac-ca12-d98c689b5230" + "height": 221 + } }, "cell_type": "code", "source": [ "_ = linear_regression(learning_rate=0.0000006, n_epochs=1000)" ], - "execution_count": 36, + "execution_count": 22, "outputs": [ { "output_type": "stream", "text": [ - "Loss after epoch 0 is 0.06481158\n", - "Loss after epoch 50 is 0.0647882\n", - "Loss after epoch 100 is 0.06476484\n", - "Loss after epoch 150 is 0.064741485\n", - "Loss after epoch 200 is 0.064718165\n", - "Loss after epoch 250 is 0.06469486\n", - "Loss after epoch 300 is 0.064671576\n", - "Loss after epoch 350 is 0.06464831\n", - "Loss after epoch 400 is 0.06462506\n", - "Loss after epoch 450 is 0.064601846\n", - "Loss after epoch 500 is 0.0645787\n", - "Loss after epoch 550 is 0.06455557\n", - "Loss after epoch 600 is 0.064532466\n", - "Loss after epoch 650 is 0.06450938\n", - "Loss after epoch 700 is 0.064486355\n", - "Loss after epoch 750 is 0.06446349\n", - "Loss after epoch 800 is 0.06444063\n", - "Loss after epoch 850 is 0.0644178\n", - "Loss after epoch 900 is 0.06439498\n", - "Loss after epoch 950 is 0.06437224\n", + "Loss after epoch 0 is 0.6203117\n", + "Loss after epoch 100 is 0.6177678\n", + "Loss after epoch 200 is 0.61523753\n", + "Loss after epoch 300 is 0.61271757\n", + "Loss after epoch 400 is 0.6102085\n", + "Loss after epoch 500 is 0.6077123\n", + "Loss after epoch 600 is 0.6052247\n", + "Loss after epoch 700 is 0.60275173\n", + "Loss after epoch 800 is 0.6002867\n", + "Loss after epoch 900 is 0.59783566\n", "Now testing the model in the test set\n", - "The final loss is: 0.3309085\n" + "The final loss is: 0.1342217\n" ], "name": "stdout" } @@ -894,51 +879,54 @@ "metadata": { "id": "JKiHjGN15HPX", "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# YOUR CODE HERE" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "hc3syMCfF72T", - "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "f6811b2f-a3ce-4dd5-a556-deae462a4977" }, "cell_type": "code", "source": [ - "W = tf.Variable(np.random.random_sample(), name='weight_2')\n", - "b = tf.Variable(np.random.random_sample(), name='bias_2')\n", - "\n", - "pred_y = (W*x) + b\n", - "\n", - "lambd = 0.01\n", - "L2_regularization = lambd * tf.tensordot(W, W, 0)\n", - "loss = tf.reduce_mean(tf.square(y - pred_y) + L2_regularization)\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=0.0000003).minimize(loss)" + "# YOUR CODE HERE\n", + "_ = linear_regression(learning_rate=0.000008, n_epochs=10000, interval= 1000)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 0.6203117\n", + "Loss after epoch 1000 is 0.5953937\n", + "Loss after epoch 2000 is 0.57157826\n", + "Loss after epoch 3000 is 0.54881245\n", + "Loss after epoch 4000 is 0.52705497\n", + "Loss after epoch 5000 is 0.50625724\n", + "Loss after epoch 6000 is 0.48637694\n", + "Loss after epoch 7000 is 0.46737328\n", + "Loss after epoch 8000 is 0.44920772\n", + "Loss after epoch 9000 is 0.43184412\n", + "Now testing the model in the test set\n", + "The final loss is: 0.045880605\n" + ], + "name": "stdout" + } + ] }, { "metadata": { "id": "FL4NT2CoIQAp", "colab_type": "code", + "outputId": "8ab4fb03-3cda-46ef-d749-ce41742e2d4d", "colab": { "base_uri": "https://localhost:8080/", "height": 386 - }, - "outputId": "8ab4fb03-3cda-46ef-d749-ce41742e2d4d" + } }, "cell_type": "code", "source": [ - "final_preds, final_loss = linear_regression(n_epochs=1000)" + "" ], - "execution_count": 39, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -969,62 +957,6 @@ "name": "stdout" } ] - }, - { - "metadata": { - "id": "DLXJjQnYIg6z", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "You can see after adding relularization, loss becomes very small and my model doesn't overfit (because, traning loss and test loss are much similar) " - ] - }, - { - "metadata": { - "id": "ij_jrW_PIVvQ", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 347 - }, - "outputId": "98372027-bba5-44ac-e047-425437eb883a" - }, - "cell_type": "code", - "source": [ - "plt.plot(test_X, test_Y, 'g', label='True Function')\n", - "plt.plot(test_X, final_preds, 'r', label='Predicted Function')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": 40, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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7Mlm+731qXY13W0+IvYS5I+7kqv5XY7Pof0+Rjkr/94u0EdM0ySnJ4u1dmfxr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\nY83f0/gscfZGbNlZ2LZtxXA6m3aJjKR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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "metadata": { - "id": "nQ0XTZi2Iv2H", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "" - ], - "execution_count": 0, - "outputs": [] } ] } \ No newline at end of file From 4853f05767f2cb1ab6dde0aaa18fe6b33f02757c Mon Sep 17 00:00:00 2001 From: AGCreates <43198265+AGCreates@users.noreply.github.com> Date: Sat, 26 Jan 2019 02:30:46 +0530 Subject: [PATCH 2/3] Assignment 4 completed by AGCreates --- AGCreates.ipynb | 618 +++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 584 insertions(+), 34 deletions(-) diff --git a/AGCreates.ipynb b/AGCreates.ipynb index 0a721fb..988230d 100644 --- a/AGCreates.ipynb +++ b/AGCreates.ipynb @@ -5,7 +5,8 @@ "colab": { "name": "First_Date_with_TensorFlow.ipynb", "version": "0.3.2", - "provenance": [] + "provenance": [], + "include_colab_link": true }, "kernelspec": { "name": "python3", @@ -13,6 +14,16 @@ } }, "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, { "metadata": { "id": "2XXfXed5YLbe", @@ -88,7 +99,11 @@ "metadata": { "id": "vmMcjzTxbWzw", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "87cd3421-2bd4-4f14-efa0-ea5c9225e13b" }, "cell_type": "code", "source": [ @@ -97,8 +112,18 @@ "print (t2)\n", "print (t3)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Tensor(\"Const:0\", shape=(), dtype=float32)\n", + "Tensor(\"Const_1:0\", shape=(2,), dtype=float32)\n", + "Tensor(\"Const_2:0\", shape=(2, 3, 2), dtype=float32)\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -118,7 +143,11 @@ "metadata": { "id": "ol6O5I7Tb2nb", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "bcb98376-cb10-4986-f5ed-ac23ace6a19a" }, "cell_type": "code", "source": [ @@ -130,8 +159,26 @@ "print (sess.run(t3))\n", "sess.close()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "2.0\n", + "=======================\n", + "[1. 2.]\n", + "=======================\n", + "[[[1. 9.]\n", + " [2. 3.]\n", + " [4. 5.]]\n", + "\n", + " [[1. 9.]\n", + " [2. 3.]\n", + " [4. 5.]]]\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -177,7 +224,11 @@ "metadata": { "id": "FyVz0GNqgreZ", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "a8179924-90eb-45c2-a6ab-0a6c494af463" }, "cell_type": "code", "source": [ @@ -193,8 +244,17 @@ "print ('Comp Graph 1 Alt: ', sess.run(comp_graph_1_alt))\n", "sess.close()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Comp Graph 1 : 7663\n", + "Comp Graph 1 Alt: 7663\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -212,7 +272,11 @@ "metadata": { "id": "4856BTvRhiBb", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "9968249b-795f-4aa7-f81a-47da4564d9f8" }, "cell_type": "code", "source": [ @@ -231,8 +295,18 @@ "print ('Part 2 Result: ', part2_res)\n", "sess.close()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Complete Result: 3.5897436\n", + "Part 1 Result: -4.0\n", + "Part 2 Result: 3.5897436\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -257,18 +331,43 @@ "metadata": { "id": "-uHNe1BolJY0", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "61fa96aa-a074-4b18-91da-b422f6e7b2df" }, "cell_type": "code", "source": [ "# Build the graph\n", "# YOUR CODE HERE\n", + "comp_graph_part_1 = tf.constant([9, 10], dtype=tf.float32) * tf.constant([7, 8.65], dtype=tf.float32)\n", + "comp_graph_part_1 = comp_graph_part_1 / 5.6\n", + "comp_graph_part_2 = tf.constant([7.65, 9], dtype=tf.float32) + tf.constant([13.5, 7.19], dtype=tf.float32)\n", "\n", + "comp_graph_complete = tf.minimum(comp_graph_part_1, comp_graph_part_2)\n", + "\n", + "with tf.Session() as sess:\n", + " part1_res, part2_res, total_res = sess.run([comp_graph_part_1, comp_graph_part_2, comp_graph_complete])\n", + " print ('Complete Result: ', total_res)\n", + " print ('Part 1 Result: ', part1_res)\n", + " print ('Part 2 Result: ', part2_res)\n", + "# Execute \n", "# Execute \n", "# YOUR CODE HERE" ], - "execution_count": 0, - "outputs": [] + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Complete Result: [11.25 15.446429]\n", + "Part 1 Result: [11.25 15.446429]\n", + "Part 2 Result: [21.15 16.19]\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -289,18 +388,58 @@ "metadata": { "id": "0ZhYwAlLmEvB", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 153 + }, + "outputId": "dbfe7f3b-882c-4a8e-d9dc-8e40c9565c8c" }, "cell_type": "code", "source": [ "# Build the graph\n", "# YOUR CODE HERE\n", + "comp_graph_part_1 = tf.constant(([7.2, 3.4],\n", + " [7.5, 8.6]), dtype=tf.float32)\n", + "comp_graph_part_1 = tf.reduce_mean(comp_graph_part_1, 1)\n", + "comp_graph_part_2 = tf.constant(([7, 9],\n", + " [8, 6]), dtype=tf.float32)\n", + "p1_graph = comp_graph_part_1 * comp_graph_part_2\n", + "\n", + "comp_graph_part_3 = tf.constant(([2.79, 3.81, 5.6],\n", + " [7.3, 5.67, 8.9]), dtype=tf.float32)\n", + "comp_graph_part_4 = tf.constant(([2.6, 18.1],\n", + " [7.86, 9.81],\n", + " [9.36, 10.11]), dtype=tf.float32)\n", + "comp_graph_part_4 = tf.transpose(comp_graph_part_4)\n", + "p2_graph = tf.reduce_sum(comp_graph_part_3 * comp_graph_part_4)\n", "\n", + "comp_graph_complete = p1_graph + p2_graph\n", + "\n", + "with tf.Session() as sess:\n", + " part1_res, part2_res, total_res = sess.run([p1_graph, p2_graph, comp_graph_complete])\n", + " print ('Complete Result: \\n', total_res)\n", + " print ('Part 1 Result: \\n', part1_res)\n", + " print ('Part 2 Result: \\n', part2_res)\n", "# Execute \n", "# YOUR CODE HERE" ], - "execution_count": 0, - "outputs": [] + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Complete Result: \n", + " [[404.4483 439.7983 ]\n", + " [409.7483 415.64832]]\n", + "Part 1 Result: \n", + " [[37.100002 72.450005]\n", + " [42.4 48.300003]]\n", + "Part 2 Result: \n", + " 367.3483\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -318,18 +457,57 @@ "metadata": { "id": "GQWyCvsQmMcL", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + }, + "outputId": "c9161335-46db-4fc6-ccc3-3af3a064058b" }, "cell_type": "code", "source": [ "# Build the graph\n", "# YOUR CODE HERE\n", + "t1 = tf.constant(([7.36, 8.93, 10.41],\n", + " [5.31, 9.38, 7.99]), dtype=tf.float32)\n", + "t2 = tf.constant(([7.99, 10.36],\n", + " [5.36, 7.98],\n", + " [8.91, 5.67]), dtype=tf.float32)\n", + "t2 = tf.transpose(t2)\n", + "comp_graph_part_1 = tf.reduce_sum(t1 * t2)\n", + "\n", + "comp_graph_part_2 = (tf.constant(7.0) + comp_graph_part_1) / 19.6\n", "\n", + "comp_graph_tot = comp_graph_part_2 / tf.constant(([1, 5.6, 6.1, 8],\n", + " [0, 0, 7.98, 9],\n", + " [0, 0, 7.6, 7],\n", + " [0, 0, 0, 8.98]), dtype=tf.float32)\n", + "\n", + "with tf.Session() as sess:\n", + " part1_res, part2_res, total_result = sess.run([comp_graph_part_1, comp_graph_part_2, comp_graph_tot])\n", + " print ('Total Result: \\n', total_result)\n", + " print ('Part 1 Result: \\n', part1_res)\n", + " print ('Part 2 Result: \\n', part2_res)\n", "# Execute \n", "# YOUR CODE HERE" ], - "execution_count": 0, - "outputs": [] + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Total Result: \n", + " [[19.46896 3.4766 3.1916327 2.43362 ]\n", + " [ inf inf 2.4397192 2.1632178]\n", + " [ inf inf 2.5617054 2.78128 ]\n", + " [ inf inf inf 2.1680357]]\n", + "Part 1 Result: \n", + " 374.5916\n", + "Part 2 Result: \n", + " 19.46896\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -388,7 +566,11 @@ "metadata": { "id": "1h1-D8K1uT48", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "0a4ef3ff-705b-4386-8830-9c05faa5c52c" }, "cell_type": "code", "source": [ @@ -396,8 +578,21 @@ "plt.plot(train_X[:10], train_Y[:10], 'g')\n", "plt.show()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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wMjQSHY30GAuZiOgvLj24iJF7h+NS9gU0tHsZK7qvgW9df9GxqBYwFB2AiEgOJEnCmrMr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\nIRPpod/un0BM8lw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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { @@ -408,7 +603,7 @@ "source": [ "** Question **
\n", "Why did I created a session to plot the graph?
\n", - "[Ans]" + "[Ans] tf.session.run is used to evaluate a tensor. So in order to visualize the characteristics of the plot and to evaluate and find the values of the tensors we need to use session." ] }, { @@ -490,7 +685,11 @@ "metadata": { "id": "ttI7ZT-ozAm1", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 721 + }, + "outputId": "0e560c0e-8254-4ffb-e23d-4200dad1df80" }, "cell_type": "code", "source": [ @@ -524,8 +723,49 @@ " plt.legend()\n", " plt.show()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48241.758\n", + "Loss after epoch 50 is 29.843296\n", + "Loss after epoch 100 is 29.832943\n", + "Loss after epoch 150 is 29.822683\n", + "Loss after epoch 200 is 29.812458\n", + "Loss after epoch 250 is 29.802202\n", + "Loss after epoch 300 is 29.791937\n", + "Loss after epoch 350 is 29.781736\n", + "Loss after epoch 400 is 29.771505\n", + "Loss after epoch 450 is 29.761292\n", + "Loss after epoch 500 is 29.751057\n", + "Loss after epoch 550 is 29.74081\n", + "Loss after epoch 600 is 29.730623\n", + "Loss after epoch 650 is 29.72042\n", + "Loss after epoch 700 is 29.710188\n", + "Loss after epoch 750 is 29.7\n", + "Loss after epoch 800 is 29.689804\n", + "Loss after epoch 850 is 29.679585\n", + "Loss after epoch 900 is 29.669428\n", + "Loss after epoch 950 is 29.659227\n", + "Now testing the model in the test set\n", + "The final loss is: 35.69291\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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FQoqrHUsEIM21q1hmTsO8YC6a9HQ8pctgj47F+e82oM2ehUj8nRSyEHmAoijM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\njjFcc1yjdFgZRj06nmYlW8hiCgFMvyc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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { @@ -550,8 +790,24 @@ "cell_type": "code", "source": [ "def linear_regression(learning_rate=0.000005, n_epochs=100, interval=50):\n", + " \n", + "\n", " # YOUR CODE HERE\n", - " pass" + " with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " \n", + " for epoch in range(n_epochs):\n", + " pred_train, current_loss = sess.run([optimizer, loss], feed_dict={x:train_X, y:train_Y})\n", + "\n", + " if epoch % interval == 0:\n", + " print ('Loss after epoch', epoch, ' is ', current_loss)\n", + "\n", + " print ('Now testing the model in the test set')\n", + " final_pred, final_loss = sess.run([pred_y, loss], feed_dict={x:test_X, y:test_Y})\n", + " \n", + " print ('The final loss is: ', final_loss)\n", + " \n", + " return\n" ], "execution_count": 0, "outputs": [] @@ -560,15 +816,38 @@ "metadata": { "id": "A6MaclhK4rc6", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "9988175d-c6f6-4cea-81a1-dc213bf5710c" }, "cell_type": "code", "source": [ "# Okay! Now let's tweak!\n", "linear_regression(learning_rate=0.000034, n_epochs=500)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48241.758\n", + "Loss after epoch 50 is 29.843296\n", + "Loss after epoch 100 is 29.832943\n", + "Loss after epoch 150 is 29.822683\n", + "Loss after epoch 200 is 29.812458\n", + "Loss after epoch 250 is 29.802202\n", + "Loss after epoch 300 is 29.791937\n", + "Loss after epoch 350 is 29.781736\n", + "Loss after epoch 400 is 29.771505\n", + "Loss after epoch 450 is 29.761292\n", + "Now testing the model in the test set\n", + "The final loss is: 35.814262\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -601,7 +880,278 @@ }, "cell_type": "code", "source": [ - "# YOUR CODE HERE" + "# YOUR CODE HERE\n", + "def linear_regression(learning_rate=0.0000001, n_epochs=1000, interval=100):\n", + " # YOUR CODE HERE\n", + " with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " \n", + " for epoch in range(n_epochs):\n", + " pred_train, current_loss = sess.run([optimizer, loss], feed_dict={x:train_X, y:train_Y})\n", + "\n", + " if epoch % interval == 0:\n", + " print ('Loss after epoch', epoch, ' is ', current_loss)\n", + "\n", + " print ('Now testing the model in the test set')\n", + " final_pred, final_loss = sess.run([pred_y, loss], feed_dict={x:test_X, y:test_Y})\n", + " \n", + " print ('The final loss is: ', final_loss)\n", + " \n", + " return\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "0knOp0SUmOoy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + }, + "outputId": "087d8c9e-c3e5-4d8d-93a6-14c57d3ac642" + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.8, n_epochs=200000, interval=40000)" + ], + "execution_count": 44, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48241.758\n", + "Loss after epoch 40000 is 22.710217\n", + "Loss after epoch 80000 is 17.332392\n", + "Loss after epoch 120000 is 13.283219\n", + "Loss after epoch 160000 is 10.234737\n", + "Now testing the model in the test set\n", + "The final loss is: 9.740068\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "4smcXWZdnYy1", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "ce2609e8-e1b0-4c10-ea16-84cdadceeb9f" + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.08, n_epochs=300000, interval=30000)" + ], + "execution_count": 45, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48241.758\n", + "Loss after epoch 30000 is 24.310637\n", + "Loss after epoch 60000 is 19.8307\n", + "Loss after epoch 90000 is 16.209751\n", + "Loss after epoch 120000 is 13.283219\n", + "Loss after epoch 150000 is 10.9177685\n", + "Loss after epoch 180000 is 9.004981\n", + "Loss after epoch 210000 is 7.460944\n", + "Loss after epoch 240000 is 6.211317\n", + "Loss after epoch 270000 is 5.2022204\n", + "Now testing the model in the test set\n", + "The final loss is: 5.4466424\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "49PIqfuWqIBm", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "b54c1e77-b080-4f67-87af-9dc0c3e511c6" + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.8, n_epochs=500000, interval=50000)" + ], + "execution_count": 46, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48241.758\n", + "Loss after epoch 50000 is 21.219416\n", + "Loss after epoch 100000 is 15.16438\n", + "Loss after epoch 150000 is 10.9177685\n", + "Loss after epoch 200000 is 7.9400454\n", + "Loss after epoch 250000 is 5.8506804\n", + "Loss after epoch 300000 is 4.386475\n", + "Loss after epoch 350000 is 3.3583517\n", + "Loss after epoch 400000 is 2.6385438\n", + "Loss after epoch 450000 is 2.132647\n", + "Now testing the model in the test set\n", + "The final loss is: 2.2477086\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "I0IaSFJ1q2LT", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "d5666f1e-b748-4f41-9a66-b68ef24da17e" + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.08, n_epochs=500000, interval=50000)" + ], + "execution_count": 47, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48241.758\n", + "Loss after epoch 50000 is 21.219416\n", + "Loss after epoch 100000 is 15.16438\n", + "Loss after epoch 150000 is 10.9177685\n", + "Loss after epoch 200000 is 7.9400454\n", + "Loss after epoch 250000 is 5.8506804\n", + "Loss after epoch 300000 is 4.386475\n", + "Loss after epoch 350000 is 3.3583517\n", + "Loss after epoch 400000 is 2.6385438\n", + "Loss after epoch 450000 is 2.132647\n", + "Now testing the model in the test set\n", + "The final loss is: 2.2477086\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "4b7eBOolq412", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "outputId": "b7245316-aa82-4f24-e7be-085bc9b6e95e" + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.8, n_epochs=1000000, interval=50000)" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48241.758\n", + "Loss after epoch 50000 is 21.219416\n", + "Loss after epoch 100000 is 15.16438\n", + "Loss after epoch 150000 is 10.9177685\n", + "Loss after epoch 200000 is 7.9400454\n", + "Loss after epoch 250000 is 5.8506804\n", + "Loss after epoch 300000 is 4.386475\n", + "Loss after epoch 350000 is 3.3583517\n", + "Loss after epoch 400000 is 2.6385438\n", + "Loss after epoch 450000 is 2.132647\n", + "Loss after epoch 500000 is 1.7783551\n", + "Loss after epoch 550000 is 1.5294437\n", + "Loss after epoch 600000 is 1.3565342\n", + "Loss after epoch 650000 is 1.2327821\n", + "Loss after epoch 700000 is 1.1488175\n", + "Loss after epoch 750000 is 1.088131\n", + "Loss after epoch 800000 is 1.0485603\n", + "Loss after epoch 850000 is 1.0154438\n", + "Loss after epoch 900000 is 0.99606717\n", + "Loss after epoch 950000 is 0.9841915\n", + "Now testing the model in the test set\n", + "The final loss is: 1.2012733\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "cI9eNZeHsnz8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561 + }, + "outputId": "67fa45be-856c-49fa-8835-9d629e99ba7c" + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.8, n_epochs=3000000, interval=100000)" + ], + "execution_count": 49, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48241.758\n", + "Loss after epoch 100000 is 15.16438\n", + "Loss after epoch 200000 is 7.9400454\n", + "Loss after epoch 300000 is 4.386475\n", + "Loss after epoch 400000 is 2.6385438\n", + "Loss after epoch 500000 is 1.7783551\n", + "Loss after epoch 600000 is 1.3565342\n", + "Loss after epoch 700000 is 1.1488175\n", + "Loss after epoch 800000 is 1.0485603\n", + "Loss after epoch 900000 is 0.99606717\n", + "Loss after epoch 1000000 is 0.9739313\n", + "Loss after epoch 1100000 is 0.9582495\n", + "Loss after epoch 1200000 is 0.9526469\n", + "Loss after epoch 1300000 is 0.9526469\n", + "Loss after epoch 1400000 is 0.9526469\n", + "Loss after epoch 1500000 is 0.9526469\n", + "Loss after epoch 1600000 is 0.9526469\n", + "Loss after epoch 1700000 is 0.9526469\n", + "Loss after epoch 1800000 is 0.9526469\n", + "Loss after epoch 1900000 is 0.9526469\n", + "Loss after epoch 2000000 is 0.9526469\n", + "Loss after epoch 2100000 is 0.9526469\n", + "Loss after epoch 2200000 is 0.9526469\n", + "Loss after epoch 2300000 is 0.9526469\n", + "Loss after epoch 2400000 is 0.9526469\n", + "Loss after epoch 2500000 is 0.9526469\n", + "Loss after epoch 2600000 is 0.9526469\n", + "Loss after epoch 2700000 is 0.9526469\n", + "Loss after epoch 2800000 is 0.9526469\n", + "Loss after epoch 2900000 is 0.9526469\n", + "Now testing the model in the test set\n", + "The final loss is: 1.1648217\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "mjeM--rNujcm", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" ], "execution_count": 0, "outputs": [] From 648500dea99e7e25b9740d161bdf5d7651d62cde Mon Sep 17 00:00:00 2001 From: AGCreates <43198265+AGCreates@users.noreply.github.com> Date: Sat, 26 Jan 2019 02:39:30 +0530 Subject: [PATCH 3/3] Delete First_Date_with_TensorFlow.ipynb --- First_Date_with_TensorFlow.ipynb | 962 ------------------------------- 1 file changed, 962 deletions(-) delete mode 100644 First_Date_with_TensorFlow.ipynb diff --git a/First_Date_with_TensorFlow.ipynb b/First_Date_with_TensorFlow.ipynb deleted file mode 100644 index a30a52c..0000000 --- a/First_Date_with_TensorFlow.ipynb +++ /dev/null @@ -1,962 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "First_Date_with_TensorFlow.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": "2XXfXed5YLbe", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "# First Date with TensorFlow\n", - "\n", - "Hi all,
\n", - "\n", - "You know what's important for understanding Deep Learning / Machine Learning?
\n", - "Intuition. Period.\n", - "\n", - "And Intuition comes when you run the code multiple times.\n", - "\n", - "So, today I can write a couple of defination and say this is this, this is that.
\n", - "You Google half of the things up. You find answers which you need to Google further.
\n", - "In the process, you probably won't even remember what's the first thing you started out with!\n", - "\n", - "So?\n", - "\n", - "Hence on, I will execute cells with code.
\n", - "The neurons in your brain will optimize a function to get a hold of what each function is doing.
\n", - "**No Theory Just Code.**\n", - "\n", - "I will at max give a defination that extends for a line. That's it.
\n", - "Let's get started!\n", - "\n", - "
\n", - "\n", - "**RECOMMENDED!**
\n", - "Write the code in the cells using the signals sent by your brain to your fingers!
\n", - "Don't just `shift+enter` the cells.\n", - "\n", - "[Source](https://github.com/iArunava/TensorFlow-NoteBooks)" - ] - }, - { - "metadata": { - "id": "gYWUpE-bYKWP", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# Essential imports\n", - "import numpy as np\n", - "import tensorflow as tf\n", - "import matplotlib.pyplot as plt" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "eKpz5NCIYMdi", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# Let's define some tensors\n", - "t1 = tf.constant(2.0, dtype=tf.float32)\n", - "t2 = tf.constant([1.0, 2.0], dtype=tf.float32)\n", - "t3 = tf.constant([[[1.0, 9.0], [2.0, 3.0], [4.0, 5.0]], \n", - " [[1.0, 9.0], [2.0, 3.0], [4.0, 5.0]]])" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "vmMcjzTxbWzw", - "colab_type": "code", - "outputId": "8291527d-4aa9-4d4c-f944-dd2aaf5e5af3", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 68 - } - }, - "cell_type": "code", - "source": [ - "# Let's print them out!\n", - "print (t1)\n", - "print (t2)\n", - "print (t3)" - ], - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Tensor(\"Const:0\", shape=(), dtype=float32)\n", - "Tensor(\"Const_1:0\", shape=(2,), dtype=float32)\n", - "Tensor(\"Const_2:0\", shape=(2, 3, 2), dtype=float32)\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "10ahnfjYbcop", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Where's Waldo?
\n", - "I mean, the value?
\n", - "\n", - "So, the thing is you can't print the value of tensors directly.
\n", - "You have to use `session`, so let's do that!" - ] - }, - { - "metadata": { - "id": "ol6O5I7Tb2nb", - "colab_type": "code", - "outputId": "2d9d1191-8b41-4134-a138-d4be2a9721a8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 204 - } - }, - "cell_type": "code", - "source": [ - "sess = tf.Session()\n", - "print (sess.run(t1))\n", - "print (\"=======================\")\n", - "print (sess.run(t2))\n", - "print (\"=======================\")\n", - "print (sess.run(t3))\n", - "sess.close()" - ], - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "text": [ - "2.0\n", - "=======================\n", - "[1. 2.]\n", - "=======================\n", - "[[[1. 9.]\n", - " [2. 3.]\n", - " [4. 5.]]\n", - "\n", - " [[1. 9.]\n", - " [2. 3.]\n", - " [4. 5.]]]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "rXKfVs_zb-kU", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Aaahaa!! Just printed those tensors!!!
\n", - "Feels good!
\n", - "\n", - "For some of you, who are like, dude you got \"No Theory Just Code\" in bold
\n", - "And you are still using the markdown cells for the theory ?!\n", - "\n", - "I am just gonna say I am a unreasonable man.
\n", - "\n", - "\n", - "So, you are programming with tf.
\n", - "What ever you do is broken down to 2 basic steps:\n", - "- Building the computational Graph!\n", - "- Execute that graph using `session`!\n", - "\n", - "That's all!\n", - "\n", - "
\n", - "\n", - "Let's compare this 2 steps with what we did above!
\n", - "So, I defined 3 `tensor`s and these 3 `tensor`s formed my computational Graph.
\n", - "And then I executed each tensor in this graph using a `session`.\n", - "\n", - "That simple!\n", - "\n", - "
\n", - "\n", - "Now, let's define a few more computational graphs and execute them with sessions.\n", - "\n", - "Okay, to start with let's build this computational graph!\n", - "\n", - "![Comp Graph 1](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_1.jpg)" - ] - }, - { - "metadata": { - "id": "FyVz0GNqgreZ", - "colab_type": "code", - "outputId": "b1f861fc-981e-4df4-b8c5-0c18a0a0b558", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 51 - } - }, - "cell_type": "code", - "source": [ - "# Let's define the graph\n", - "comp_graph_1 = tf.multiply(tf.add(78, 19), 79)\n", - "\n", - "# Alternatively\n", - "comp_graph_1_alt = (tf.constant(78) + tf.constant(19)) * tf.constant(79)\n", - "\n", - "# Let's execute using session\n", - "sess = tf.Session()\n", - "print ('Comp Graph 1 : ', sess.run(comp_graph_1))\n", - "print ('Comp Graph 1 Alt: ', sess.run(comp_graph_1_alt))\n", - "sess.close()" - ], - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Comp Graph 1 : 7663\n", - "Comp Graph 1 Alt: 7663\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "SVMMtuFYhaQB", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Let's define a sligtly more involved graph!\n", - "\n", - "![alt text](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_2.jpg)" - ] - }, - { - "metadata": { - "id": "4856BTvRhiBb", - "colab_type": "code", - "outputId": "35451a1d-7286-4225-a8ab-c3f5655da3e6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 68 - } - }, - "cell_type": "code", - "source": [ - "# Let build the graph\n", - "# We need to cast cause the tensors operated on should be of the same type\n", - "comp_graph_part_1 = tf.cast(tf.subtract(tf.add(7, 8), tf.add(9, 10)), \n", - " dtype=tf.float32)\n", - "comp_graph_part_2 = tf.divide(tf.cast(tf.multiply(7, 10), dtype=tf.float32), tf.constant(19.5))\n", - "comp_graph_complete = tf.maximum(comp_graph_part_1, comp_graph_part_2)\n", - "\n", - "# Let's execute\n", - "sess = tf.Session()\n", - "part1_res, part2_res, total_res = sess.run([comp_graph_part_1, comp_graph_part_2, comp_graph_complete])\n", - "print ('Complete Result: ', total_res)\n", - "print ('Part 1 Result: ', part1_res)\n", - "print ('Part 2 Result: ', part2_res)\n", - "sess.close()" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Complete Result: 3.5897436\n", - "Part 1 Result: -4.0\n", - "Part 2 Result: 3.5897436\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "B-_ZDtEbj4N0", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Cool! Let's go! Build another graph and execute it with sessions.
\n", - "\n", - "But this time, it's all you!\n", - "\n", - "Build this graph and execute it with `session`!\n", - "\n", - "![alt text](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_3.jpg)\n", - "\n", - "_Remember that `tensors` operated on should be of the same type!_
\n", - "_Search up errors and other help you need on Google_" - ] - }, - { - "metadata": { - "id": "-uHNe1BolJY0", - "colab_type": "code", - "outputId": "4039c026-a7f0-47d0-99a0-d411ae6a720b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 68 - } - }, - "cell_type": "code", - "source": [ - "# Build the graph\n", - "# YOUR CODE HERE\n", - "comp_graph_part_1 = tf.constant([9, 10], dtype=tf.float32) * tf.constant([7, 8.65], dtype=tf.float32)\n", - "comp_graph_part_1 = comp_graph_part_1 / 5.6\n", - "comp_graph_part_2 = tf.constant([7.65, 9], dtype=tf.float32) + tf.constant([13.5, 7.19], dtype=tf.float32)\n", - "\n", - "comp_graph_complete = tf.minimum(comp_graph_part_1, comp_graph_part_2)\n", - "\n", - "with tf.Session() as sess:\n", - " part1_res, part2_res, total_res = sess.run([comp_graph_part_1, comp_graph_part_2, comp_graph_complete])\n", - " print ('Complete Result: ', total_res)\n", - " print ('Part 1 Result: ', part1_res)\n", - " print ('Part 2 Result: ', part2_res)\n", - "# Execute \n", - "# YOUR CODE HERE" - ], - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Complete Result: [11.25 15.446429]\n", - "Part 1 Result: [11.25 15.446429]\n", - "Part 2 Result: [21.15 16.19]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "qmap38WelREN", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Let's do another!
\n", - "It's fun! Isn't it?!\n", - "\n", - "Build and execute this one!\n", - "\n", - "![alt text](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_4.jpg)" - ] - }, - { - "metadata": { - "id": "0ZhYwAlLmEvB", - "colab_type": "code", - "outputId": "cef4f995-b664-4b1f-9b24-cf1d3f44e638", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 153 - } - }, - "cell_type": "code", - "source": [ - "# Build the graph\n", - "# YOUR CODE HERE\n", - "comp_graph_part_1 = tf.constant(([7.2, 3.4],\n", - " [7.5, 8.6]), dtype=tf.float32)\n", - "comp_graph_part_1 = tf.reduce_mean(comp_graph_part_1, 1)\n", - "comp_graph_part_2 = tf.constant(([7, 9],\n", - " [8, 6]), dtype=tf.float32)\n", - "p1_graph = comp_graph_part_1 * comp_graph_part_2\n", - "\n", - "comp_graph_part_3 = tf.constant(([2.79, 3.81, 5.6],\n", - " [7.3, 5.67, 8.9]), dtype=tf.float32)\n", - "comp_graph_part_4 = tf.constant(([2.6, 18.1],\n", - " [7.86, 9.81],\n", - " [9.36, 10.11]), dtype=tf.float32)\n", - "comp_graph_part_4 = tf.transpose(comp_graph_part_4)\n", - "p2_graph = tf.reduce_sum(comp_graph_part_3 * comp_graph_part_4)\n", - "\n", - "comp_graph_complete = p1_graph + p2_graph\n", - "\n", - "with tf.Session() as sess:\n", - " part1_res, part2_res, total_res = sess.run([p1_graph, p2_graph, comp_graph_complete])\n", - " print ('Complete Result: \\n', total_res)\n", - " print ('Part 1 Result: \\n', part1_res)\n", - " print ('Part 2 Result: \\n', part2_res)\n", - "# Execute \n", - "# YOUR CODE HERE" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Complete Result: \n", - " [[404.4483 439.7983 ]\n", - " [409.7483 415.64832]]\n", - "Part 1 Result: \n", - " [[37.100002 72.450005]\n", - " [42.4 48.300003]]\n", - "Part 2 Result: \n", - " 367.3483\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "BnB0b6qCmGmg", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "And a final one, before we move on to the next part!\n", - "\n", - "![alt text](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_5.jpg)" - ] - }, - { - "metadata": { - "id": "GQWyCvsQmMcL", - "colab_type": "code", - "outputId": "c841d00d-a993-4140-b5b7-61ab3bddb87b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 170 - } - }, - "cell_type": "code", - "source": [ - "# Build the graph\n", - "# YOUR CODE HERE\n", - "t1 = tf.constant(([7.36, 8.93, 10.41],\n", - " [5.31, 9.38, 7.99]), dtype=tf.float32)\n", - "t2 = tf.constant(([7.99, 10.36],\n", - " [5.36, 7.98],\n", - " [8.91, 5.67]), dtype=tf.float32)\n", - "t2 = tf.transpose(t2)\n", - "comp_graph_part_1 = tf.reduce_sum(t1 * t2)\n", - "\n", - "comp_graph_part_2 = (tf.constant(7.0) + comp_graph_part_1) / 19.6\n", - "\n", - "comp_graph_tot = comp_graph_part_2 / tf.constant(([1, 5.6, 6.1, 8],\n", - " [0, 0, 7.98, 9],\n", - " [0, 0, 7.6, 7],\n", - " [0, 0, 0, 8.98]), dtype=tf.float32)\n", - "\n", - "with tf.Session() as sess:\n", - " part1_res, part2_res, total_result = sess.run([comp_graph_part_1, comp_graph_part_2, comp_graph_tot])\n", - " print ('Total Result: \\n', total_result)\n", - " print ('Part 1 Result: \\n', part1_res)\n", - " print ('Part 2 Result: \\n', part2_res)\n", - "# Execute \n", - "# YOUR CODE HERE" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Total Result: \n", - " [[19.46896 3.4766 3.1916327 2.43362 ]\n", - " [ inf inf 2.4397192 2.1632178]\n", - " [ inf inf 2.5617054 2.78128 ]\n", - " [ inf inf inf 2.1680357]]\n", - "Part 1 Result: \n", - " 374.5916\n", - "Part 2 Result: \n", - " 19.46896\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "12NC7XTPsJw7", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "# Linear Regression\n", - "\n", - "Okay, now we will create a dummy dataset and perform linear regression on this dataset!\n", - "\n", - "\n", - "To get you in the habit of looking up for the documentation, I am not providing what some of the following functions does, Google them up!" - ] - }, - { - "metadata": { - "id": "hW31RZkjtNwI", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# Create the dataset\n", - "X = np.linspace(-30.0, 300.0, 300)\n", - "Y = 2 * np.linspace(-30.0, 250.0, 300) + np.random.randn(*X.shape)\n", - "\n", - "# Normalize the dataset\n", - "X = X / np.max(X)\n", - "Y = Y / np.max(Y)\n", - "\n", - "# Divide it into train and test\n", - "train_X = X[:250]\n", - "train_Y = Y[:250]\n", - "\n", - "test_X = X[250:]\n", - "test_Y = Y[250:]" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "LQKy6U33y4lt", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# Let's define the hyperparameters\n", - "learning_rate = 0.00001\n", - "n_epochs = 60\n", - "interval = 20" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "1h1-D8K1uT48", - "colab_type": "code", - "outputId": "bf8df7f9-8ef1-4f92-e635-0c30075c120b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 347 - } - }, - "cell_type": "code", - "source": [ - "# let's viz the first 10 datapoints of the dataset\n", - "plt.plot(train_X[:10], train_Y[:10], 'g')\n", - "plt.show()" - ], - "execution_count": 14, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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Cu3J2AjCo9RAmhUymc5Pgv9zG0We2lSvOrZmdm833yEXEfIZhsPVwKnPSE0g/\nsgOAga1v5pEek/UjZSIuTkUuUosZhsHHhz9iTnoCO49sB2BA68E82iNWBS4igIpcpFYyDINPsrYy\nJz2BHT+nATCg1SAeCYklOOBak9OJSG2iIhepRQzD4NOsj5mTnsD2n7cBcFOrgTzSI5YugV1NTici\ntZGKXKSW+CzrE55On01a9mcA9G85gEdDpqjAReS8VOQiJtuW9SlPp89mW/anAES2vIlHQ6ZwbWA3\nk5OJiCNQkYuYZFvWp8xJT+Cz7E8AuLFFfx4NmULXoO5VbCki8j8qcpEalpb9GXPSE/g062MA/tYi\nkkdDptAtqIfJyUTEEanIRWrI9uxtzElP4JOsrcCvBf5ISCzdg0JMTiYijkxFLlLNtv+c9muBH/4I\ngH4tbuSRHrH0aNrT3GAi4hRU5CLVZMfP25mTnsDHh1MBiGj+Nx4JiSWk6YX9kiERkQuhIhexs50/\n72BO+my2/lbgNzTvxyM9ptCzmQpcROxPRS5iJ+lHdjAnPYGPDm0BoO8VETwSMoXQZteZnExEnJmK\nXOQS7TqykznpCaQe2gxAnysieCQkluuaXW9yMhFxBSpyERvtzklnTnoCWw5+CEDvK27g0R6xXHdZ\nmMnJRMSVqMhFLlJGzi7mpCew+eAHAPS+vC+PhMRy/WXhJicTEVekIhe5QBk5u5ibnsiHBzcB0Ovy\nPjwaMkUFLiKmUpGLVGFPzm7m7krkg5/eByD8st48GjKFsMt7mZxMRERFLnJOZ8rO8H8f/oO3v3sT\ngOsvC+exkDjCL+9tcjIRkf9RkYv8hbLyMsZ9eD/vfLeW7kE9mHrddHpd3sfsWCIiZ1GRi/yJYRhM\n+eQR3vluLWGX9WL1zW9Sz6Oe2bFERP6Sm9kBRGqbubsSeWXfYq5p3JlXB65SiYtIraYiF/mDV75c\nzJz0BFr4tWL1zWvwq9vA7EgiIuelIhf5zTvfrWXyxxNp4tWE14e8RZBPU7MjiYhUSUUuAnxyeCsP\nfnAf3nV8WDV4DW0atDU7kojIBVGRi8vba83k3vfuAmDZwNfoEtjV5EQiIhdOn1oXl/bD8e+5c/3t\nnCwp5KX+r9DnihvMjiQiclFU5OKycopyGP7OreSespLYJ4mhV95mdiQRkYumS+vikgrOHCd6/R38\nVPAjk3pMZnSn+82OJCJiExW5uJzTpae59727+DL3C+65ejSPhcSZHUlExGYqcnEpZeVlPPjhfXyW\n/Qk3t7mFp/okYbFYzI4lImIzFbm4DMMwGL9hPO9+v47wy3rz/I0v4e7mbnYsEZFLoiIXl/F0+mz+\nvfvfdGoSzKuD9OhVEXEOKnJFP3LHAAAYKUlEQVRxCUu+fImkXU/RplEbVt28Bl9PP7MjiYjYhX78\nTJze29++yZSPHyHAK5BNIzfhVxZodiQREbvRGbk4tY8Pf8S4D+/Hp059Vt+8hrb+evSqiDgXm4q8\npKSESZMmER0dzciRIzl06NBZ6xw/fpwxY8YwYcKESst37tzJ9ddfT2pqasWy/fv3M2LECEaMGEF8\nfLwtkUTOknl0D/e+dxcWLLw6aBWdA7qYHUlExO5sKvL169fj5+fHqlWrGDt2LElJSWetEx8fT/fu\n3SstO3jwIEuXLqVbt26Vls+aNYu4uDhWr15NYWEhW7dutSWWSIXv878l+t07KCo5yQuRi+l1eR+z\nI4mIVAubijwtLY3IyEgAwsLCyMjIOGudmTNnnlXkAQEBLFiwAF9f34plxcXFZGVlERwcDEBERARp\naWm2xBIBIOfkEYa/cxu5p3J5uu8zDGl7i9mRRESqjU0fdsvNzcXf3x8ANzc3LBYLxcXFeHp6VqxT\nv379s7bz8vI6a1leXh5+fv/7BHHjxo2xWq3nff9Gjbzx8LD/z/8GBPhWvZKTcbaZ80/nc/eaYRw8\n8RMzbpjBI30fPmsdZ5v5QrjizOCac2tm11NlkaekpJCSklJpWWZmZqWvDcOwW6AL2VdeXpHd3u93\nAQG+WK0n7L7f2szZZj5VeooR62/ni5wv+Hun+xh71T/Pms/ZZr4QrjgzuObcmtm5nesbliqLPCoq\niqioqErLYmNjsVqtdOzYkZKSEgzDqHQ2fjH8/f3Jz8+v+DonJ4fAQP14kFyc0vJSxn4whrTszxjS\n9lZm95qjR6+KiEuw6R55eHg4GzduBCA1NZXQ0FCbA9SpU4c2bdqwa9cuADZt2kTv3r1t3p+4HsMw\neGzr/+O9H9bT+/K+evSqiLgUm+6RDxo0iG3bthEdHY2npyeJiYkALFq0iJCQEIKDg4mJiaGgoICc\nnBxGjRrFuHHjOHPmDIsXL+b7779n3759LF++nCVLlhAXF8e0adMoLy+nS5cuhIWF2XVIcW6JO59k\nxX+XERxwLa8MXEld97pmRxIRqTEWw543uGtIddwPcaX7LL9zhplf/uJF4j59jFZ+rVl/+wcEep//\ntowzzHyxXHFmcM25NbNzO9c9cj3ZTRzW2m/WMPXTyQR6B/H6kLVVlriIiDNSkYtD+ujQFsZvfoD6\nnr6svvlNWjVobXYkERFTqMjF4Xx+NIOY9+7GzeLG8oGr6dSks9mRRERMo99+Jg7lu/xviF5/B6fL\nTrH4puWEXd7L7EgiIqZSkYvDOHLyZ4a/cxu/nP6FpBuSGdxmiNmRRERMp0vr4hCOn8nnzndu59CJ\ng8T2fJxRV8eYHUlEpFZQkUutd6r0FCM33Ml/j+1jTOcH+H/dHzU7kohIraEil1qttLyUf2z6Ozt+\nTuPWK29nVq+n9ehVEZE/UJFLrWUYBo989DAbf9xAnysieO5v/8bNor+yIiJ/pH8VpdaaveMJXtu/\nnGsDuvLKgBV69KqIyF9QkUuttCjzeZ7NSKJNg7a8dvMa6nu69u8bFhE5FxW51Dprvn6dxz+LJci7\nKa8PWUsTryZmRxIRqbVU5FKrbDn4IQ9tGYufZwNW3/wmLfxamh1JRKRWU5FLrZGRs4vRG0fhbnFn\n+aDVXNOkk9mRRERqPT3ZTWqFb/K+5q53h3G67BRLB6zk+svCzY4kIuIQVORiuuzCLO585zaOnT7G\nvBueY2DrwWZHEhFxGLq0LqbKO32MEetv53DhIeJCpzHy6nvNjiQi4lBU5GKaopIiRm64k/3H/sv9\nncfycLdJZkcSEXE4KnIxRWl5KQ9siiH9yA5ubzeMJ3sl6tGrIiI2UJFLjTMMg0kfTWDTTxu5oXk/\nkvu9qEeviojYSP96So2buX06q/avoGtgN5YMWIGnu6fZkUREHJaKXGrUi5kLeG7PM7RteCUrB79B\n/Tr1zY4kIuLQVORSY1IOrGbaZ3E09WmmR6+KiNiJfo5cql3WicPM2jGDN77+Dw3qNuQ/N79Fc98W\nZscSEXEKKnKpNoUlhSzYM58XPn+OU6WnCA64lqS+z3JV46vNjiYi4jRU5GJ3ZeVlvH5gFbN3PEFO\n0RGa+jTjqT7zGN4hWp9OFxGxMxW52NWnWR8z7bM4vsz9Ai8PLx7pEcv4rg/jU8fH7GgiIk5JRS52\n8V3+N8xIm8bGH94FYHiHaKaGxtOs/mUmJxMRcW4qcrkkeaePMW/X0yz+chGl5aVc1yyMJ8Jnc21g\nN7OjiYi4BBW52KSkrIRX9r3M3PRE8s7k0dKvFfHXz2RwmyF61KqISA1SkctFMQyDTT9tZPq2qXyX\n/y1+ng2YHjaLMZ0foK57XbPjiYi4HBW5XLAvc/cS/1kcn2Rtxd3izuhO9/NIyBQ92EVExEQqcqlS\nTlEOiTue5LX/LsfA4MYW/ZkeNov2/h3MjiYi4vJU5HJOp0pP8eLnC3g2Yx5FpSe5yv9qpofNIqLF\n38yOJiIiv1GRy1kMw+DNb1KYuX06WYWHaeIVwBPhs7nrqlF4uOmvjIhIbaJ/laWSnT/vIH7bFHbn\n7KKue10mdJ3Iw90n4uvpZ3Y0ERH5CzYVeUlJCbGxsWRnZ+Pu7k5CQgLNmzevtM7x48eZOHEiPj4+\nJCcnVyzfuXMnDz/8MLNnzyYiIgKAUaNGUVRUhLe3NwCTJ0+mU6dOts4kNjhY8BNPpsXz9ndvAnDr\nlbcz9brptPRrZW4wERE5L5uKfP369fj5+ZGUlMSnn35KUlIS8+fPr7ROfHw83bt3Z//+/RXLDh48\nyNKlS+nW7eyHhSQkJNC+fXtb4sglOFFcwPzdSSz64nnOlJ2hW2B3nghPpGezULOjiYjIBbDpN1ik\npaURGRkJQFhYGBkZGWetM3PmTLp3715pWUBAAAsWLMDX19eWtxU7Ki0v5d+7/k3oymt5bs8zNPEK\n4IUbX2bDHZtV4iIiDsSmM/Lc3Fz8/f0BcHNzw2KxUFxcjKenZ8U69evXP2s7Ly+vc+4zOTmZvLw8\n2rZtS1xcHPXq1Tvnuo0aeePh4W5L9PMKCHCNbzA2fbeJSZsm8eXRL/Gp48PMiJlMvH4iXnXOfXyc\niasc5z9yxZnBNefWzK6nyiJPSUkhJSWl0rLMzMxKXxuGcUkh7rnnHjp06ECLFi2Ij49n5cqVjBkz\n5pzr5+UVXdL7/ZWAAF+s1hN2329t8vWxA8Rvi2PzwQ+wYOG+rvfxcJfJBHkHUZhfSiHOPT+4xnH+\nM1ecGVxzbs3s3M71DUuVRR4VFUVUVFSlZbGxsVitVjp27EhJSQmGYVQ6G79Yv1+mB+jXrx8bNmyw\neV9ytl9O/cKc9Nks27eEMqOM3pf3ZUb4bCKuCnOZ/wBERJyVTffIw8PD2bhxIwCpqamEhtp+T9Uw\nDGJiYigoKABgx44dtGvXzub9yf+cKTvDwj3JhK68liVfvkSrBq1ZPug/vDF0HZ2adDY7noiI2IFN\n98gHDRrEtm3biI6OxtPTk8T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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "metadata": { - "id": "jrsUps0nu8vj", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "** Question **
\n", - "Why did I created a session to plot the graph?
\n", - "Ans) tf.session.run is used to evaluate a tensor. So in order to visualize the characteristics of the plot and to evaluate and find the values of the tensors we need to use session." - ] - }, - { - "metadata": { - "id": "P3-iuxE4sjAf", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# Let's define the placeholders\n", - "\n", - "# Placeholders?\n", - "# The input to the model changes on iteration\n", - "# So we cannot have a constant in the input as we did before\n", - "# And thus we need placeholders which we can change on each \n", - "# iteration of the training\n", - "\n", - "x = tf.placeholder(tf.float32, name='x')\n", - "y = tf.placeholder(tf.float32, name='y')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "8hPRkaoxvRyV", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# Let's define the linear regression model\n", - "\n", - "# tf.Variable?\n", - "# We define the model parameters as tf.Variables\n", - "# as they get updated throghout the training.\n", - "# And variables denotes something which changes overtime.\n", - "\n", - "W = tf.Variable(np.random.random_sample(), name='weight_1')\n", - "b = tf.Variable(np.random.random_sample(), name='bias_1')\n", - "\n", - "pred_y = (W*x) + b" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "cSw1P8bkv96r", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# Let's define the loss function\n", - "# We are going to use the mean squared loss\n", - "loss = tf.reduce_mean(tf.square(y - pred_y))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "5G4uQqjsygNj", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# Let's define the optimizer\n", - "# And specify the which value (i.e. loss) it has to minimize\n", - "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "ttI7ZT-ozAm1", - "colab_type": "code", - "outputId": "14fb80da-8552-4dad-bd0b-46ef731fcff4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 432 - } - }, - "cell_type": "code", - "source": [ - "# So the graph is now built\n", - "# Now let's execute the graph using session\n", - "# i.e. lets train the model\n", - "\n", - "# What it is to train a model?\n", - "# To update the paramters in the graph (i.e. tf.Variables)\n", - "# So that the loss is minimized\n", - "\n", - "# Okay let's start!\n", - "with tf.Session() as sess:\n", - " # We need to initialize the variables in our graph\n", - " sess.run(tf.global_variables_initializer())\n", - " \n", - " for epoch in range(n_epochs):\n", - " _, curr_loss = sess.run([optimizer, loss], feed_dict={x:train_X, y:train_Y})\n", - " \n", - " if epoch % interval == 0:\n", - " print ('Loss after epoch', epoch, ' is ', curr_loss)\n", - " \n", - " print ('Now testing the model in the test set')\n", - " final_preds, final_loss = sess.run([pred_y, loss], feed_dict={x:test_X, y:test_Y})\n", - " \n", - " \n", - " print ('The final loss is: ', final_loss)\n", - " \n", - " # Plotting the final predictions against the true predictions\n", - " plt.plot(test_X, test_Y, 'g', label='True Function')\n", - " plt.plot(test_X, final_preds, 'r', label='Predicted Function')\n", - " plt.legend()\n", - " plt.show()" - ], - "execution_count": 19, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 0.6203117\n", - "Loss after epoch 20 is 0.61980206\n", - "Loss after epoch 40 is 0.61929274\n", - "Now testing the model in the test set\n", - "The final loss is: 0.14766909\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "metadata": { - "id": "jgmH3wwt1src", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "Okay, so we are doing good!
\n", - "\n", - "Now, let me just put everything here into one function so that you can tweak the hyperparameters easily!\n", - "\n", - "Or better, do it yourself!" - ] - }, - { - "metadata": { - "id": "OZ5TY7B_4E_v", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "def linear_regression(learning_rate=0.0000001, n_epochs=1000, interval=100):\n", - " # YOUR CODE HERE\n", - " with tf.Session() as sess:\n", - " sess.run(tf.global_variables_initializer())\n", - " \n", - " for epoch in range(n_epochs):\n", - " pred_train, current_loss = sess.run([optimizer, loss], feed_dict={x:train_X, y:train_Y})\n", - "\n", - " if epoch % interval == 0:\n", - " print ('Loss after epoch', epoch, ' is ', current_loss)\n", - "\n", - " print ('Now testing the model in the test set')\n", - " final_pred, final_loss = sess.run([pred_y, loss], feed_dict={x:test_X, y:test_Y})\n", - " \n", - " print ('The final loss is: ', final_loss)\n", - " \n", - " return final_pred, final_loss" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "A6MaclhK4rc6", - "colab_type": "code", - "outputId": "9161a302-2016-458b-c42b-34b2204611f0", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 136 - } - }, - "cell_type": "code", - "source": [ - "# Okay! Now let's tweak!\n", - "_ = linear_regression(learning_rate=0.000034, n_epochs=500)" - ], - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 0.6203117\n", - "Loss after epoch 100 is 0.6177678\n", - "Loss after epoch 200 is 0.61523753\n", - "Loss after epoch 300 is 0.61271757\n", - "Loss after epoch 400 is 0.6102085\n", - "Now testing the model in the test set\n", - "The final loss is: 0.14125724\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "peoHmV2M40uU", - "colab_type": "code", - "outputId": "85832b32-46e3-4612-c232-b822c3fae1c6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 221 - } - }, - "cell_type": "code", - "source": [ - "_ = linear_regression(learning_rate=0.0000006, n_epochs=1000)" - ], - "execution_count": 22, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 0.6203117\n", - "Loss after epoch 100 is 0.6177678\n", - "Loss after epoch 200 is 0.61523753\n", - "Loss after epoch 300 is 0.61271757\n", - "Loss after epoch 400 is 0.6102085\n", - "Loss after epoch 500 is 0.6077123\n", - "Loss after epoch 600 is 0.6052247\n", - "Loss after epoch 700 is 0.60275173\n", - "Loss after epoch 800 is 0.6002867\n", - "Loss after epoch 900 is 0.59783566\n", - "Now testing the model in the test set\n", - "The final loss is: 0.1342217\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "KjY_KnlE5ClG", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "## Drive the loss to a minimum." - ] - }, - { - "metadata": { - "id": "JKiHjGN15HPX", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 221 - }, - "outputId": "f6811b2f-a3ce-4dd5-a556-deae462a4977" - }, - "cell_type": "code", - "source": [ - "# YOUR CODE HERE\n", - "_ = linear_regression(learning_rate=0.000008, n_epochs=10000, interval= 1000)" - ], - "execution_count": 23, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 0.6203117\n", - "Loss after epoch 1000 is 0.5953937\n", - "Loss after epoch 2000 is 0.57157826\n", - "Loss after epoch 3000 is 0.54881245\n", - "Loss after epoch 4000 is 0.52705497\n", - "Loss after epoch 5000 is 0.50625724\n", - "Loss after epoch 6000 is 0.48637694\n", - "Loss after epoch 7000 is 0.46737328\n", - "Loss after epoch 8000 is 0.44920772\n", - "Loss after epoch 9000 is 0.43184412\n", - "Now testing the model in the test set\n", - "The final loss is: 0.045880605\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "FL4NT2CoIQAp", - "colab_type": "code", - "outputId": "8ab4fb03-3cda-46ef-d749-ce41742e2d4d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 386 - } - }, - "cell_type": "code", - "source": [ - "" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 0.01542704\n", - "Loss after epoch 50 is 0.015424165\n", - "Loss after epoch 100 is 0.01542129\n", - "Loss after epoch 150 is 0.015418426\n", - "Loss after epoch 200 is 0.015415577\n", - "Loss after epoch 250 is 0.015412727\n", - "Loss after epoch 300 is 0.015409879\n", - "Loss after epoch 350 is 0.015407033\n", - "Loss after epoch 400 is 0.015404186\n", - "Loss after epoch 450 is 0.015401339\n", - "Loss after epoch 500 is 0.015398496\n", - "Loss after epoch 550 is 0.015395651\n", - "Loss after epoch 600 is 0.015392808\n", - "Loss after epoch 650 is 0.015389967\n", - "Loss after epoch 700 is 0.015387124\n", - "Loss after epoch 750 is 0.015384283\n", - "Loss after epoch 800 is 0.015381445\n", - "Loss after epoch 850 is 0.015378606\n", - "Loss after epoch 900 is 0.015375769\n", - "Loss after epoch 950 is 0.01537293\n", - "Now testing the model in the test set\n", - "The final loss is: 0.05965931\n" - ], - "name": "stdout" - } - ] - } - ] -} \ No newline at end of file