From 2a294efe61cb03f6f7a7d58dff7462cc3615e834 Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Tue, 25 Sep 2018 00:06:44 +0530 Subject: [PATCH 01/12] Created using Colaboratory --- Anubhab-Bob.ipynb | 70 +++++++++++++++++++++++++++-------------------- 1 file changed, 40 insertions(+), 30 deletions(-) diff --git a/Anubhab-Bob.ipynb b/Anubhab-Bob.ipynb index 9e2543a..1824a7c 100644 --- a/Anubhab-Bob.ipynb +++ b/Anubhab-Bob.ipynb @@ -1,32 +1,42 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Anubhab-Bob.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/Anubhab-Bob/Assignment-2/blob/Anubhab-Bob/Anubhab-Bob.ipynb)" + ] + }, + { + "metadata": { + "id": "A5gF9gMFq3Ck", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 6882ccdde1cb7d315eadc7a79bdf67dec16a3f5c Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Tue, 25 Sep 2018 00:10:43 +0530 Subject: [PATCH 02/12] Created using Colaboratory --- Anubhab-Bob.ipynb | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/Anubhab-Bob.ipynb b/Anubhab-Bob.ipynb index 1824a7c..50191de 100644 --- a/Anubhab-Bob.ipynb +++ b/Anubhab-Bob.ipynb @@ -5,8 +5,7 @@ "colab": { "name": "Anubhab-Bob.ipynb", "version": "0.3.2", - "provenance": [], - "include_colab_link": true + "provenance": [] }, "kernelspec": { "display_name": "Python 3", @@ -15,16 +14,6 @@ } }, "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "[View in Colaboratory](https://colab.research.google.com/github/Anubhab-Bob/Assignment-2/blob/Anubhab-Bob/Anubhab-Bob.ipynb)" - ] - }, { "metadata": { "id": "A5gF9gMFq3Ck", From a4c4fc24599a19e85776904bf22cca391ba4cf81 Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Thu, 27 Sep 2018 00:18:44 +0530 Subject: [PATCH 03/12] Completed Lists Question. --- Anubhab_Bob_Assignment_2_Lists.ipynb | 298 +++++++++++++++++++++++++++ 1 file changed, 298 insertions(+) create mode 100644 Anubhab_Bob_Assignment_2_Lists.ipynb diff --git a/Anubhab_Bob_Assignment_2_Lists.ipynb b/Anubhab_Bob_Assignment_2_Lists.ipynb new file mode 100644 index 0000000..cfafc21 --- /dev/null +++ b/Anubhab_Bob_Assignment_2_Lists.ipynb @@ -0,0 +1,298 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Anubhab_Bob (1).ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/Anubhab-Bob/Assignment-2/blob/Anubhab-Bob/Anubhab_Bob_Assignment_2_Lists.ipynb)" + ] + }, + { + "metadata": { + "id": "A5gF9gMFq3Ck", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dummy_list=[1, 2, 3, 4, 5, 3]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "QkJs_byK_qPg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "6517822b-da14-4727-f436-c101ba3ec661" + }, + "cell_type": "code", + "source": [ + "print(dummy_list)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 3]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "F2QMHeQAB4Ja", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(sorted(dummy_list, reverse=True))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "7XeXBwK6CD1i", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "3cefa005-b90a-4e72-8be7-24e06ea943fb" + }, + "cell_type": "code", + "source": [ + "dummy_list_2 = [2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", + "dummy_list.extend(dummy_list_2)\n", + "print(dummy_list)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 3, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "ujmdan92Du6g", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dummy_dict={}" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TPaUVjDSEqyv", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "i=0\n", + "while i!= len(dummy_list):\n", + " if dummy_list[i] not in dummy_dict:\n", + " dummy_dict[dummy_list[i]]=1\n", + " else:\n", + " for key,value in dummy_dict.items():\n", + " if key == dummy_list[i]:\n", + " dummy_dict[key] = value + 1\n", + " break\n", + " #print(\"Key: \" + str(key))\n", + " #print (\"Value: \" + str(value))\n", + " i = i + 1\n", + " #print(i)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TY6jC_gyQbPP", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for key, value in dummy_dict.items():\n", + " print(\"Key: \" + str(key))\n", + " print (\"Value: \" + str(value))\n", + " #print('\\n')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "G0Ft30gsucvR", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 510 + }, + "outputId": "b3baa9d8-accc-4b8e-e76c-bd35a1052f3f" + }, + "cell_type": "code", + "source": [ + "for key in sorted(dummy_dict.keys()):\n", + " print (\"Key :\" + str(key) + '\\t\\t\\t' + \"Value :\" + str(dummy_dict[key]))\n", + "\n", + "print(\"\\n\\n\")\n", + "for key in sorted(dummy_dict.keys(), reverse=True):\n", + " print (\"Key :\" + str(key) + '\\t\\t\\t' + \"Value :\" + str(dummy_dict[key]))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Key :0\t\t\tValue :1\n", + "Key :1\t\t\tValue :2\n", + "Key :2\t\t\tValue :2\n", + "Key :3\t\t\tValue :2\n", + "Key :4\t\t\tValue :2\n", + "Key :5\t\t\tValue :1\n", + "Key :9.45\t\t\tValue :1\n", + "Key :12.01\t\t\tValue :1\n", + "Key :12.02\t\t\tValue :1\n", + "Key :16\t\t\tValue :1\n", + "Key :45.67\t\t\tValue :1\n", + "Key :90\t\t\tValue :1\n", + "Key :200\t\t\tValue :1\n", + "\n", + "\n", + "\n", + "Key :200\t\t\tValue :1\n", + "Key :90\t\t\tValue :1\n", + "Key :45.67\t\t\tValue :1\n", + "Key :16\t\t\tValue :1\n", + "Key :12.02\t\t\tValue :1\n", + "Key :12.01\t\t\tValue :1\n", + "Key :9.45\t\t\tValue :1\n", + "Key :5\t\t\tValue :1\n", + "Key :4\t\t\tValue :2\n", + "Key :3\t\t\tValue :2\n", + "Key :2\t\t\tValue :2\n", + "Key :1\t\t\tValue :2\n", + "Key :0\t\t\tValue :1\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "DtOAyHUtvm8Z", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "7414ec65-9a45-4354-fa7a-4e8e8370fde8" + }, + "cell_type": "code", + "source": [ + "x=200\n", + "dummy_list.remove(x)\n", + "print(dummy_list)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 3, 2, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "Adi6fYUBwnFG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ccced705-37e8-45c6-e5ba-a223ace45e16" + }, + "cell_type": "code", + "source": [ + "del dummy_list[5]\n", + "print (dummy_list)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 2, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "OTEasC7uxmOX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "187e88b3-6f25-43ef-f133-ef3edc147094" + }, + "cell_type": "code", + "source": [ + "dummy_list.clear()\n", + "print(dummy_list)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[]\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file From 0f3a8f8c5c767a843a69f9da8924d462d5ff3e8e Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Sat, 29 Sep 2018 00:36:46 +0530 Subject: [PATCH 04/12] Please check this. I really don't know if it is alright. --- Assignment 2(Complete).ipynb | 364 +++++++++++++++++++++++++++++++++++ 1 file changed, 364 insertions(+) create mode 100644 Assignment 2(Complete).ipynb diff --git a/Assignment 2(Complete).ipynb b/Assignment 2(Complete).ipynb new file mode 100644 index 0000000..419db63 --- /dev/null +++ b/Assignment 2(Complete).ipynb @@ -0,0 +1,364 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Anubhab_Bob (1).ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/Anubhab-Bob/Assignment-2/blob/Anubhab-Bob/Assignment%202(Complete).ipynb)" + ] + }, + { + "metadata": { + "id": "A5gF9gMFq3Ck", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dummy_list=[1, 2, 3, 4, 5, 3]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "QkJs_byK_qPg", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(dummy_list)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "F2QMHeQAB4Ja", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(sorted(dummy_list, reverse=True))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "7XeXBwK6CD1i", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dummy_list_2 = [2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", + "dummy_list.extend(dummy_list_2)\n", + "print(dummy_list)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ujmdan92Du6g", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dummy_dict={}" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TPaUVjDSEqyv", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "i=0\n", + "while i!= len(dummy_list):\n", + " if dummy_list[i] not in dummy_dict:\n", + " dummy_dict[dummy_list[i]]=1\n", + " else:\n", + " for key,value in dummy_dict.items():\n", + " if key == dummy_list[i]:\n", + " dummy_dict[key] = value + 1\n", + " break\n", + " #print(\"Key: \" + str(key))\n", + " #print (\"Value: \" + str(value))\n", + " i = i + 1\n", + " #print(i)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TY6jC_gyQbPP", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for key, value in dummy_dict.items():\n", + " print(\"Key: \" + str(key))\n", + " print (\"Value: \" + str(value))\n", + " #print('\\n')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "G0Ft30gsucvR", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for key in sorted(dummy_dict.keys()):\n", + " print (\"Key :\" + str(key) + '\\t\\t\\t' + \"Value :\" + str(dummy_dict[key]))\n", + "\n", + "print(\"\\n\\n\")\n", + "for key in sorted(dummy_dict.keys(), reverse=True):\n", + " print (\"Key :\" + str(key) + '\\t\\t\\t' + \"Value :\" + str(dummy_dict[key]))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DtOAyHUtvm8Z", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "x=200\n", + "dummy_list.remove(x)\n", + "print(dummy_list)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Adi6fYUBwnFG", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "del dummy_list[5]\n", + "print (dummy_list)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "OTEasC7uxmOX", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dummy_list.clear()\n", + "print(dummy_list)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "XI0_hACCQlA_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VV7zXbLnYp2t", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "e2e0fda2-13a1-40e3-a28e-2fa1253cdd70" + }, + "cell_type": "code", + "source": [ + "b = np.linspace(1.3,2.5, num=64)\n", + "print (b)" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1.3 1.31904762 1.33809524 1.35714286 1.37619048 1.3952381\n", + " 1.41428571 1.43333333 1.45238095 1.47142857 1.49047619 1.50952381\n", + " 1.52857143 1.54761905 1.56666667 1.58571429 1.6047619 1.62380952\n", + " 1.64285714 1.66190476 1.68095238 1.7 1.71904762 1.73809524\n", + " 1.75714286 1.77619048 1.7952381 1.81428571 1.83333333 1.85238095\n", + " 1.87142857 1.89047619 1.90952381 1.92857143 1.94761905 1.96666667\n", + " 1.98571429 2.0047619 2.02380952 2.04285714 2.06190476 2.08095238\n", + " 2.1 2.11904762 2.13809524 2.15714286 2.17619048 2.1952381\n", + " 2.21428571 2.23333333 2.25238095 2.27142857 2.29047619 2.30952381\n", + " 2.32857143 2.34761905 2.36666667 2.38571429 2.4047619 2.42380952\n", + " 2.44285714 2.46190476 2.48095238 2.5 ]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "JYYLSWaQRKJC", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "n = int(input())\n", + "x = np.array([1, 2, 3])\n", + "y = np.tile(x,(3*n))\n", + "print (y)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FGwwjf_pTMRs", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "b = np.arange(20)\n", + "\n", + "#print(b)\n", + "\n", + "mask = b%2!=0 #perform computations on the list \n", + "\n", + "#print(mask)\n", + "\n", + "print(b[mask]) #applying the mask on the numpy array" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ZRDXe1q4Yw6C", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#expected output array([2, 4])\n", + "a = np.array([1,2,3,2,3,4,3,4,5,6])\n", + "b = np.array([7,2,10,2,7,4,9,4,9,8])\n", + "np.intersect1d(a,b)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vrIl330LZDO3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "a = np.arange(10)\n", + "print (a)\n", + "a.shape = a.size//-1, 5\n", + "print(a)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "atGd8_9_Zjc-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "a = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "np.array(a).tolist()\n", + "print(a)\n", + "print('\\n')\n", + "np.asarray(a)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "L8JI_PAmbcbp", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "d = np.ones((5,5))\n", + "d[1:-1,1:-1] = 0\n", + "print(d)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "SY2a-sGxU4Q8", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "x = np.zeros((8,8),dtype=int)\n", + "x[1::2,::2] = 1\n", + "x[::2,1::2] = 1\n", + "print(x)" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 7ef642d45c87c1927a532f1b18f4781640e24a89 Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Thu, 4 Oct 2018 02:46:32 +0530 Subject: [PATCH 05/12] Created using Colaboratory --- Assignment_2.ipynb | 654 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 654 insertions(+) create mode 100644 Assignment_2.ipynb diff --git a/Assignment_2.ipynb b/Assignment_2.ipynb new file mode 100644 index 0000000..2a5821e --- /dev/null +++ b/Assignment_2.ipynb @@ -0,0 +1,654 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Assignment 2.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/Anubhab-Bob/Assignment-2/blob/Anubhab-Bob/Assignment_2.ipynb)" + ] + }, + { + "metadata": { + "id": "A5gF9gMFq3Ck", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dummy_list=[1, 2, 3, 4, 5, 3]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "QkJs_byK_qPg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "54a5acbb-99c1-4976-8427-928829866b40" + }, + "cell_type": "code", + "source": [ + "print(dummy_list)" + ], + "execution_count": 43, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 3]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "F2QMHeQAB4Ja", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "f25ee66b-52c9-4a33-c6c1-e4e5b39429cd" + }, + "cell_type": "code", + "source": [ + "dummy_list.reverse()\n", + "print(dummy_list)\n", + "#for o in reversed(dummy_list):\n", + "# print(o)" + ], + "execution_count": 44, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[3, 5, 4, 3, 2, 1]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "7XeXBwK6CD1i", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "7bac405f-c1a1-47af-f9d1-2bd4da1d9295" + }, + "cell_type": "code", + "source": [ + "dummy_list_2 = [2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", + "dummy_list.extend(dummy_list_2)\n", + "print(dummy_list)" + ], + "execution_count": 45, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[3, 5, 4, 3, 2, 1, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "ujmdan92Du6g", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dummy_dict={}" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TPaUVjDSEqyv", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "i=0\n", + "while i!= len(dummy_list):\n", + " if dummy_list[i] not in dummy_dict:\n", + " dummy_dict[dummy_list[i]]=1\n", + " else:\n", + " for key,value in dummy_dict.items():\n", + " if key == dummy_list[i]:\n", + " dummy_dict[key] = value + 1\n", + " break\n", + " #print(\"Key: \" + str(key))\n", + " #print (\"Value: \" + str(value))\n", + " i = i + 1\n", + " #print(i)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TY6jC_gyQbPP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 890 + }, + "outputId": "d9ebd07a-f660-466d-8dc6-f63c75a3562d" + }, + "cell_type": "code", + "source": [ + "for key, value in dummy_dict.items():\n", + " print(\"Key: \" + str(key))\n", + " print (\"Value: \" + str(value))\n", + " print('\\n')" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Key: 3\n", + "Value: 2\n", + "\n", + "\n", + "Key: 5\n", + "Value: 1\n", + "\n", + "\n", + "Key: 4\n", + "Value: 2\n", + "\n", + "\n", + "Key: 2\n", + "Value: 2\n", + "\n", + "\n", + "Key: 1\n", + "Value: 2\n", + "\n", + "\n", + "Key: 200\n", + "Value: 1\n", + "\n", + "\n", + "Key: 16\n", + "Value: 1\n", + "\n", + "\n", + "Key: 0\n", + "Value: 1\n", + "\n", + "\n", + "Key: 9.45\n", + "Value: 1\n", + "\n", + "\n", + "Key: 45.67\n", + "Value: 1\n", + "\n", + "\n", + "Key: 90\n", + "Value: 1\n", + "\n", + "\n", + "Key: 12.01\n", + "Value: 1\n", + "\n", + "\n", + "Key: 12.02\n", + "Value: 1\n", + "\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "G0Ft30gsucvR", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + }, + "outputId": "43c82359-3219-4ac8-c68a-22f99a666d04" + }, + "cell_type": "code", + "source": [ + "'''for key in sorted(dummy_dict.keys()):\n", + " print (\"Key :\" + str(key) + '\\t\\t\\t' + \"Value :\" + str(dummy_dict[key]))\n", + "\n", + "print(\"\\n\\n\")\n", + "for key in sorted(dummy_dict.keys(), reverse=True):\n", + " print (\"Key :\" + str(key) + '\\t\\t\\t' + \"Value :\" + str(dummy_dict[key]))'''\n", + "\n", + "dummy_list.sort()\n", + "print(dummy_list)\n", + "print('\\n')\n", + "dummy_list.sort(reverse=True)\n", + "print(dummy_list)\n", + "#print(sorted(dummy_list, reversed=True))" + ], + "execution_count": 49, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 9.45, 12.01, 12.02, 16, 45.67, 90, 200]\n", + "\n", + "\n", + "[200, 90, 45.67, 16, 12.02, 12.01, 9.45, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "DtOAyHUtvm8Z", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "247c5ea9-d230-42ab-f6ce-bdd0b0b3ae19" + }, + "cell_type": "code", + "source": [ + "x=200\n", + "dummy_list.remove(x)\n", + "print(dummy_list)" + ], + "execution_count": 50, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[90, 45.67, 16, 12.02, 12.01, 9.45, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "Adi6fYUBwnFG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "73231223-6081-466f-937b-b759e280aaa6" + }, + "cell_type": "code", + "source": [ + "del dummy_list[5]\n", + "print (dummy_list)" + ], + "execution_count": 51, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[90, 45.67, 16, 12.02, 12.01, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "OTEasC7uxmOX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "18486a51-d854-4d83-982b-2eb7647e86c6" + }, + "cell_type": "code", + "source": [ + "dummy_list.clear()\n", + "print(dummy_list)" + ], + "execution_count": 52, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "5Tw-3o-kwdZS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Now the Numpy part" + ] + }, + { + "metadata": { + "id": "XI0_hACCQlA_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VV7zXbLnYp2t", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 202 + }, + "outputId": "d318cb7b-4b2e-41fa-d1fd-ad931ec931a9" + }, + "cell_type": "code", + "source": [ + "b = np.linspace(1.3,2.5, num=64)\n", + "print (b)" + ], + "execution_count": 54, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1.3 1.31904762 1.33809524 1.35714286 1.37619048 1.3952381\n", + " 1.41428571 1.43333333 1.45238095 1.47142857 1.49047619 1.50952381\n", + " 1.52857143 1.54761905 1.56666667 1.58571429 1.6047619 1.62380952\n", + " 1.64285714 1.66190476 1.68095238 1.7 1.71904762 1.73809524\n", + " 1.75714286 1.77619048 1.7952381 1.81428571 1.83333333 1.85238095\n", + " 1.87142857 1.89047619 1.90952381 1.92857143 1.94761905 1.96666667\n", + " 1.98571429 2.0047619 2.02380952 2.04285714 2.06190476 2.08095238\n", + " 2.1 2.11904762 2.13809524 2.15714286 2.17619048 2.1952381\n", + " 2.21428571 2.23333333 2.25238095 2.27142857 2.29047619 2.30952381\n", + " 2.32857143 2.34761905 2.36666667 2.38571429 2.4047619 2.42380952\n", + " 2.44285714 2.46190476 2.48095238 2.5 ]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "JYYLSWaQRKJC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + }, + "outputId": "29491d17-ac3e-4fab-85b6-91862e8f894a" + }, + "cell_type": "code", + "source": [ + "n = int(input())\n", + "x = np.array([1, 2, 3])\n", + "y = np.tile(x,(3*n))\n", + "print (y)" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "stream", + "text": [ + "3\n", + "[1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "FGwwjf_pTMRs", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "66dfd586-66ac-4e7f-af97-386793d75a76" + }, + "cell_type": "code", + "source": [ + "b = np.arange(20)\n", + "\n", + "#print(b)\n", + "\n", + "mask = b%2!=0 #perform computations on the list \n", + "\n", + "#print(mask)\n", + "\n", + "print(b[mask]) #applying the mask on the numpy array" + ], + "execution_count": 56, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[ 1 3 5 7 9 11 13 15 17 19]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "ZRDXe1q4Yw6C", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "25134caa-83c5-46db-a54c-bd968458254f" + }, + "cell_type": "code", + "source": [ + "#expected output array([2, 4])\n", + "a = np.array([1,2,3,2,3,4,3,4,5,6])\n", + "b = np.array([7,2,10,2,7,4,9,4,9,8])\n", + "np.intersect1d(a,b)" + ], + "execution_count": 57, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([2, 4])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 57 + } + ] + }, + { + "metadata": { + "id": "vrIl330LZDO3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + }, + "outputId": "31437943-9a45-4f7e-af44-8d9575e8a6e1" + }, + "cell_type": "code", + "source": [ + "a = np.arange(10)\n", + "print (a)\n", + "print('\\n')\n", + "a.shape = a.size//-1, 5\n", + "print(a)" + ], + "execution_count": 58, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0 1 2 3 4 5 6 7 8 9]\n", + "\n", + "\n", + "[[0 1 2 3 4]\n", + " [5 6 7 8 9]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "atGd8_9_Zjc-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + }, + "outputId": "836cbdae-f78f-4dfc-8bc1-43dab74012dc" + }, + "cell_type": "code", + "source": [ + "a = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "np.array(a).tolist()\n", + "print(a)\n", + "print('\\n')\n", + "np.asarray(a)" + ], + "execution_count": 59, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "\n", + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 59 + } + ] + }, + { + "metadata": { + "id": "L8JI_PAmbcbp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + }, + "outputId": "0ba45318-d34a-4b6d-c09d-42ccffa782f5" + }, + "cell_type": "code", + "source": [ + "d = np.ones((5,5))\n", + "d[1:-1,1:-1] = 0\n", + "print(d)" + ], + "execution_count": 60, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[1. 1. 1. 1. 1.]\n", + " [1. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 1.]\n", + " [1. 1. 1. 1. 1.]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "SY2a-sGxU4Q8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 151 + }, + "outputId": "70bdeca0-5a5e-46dd-ed88-694e3f48dd4a" + }, + "cell_type": "code", + "source": [ + "x = np.zeros((8,8),dtype=int)\n", + "x[1::2,::2] = 1\n", + "x[::2,1::2] = 1\n", + "print(x)" + ], + "execution_count": 61, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]\n", + " [0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]\n", + " [0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]\n", + " [0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]]\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file From 9521388600e1de3fbeb1c4573b4a625af13dc06e Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Mon, 8 Oct 2018 19:13:16 +0530 Subject: [PATCH 06/12] Created using Colaboratory --- Assignment_2.ipynb | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/Assignment_2.ipynb b/Assignment_2.ipynb index 2a5821e..dafb726 100644 --- a/Assignment_2.ipynb +++ b/Assignment_2.ipynb @@ -3,7 +3,7 @@ "nbformat_minor": 0, "metadata": { "colab": { - "name": "Assignment 2.ipynb", + "name": "Assignment_2.ipynb", "version": "0.3.2", "provenance": [], "include_colab_link": true @@ -52,7 +52,7 @@ "source": [ "print(dummy_list)" ], - "execution_count": 43, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -80,7 +80,7 @@ "#for o in reversed(dummy_list):\n", "# print(o)" ], - "execution_count": 44, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -107,7 +107,7 @@ "dummy_list.extend(dummy_list_2)\n", "print(dummy_list)" ], - "execution_count": 45, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -173,7 +173,7 @@ " print (\"Value: \" + str(value))\n", " print('\\n')" ], - "execution_count": 48, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -261,7 +261,7 @@ "print(dummy_list)\n", "#print(sorted(dummy_list, reversed=True))" ], - "execution_count": 49, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -291,7 +291,7 @@ "dummy_list.remove(x)\n", "print(dummy_list)" ], - "execution_count": 50, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -317,7 +317,7 @@ "del dummy_list[5]\n", "print (dummy_list)" ], - "execution_count": 51, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -343,7 +343,7 @@ "dummy_list.clear()\n", "print(dummy_list)" ], - "execution_count": 52, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -392,7 +392,7 @@ "b = np.linspace(1.3,2.5, num=64)\n", "print (b)" ], - "execution_count": 54, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -430,7 +430,7 @@ "y = np.tile(x,(3*n))\n", "print (y)" ], - "execution_count": 55, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -464,7 +464,7 @@ "\n", "print(b[mask]) #applying the mask on the numpy array" ], - "execution_count": 56, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -492,7 +492,7 @@ "b = np.array([7,2,10,2,7,4,9,4,9,8])\n", "np.intersect1d(a,b)" ], - "execution_count": 57, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -526,7 +526,7 @@ "a.shape = a.size//-1, 5\n", "print(a)" ], - "execution_count": 58, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -559,7 +559,7 @@ "print('\\n')\n", "np.asarray(a)" ], - "execution_count": 59, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -600,7 +600,7 @@ "d[1:-1,1:-1] = 0\n", "print(d)" ], - "execution_count": 60, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -632,7 +632,7 @@ "x[::2,1::2] = 1\n", "print(x)" ], - "execution_count": 61, + "execution_count": 0, "outputs": [ { "output_type": "stream", From 553a13a10f351a3662cd0501382bd639ea6b74c7 Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Fri, 28 Dec 2018 01:24:22 +0530 Subject: [PATCH 07/12] Assignment 4 completed. Please check and report if everything is okay. Thank you. --- First_Date_with_TensorFlow (Complete).ipynb | 1104 +++++++++++++++++++ 1 file changed, 1104 insertions(+) create mode 100644 First_Date_with_TensorFlow (Complete).ipynb diff --git a/First_Date_with_TensorFlow (Complete).ipynb b/First_Date_with_TensorFlow (Complete).ipynb new file mode 100644 index 0000000..6ce7cc9 --- /dev/null +++ b/First_Date_with_TensorFlow (Complete).ipynb @@ -0,0 +1,1104 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of 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([[[1.45, 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": "1396b865-0a11-4b31-a1d5-9510e6b20301", + "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": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Tensor(\"Const_3:0\", shape=(), dtype=float32)\n", + "Tensor(\"Const_4:0\", shape=(2,), dtype=float32)\n", + "Tensor(\"Const_5:0\", 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": "1c005d9c-951d-48be-94e5-8681eaefca4d", + "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": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "2.0\n", + "=======================\n", + "[1. 2.]\n", + "=======================\n", + "[[[1.45 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": "e345fccd-ca78-49bf-b9dd-ae0a6d29da9f", + "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": 8, + "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": "50a23939-418c-4565-bd87-b66b9ee7b2bf", + "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": 9, + "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": "25007c20-00d5-4239-ab31-3e319120d77b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + } + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "# YOUR CODE HERE\n", + "node_1 = tf.constant([9, 10], dtype=tf.float32, name='leaf_node1')\n", + "node_2 = tf.constant([7, 8.65], dtype=tf.float32, name='leaf_node2')\n", + "node_3 = tf.constant(5.6, dtype=tf.float32, name='leaf_node3')\n", + "node_4 = tf.constant([7.65, 9], dtype=tf.float32, name='leaf_node4')\n", + "node_5 = tf.constant([13.5, 7.18], dtype=tf.float32, name='leaf_node5')\n", + "\n", + "cg_part_1=(node_1 * node_2)\n", + "\n", + "cg_part_2=(node_4 + node_5)\n", + "\n", + "cg_part_3=tf.divide(cg_part_1, node_3)\n", + "\n", + "comp_graph_2 = tf.minimum(((node_1 * node_2) / node_3),\n", + " (node_4 + node_5))\n", + "\n", + "comp_graph_2_alt=tf.minimum(cg_part_2, cg_part_3)\n", + "# Execute \n", + "# YOUR CODE HERE\n", + "with tf.Session() as sess:\n", + " print(sess.run(comp_graph_2))\n", + " part1, part2, part3, total = sess.run([cg_part_1, cg_part_2, cg_part_3, comp_graph_2_alt])\n", + "print (\"=======================\")\n", + "print ('Part 1 Result: ', part1)\n", + "print ('Part 2 Result: ', part2)\n", + "print ('Part 3 Result: ', part3)\n", + "print ('Final Result: ', total)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[11.25 15.446429]\n", + "=======================\n", + "Part 1 Result: [63. 86.5]\n", + "Part 2 Result: [21.15 16.18]\n", + "Part 3 Result: [11.25 15.446429]\n", + "Final Result: [11.25 15.446429]\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": "4b97a028-dc5d-4d7b-b640-b21e85c7b8bd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + } + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "# YOUR CODE HERE\n", + "node_6 = tf.constant([[1.2 , 3.4],\n", + " [7.5 , 8.6]], dtype = tf.float32, name='leaf_node6')\n", + "node_7 = tf.constant([[7 , 9],\n", + " [8 , 6]] , dtype = tf.float32, name='leaf_node7')\n", + "node_8 = tf.constant([[2.6 , 18.1],\n", + " [7.86 , 9.81],\n", + " [9.36 , 10.41]] , dtype = tf.float32, name='leaf_node8')\n", + "node_9 = tf.constant([[2.79 , 3.81 , 5.6],\n", + " [7.3 , 5.67 , 8.9]] , dtype = tf.float32, name='leaf_node9')\n", + "\n", + "cg_1 = tf.reduce_mean(node_6, axis = 1)\n", + "cg_2 = tf.transpose(node_8)\n", + "cg_3 = tf.multiply(node_7 , cg_1)\n", + "cg_4 = tf.multiply(node_9 , cg_2)\n", + "cg_5 = tf.reduce_sum(cg_4)\n", + "cg_com = cg_3 + cg_5\n", + "\n", + "#cg_1alt = (node_7 * (np.mean(node_6, axis = 1)))\n", + "#cg_1alt = (node_7 * (tf.reduce_mean(node_6, axis = 1)))\n", + "#cg_2alt = np.sum((node_9 * tf.transpose(node_8)))\n", + "#cg_2alt = tf.reduce_sum((node_9 * tf.transpose(node_8)))\n", + "#cg_comalt = cg_1alt + cg_2alt\n", + "\n", + "# Execute \n", + "# YOUR CODE HERE\n", + "with tf.Session() as sess:\n", + " print(sess.run(cg_com))\n", + " part_1, part_2, part_3, part_4, part_5, final = sess.run(\n", + " [cg_1, cg_2, cg_3, cg_4, cg_5, cg_com])\n", + "print (\"=======================\")\n", + "print ('Part 1 Result: ', part_1)\n", + "print ('Part 2 Result: ', part_2)\n", + "print ('Part 3 Result: ', part_3)\n", + "print ('Part 2 Result: ', part_4)\n", + "print ('Part 3 Result: ', part_5)\n", + "print ('Final Result: ', final)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[386.1183 442.4683 ]\n", + " [388.41827 418.3183 ]]\n", + "=======================\n", + "Part 1 Result: [2.3000002 8.05 ]\n", + "Part 2 Result: [[ 2.6 7.86 9.36]\n", + " [18.1 9.81 10.41]]\n", + "Part 3 Result: [[16.100002 72.450005]\n", + " [18.400002 48.300003]]\n", + "Part 2 Result: [[ 7.2539997 29.9466 52.415997 ]\n", + " [132.13 55.622704 92.648994 ]]\n", + "Part 3 Result: 370.01828\n", + "Final Result: [[386.1183 442.4683 ]\n", + " [388.41827 418.3183 ]]\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": "69060a80-9d7f-4b1a-b125-683be586a07a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + } + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "# YOUR CODE HERE\n", + "\n", + "node_10 = tf.constant([[7.36 , 8.91 , 10.41],\n", + " [5.31 , 9.38 , 7.99]] , dtype = tf.float32, name='leaf_node10')\n", + "node_11 = tf.constant([[7.99 , 10.36],\n", + " [5.36 , 7.98],\n", + " [8.91 , 5.67]] , dtype = tf.float32, name='leaf_node11')\n", + "node_12 = tf.constant([[1.0 , 5.6 , 6.1 , 8.0],\n", + " [0 , 0 , 7.98 , 9.0],\n", + " [0 , 0 , 7.6 , 7],\n", + " [0 , 0 , 0 , 8.98]] , dtype = tf.float32, name='leaf_node12')\n", + "\n", + "c_graph1 = tf.transpose(node_11)\n", + "c_graph2 = (node_10 * c_graph1)\n", + "c_graph3 = tf.reduce_sum(c_graph2)\n", + "c_graph4 = tf.add(c_graph3 , 7.0)\n", + "c_graph5 = tf.divide(c_graph4 , 19.6)\n", + "c_graph_final = tf.divide(c_graph5 , node_12)\n", + "\n", + "c_graph_alt = (((tf.reduce_sum(node_10 * tf.transpose(node_11))) + 7.0) / 19.6/node_12)\n", + "\n", + "# Execute \n", + "# YOUR CODE HERE\n", + "\n", + "with tf.Session() as sess:\n", + " print(sess.run(c_graph_final))\n", + " pt_1, pt_2, pt_3, pt_4, pt_5, rslt = sess.run(\n", + " [c_graph1, c_graph2, c_graph3, c_graph4, c_graph5, c_graph_final])\n", + "print (\"\\n\\n=======================\\n\\n\")\n", + "print ('Part 1 Result: ', pt_1)\n", + "print ('Part 2 Result: ', pt_2)\n", + "print ('Part 3 Result: ', pt_3)\n", + "print ('Part 2 Result: ', pt_4)\n", + "print ('Part 3 Result: ', pt_5)\n", + "print ('Final Result: ', rslt)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[19.463488 3.475623 3.1907358 2.432936 ]\n", + " [ inf inf 2.4390335 2.1626098]\n", + " [ inf inf 2.5609853 2.7804983]\n", + " [ inf inf inf 2.1674263]]\n", + "\n", + "\n", + "=======================\n", + "\n", + "\n", + "Part 1 Result: [[ 7.99 5.36 8.91]\n", + " [10.36 7.98 5.67]]\n", + "Part 2 Result: [[58.8064 47.7576 92.7531 ]\n", + " [55.011597 74.8524 45.3033 ]]\n", + "Part 3 Result: 374.48438\n", + "Part 2 Result: 381.48438\n", + "Part 3 Result: 19.463488\n", + "Final Result: [[19.463488 3.475623 3.1907358 2.432936 ]\n", + " [ inf inf 2.4390335 2.1626098]\n", + " [ inf inf 2.5609853 2.7804983]\n", + " [ inf inf inf 2.1674263]]\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", + "# 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 = 0.000005\n", + "n_epochs = 1000\n", + "interval = 50" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "1h1-D8K1uT48", + "colab_type": "code", + "outputId": "2189ad35-c490-4fe6-b530-27517fff8cc8", + "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": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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5WkewkImIBLv5/AZmnJyCPx+fgomBCSa39Idfs4kwMzITHY3KEQuZiEiQ9Pw0LDo3Hxuu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\nQiadpZJUOPJXBNb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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]" + ] + }, + { + "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(0.0, name='weight_1')\n", + "b = tf.Variable(0.0, 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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 721 + }, + "outputId": "d0ff4839-1198-44d8-bd4d-79d92dc1a528" + }, + "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", + " print ('The final loss is: ', final_loss)\n", + " \n", + "# Plotting the final predictions against the true predictions\n", + "plt.plot(test_X[:10], test_Y[:10], 'g', label='True Function')\n", + "plt.plot(test_X[:10], final_preds[:10], 'r', label='Predicted Function')\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48290.105\n", + "Loss after epoch 50 is 29.782778\n", + "Loss after epoch 100 is 29.7724\n", + "Loss after epoch 150 is 29.762121\n", + "Loss after epoch 200 is 29.751879\n", + "Loss after epoch 250 is 29.741611\n", + "Loss after epoch 300 is 29.731339\n", + "Loss after epoch 350 is 29.721113\n", + "Loss after epoch 400 is 29.710855\n", + "Loss after epoch 450 is 29.700636\n", + "Loss after epoch 500 is 29.690397\n", + "Loss after epoch 550 is 29.680191\n", + "Loss after epoch 600 is 29.66994\n", + "Loss after epoch 650 is 29.65969\n", + "Loss after epoch 700 is 29.6495\n", + "Loss after epoch 750 is 29.639277\n", + "Loss after epoch 800 is 29.629047\n", + "Loss after epoch 850 is 29.618847\n", + "Loss after epoch 900 is 29.608654\n", + "Loss after epoch 950 is 29.598469\n", + "Now testing the model in the test set\n", + "The final loss is: 33.011585\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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DQCaiAnE7IRxdf+4AlVaFzc2DULdkfbFHoncRBFiGnIDdnOmv+qY7d9P3TZcu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\nIwYy5Yjln6dhN2vaq77pdh2gCBgP3Yf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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", + "outputId": "5f7cfdb6-e69e-40ea-a4e1-4e0eca574301", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 415 + } + }, + "cell_type": "code", + "source": [ + "def linear_regression(learning_rate=0.000005, n_epochs=100, interval=50):\n", + " # YOUR CODE HERE\n", + "\n", + " x = tf.placeholder(tf.float32 , name = 'x')\n", + " y = tf.placeholder(tf.float32 , name = 'y')\n", + " W = tf.Variable(0.0 , name = 'Weight_1')\n", + " b = tf.Variable(0.0 , name = 'bias_1')\n", + " pred_y = (W*x) + b\n", + " loss = tf.reduce_mean(tf.square(y - pred_y))\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)\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", + " \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", + " print ('The final loss is: ', final_loss)\n", + " plt.plot(test_X[:10], test_Y[:10], 'g', label='True Function')\n", + " plt.plot(test_X[:10], final_preds[:10], 'r', label='Predicted Function')\n", + " plt.legend()\n", + " plt.show()\n", + "pass\n", + "linear_regression()" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48290.105\n", + "Loss after epoch 50 is 29.782778\n", + "Now testing the model in the test set\n", + "The final loss is: 33.2231\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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9501nJClkIYQqZh2cxpoTK6mStxprX92It95b7UgirRQFw+4vMU+bhP7UCRQv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\n7wpbzmxky5mNXIy/AEBhcxGmvTCbpqW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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "A6MaclhK4rc6", + "colab_type": "code", + "outputId": "065e7aa3-2d40-4566-9b49-d3237c69e4e5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 548 + } + }, + "cell_type": "code", + "source": [ + "# Okay! Now let's tweak!\n", + "linear_regression(learning_rate=0.000034, n_epochs=500)" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48290.105\n", + "Loss after epoch 50 is 29.723145\n", + "Loss after epoch 100 is 29.653582\n", + "Loss after epoch 150 is 29.584167\n", + "Loss after epoch 200 is 29.514908\n", + "Loss after epoch 250 is 29.445837\n", + "Loss after epoch 300 is 29.376942\n", + "Loss after epoch 350 is 29.30818\n", + "Loss after epoch 400 is 29.239614\n", + "Loss after epoch 450 is 29.171177\n", + "Now testing the model in the test set\n", + "The final loss is: 32.455235\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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BU6y42tGyBClkIUSGOhl9gg7ftMajeFjzxloq562qdiTxEPqj4VgnjsUYtgcA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\nJRER+a4hM8iO+QvhSJjHd00TEZFPGlpffMsxJiI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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "peoHmV2M40uU", + "colab_type": "code", + "outputId": "cc3b5c21-06bd-4aee-9476-1f50063bf5e5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 715 + } + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.0000006, n_epochs=1000)" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48290.105\n", + "Loss after epoch 50 is 5544.9077\n", + "Loss after epoch 100 is 660.0498\n", + "Loss after epoch 150 is 101.81439\n", + "Loss after epoch 200 is 38.018833\n", + "Loss after epoch 250 is 30.727413\n", + "Loss after epoch 300 is 29.893017\n", + "Loss after epoch 350 is 29.796562\n", + "Loss after epoch 400 is 29.78446\n", + "Loss after epoch 450 is 29.781971\n", + "Loss after epoch 500 is 29.780622\n", + "Loss after epoch 550 is 29.77936\n", + "Loss after epoch 600 is 29.778126\n", + "Loss after epoch 650 is 29.776915\n", + "Loss after epoch 700 is 29.77566\n", + "Loss after epoch 750 is 29.774433\n", + "Loss after epoch 800 is 29.77318\n", + "Loss after epoch 850 is 29.771988\n", + "Loss after epoch 900 is 29.77074\n", + "Loss after epoch 950 is 29.76949\n", + "Now testing the model in the test set\n", + "The final loss is: 33.224064\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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9sfXog5Ijh9rp0h0pZCGEKqYemMiKE0spn6MiK19dh4/eR+1IIrkUBeOuL7FM\nHIv+1AnPvOmOnbD1HYg7T16106VbUshCiDS3/PhSpvw+gUL+RVjfZAv+poy1jV6GpSgY9oZhmTAa\nQ/hBFK0We6u2nnnThYuonS7dk0IWQqSpHRe2M2hvP3L65mRjk63ktuRRO5JIBv1vv2KZMBrj/n0A\nOJq+5Zk3Xaq0yskyDilkIUSa+fHaXj76thNmg4V1r2+hWLYSakcSj6E7fswzb/qbXQA4GjTCNiQU\nZ6UglZNlPFLIQog0cSzyCO992QYFheWN11A5VxW1I4lH0J0/h3nyOHy2bwUgoWZtrENG4KxRU+Vk\nGZcUshAi1V2KvUjrnc2wJsax+OVl1ClYT+1I4j9oI678NW/a7SaxchXPxg9168u86VQmhSyESFW3\nbbdpteNtIuNvM+HFKbxZ4h21I4mH0Ny65Zk3vXKZZ9506TJYB4eS8FoTKeI0IoUshEg19xPu0WZn\nM/64d4l+1QYSXLGL2pHEP2ju3vHMm16y0DNvunARrCFDcbzTQuZNpzEpZCFEqnC4HHT46l2ORR2h\nfbkODHpuuNqRxP+jibuP7+IH86bv38OVNx+2UeOxt20PBoPa8TIlKWQhRIr7cyTmvms/8GrRJkx6\nabqMxPQWdju+C+d65k1HR+POkYO4UeOJ7xAMvr5qp8vUpJCFEClKURSG/jiQHRe2UyNvLRY2Wope\nKz9qVJeYiM+61TBjMn7XruHO4o910DDiu3RD8cuidjqBFLIQIoVNPziZZceXUDZ7eVa9th5fvTzr\nUpXLhWnbZiyTx6P74xL4+mLr2Rdbj94oAdnVTif+HylkIcRjudwuHC4HCS4Hjn/9spPgSsDutHP4\ndjiTfhtHoSyF2dB0K1lN2dSOnnkpCsavvsAycQz606dQDAbigz/Ed8xIrHo/tdOJh5BCFsKL/VmE\nSaX3t98dOJx/FWOCy5F0ucNlx+FKwOG043D/eb0Hf/fge+1/FuzjjuGy43Q7k505h08ONjTdSh6L\nbDKgCkXB8MMez7zpQ+EoWi3xbdph6z8Id6HC+AZmgcj7aqcUDyGFLIQX+vXGL3T5piPXrdfS5PYM\nWgNGnQkfnQmjzoRJZ8Lf6I9J74NRa8RH74NRZ8Sk88H04Pe/XV9vwqQ1YdL78GrR1ymatVia5BZ/\np//1F8+86Z9+BMD+5jvYQobiKllK5WQiOaSQhfAyX1zcwUffBpPoTqR2vhcflOH/L8sHpfigJH10\nPg8K0vO1UWdM+h7T//vlOcaDYtX7YNI++F1nQqvRqn23xTPQHzuCecIYTN99A4Cj0StYB4fiqlhJ\n5WTiSUghC+FFlh5bzNB9A/HTuLNYAAAgAElEQVTVm1nz+hrqF2qkdiThxXTnzmKeNA6fz7cBkFD7\nRc+86eeeVzmZeBpSyEJ4AbfiZtwvo5hzaAY5fQNZ9/pm2XxB/CftlctYpk7EtHGdZ950lapYh35M\n4kt1ZcxlOiaFLITKElwJ9NnTnc1nN1Asa3HWN9lKkaxF1Y4lvJD21k3MM6bgs2o5msREnGXLeeZN\nN35NijgDkEIWQkX3E+7RcVd79l7dQ7Xc/2P1axvJ4ZtD7VjCy2juRGOeOwvfpYvQxMfjKlIU66Bh\nON5qJvOmMxApZCFUctN6gzY7m3Mi+hiNi7zGwkafYjaY1Y4lvIgm7j6+C+fhu2CuZ950vvzYxg7C\n3vpdmTedAUkhC6GCs3fO0HrnO1yNi+D98sFMeHGKjJcUf4mPx3fZEsxzpnvmTefMSVzIBOLfDwYf\nH7XTiVQiPwGESGO/3PiZ975sRYwjhqHPj6B31f6y8YLwSEjAZ+0qzNMno7t5A7d/VqxDQrF1/gj8\nZLpWRieFLEQa2nHhM7p91wmX4mJ2/QW0LvOu2pGEN3C5MG3ZiGXyBHRX/kAxm7H17o+tey+UbAFq\npxNpRApZiDSy5OhChv04CLPBwspX1lOvUAO1Iwm1KQrGL3ZgmTQW/ZnTKEYjts5dsfUegJIrl9rp\nRBqTQhYilbkVNyHfhjDlpykE+uZiXZPNVAoMUjuWUJOiYNjzPZYJYzAcOYSi0xH/7nueedMFCqqd\nTqhEClmIVORwOei9uxtbz22iRLaSrGuyhcL+RdSOJVRk+OUnzONHY/zlJwDsbzfzzJsuXlLlZEJt\nUshCpJJ7jlg67mrHvms/ULNATZa9vJbsPvIZ48xKf+QQlgljMO7+DgDHK69iHTQcV4WKKicT3kIK\nWYhUcCPuOm2+aM7J6OO8WrQJW9psJC4m+VsYioxDd+Y0lknjMO38DICEF+tgHRKKs/pzKicT3kYK\nWYgUdubOaVrvfIdrcVfpWKET41+Ygq/BlzhkD9rMRHv5DyxTJmDavMEzb7padaxDRnjmTQvxEFLI\nQqSgn6/v572v2hDriGF4jZH0rNJXPmOcyWhv3sA8fTI+a1Y+mDddHuvQESS83FjmTYtHkkIWIoV8\nfn4b3b7rjBs3cxssomXpNmpHEmlIEx2Nec4MfD9djMZux1msOLZBw3C8+Q5oZb9p8XhSyEKkgMVH\n5hO6fwhmg4VljVdTt2B9tSOJNKK5fw/fBXPxXTgPbdx9XPkLYBswGHurtqCXH7Ei+eTRIsQzcCtu\nRv0UyoIjc8hlzs26JluomLOS2rFEWrDZ8P30E8+86bt3cecMJG7IcOLbd5R50+KpSCEL8ZQcLge9\nvu/KtvNbKJmtFOuabKGQf2G1Y4nUlpCAz+oVmGdMQXfrJu6s2Ygb9jHxwV1k3rR4JlLIQjyFWEcM\nHb56l/3X9/Fcnhqsem09AT7Z1Y4lUpPLhWnTeixTJ6K7chnFbMHadwDx3XqhZM2mdjqRAUghC/GE\nrsddo83OZpy6c5LXijZlQaMl+Op91Y4lUovbjfGLz7FMHIv+3FnPvOku3bD16o8SGKh2OpGBJOvU\nP7vdTsOGDdm6dSsAK1eupHz58lit1qTrlC9fnvbt2yf9crlcqZNYCBWdij7Ja1sacurOSYIrfsjS\nV1ZKGWdUioLx+2/I1qgOWYPfQ3fxAvHtO3Dn18NYx0yUMhYpLlnPkBcsWEDWrFkB2L59O9HR0eT6\nx04kfn5+rFq1KuUTCuElfrr2I+991YZ7CbGE1hxNj6De8hnjDMrw834s40Zh+O0XFI0G+zstsIUM\nwVWshNrRRAb22EK+cOEC58+fp27dugA0bNgQPz8/duzYkdrZhPAan53fSvfvPkRBYX7DT2heqpXa\nkUQq0B8OxzJ+NMaw3QA4Gr+OdfBwXOXKqxtMZAqPfcl60qRJDB48OOnPfv9xFmFCQgL9+/endevW\nLFu2LOUSCqGyhUfm0vmbDhh1JtY12SJlnAHpTp/Cv8O7BLxcF2PYbhJeqsfdr77n3sp1UsYizTzy\nGfL27dsJCgqiYMHH788ZEhLCG2+8gUajoV27dlSvXp2KFR+9i0lAgBm9XvdkiZ9QYGCWVD1+ZpBZ\n19CtuBnwzQBm/DKDvH55+erdr6icp/JTHy+zrmNKS9F1vHABRo6ENWtAUaBmTRg3DmO9ehhT7la8\njjwWU0ZKr+MjCzksLIyIiAjCwsK4efMmRqORPHnyUKtWrX9dt02bv8YE1qhRg7Nnzz62kO/etT1l\n7OQJDMxCZKQM9H8WmXUNHS4HPb7rwmcXtlIqoDTrmmwhn67QU69FZl3HlJZS66i9cR3ztMn4rF2J\nxunEWb4i1qGhJDR8xTNvOgP/W8ljMWU87To+qsQfWcgzZ85M+nrOnDnkz5//oWV88eJF5s2bx9Sp\nU3G5XISHh9O4ceMnDiqEN4h1xPD+V2356fqPPJ+3JitfXSefMc4gNFFRmGdPx3fZJ2gcDpzFS2Ab\nPBxH07dk3rRQ3RN/DnnBggX89NNPREZG0rlzZ4KCgggJCSFPnjw0b94crVZL/fr1qVRJxgeK9Ofa\n/au0+aIZp++cokmxN5nf8BN89DIGMb3T3IvFd/4cfBfNR2uNw1WgINaBQ3C0aC3zpoXX0CiKoqh1\n46n9som8NPPsMtManow+QZudzbhhvU7nil0ZXXsCOm3KnOOQmdYxNT3xOlqt+C5djHnuDLQxMbgD\nc2HtNxB7uw5gMqVaTm8mj8WUkeYvWQuRWfx4bS/vf9WW+wn3+LjmWLoF9ZTPGKdnDgc+q5djmT4F\nbeRt3NmyETd8FPHBH4LFonY6IR5KCllketvObabn911RUFjQcAnNSrVUO5J4Wk6nZ970lAnorkZ4\n5k33G0j8Rz1l3rTwelLIItNSFIUFR+Yy8qdhZDH6s7zxGl4sUEftWOJpuN2YdmzHPGkc+vPnUEwm\nbF26Y+vVT0ZcinRDCllkSm7FzYj9Q1h8dAF5LHlZ9/oWyuesoHYs8aQUBeN3X2MZPwb9iWMoej3x\n7Tti6x+CO19+tdMJ8USkkEWmY3fa6fF9Fz6/sI3SAWVY12QLBbI8fviN8C6G/fs886YP/OaZN928\nFdaBQ3AXLaZ2NCGeihSyyFRi7Hd5f1dbfr6+n5r5arOi8Vqy+QSoHUs8AX34ASzjx2DcuwcAx2tN\nsQ4ahqtsOZWTCfFspJBFpnH1fgRtdjbjzN3TvFH8beY2WCSfMU5HdCdPwIyJBHz2GQAJdetjHRKK\ns0o1lZMJkTKkkEWmcCLqOG2+aMZN6w26VOrGqNrj0WpkMlN6oL14Acvk8Zi2bQZFIfG5GliHjiCx\n1gtqRxMiRUkhiwwt1hHDJ0cXMv/wHOIS7zOq1ng+CuqhdiyRDNprVzFPn4zP2lVoXC4SK1bGMHE8\nMdVf8MybFiKDkUIWGdIdezSLj8znk2OLuJ9wjwBTAIsbLeOtks3UjiYeQxMZiXn2NHyXL/XMmy5Z\nCuugYSQ0eZPA3Fkz9MYPInOTQhYZSqQtkoVH5vLp8U+wJsaR0zeQvjXH0KFCMH6Gh+/lLbyDJjYG\n3/mzMS9agMZmxVWwkGfedPNWMm9aZAryKBcZwi3rTeYensXKE58S74wntzkPg58bRvtyHTEbzGrH\nE49iteK7ZCHmubPQxsbgypUbW+go7O3ez7TzpkXmJIUs0rVr968y9/BMVp9cgcPlIL9fAXpU6cO7\nZd+TM6i9ncOB78pPMc+YijYqEndAAHGhoz3zps3ynyiR+Ughi3Tpyr3LzAqfzvrTq0l0J1LIvwi9\nq/ajVem2GHVGteOJR3E68dmwFvPUieiuXcVt8cPafxDxH/VA8c+qdjohVCOFLNKVi7EXmHVwGpvO\nrsfpdlIsa3H6VBtAs5ItMegMascTj+J2Y/psq2fe9MULKD4+2Lr1wtazL0qOHGqnE0J1UsgiXTh3\n9ywzDk5h67lNuBU3pQJK07faQN4s8Q56rTyMvZqiYPxmF5YJY9CfPO6ZN90hGFvfgbjz5lM7nRBe\nQ36SCa92KvokMw5O5rPz21BQKJu9PP2rh9Ck+Jsy2CMdMOz7Acv4URgOHvDMm27ZBuuAwbiLFFU7\nmhBeRwpZeKVjkUeYdmAyX17aAUClwCD6VQuhcdHXpIjTAf2B37BMGINx3w8AOJq86Zk3XbqMysmE\n8F5SyMKrhN86wPQDk/nm8i4AquWuTr9qITQs/Aoamc7k9XQnjmOZOAbT118BkFC/oWfedOUqKicT\nwvtJIQuv8OuNX5h+YBJ7Ir4H4Pm8NelffRB1CtSTIk4HdBfOYZ48Hp9tWwBIfL6mZ950zdoqJxMi\n/ZBCFqpRFIWfrv/ItAOT+PHaXgBezF+HftVDqJXvBSnidEB7NQLztEn4rF/jmTddKQjbkOEk1G8k\n86aFeEJSyCLNKYpCWMRuph+czK83fgagXsEG9Ks+iOfz1lA5nUgOze3bmGdNxXfFp2gSEnCWKo11\n0HASmrwhRSzEU5JCFmlGURS+u/w10w9O5uCtAwC8XLgx/aqHUDV3dZXTieTQxNzFPG82vp8sQGOz\n4SpU+K950zqd2vGESNekkEWqcytuvrr0BTMOTuFo5GEAXi/2Bv2qDaRiYGWV04lkiYvD/MkCfOfN\nRnsvFlfuPNg+Hov93ffAKJPRhEgJUsgi1bjcLnZe/IzpB6Zw6s4JNGh4q8Q79Kk2kHI5yqsdTySH\n3Y7viqWYZ01DGxXlmTf98VjiO3aSedNCpDApZJHinG4n289vYcaBKZyLOYtWo6VFqdb0qTaAkgGl\n1I4nkiMxEZ/1azBPm4Tu+jXcflmwDhxCfNfuKFn81U4nRIYkhSxSTKIrkS3nNjLj4BQuxV5Er9XT\ntkx7elXrR7GsxdWOJ5LD7ca0bTPmyePRX7romTfdvTe2nn1Qssu8aSFSkxSyeGYJrgTWn17D7PDp\nXLl/GYPWwHvlPqBX1b4U8i+sdjyRHIqCcdeXWCaORX/qBIrBQHzHTp5503nyqp1OiExBClk8NbvT\nzppTK5l7aCbX4q5i0pnoVLELPar0IZ9ffrXjieRQFAx7w7BMGI0h/CCKVou9VVvPvOnCRdROJ0Sm\nIoUsnpgt0caqk8uYe2gWt2w3MevNdK3cg+5BvchtyaN2PJFM+t9+xTJhNMb9+wBwNH3LM2+6VGmV\nkwmROUkhiyey88LnhOztS1R8JBaDH72q9KNrUA9y+uZUO5pIJt2xo555099+DYCjQSNsQ0JxVgpS\nOZkQmZsUskiWeGc8I/YPZcWJpfjqfelXPYQPK31Edh850Se90J0/h3nSOHw+2wpAQo1aWId+jLNG\nTZWTCSFAClkkw+k7p+jyTUdO3TlJuRwVWNxoGaWyy8ua6YU24grmqRPx2bAWjdtNYuUqWIeEkliv\ngYy5FMKLSCGL/6QoCp8c/ITeu3oT74zngwqdGVlrHD56H7WjiWTQ3LqFZeYUfFYuQ5OYiLN0Gc+8\n6debShEL4YWkkMVDxTpi6B/Wm88vbCObKRvzGy7h9WJN1Y4lkkFz9w7mubPwXbIQTXw8rsJFPPOm\nm7WUedNCeDEpZPEvB27+Rtdvg7ly/zIvFHqB2XUWUSBLQbVjicfQxN3Hd9F8fOfPQXv/Hq48ebGN\nGo+9bXuZNy1EOiCFLJK4FTdzD81kwq9jcCtu+lcfxMRXx3I3Ol7taOJR4uPxXb4U8+xpaKOjcWfP\nTtyo8cR3CAZfX7XTCSGSSQpZAHDLdovu333I3qt7yGPJy4KGS6id/0X0WnmIeK3ERHzWrsI8fTK6\nG9dxZ/HHOmgY8V26ofhlUTudEOIJyU9bwe4r39Lj+y5ExUfxcuHGzKq/gBy+8nEmr+VyYdq6Ccvk\n8egu/4Hi64utZ19s3XvJvGkh0jEp5EwswZXA+F9HM//wbIxaI+NemESnil3RyBm43klRMH65E8uk\nsehPn/LMmw7+EFufAbhzy4Q0IdI7KeRM6lLsRbp++wGHbodTLGtxPnl5ORUDK6sdSzyMomAI2+2Z\nN334EIpWS3ybdtj6D8JdSDbvECKjkELOhLae28SAsD7EJd6nVem2THhpKn4GP7VjiYfQ//KzZ970\nz/sBsL/5DraQobhKyr7SQmQ0UsiZiDXRytB9A1l3ejUWgx/zGiymRenWascSD6E/dgTzhDGYvvsG\nAEfDlz3zpivKqxhCZFRSyJnEsaijdPmmI+djzlE5sAqLGi2lWLYSascS/6A7ewbLpHGYdmwHIKHW\nC1iHjMD5fA2VkwkhUpsUcganKAqfHl/Mx/uHkeBOoGvlHgyvMRKjTgZFeBPtlctYpk7EtHGdZ950\nUBWsQz8msU49GXMpRCYhhZyB3bFH02dPD3Zd+oIcPjmY02AhDQu/onYs8f9ob93EPGMKPquWe+ZN\nlymLdXAoCa++LkUsRCYjhZxB/Xx9Px9924nr1mu8mL8O8xouJo8lr9qxxAOaO9GeedNLF3nmTRcp\nijVkKI63m8u8aSEyKSnkDMbldjH94GSmHZiEBg1Dnx9Bzyp90Wnlh7w30Ny/55k3vWCuZ9503nzY\nxkzE3qYdGAxqxxNCqEgKOQO5HneNj77rxM/X91PAryALG33Kc3mfVzuWAM+86flzPPOm79zBnSMH\ncaPHE9+hE/jIdpZCCCnkDGPXpS/pvfsj7jru0qTYm0yvO5tsPgFqxxIJCfisWQmzpuJ3/Tpu/6xY\nBw8n/sOPZN60EOJvpJDTObvTzuifQ1lybBE+Oh+m1JnJe+U6yvhLtblcmDZvwDJlIrorf4DZjK1X\nP8+86YDsaqcTQnghKeR07Pzdc3T+pgMnoo9ROqAMi19eTtkc5dSOlbkpCsadn2OZPA79mdMoRiO2\nTl0wjxmJVWdRO50QwotJIadDiqKw/vQahuwbgM1po325joypPQGzwax2tMxLUTDs+Q7LhLEYjhxC\n0emIb9veM2+6YCHMgVkg8r7aKYUQXkwKOZ25n3CPgT/0Zeu5Tfgbs7Lk5RW8UeJttWNlaoZffsI8\nfjTGX34CwP52M8+86eIlVU4mhEhPpJDTkUO3DtLl2w/4494lqud+joWNllLIX3b7UYv+yCEsE8Zg\n3P0dAI5XXsU6aDiuChVVTiaESI+kkNMBt+JmweG5jPt1JC63i95V+xPyv6EYdPK5VTXozpz2zJve\n+RkACS/WwTokFGf151ROJoRIz7TJuZLdbqdhw4Zs3boVgJUrV1K+fHmsVmvSdT7//HOaNWtGixYt\n2LRpU+qkzYQibZG0/aI5o34eTnafHGx64zOG1fhYylgF2j8ukaVHFwLq1MC08zMSq1UnZvPnxG7Z\nIWUshHhmyXqGvGDBArJmzQrA9u3biY6OJleuXEmX22w25s2bx+bNmzEYDDRv3pxGjRqRLVu21Emd\nSfwQsYfu33/IbdstGhRqxOz6Cwk0B6odK9PR3ryBefpkfFavQON04ixbHuvQESS83FjmTQshUsxj\nC/nChQucP3+eunXrAtCwYUP8/PzYsWNH0nWOHDlCxYoVyZLFM+igatWqhIeHU79+/dRJncEluhKZ\n9Ns45hyagV6rZ2StcXSt3B2tJlkvaIgUoomOxjxnBr6fLkZjt+MsVhzboGE43nwHtPJvIYRIWY8t\n5EmTJhEaGsr27Z79Wf38/P51naioKLJn/2vYQfbs2YmMjEzBmJnHlXuX6fLtBxy89TtF/Iuy+OVl\nBOWqqnasTEVzLxbfBXPxXTQfbdx9XPkLYBswGHurtqCX0y6EEKnjkT9dtm/fTlBQEAULFnyigyqK\nkqzrBQSY0etTd9ODwMD0M55w04lNdN7RmVhHLG0rtmXB6wvwN/mrHStdreEzsdlg7lyYNAnu3IFc\nuWDcWHQffkgWHx+edRUyzTqmMlnHZydrmDJSeh0fWchhYWFEREQQFhbGzZs3MRqN5MmTh1q1av3t\nerly5SIqKirpz7dv3yYoKOixN373ru0pYydPYGAWItPBMAZboo3Q/YNZdXI5Zr2Z2fUX0Kp0Wxz3\nNESibv70sobPJCEBn1XLMc+Ygu72LdxZs2Eb9jHxwV3Azw/uJ3p+PYNMsY5pQNbx2ckapoynXcdH\nlfgjC3nmzJlJX8+ZM4f8+fP/q4wBKleuzPDhw7l37x46nY7w8HCGDh36xEEzo1PRJ/nwmw6cuXua\nCjkrsbjRMkoEyECJNOF0euZNT52I7splFLMFa98BxHfrhZJVTkgUQqStJ35DbMGCBfz0009ERkbS\nuXNngoKCCAkJoX///gQHB6PRaOjevXvSCV7i3xRF4bebv7LxzFo2nVmP3WWnc8WuhNYcjY9etuJL\ndW43xi8+xzJxLPpzZz3zprt0w9arP0qgnMUuhFCHRknuG76pILVfNvG2l2Yi7l9h45l1bDyzjkux\nFwHIZ8nPxJem0bjoayqnezhvW8Nnoig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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "KjY_KnlE5ClG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Drive the loss to a minimum." + ] + }, + { + "metadata": { + "id": "JKiHjGN15HPX", + "colab_type": "code", + "outputId": "4bcb2a50-5858-46ea-e393-667d80049851", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1049 + } + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "linear_regression(learning_rate=0.0000556, n_epochs=200000, interval=5000)" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 48290.105\n", + "Loss after epoch 5000 is 20.356878\n", + "Loss after epoch 10000 is 13.997063\n", + "Loss after epoch 15000 is 9.710622\n", + "Loss after epoch 20000 is 6.821608\n", + "Loss after epoch 25000 is 4.874446\n", + "Loss after epoch 30000 is 3.5620654\n", + "Loss after epoch 35000 is 2.6775396\n", + "Loss after epoch 40000 is 2.081378\n", + "Loss after epoch 45000 is 1.679563\n", + "Loss after epoch 50000 is 1.4087588\n", + "Loss after epoch 55000 is 1.226228\n", + "Loss after epoch 60000 is 1.1032239\n", + "Loss after epoch 65000 is 1.0203019\n", + "Loss after epoch 70000 is 0.9644026\n", + "Loss after epoch 75000 is 0.92674285\n", + "Loss after epoch 80000 is 0.90138215\n", + "Loss after epoch 85000 is 0.8842748\n", + "Loss after epoch 90000 is 0.87272763\n", + "Loss after epoch 95000 is 0.864965\n", + "Loss after epoch 100000 is 0.85972536\n", + "Loss after epoch 105000 is 0.85620254\n", + "Loss after epoch 110000 is 0.85381925\n", + "Loss after epoch 115000 is 0.85222006\n", + "Loss after epoch 120000 is 0.8511251\n", + "Loss after epoch 125000 is 0.85038906\n", + "Loss after epoch 130000 is 0.8499146\n", + "Loss after epoch 135000 is 0.8495802\n", + "Loss after epoch 140000 is 0.8493226\n", + "Loss after epoch 145000 is 0.84920985\n", + "Loss after epoch 150000 is 0.84911305\n", + "Loss after epoch 155000 is 0.8490328\n", + "Loss after epoch 160000 is 0.8489686\n", + "Loss after epoch 165000 is 0.8489226\n", + "Loss after epoch 170000 is 0.8489226\n", + "Loss after epoch 175000 is 0.8489226\n", + "Loss after epoch 180000 is 0.8489226\n", + "Loss after epoch 185000 is 0.8489226\n", + "Loss after epoch 190000 is 0.8489226\n", + "Loss after epoch 195000 is 0.8489226\n", + "Now testing the model in the test set\n", + "The final loss is: 0.95510364\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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TX52ap4Ft8fMh7Md4obl/j6TadVM+d12rdp5tX+RvcjNaIYT4j68urGHfX3t4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\nEDkmnbYoFPTJetbsmUh0u1cYs+EKaLS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YzeBeX55p70tAdmdLoFg4Hw7Hv4a+ixfqklPR\nOGbsLccLIfC34vcRlxsNO4kdska8i9EPB7dbvcbipbS7xx6aBvtoGuyjabT75XEyEZ0OTlkb4Jz4\nKiQNDdCOn4j6N9+C8PRsdXiTvgk5pR9jXf5aFFZ9g86Ortg6eicGPTCknQsnIiJLwtC+x+xKz0Kx\ncAEcvs6DvksXqDb8FY1jQ1ode/3Gdews2oaMk+tQVncRUokU4x6ZgD8+HY/e7o+2c+VERGRpGNr3\nik4Hp40ZcH7zVUi0WmhDJqA+8S2ILi2fp76muYZ3CrKwqfBtVGurIbOT4fl+kZg3IBoPufY0Q/FE\nRGSJGNr3gPR8KTrHLoBD3hHoPT2hWpeFxj+MbzHuouo7ZJxMx/tF26Bp0sC9kzuW/HYZIvrP5WYp\nRETUAkPblPR6OP01E85vrIREo0FDcAjqklIgvLyaDSu4egrr8lPxj9KPoRd6dHfpgfk+0VD2mQFn\nB2czFU9ERJaOoW0i0gvnoVgUBccjX0Dv4YG6tRvQEDIB+N+jWUIIHPo+F+vyU/Gf7w8CAPp69kf0\nwFj8odd4ONg5mLN8IiKyAgztu6XXQ/buRri89gokajUaRo9F3eo1EP/b8a1J34RPSrOxLn8tCqpO\nAQCGdnsWUQNj4d8jgM9bExGR0Rjad0F68bubs+svPofezQ11f0lDw4RJgEQC9Q013i/ahoxT61Cm\n+g5SiRQhvSYgauBC+Hj7mrt0IiKyQgztX0Ovh2zLJrisfBkS9XU0BI1BXXIqxH334ZrmGjYVvo13\nCrIMK8Fn9Y3APJ9oPOzay9yVExGRFWNot5G07CIUcdFw/Pw/N2fXyW+jITQcZXVlyPz8j9hxZivU\nTWq4dXLD4t8uRUS/ufCSe935wERERHfA0DaWEJC99y6cE1ZAer0eDSODUP/WWpyyq8T6/RH4x7mP\noRM6dHfpgfgBf4byiRlwcWj52eNERES/FkPbCNLyMijiYuB46CD0rm5QpWdi3+CuWPf1fOSWHwAA\nPOHZD9EDYxHSawJXghMR0T3B0L4dISDbtgXOr/wJ0vo6aANG4O8xo7D6Uia++eQkAOCZbsMQPTAW\n/j0CuRKciIjuKYb2LUgvfQ/F4hg4Hvw39AoFPl0ajqj7juLiyX2QQIKxvcYh2icWA+97ytylEhGR\njWBo/5IQkL2/Dc4vvwRpnQrFTz2M0JHVKOz0ATqpO2Fm3wjM50pwIiIyA4b2z0h/uAyXxTHo9O99\n0MgdsXicIzIHnIerzA1x/V5ERP958JZ7m7tMIiKyUQxtABACnT7YAac//REOdfXY2wuI+EMjRLfu\neG1AFKb2mQEXx1t/KDkREVF7sPnQlvxwGTeipsHr8DGoHIEFY4EvAvtgue8ijH8klCvBiYjIYths\naOt0TchLjEaf1zLRRaPD/p5AxgtPY1LgMrzxmxFcCU5ERBbH5kJb06TBJ19k4NGEJIw4rUGdI7B+\n1pPotTgFmff/n7nLIyIiuiWbCW0hBLJOrcOlTYl4M7seHlqg6MkHoEl9G2H9hpm7PCIiojuSmruA\n9nK9QYXHFq9A5s56OMMBl19LwOP55ejOwCYiIithMzNtF70DJnzfGZrBfaFOzYDDQz0Bqc38zUJE\nRB2AzYQ25HLUnD4POHA1OBERWSfbmmoysImIyIrZVmgTERFZMYY2ERGRlWBoExERWQmGNhERkZVg\naBMREVkJhjYREZGVYGgTERFZCaM3V9FqtQgODsaCBQswaNAgLF26FDqdDl5eXkhOTkZJSQmSkpIM\n48+dO4f169fD19fX8L3p06dDrVZDLpcDAJYtW4Z+/fqZ8HSIiIg6LqNDOyMjA66urgCAtLQ0KJVK\njBo1CikpKdi1axeUSiW2bt0KAFCpVFiwYAF8fHxaHCcxMRGPPvqoiconIiKyHUZdHi8tLcW5c+fg\n5+cHAMjLy0NAQAAAwN/fH0eOHGk2/p133sHMmTMh5d7eREREJmPUTDspKQkvv/wysrOzAQAajQaO\njo4AAE9PT1y9etUwVqvV4vDhw4iNjW31WGlpaaipqUGvXr0QHx8PmUx2y9/r7i6Hvb2d0Sfza3h5\nKe7p8W0F+3j32EPTYB9Ng300DVP38Y5T4ezsbPj4+KBHjx6tvi+EaPZ6//798PPza3WWPWPGDCxd\nuhTbt2+HRCLB9u3bb/u773VgExERWZM7zrRzc3NRXl6O3NxcVFRUwNHREXK5HFqtFjKZDFeuXIG3\nt7dh/MGDBzFlypRWjzVixAjD18OHD8fu3btNcApERES24Y6hnZqaavg6PT0d3bp1Q35+Pvbs2YOQ\nkBDs3bsXQ4cONYwpLCzE448/3uI4QgjMnj0baWlp6Ny5M/Ly8tC7d28TnQYREVHH96tWisXExCA7\nOxtKpRK1tbUYN26c4T2VSgUXFxfD60OHDmHHjh2QSCQICwvDrFmzMHXqVFRUVGDq1Kl3fwZEREQ2\nQiJ+eVOaiIiILBKfySIiIrISDG0iIiIrYfSOaNZq9erVOH78OJqamjB37lyMHDkSAPD5559jzpw5\nKC4uBgAUFRUhPj4eABAQEICoqCiz1WyJjO3jmjVrkJeXByEEAgMDERkZac6yLc4v+3jgwAGcPn0a\nbm5uAICIiAj4+fkhJycHW7ZsgVQqRVhYGCZNmmTmyi2HsT3cvXs3Nm3aBKlUikGDBiEuLs7MlVsW\nY/v4k8WLF8PR0RGrVq0yU8WWydg+mixjRAd25MgRMWfOHCGEENXV1eLZZ58VQgih1WrFtGnTxJAh\nQwxjQ0NDRWFhodDpdCIuLk6o1WpzlGyRjO1jcXGxCA8PF0IIodPpRFBQkKisrDRLzZaotT4uW7ZM\nHDhwoNm469evi5EjRwqVSiU0Go0YM2aMqKmpMUfJFsfYHqrVauHv7y/q6uqEXq8XoaGh4uzZs+Yo\n2SIZ28efHD58WEycOFEsW7asPcu0eG3po6kypkPPtJ9++mk8+eSTAIDOnTtDo9FAp9MhMzMTSqUS\nycnJAICqqiqo1Wr07dsXAJCSkmK2mi2RsX1UKBRoaGhAY2MjdDodpFIpnJyczFm6RblVH3/p1KlT\n6N+/PxSKmzsp+fr64sSJExg+fHi71muJjO2hk5MTcnJyDE+yuLm5oba2tl1rtWTG9hEAGhsbkZGR\ngfnz52Pfvn3tWabFM7aPpsyYDn1P287OzvCJYrt27cKwYcNQVlaGoqIijBo1yjDu0qVLcHV1xfLl\nyzF58mRs3rzZTBVbJmP72LVrVwQFBcHf3x/+/v6YPHlys8f/bF1rfbSzs8O2bdswY8YMxMXFobq6\nGlVVVfDw8DD8nIeHR7Otgm2ZsT0EYPi3V1xcjEuXLmHAgAFmq9vStKWPWVlZmDJlCv8vt8LYPpo0\nY+7q2oCV2LdvnwgNDRUqlUpERkaKixcvCiGE8Pf3F0IIkZ+fL4YOHSqqq6uFWq0WY8eOFSUlJeYs\n2SLdqY9lZWVi4sSJQq1WC5VKJUaPHi2qqqrMWbJF+nkfv/zyS/Htt98KIYTIysoSK1euFDk5OeKN\nN94wjE9JSRE7d+40V7kW6U49/MmFCxdEcHCw4X1q7k59vHDhgnjhhReEEEIcPXqUl8dv4U59NGXG\ndOiZNnBzoVRmZiY2btwItVqN8+fP48UXX0RYWBgqKysxbdo0eHp6onfv3nB3d4eTkxOeeuopnD17\n1tylWxRj+lhQUIABAwbAyckJCoUCjz32GEpKSsxdukX5eR8VCgUGDRqEPn36ALi5tW9JSQm8vb1R\nVVVl+JnKyspmWwXbOmN6CAAVFRWIiorCqlWrDO/T/zOmj7m5ubh8+TLCwsKwcuVK5ObmYuPGjWau\n3LIY00eTZowp/9qwNCqVSgQHB99ytvfTDFEIIcLDw0VNTY3Q6XQiPDxcnDlzpr3KtHjG9rGgoECE\nhYUJnU4nGhsbxZgxY0R5eXl7lmrRWutjdHS0KCsrE0IIsW3bNpGQkCA0Go0IDAwUP/74o6ivrzcs\nSiPjeyiEEM8//7z46quvzFKnpWtLH3/CmXZLbemjqTKmQy9E2717N2pqarBo0SLD95KSkvDAAw+0\nGPvSSy8hMjISEokEQ4cObXX/dFtlbB/79euHIUOGQKlUAgBCQ0PRvXv3dq3VkrXWxwkTJmDRokVw\ncnKCXC5HYmIiZDIZlixZgoiICEgkEkRFRRkWpdk6Y3t44cIFHDt2DGlpaYZxs2bNQkBAgDnKtjjG\n9pFury19NFXGcBtTIiIiK9Hh72kTERF1FAxtIiIiK8HQJiIishIMbSIiIivB0CYiIrISDG0iIiIr\nwdAmIiKyEgxtIiIiK/FfBtXV/JeORnAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From 98b265ac9e61d5ff08035d6b26189c44eb8f1a72 Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Fri, 28 Dec 2018 01:32:25 +0530 Subject: [PATCH 08/12] Completed Assignment 2. Please take a look. --- Assignment_2.ipynb | 128 ++++++++++++++++++++++----------------------- 1 file changed, 64 insertions(+), 64 deletions(-) diff --git a/Assignment_2.ipynb b/Assignment_2.ipynb index dafb726..530a016 100644 --- a/Assignment_2.ipynb +++ b/Assignment_2.ipynb @@ -22,7 +22,7 @@ "colab_type": "text" }, "source": [ - "[View in Colaboratory](https://colab.research.google.com/github/Anubhab-Bob/Assignment-2/blob/Anubhab-Bob/Assignment_2.ipynb)" + "\"Open" ] }, { @@ -42,17 +42,17 @@ "metadata": { "id": "QkJs_byK_qPg", "colab_type": "code", + "outputId": "65790928-5ac4-4cac-9033-5cd5c6b6e3f4", "colab": { "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "54a5acbb-99c1-4976-8427-928829866b40" + "height": 33 + } }, "cell_type": "code", "source": [ "print(dummy_list)" ], - "execution_count": 0, + "execution_count": 2, "outputs": [ { "output_type": "stream", @@ -67,11 +67,11 @@ "metadata": { "id": "F2QMHeQAB4Ja", "colab_type": "code", + "outputId": "ffd98b19-842c-4173-fd00-86956c639f63", "colab": { "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "f25ee66b-52c9-4a33-c6c1-e4e5b39429cd" + "height": 33 + } }, "cell_type": "code", "source": [ @@ -80,7 +80,7 @@ "#for o in reversed(dummy_list):\n", "# print(o)" ], - "execution_count": 0, + "execution_count": 3, "outputs": [ { "output_type": "stream", @@ -95,11 +95,11 @@ "metadata": { "id": "7XeXBwK6CD1i", "colab_type": "code", + "outputId": "b18002de-a2a2-4855-e2da-82dc314d6f59", "colab": { "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "7bac405f-c1a1-47af-f9d1-2bd4da1d9295" + "height": 33 + } }, "cell_type": "code", "source": [ @@ -107,7 +107,7 @@ "dummy_list.extend(dummy_list_2)\n", "print(dummy_list)" ], - "execution_count": 0, + "execution_count": 4, "outputs": [ { "output_type": "stream", @@ -160,11 +160,11 @@ "metadata": { "id": "TY6jC_gyQbPP", "colab_type": "code", + "outputId": "c6e40202-7c20-4caf-93dc-12e0c76ad1f2", "colab": { "base_uri": "https://localhost:8080/", - "height": 890 - }, - "outputId": "d9ebd07a-f660-466d-8dc6-f63c75a3562d" + "height": 886 + } }, "cell_type": "code", "source": [ @@ -173,7 +173,7 @@ " print (\"Value: \" + str(value))\n", " print('\\n')" ], - "execution_count": 0, + "execution_count": 7, "outputs": [ { "output_type": "stream", @@ -239,11 +239,11 @@ "metadata": { "id": "G0Ft30gsucvR", "colab_type": "code", + "outputId": "8f9a521d-8ceb-475c-fd71-cd4a4b193d64", "colab": { "base_uri": "https://localhost:8080/", "height": 84 - }, - "outputId": "43c82359-3219-4ac8-c68a-22f99a666d04" + } }, "cell_type": "code", "source": [ @@ -261,7 +261,7 @@ "print(dummy_list)\n", "#print(sorted(dummy_list, reversed=True))" ], - "execution_count": 0, + "execution_count": 8, "outputs": [ { "output_type": "stream", @@ -279,11 +279,11 @@ "metadata": { "id": "DtOAyHUtvm8Z", "colab_type": "code", + "outputId": "23b3a649-a150-40d2-909a-09506e32c10f", "colab": { "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "247c5ea9-d230-42ab-f6ce-bdd0b0b3ae19" + "height": 33 + } }, "cell_type": "code", "source": [ @@ -291,7 +291,7 @@ "dummy_list.remove(x)\n", "print(dummy_list)" ], - "execution_count": 0, + "execution_count": 9, "outputs": [ { "output_type": "stream", @@ -306,18 +306,18 @@ "metadata": { "id": "Adi6fYUBwnFG", "colab_type": "code", + "outputId": "6fa54ac4-c156-4ce3-a0cf-03ffbb5b6d15", "colab": { "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "73231223-6081-466f-937b-b759e280aaa6" + "height": 33 + } }, "cell_type": "code", "source": [ "del dummy_list[5]\n", "print (dummy_list)" ], - "execution_count": 0, + "execution_count": 10, "outputs": [ { "output_type": "stream", @@ -332,18 +332,18 @@ "metadata": { "id": "OTEasC7uxmOX", "colab_type": "code", + "outputId": "fa25c22f-b308-4b2a-ac63-90d0dbf4faaa", "colab": { "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "18486a51-d854-4d83-982b-2eb7647e86c6" + "height": 33 + } }, "cell_type": "code", "source": [ "dummy_list.clear()\n", "print(dummy_list)" ], - "execution_count": 0, + "execution_count": 11, "outputs": [ { "output_type": "stream", @@ -381,18 +381,18 @@ "metadata": { "id": "VV7zXbLnYp2t", "colab_type": "code", + "outputId": "7a0112f4-dc8d-4621-9c9f-49f64395f5d2", "colab": { "base_uri": "https://localhost:8080/", - "height": 202 - }, - "outputId": "d318cb7b-4b2e-41fa-d1fd-ad931ec931a9" + "height": 201 + } }, "cell_type": "code", "source": [ "b = np.linspace(1.3,2.5, num=64)\n", "print (b)" ], - "execution_count": 0, + "execution_count": 13, "outputs": [ { "output_type": "stream", @@ -417,11 +417,11 @@ "metadata": { "id": "JYYLSWaQRKJC", "colab_type": "code", + "outputId": "b3b3c270-f675-4506-e784-beee8ca1e2f7", "colab": { "base_uri": "https://localhost:8080/", "height": 50 - }, - "outputId": "29491d17-ac3e-4fab-85b6-91862e8f894a" + } }, "cell_type": "code", "source": [ @@ -430,7 +430,7 @@ "y = np.tile(x,(3*n))\n", "print (y)" ], - "execution_count": 0, + "execution_count": 14, "outputs": [ { "output_type": "stream", @@ -446,11 +446,11 @@ "metadata": { "id": "FGwwjf_pTMRs", "colab_type": "code", + "outputId": "6cb8a5f9-16c3-4d19-c99f-709cdb74f778", "colab": { "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "66dfd586-66ac-4e7f-af97-386793d75a76" + "height": 33 + } }, "cell_type": "code", "source": [ @@ -464,7 +464,7 @@ "\n", "print(b[mask]) #applying the mask on the numpy array" ], - "execution_count": 0, + "execution_count": 15, "outputs": [ { "output_type": "stream", @@ -479,11 +479,11 @@ "metadata": { "id": "ZRDXe1q4Yw6C", "colab_type": "code", + "outputId": "1a88b34c-7a90-4b40-c9d7-7e16fbe9b027", "colab": { "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "25134caa-83c5-46db-a54c-bd968458254f" + "height": 33 + } }, "cell_type": "code", "source": [ @@ -492,7 +492,7 @@ "b = np.array([7,2,10,2,7,4,9,4,9,8])\n", "np.intersect1d(a,b)" ], - "execution_count": 0, + "execution_count": 16, "outputs": [ { "output_type": "execute_result", @@ -504,7 +504,7 @@ "metadata": { "tags": [] }, - "execution_count": 57 + "execution_count": 16 } ] }, @@ -512,11 +512,11 @@ "metadata": { "id": "vrIl330LZDO3", "colab_type": "code", + "outputId": "25c61d75-1de8-4e4e-e596-40453739cba6", "colab": { "base_uri": "https://localhost:8080/", - "height": 101 - }, - "outputId": "31437943-9a45-4f7e-af44-8d9575e8a6e1" + "height": 100 + } }, "cell_type": "code", "source": [ @@ -526,7 +526,7 @@ "a.shape = a.size//-1, 5\n", "print(a)" ], - "execution_count": 0, + "execution_count": 17, "outputs": [ { "output_type": "stream", @@ -545,11 +545,11 @@ "metadata": { "id": "atGd8_9_Zjc-", "colab_type": "code", + "outputId": "67882dcf-e6ff-4a67-b531-0195b7d8177d", "colab": { "base_uri": "https://localhost:8080/", "height": 84 - }, - "outputId": "836cbdae-f78f-4dfc-8bc1-43dab74012dc" + } }, "cell_type": "code", "source": [ @@ -559,7 +559,7 @@ "print('\\n')\n", "np.asarray(a)" ], - "execution_count": 0, + "execution_count": 18, "outputs": [ { "output_type": "stream", @@ -580,7 +580,7 @@ "metadata": { "tags": [] }, - "execution_count": 59 + "execution_count": 18 } ] }, @@ -588,11 +588,11 @@ "metadata": { "id": "L8JI_PAmbcbp", "colab_type": "code", + "outputId": "dac71dd3-e9f4-4a36-be66-04a92d7ecb3f", "colab": { "base_uri": "https://localhost:8080/", - "height": 101 - }, - "outputId": "0ba45318-d34a-4b6d-c09d-42ccffa782f5" + "height": 100 + } }, "cell_type": "code", "source": [ @@ -600,7 +600,7 @@ "d[1:-1,1:-1] = 0\n", "print(d)" ], - "execution_count": 0, + "execution_count": 19, "outputs": [ { "output_type": "stream", @@ -619,11 +619,11 @@ "metadata": { "id": "SY2a-sGxU4Q8", "colab_type": "code", + "outputId": "96dfeeb7-b231-47f2-be76-0bcd1696699a", "colab": { "base_uri": "https://localhost:8080/", - "height": 151 - }, - "outputId": "70bdeca0-5a5e-46dd-ed88-694e3f48dd4a" + "height": 150 + } }, "cell_type": "code", "source": [ @@ -632,7 +632,7 @@ "x[::2,1::2] = 1\n", "print(x)" ], - "execution_count": 0, + "execution_count": 20, "outputs": [ { "output_type": "stream", From 734ca78386d280518e4e6f92b54f53d183dbe202 Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Fri, 28 Dec 2018 01:35:45 +0530 Subject: [PATCH 09/12] Delete First_Date_with_TensorFlow (Complete).ipynb This had been uploaded in this repository by mistake. --- First_Date_with_TensorFlow (Complete).ipynb | 1104 ------------------- 1 file changed, 1104 deletions(-) delete mode 100644 First_Date_with_TensorFlow (Complete).ipynb diff --git a/First_Date_with_TensorFlow (Complete).ipynb b/First_Date_with_TensorFlow (Complete).ipynb deleted file mode 100644 index 6ce7cc9..0000000 --- a/First_Date_with_TensorFlow (Complete).ipynb +++ /dev/null @@ -1,1104 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Copy of 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([[[1.45, 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": "1396b865-0a11-4b31-a1d5-9510e6b20301", - "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": 6, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Tensor(\"Const_3:0\", shape=(), dtype=float32)\n", - "Tensor(\"Const_4:0\", shape=(2,), dtype=float32)\n", - "Tensor(\"Const_5:0\", 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": "1c005d9c-951d-48be-94e5-8681eaefca4d", - "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": 7, - "outputs": [ - { - "output_type": "stream", - "text": [ - "2.0\n", - "=======================\n", - "[1. 2.]\n", - "=======================\n", - "[[[1.45 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": "e345fccd-ca78-49bf-b9dd-ae0a6d29da9f", - "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": 8, - "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": "50a23939-418c-4565-bd87-b66b9ee7b2bf", - "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": 9, - "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": "25007c20-00d5-4239-ab31-3e319120d77b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 119 - } - }, - "cell_type": "code", - "source": [ - "# Build the graph\n", - "# YOUR CODE HERE\n", - "node_1 = tf.constant([9, 10], dtype=tf.float32, name='leaf_node1')\n", - "node_2 = tf.constant([7, 8.65], dtype=tf.float32, name='leaf_node2')\n", - "node_3 = tf.constant(5.6, dtype=tf.float32, name='leaf_node3')\n", - "node_4 = tf.constant([7.65, 9], dtype=tf.float32, name='leaf_node4')\n", - "node_5 = tf.constant([13.5, 7.18], dtype=tf.float32, name='leaf_node5')\n", - "\n", - "cg_part_1=(node_1 * node_2)\n", - "\n", - "cg_part_2=(node_4 + node_5)\n", - "\n", - "cg_part_3=tf.divide(cg_part_1, node_3)\n", - "\n", - "comp_graph_2 = tf.minimum(((node_1 * node_2) / node_3),\n", - " (node_4 + node_5))\n", - "\n", - "comp_graph_2_alt=tf.minimum(cg_part_2, cg_part_3)\n", - "# Execute \n", - "# YOUR CODE HERE\n", - "with tf.Session() as sess:\n", - " print(sess.run(comp_graph_2))\n", - " part1, part2, part3, total = sess.run([cg_part_1, cg_part_2, cg_part_3, comp_graph_2_alt])\n", - "print (\"=======================\")\n", - "print ('Part 1 Result: ', part1)\n", - "print ('Part 2 Result: ', part2)\n", - "print ('Part 3 Result: ', part3)\n", - "print ('Final Result: ', total)" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[11.25 15.446429]\n", - "=======================\n", - "Part 1 Result: [63. 86.5]\n", - "Part 2 Result: [21.15 16.18]\n", - "Part 3 Result: [11.25 15.446429]\n", - "Final Result: [11.25 15.446429]\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": "4b97a028-dc5d-4d7b-b640-b21e85c7b8bd", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 238 - } - }, - "cell_type": "code", - "source": [ - "# Build the graph\n", - "# YOUR CODE HERE\n", - "node_6 = tf.constant([[1.2 , 3.4],\n", - " [7.5 , 8.6]], dtype = tf.float32, name='leaf_node6')\n", - "node_7 = tf.constant([[7 , 9],\n", - " [8 , 6]] , dtype = tf.float32, name='leaf_node7')\n", - "node_8 = tf.constant([[2.6 , 18.1],\n", - " [7.86 , 9.81],\n", - " [9.36 , 10.41]] , dtype = tf.float32, name='leaf_node8')\n", - "node_9 = tf.constant([[2.79 , 3.81 , 5.6],\n", - " [7.3 , 5.67 , 8.9]] , dtype = tf.float32, name='leaf_node9')\n", - "\n", - "cg_1 = tf.reduce_mean(node_6, axis = 1)\n", - "cg_2 = tf.transpose(node_8)\n", - "cg_3 = tf.multiply(node_7 , cg_1)\n", - "cg_4 = tf.multiply(node_9 , cg_2)\n", - "cg_5 = tf.reduce_sum(cg_4)\n", - "cg_com = cg_3 + cg_5\n", - "\n", - "#cg_1alt = (node_7 * (np.mean(node_6, axis = 1)))\n", - "#cg_1alt = (node_7 * (tf.reduce_mean(node_6, axis = 1)))\n", - "#cg_2alt = np.sum((node_9 * tf.transpose(node_8)))\n", - "#cg_2alt = tf.reduce_sum((node_9 * tf.transpose(node_8)))\n", - "#cg_comalt = cg_1alt + cg_2alt\n", - "\n", - "# Execute \n", - "# YOUR CODE HERE\n", - "with tf.Session() as sess:\n", - " print(sess.run(cg_com))\n", - " part_1, part_2, part_3, part_4, part_5, final = sess.run(\n", - " [cg_1, cg_2, cg_3, cg_4, cg_5, cg_com])\n", - "print (\"=======================\")\n", - "print ('Part 1 Result: ', part_1)\n", - "print ('Part 2 Result: ', part_2)\n", - "print ('Part 3 Result: ', part_3)\n", - "print ('Part 2 Result: ', part_4)\n", - "print ('Part 3 Result: ', part_5)\n", - "print ('Final Result: ', final)" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[[386.1183 442.4683 ]\n", - " [388.41827 418.3183 ]]\n", - "=======================\n", - "Part 1 Result: [2.3000002 8.05 ]\n", - "Part 2 Result: [[ 2.6 7.86 9.36]\n", - " [18.1 9.81 10.41]]\n", - "Part 3 Result: [[16.100002 72.450005]\n", - " [18.400002 48.300003]]\n", - "Part 2 Result: [[ 7.2539997 29.9466 52.415997 ]\n", - " [132.13 55.622704 92.648994 ]]\n", - "Part 3 Result: 370.01828\n", - "Final Result: [[386.1183 442.4683 ]\n", - " [388.41827 418.3183 ]]\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": "69060a80-9d7f-4b1a-b125-683be586a07a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 357 - } - }, - "cell_type": "code", - "source": [ - "# Build the graph\n", - "# YOUR CODE HERE\n", - "\n", - "node_10 = tf.constant([[7.36 , 8.91 , 10.41],\n", - " [5.31 , 9.38 , 7.99]] , dtype = tf.float32, name='leaf_node10')\n", - "node_11 = tf.constant([[7.99 , 10.36],\n", - " [5.36 , 7.98],\n", - " [8.91 , 5.67]] , dtype = tf.float32, name='leaf_node11')\n", - "node_12 = tf.constant([[1.0 , 5.6 , 6.1 , 8.0],\n", - " [0 , 0 , 7.98 , 9.0],\n", - " [0 , 0 , 7.6 , 7],\n", - " [0 , 0 , 0 , 8.98]] , dtype = tf.float32, name='leaf_node12')\n", - "\n", - "c_graph1 = tf.transpose(node_11)\n", - "c_graph2 = (node_10 * c_graph1)\n", - "c_graph3 = tf.reduce_sum(c_graph2)\n", - "c_graph4 = tf.add(c_graph3 , 7.0)\n", - "c_graph5 = tf.divide(c_graph4 , 19.6)\n", - "c_graph_final = tf.divide(c_graph5 , node_12)\n", - "\n", - "c_graph_alt = (((tf.reduce_sum(node_10 * tf.transpose(node_11))) + 7.0) / 19.6/node_12)\n", - "\n", - "# Execute \n", - "# YOUR CODE HERE\n", - "\n", - "with tf.Session() as sess:\n", - " print(sess.run(c_graph_final))\n", - " pt_1, pt_2, pt_3, pt_4, pt_5, rslt = sess.run(\n", - " [c_graph1, c_graph2, c_graph3, c_graph4, c_graph5, c_graph_final])\n", - "print (\"\\n\\n=======================\\n\\n\")\n", - "print ('Part 1 Result: ', pt_1)\n", - "print ('Part 2 Result: ', pt_2)\n", - "print ('Part 3 Result: ', pt_3)\n", - "print ('Part 2 Result: ', pt_4)\n", - "print ('Part 3 Result: ', pt_5)\n", - "print ('Final Result: ', rslt)" - ], - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[[19.463488 3.475623 3.1907358 2.432936 ]\n", - " [ inf inf 2.4390335 2.1626098]\n", - " [ inf inf 2.5609853 2.7804983]\n", - " [ inf inf inf 2.1674263]]\n", - "\n", - "\n", - "=======================\n", - "\n", - "\n", - "Part 1 Result: [[ 7.99 5.36 8.91]\n", - " [10.36 7.98 5.67]]\n", - "Part 2 Result: [[58.8064 47.7576 92.7531 ]\n", - " [55.011597 74.8524 45.3033 ]]\n", - "Part 3 Result: 374.48438\n", - "Part 2 Result: 381.48438\n", - "Part 3 Result: 19.463488\n", - "Final Result: [[19.463488 3.475623 3.1907358 2.432936 ]\n", - " [ inf inf 2.4390335 2.1626098]\n", - " [ inf inf 2.5609853 2.7804983]\n", - " [ inf inf inf 2.1674263]]\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", - "# 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 = 0.000005\n", - "n_epochs = 1000\n", - "interval = 50" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "1h1-D8K1uT48", - "colab_type": "code", - "outputId": "2189ad35-c490-4fe6-b530-27517fff8cc8", - "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": 15, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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5WkewkImIBLv5/AZmnJyCPx+fgomBCSa39Idfs4kwMzITHY3KEQuZiEiQ9Pw0LDo3Hxuu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\nQiadpZJUOPJXBNb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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]" - ] - }, - { - "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(0.0, name='weight_1')\n", - "b = tf.Variable(0.0, 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", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 721 - }, - "outputId": "d0ff4839-1198-44d8-bd4d-79d92dc1a528" - }, - "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", - " print ('The final loss is: ', final_loss)\n", - " \n", - "# Plotting the final predictions against the true predictions\n", - "plt.plot(test_X[:10], test_Y[:10], 'g', label='True Function')\n", - "plt.plot(test_X[:10], final_preds[:10], 'r', label='Predicted Function')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": 20, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 48290.105\n", - "Loss after epoch 50 is 29.782778\n", - "Loss after epoch 100 is 29.7724\n", - "Loss after epoch 150 is 29.762121\n", - "Loss after epoch 200 is 29.751879\n", - "Loss after epoch 250 is 29.741611\n", - "Loss after epoch 300 is 29.731339\n", - "Loss after epoch 350 is 29.721113\n", - "Loss after epoch 400 is 29.710855\n", - "Loss after epoch 450 is 29.700636\n", - "Loss after epoch 500 is 29.690397\n", - "Loss after epoch 550 is 29.680191\n", - "Loss after epoch 600 is 29.66994\n", - "Loss after epoch 650 is 29.65969\n", - "Loss after epoch 700 is 29.6495\n", - "Loss after epoch 750 is 29.639277\n", - "Loss after epoch 800 is 29.629047\n", - "Loss after epoch 850 is 29.618847\n", - "Loss after epoch 900 is 29.608654\n", - "Loss after epoch 950 is 29.598469\n", - "Now testing the model in the test set\n", - "The final loss is: 33.011585\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "image/png": 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DQCaiAnE7IRxdf+4AlVaFzc2DULdkfbFHoncRBFiGnIDdnOmv+qY7d9P3TZcu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\nIwYy5Yjln6dhN2vaq77pdh2gCBgP3Yf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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", - "outputId": "5f7cfdb6-e69e-40ea-a4e1-4e0eca574301", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 415 - } - }, - "cell_type": "code", - "source": [ - "def linear_regression(learning_rate=0.000005, n_epochs=100, interval=50):\n", - " # YOUR CODE HERE\n", - "\n", - " x = tf.placeholder(tf.float32 , name = 'x')\n", - " y = tf.placeholder(tf.float32 , name = 'y')\n", - " W = tf.Variable(0.0 , name = 'Weight_1')\n", - " b = tf.Variable(0.0 , name = 'bias_1')\n", - " pred_y = (W*x) + b\n", - " loss = tf.reduce_mean(tf.square(y - pred_y))\n", - " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)\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", - " \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", - " print ('The final loss is: ', final_loss)\n", - " plt.plot(test_X[:10], test_Y[:10], 'g', label='True Function')\n", - " plt.plot(test_X[:10], final_preds[:10], 'r', label='Predicted Function')\n", - " plt.legend()\n", - " plt.show()\n", - "pass\n", - "linear_regression()" - ], - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 48290.105\n", - "Loss after epoch 50 is 29.782778\n", - "Now testing the model in the test set\n", - "The final loss is: 33.2231\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "image/png": 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9501nJClkIYQqZh2cxpoTK6mStxprX92It95b7UgirRQFw+4vMU+bhP7UCRQv\nL5K6dsM6eDiugoXUTpdlSSELITLd6j9WMvP3qRT3L8mm5tvwN2avbfSyLUXBa1845qkT8Io4hKLV\nYmvbAcuwkbhKlFQ7XZYnhSyEyFQ7I3cwYt8Q8vnkY0vz7RQwF1Q7kkgD/W+/Yp46AcOB/QDYW7zp\nnjddvoLKybIPKWQhRKb58do+en/bDZOXmY2vbaN07rJqRxIPoTt+zD1v+tuvAbC/1ARraBiO6kEq\nJ8t+pJCFEJniePRR3v2yPQoKq5uup0b+mmpHEg+gO38O0/TJeH+2HYDkevWxhL6Po249lZNlX1LI\nQogMdzH+Au12vY0lJZHlL6/ixWIN1Y4k/oM26gqmWdPw3rwBjctFSo2a7o0fGjSSMZcZTApZCJGh\nbltv03bnW0Qn3Wbq8zN5o2xLtSOJ+9DcuoV53ky8165Ck5KCo0JFLCPDSG7WXIo4k0ghCyEyzL3k\nBNrveptLCRcZUns4wdV6qh1J/IPm7h1Mi+bjs+IDNElJOEuUxBIyCnvL1jJvOpNJIQshMoTdaafL\nV+9wPOYonSp3YcTTY9SOJP4fTeI9fJYtwWfJQrT3EnAWKox1wlRsHTqBl5fa8XIkKWQhRLr7ayTm\n/ms/8Gqp5kx/YY6MxPQUSUn4LF2EacFstLGxuPLmJXH8FJK6BIOPj9rpcjQpZCFEulIUhVE/Dmdn\n5A7qFnqWD5qsRK+VXzWqS0nBe8M6mDcT32vXcPn5YxkxmqSefVB8/dROJ5BCFkKkszmHZrDqjxVU\nylOFdc024aOXR12qcjoxbv8E84wp6C5fAh8frP0HY+03ECUgj9rpxP8jhSyEeCiny4ndaSfZacf+\nrz82kp3J2Bw2jtyOYPpvkynuV4LNLbaTy5hb7eg5l6Jg+HIX5umT0J8+5Z43HdwDn4njsOh91U4n\n7kMKWQgP9lcRppbe3/62Y3f8rxiTnfbUy+1OG3ZnMnaHDbvrr+v9+bU/v9f2V8E+7BhOGw6XI82Z\n83rnZXOL7RQ0yyYDqlAUvML3uOdNHzmMotWS1L4j1qEjcBUvgU+gH0TfUzuluA8pZCE80K83fqHn\nN125brmWKbfnpfXCoDPirTNi0Bkx6oz4G/wx6r0xaA14670x6AwYdd4Y//z7b9fXGzFqjRj13rxa\n6jVK5SqdKbnF3+l/+dk9b/rnAwDY3miJNWQUznLlVU4m0kIKWQgP88WFnfT+NpgUVwr1Cz//Zxn+\n/7L8sxT/LElvnfefBen+2KAzpH6P8f/9cR/jz2LVe2PU/vm3zohWo1X7xxZPQH/8KKapEzF+9w0A\n9iavYBkZhrNadZWTiUchhSwnjy/jAAAgAElEQVSEB1l5fDmj9g/HR29i/WvraVS8idqRhAfTnTvr\nnjf9+acAJNd/3j1v+ulnVE4mHocUshAewKW4mPzLeBYenks+n0A2vrZVNl8Q/0l75TLmWdMwbtno\nnjddsxaWUWNJeaGBjLnMwqSQhVBZsjOZQXv7svXsZkrnKsOm5tspmauU2rGEB9Leuolp7ky81612\nz5uuVNk9b7ppMynibEAKWQgV3UtOoOvuTuy7upfaBZ7i42ZbyOuTV+1YwsNo7sS6502vXOaeN12y\nlHve9FutZN50NiKFLIRKblpu0H5XK07EHqdpyWZ80OQjTF4mtWMJD6K5l+CeN710kXvedOEiWCeN\nwNbuHZk3nQ1JIQuhgrN3ztBuV0uuJkbRuUowU5+fKeMlxf8kJeGzaoV73vSdO7jy5SMxZCpJnYPB\n21vtdCKDyG8AITLZLzd+5t0v2xJnj2PUM+8zsNZQ2XhBuCUn471+Laa5M9HdvIHLPxeW0DCs3XuD\nr0zXyu6kkIXIRLsiP6f3d8E4FScLGi2lXcV31I4kPIHTiXHrZswzp6G7cgnFZMI6cCjWvgNQcgeo\nnU5kEilkITLJimMfMPrHEZi8zKx9ZRMNi7+kdiShNkXBsOtzzDMmoz9zGsVgwNq9F9aBw1Dy51c7\nnchkUshCZDCX4iLk2xBm/jSTQJ/8bGy+leqBQWrHEmpSFLz2fod56iS8jh5G0elIeudd97zposXU\nTidUIoUsRAZKdiYzYE9vtp/7hLK5y7Gx+TZK+JdUO5ZQkdcvP2GaMgHDLz8BYHvrbfe86TLlVE4m\n1CaFLEQGSbDH0/XrTuy/Gk69ovVY9fIG8njLe4xzKv3Rw5inTsSw5zsA7K+8imXEGJxVq6mcTHgK\nKWQhMsCNxOu0/6IVJ2P/4NVSzdnWfguJcWnfwlBkH7ozpzFPn4xx12cAJD//IpbQMBx1nlY5mfA0\nUshCpLMzd07TbldLriVepWvVbkx5biY+Xj4kInvQ5iTaSxfd86a3bnbPm65dB0vo++5500LchxSy\nEOnol+s/0emrdsTb4xhTdxz9aw6W9xjnMNqbNzDNmYH3x2vQOBw4KlXBMup9kl9uKvOmxQNJIQuR\nTnZG7qDPd91xKk4WvbSMNhXaqx1JZCJNbCymhXPx+Wg5GpsNR+kyWEeMxv5GS9DKftPi4aSQhUgH\ny48uIexAKCYvM+uabqZBsUZqRxKZRJMQj8/SRfgsW4I28R7OIkWxDhuJrW0H0MuvWJF2cm8R4gm4\nFBcTfn6fJUcWkN9UgI3Nt1EtX3W1Y4nMYLXis3I5pkVz0d69iytfIImhY0jq1FXmTYvHIoUsxGOy\nO+0M+L4Xn57fRrnc5dnYfBvF/UuoHUtktORkvD9eg2nODHS3b+HKlZvE0WNJCu4p86bFE5FCFuIx\nxNvj6Lq7Iz9e28fTBeuyrtkmArzzqB1LZCSnE+MnmzDPmobuymUUkxnL4GEk9RmAkiu32ulENiCF\nLMQjup54jfa73ubUnZM0K9WCpU1W4KP3UTuWyCguF4YvPsc8bRL6c2fd86Z79sE6YChKYKDa6UQ2\nkqZT/2w2G40bN2b79u0ArF27lipVqmCxWFKvU6VKFTp16pT6x+l0ZkxiIVR0+s4pmm1rzKk7Jwmu\n1oOVr6yVMs6uFAXD99+Qu8mL5Ap+F92FSJI6deHOr0ewTJwmZSzSXZoeIS9dupRcuXIBsGPHDmJj\nY8n/j51IfH19WbduXfonFMJD/HTtRzrv7kC8PY6wehPoFzRQ3mOcTXn9fADz5PF4/fYLikaDrWVr\nrCGhOEuXVTuayMYeWsiRkZGcP3+eBg0aANC4cWN8fX3ZuXNnRmcTwmN8dn47fb/rgYLCksYf0qp8\nW7UjiQygPxKBecoEDOF7ALA3fQ3LyDE4K1dRN5jIER76lPX06dMZOXJk6ue+/3EWYXJyMkOHDqVd\nu3asWrUq/RIKobIPji6i+zddMOiMbGy+Tco4G9KdPoV/l3cIeLkBhvA9JL/QkLtffU/C2o1SxiLT\nPPAR8o4dOwgKCqJYsYfvzxkSEsLrr7+ORqOhY8eO1KlTh2rVHryLSUCACb1e92iJH1FgoF+GHj8n\nyKlr6FJcDP9mOHN+mUMh30J89c5X1ChY47GPl1PXMb2l6zpGRsK4cbB+PSgK1KsHkydjaNgQQ/rd\niseR+2L6SO91fGAhh4eHExUVRXh4ODdv3sRgMFCwYEGeffbZf123ffv/jQmsW7cuZ8+efWgh371r\nfczYaRMY6Ed0tAz0fxI5dQ3tTjv9v+/JjvPbKR9QgY3Nt1FYV/yx1yKnrmN6S6911N64jmn2DLw3\nrHXPm65SDcuoMJIbv+KeN52N/63kvpg+HncdH1TiDyzkefPmpX68cOFCihQpct8yvnDhAosXL2bW\nrFk4nU4iIiJo2rTpIwcVwhPE2+Po/FUHfrr+I88UqsfaVzfKe4yzCU1MDKYFc/BZ9SEaux1HmbJY\nR47B3uJNmTctVPfI70NeunQpP/30E9HR0XTv3p2goCBCQkIoWLAgrVq1QqvV0qhRI6pXl/GBIuu5\ndu8q7b94m9N3TtG89Bssafwh3noZg5jVaRLi8Vmy0D1v2pKIs2gxLMNDsbduJ/OmhcfQKIqiqHXj\nGf20iTw18+Ry0hqejD1B+11vc8Nyne7VejGh/lR02vQ5xyEnrWNGeuR1tFj+N286Lg5XYH4sg4dh\n69QVjMaMC+rB5L6YPjL9KWshcooD1/bT+asOJCTHM7beJPoE9Zf3GGdldjveH6/GPGcm2ujbuHLn\nJnHMOPe8abNZ7XRC3JcUssjxPj23lf7f90JBYWnjFbxdvo3akcTjcjjc86ZnTkV3Nco9b3rIcJJ6\n95d508LjSSGLHG3pkUWM/WkUfgZ/Vjddz/NFX1Q7kngcLhfGnTswTZ+M/vw5FKMRa8++WAcMkRGX\nIsuQQhY5kktxMfbAKJYdW0JBcyE2vraNKvmqqh1LPCpFwfDd15inTER/4jiKXk9Sp65Yh4bgKlxE\n7XRCPBIpZJHj2Bw2+n/fi88it1MhoCIbm2+jqN/Dh98Iz+J1YL973vTB39zzplu1xTI8FFep0mpH\nE+KxSCGLHCXOdpfOuzvw8/UD1CtcnzVNN5DbO0DtWOIR6CMOYp4yEcO+vQDYm7XAMmI0zkqVVU4m\nxJORQhY5xrV7V2m3qyVn7p7m9TJvseilZfIe4yxEd/IEzJ1GwGefAZDcoBGW0DAcNWurnEyI9CGF\nLHKEEzF/0P6Lt7lpuUHP6n0YX38KWo1MZsoKtBciMc+YgvHTraAopDxdF8uo90l59jm1owmRrqSQ\nRbYWb49jxfFlLD68gMSUe4x/dgq9g/qpHUukgfbaVUxzZuC9YR0ap5OUqtXxmj6VuDrPuedNC5HN\nSCGLbOmu7Q7Lji1hxbFlJCTHE2AMYHmTVbxZ7m21o4mH0ERHY1owG5/VK93zpsuWwzJyDMnN3yCw\nQK5svfGDyNmkkEW2EpMUwwdHFrHyj+VYUhLJ55OPsHoT6FolGF+DbDnnyTTxcfgsWYBp2VI0VgvO\nYsXd86ZbtZV50yJHkHu5yBZuWW6y+MgC1p74CKvDSn5TAUY8PYpOlbti9pJRiR7NYsFnxQeYFs1H\nGx+HM38BrGHjsXXsnGPnTYucSQpZZGnXE6+x6PA81p1cjd1pp7C5CGH1xtOh0rv46H3UjicexG7H\nZ+1HmObOQhsT7Z43HTaBpOAeYDKpnU6ITCeFLLKkKwmXWRAxl02nPybZlUxxvxIMqDWEthU7YNTJ\noyqP5nDgvXkDplnT0F27isvsi2XoCJJ690Pxz6V2OiFUI4UsspQL8ZEsODSHLWc34nA5KJWrNINq\nDaNV+bZ46bzUjicexOXC+Nl297zpC5Eo3t5Ye/fH2n8wSr58aqcTQnVSyCJLOHf3LPMOzWLbuS24\nFBflcpdnUO1hvFWuFXqt3I09mqJg+GY35qkT0Z/8wz1vunMw1iHDcRUqrHY6ITyG/CYTHu1U7Enm\nHprBZ+c/RUGhUp7KDKkTQvPSb6DT6tSOJx7Ca/8PmKeMx+vQQfe86dbt3POmS5ZSO5oQHkcKWXik\n49FHmXNoJl9c+ByAavlqMKROCK+Wek0mbGUB+oO/YZ46EcP+HwCwv/a6e950xUoqJxPCc0khC48S\ncesgcw7O4JvLuwGolb82Q+qE0KREUzQyncnj6U78gXnaRIxffwVAcsOX3POmg2qpnEwIzyeFLDzC\nrzd+Yc7B6eyN+h6ApwvWZWidETQo1kiKOAvQRZ7DNGMK3p9uAyDlmXruedP16qucTIisQwpZqEZR\nFH66/iNzDs5g/zX3U5vPFXmBIXVCqF/4eSniLEB7NQrT7Ol4b1rvnjddrQbWUWEkN2oi86aFeERS\nyCLTKYrCD1f3MvvgdH698TMADYo1YkidEdQtVE/ldCItNLdvY5o/C581H6FJTsZRrrx73vRrr4NW\nXuMX4nFIIYtMoygK313+mjmHZnDo1kEAXi7RlMF1hlO7wFMqpxNpoYm7i2nxAnw+XIrGasVZvASW\nYSOxt24HOjnrXYgnIYUsMpxLcbH74pfMOTSDY9FHAGhWqgVD6gynemCQyulEmiQmYvpwKT6LF6BN\niMdZoCDW9ye6500bDGqnEyJbkEIWGcbpcrLrwmfMOTiTU3dOoEHDG2VaMrjOcCrnraJ2PJEWNhs+\na1Zimj8bbUwMroAAEt+fSNJ73WXetBDpTApZpDuHy8GO89uYd2gWZ++eQavR0qp8WwbVGkb5PBXU\njifSIiUF703rMc2eju76NVy+fliGh5LUqy+Kn7/a6YTIlqSQRbpJcaaw7dwW5h6aycX4C+i1etpX\n7MjAWkMonbus2vFEWrhcGD/dinn6ZHSXLrrnTfcdiLX/IJQ8edVOJ0S2JoUsnliyM5lNp9ezIGIO\nV+5dxkvrRafKXRlQazAl/EuqHU+khaJg2P0l5mmT0J86geLlRVLXblgHD8dVsJDa6YTIEaSQxWOz\nOWysP7WWRYfncS3xKkadkeBqPegXNIgifkXVjifSQlHw2heOeeoEvCIOoWi12Np2wDJsJK4SJdVO\nJ0SOIoUsHpk1xcq6k6tYdHg+t6w38dH70LNGX/oFDaSAuaDa8UQa6X/7FfPUCRgO7AfA3uJN97zp\n8vI6vxBqkEIWj2RX5OeE7BtMTFI0Jr2Z/jUH06tGPwJNgWpHE2mkO37MPW/6268BsL/UBGtoGI7q\n8hY0IdQkhSzSJMmRxPsHRrHmxEp89D4MqT2cHjX6kMdbTvTJKnTnz2GaPhnvz7YDkFyvPpbQ93HU\nleloQngCKWTxUKfvnKLnN105decklfJUZvnLq6mQp6LasUQaaaOuYJo1De/NG9C4XKTUqOne+KFB\nI5k3LYQHkUIW/0lRFD489CEDdw8kyZFElyrBjK8/BR+9j9rRRBpobt3CPG8m3mtXoUlJwVGhIpaR\nYSQ3ay5FLIQHkkIW9xVvj2No+EA+j/yUXMbcLH7pQ5qXeV3tWCINNHfvYFo0H58VH6BJSsJZoiSW\nkFHYW7aWedNCeDApZPEvB2/+Rq9vg7ly7zL1i9VnQYNlFPMrrnYs8RCaxHv4LFuCz5KFaO8l4CxU\nGOuEqdg6dAIvL7XjCSEeQgpZpHIpLhYdnsfUXyfiUlwMqRPC9Fcnczc2Se1o4kGSkvBZvRLTgtlo\nY2Nx5c1L4vgpJHUJBh95eUGIrEIKWQBwy3qLvt/1YN/VvRQ0F2JJ4w95rsgL6LVyF/FYKSl4b1iH\nac4MdDeu4/LzxzJiNEk9+6D4+qmdTgjxiOS3rWDPlW/p931PYpJieLlEU+Y3WkpeH3k7k8dyOjFu\n/wTzjCnoLl9C8fHB2n8w1n4DUQLyqJ1OCPGYpJBzsGRnMlN+ncCSIwswaA1Mqj+N7tV7o5EzcD2T\nomD4chfm6ZPQnz7lnjcd3AProGG4CsiENCGyOinkHOpi/AV6ffseh29HUDpXGZa/vIrqgTKpySMp\nCl7he9zzpo8cRtFqSWrfEevQEbiKl1A7nRAinUgh50Dbz33CsPBBJKbco02F9kx7fha+BnnN0RPp\nf/nZPW/65wMA2N5oiTVkFM5y5VVOJoRIb1LIOYglxcKo/cPZePpjzF6+LH5pOa0rtFM7lrgP/fGj\nmKZOxPjdNwDYm7yCZWQYzmrVVU4mhMgoUsg5xPGYY/T8pivn485RPTCI5U0+onTusmrHEv+gO3sG\n8/TJGHfuACC5/vPuedNPP6NyMiFERpNCzuYUReGjP5Yz9sBokl3J9KzRlzF1x2HUGdWOJv4f7ZXL\nmGdNw7hlo3vedM1aWEaNJeWFBjLmUogcQgo5G7tji2XQ3n7svvgFeb3zsvClD2hc4hW1Y4n/R3vr\nJqa5M/Fet9o9b7pSZfe86abNpIiFyGGkkLOpn68foPe33bhuucZzRV5gSeMPKWgupHYs8SfNnVj3\nvOmVy9AkJeEoVRpryCjsb74t86aFyKGkkLMZp8vJnEMzmH1wOho0hD4dxoBaQ9Bp5Ze8J9DcS3DP\nm166yD1vunARrJNGYGv3jsybFiKHk0LORq4nXqP3d934+foBivoW44MmH/F0ITkZyCMkJeGzZKF7\n3vSdO7jy5SMxZCpJnYPB21vtdEIIDyCFnE3svvglA/f05q79Ls1Lv8GcBgvI7R2gdiyRnIz3+rUw\nfxa+16/j8s+FJTQMa/fe4OurdjohhAeRQs7ibA4bE34OY8XxZXjrvJnxwlw6V3lPxl+qzenEuHUz\n5pnT0F25BCYT1oFDsfYdgJJb/qMkhPg3KeQs7Pzdc3T/pgsnYo9TIaAiy19eTaW8ldWOlbMpCoZd\nn2OeMRn9mdMoBgPW7r0wTRyHRWtSO50QwoNJIWdBiqKw6fR6QvcPw+qw0qlyVybWn4rJS37hq0ZR\n8Nr7Heapk/A6ehhFpyPpnXfd86aLFsMU6AfR99ROKYTwYFLIWcy95ASG/zCY7ec+wd+QixUvr+H1\nsm+pHStH8/rlJ0xTJmD45ScAbG+97Z43XaacysmEEFmJFHIWcvjWIXp++x6XEi5Su8BTLGvyEcX9\nZbcfteiPHsY8dSKGPd8BYH/lVSwjxuCsWk3lZEKIrEgKOQtwKS6WHlnE5F/H4XQ5GVhrKCFPjcJL\nJ+9bVYPuzGn3vOldnwGQ/PyLWELDcNR5WuVkQoisTJuWK9lsNho3bsz27dsBWLt2LVWqVMFisaRe\n5/PPP+ftt9+mdevWfPLJJxmTNgeKtkbT4YtWjP95DHm887KlxQ5G1x0rZawC7aWL+PXrScCLdTHu\n+oyU2nWI2/o58dt2ShkLIZ5Ymh4hL126lFy5cgGwY8cOYmNjyZ8/f+rlVquVxYsXs3XrVry8vGjV\nqhVNmjQhd+7cGZM6h/ghai99v+/BbestGhVvzMJGywg0BaodK8fR3ryBac4MvD9eg8bhwFGpCpZR\n75P8clOZNy2ESDcPLeTIyEjOnz9PgwYNAGjcuDG+vr7s3Lkz9TpHjx6lWrVq+Pm5N7mvVasWERER\nNGrUKGNSZ3MpzhSm/zaZhYfnotfqGffsZHrV6ItWk6YnNEQ60cTGYlo4F5+PlqOx2XCULoN1xGjs\nb7QErfxbCCHS10MLefr06YSFhbFjh3t/Vt/7TBeKiYkhT548qZ/nyZOH6OjodIyZc1xJuEzPb9/j\n0K3fKelfiuUvryIofy21Y+UomoR4fJYuwmfZErSJ93AWKYp12EhsbTuAXk67EEJkjAf+dtmxYwdB\nQUEUK1bskQ6qKEqarhcQYEKvz9hNDwID/TL0+OnpkxOf0H1nd+Lt8XSo1oGlry3F3+ivdqwstYZP\nxGqFRYtg+nS4cwfy54fJk9D16IGftzdPugo5Zh0zmKzjk5M1TB/pvY4PLOTw8HCioqIIDw/n5s2b\nGAwGChYsyLPPPvu36+XPn5+YmJjUz2/fvk1QUNBDb/zuXetjxk6bwEA/orPAMAZripWwAyNZd3I1\nJr2JBY2W0rZCB+wJGqJRN39WWcMnkpyM97rVmObORHf7Fq5cubGOHktScE/3vOl7Ke4/TyBHrGMm\nkHV8crKG6eNx1/FBJf7AQp43b17qxwsXLqRIkSL/KmOAGjVqMGbMGBISEtDpdERERDBq1KhHDpoT\nnYo9SY9vunDm7mmq5qvO8iarKBsgAyUyhcPhnjc9axq6K5dRTGYsg4eR1GcASi45IVEIkbke+QWx\npUuX8tNPPxEdHU337t0JCgoiJCSEoUOHEhwcjEajoW/fvqkneIl/UxSF327+ypYzG/jkzCZsThvd\nq/UirN4EvPWyFV+Gc7kwfPE55mmT0J8765433bMP1gFDUQLlLHYhhDo0Slpf8M0AGf20iac9NRN1\n7wpbzmxky5mNXIy/AEBhcxGmvTCbpqW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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "metadata": { - "id": "A6MaclhK4rc6", - "colab_type": "code", - "outputId": "065e7aa3-2d40-4566-9b49-d3237c69e4e5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 548 - } - }, - "cell_type": "code", - "source": [ - "# Okay! Now let's tweak!\n", - "linear_regression(learning_rate=0.000034, n_epochs=500)" - ], - "execution_count": 22, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 48290.105\n", - "Loss after epoch 50 is 29.723145\n", - "Loss after epoch 100 is 29.653582\n", - "Loss after epoch 150 is 29.584167\n", - "Loss after epoch 200 is 29.514908\n", - "Loss after epoch 250 is 29.445837\n", - "Loss after epoch 300 is 29.376942\n", - "Loss after epoch 350 is 29.30818\n", - "Loss after epoch 400 is 29.239614\n", - "Loss after epoch 450 is 29.171177\n", - "Now testing the model in the test set\n", - "The final loss is: 32.455235\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "image/png": 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BU6y42tGyBClkIUSGOhl9gg7ftMajeFjzxloq562qdiTxEPqj4VgnjsUYtgcA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\nj41n1rHj0lfYXfdeO3+hwEu0Kt2W5s+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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "metadata": { - "id": "peoHmV2M40uU", - "colab_type": "code", - "outputId": "cc3b5c21-06bd-4aee-9476-1f50063bf5e5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 715 - } - }, - "cell_type": "code", - "source": [ - "linear_regression(learning_rate=0.0000006, n_epochs=1000)" - ], - "execution_count": 23, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 48290.105\n", - "Loss after epoch 50 is 5544.9077\n", - "Loss after epoch 100 is 660.0498\n", - "Loss after epoch 150 is 101.81439\n", - "Loss after epoch 200 is 38.018833\n", - "Loss after epoch 250 is 30.727413\n", - "Loss after epoch 300 is 29.893017\n", - "Loss after epoch 350 is 29.796562\n", - "Loss after epoch 400 is 29.78446\n", - "Loss after epoch 450 is 29.781971\n", - "Loss after epoch 500 is 29.780622\n", - "Loss after epoch 550 is 29.77936\n", - "Loss after epoch 600 is 29.778126\n", - "Loss after epoch 650 is 29.776915\n", - "Loss after epoch 700 is 29.77566\n", - "Loss after epoch 750 is 29.774433\n", - "Loss after epoch 800 is 29.77318\n", - "Loss after epoch 850 is 29.771988\n", - "Loss after epoch 900 is 29.77074\n", - "Loss after epoch 950 is 29.76949\n", - "Now testing the model in the test set\n", - "The final loss is: 33.224064\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "image/png": 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9sfXog5Ijh9rp0h0pZCGEKqYemMiKE0spn6MiK19dh4/eR+1IIrkUBeOuL7FM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\nFwHIZ8nPxJem0bjoayqnezhvW8Nnoig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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "metadata": { - "id": "KjY_KnlE5ClG", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "## Drive the loss to a minimum." - ] - }, - { - "metadata": { - "id": "JKiHjGN15HPX", - "colab_type": "code", - "outputId": "4bcb2a50-5858-46ea-e393-667d80049851", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1049 - } - }, - "cell_type": "code", - "source": [ - "# YOUR CODE HERE\n", - "linear_regression(learning_rate=0.0000556, n_epochs=200000, interval=5000)" - ], - "execution_count": 28, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss after epoch 0 is 48290.105\n", - "Loss after epoch 5000 is 20.356878\n", - "Loss after epoch 10000 is 13.997063\n", - "Loss after epoch 15000 is 9.710622\n", - "Loss after epoch 20000 is 6.821608\n", - "Loss after epoch 25000 is 4.874446\n", - "Loss after epoch 30000 is 3.5620654\n", - "Loss after epoch 35000 is 2.6775396\n", - "Loss after epoch 40000 is 2.081378\n", - "Loss after epoch 45000 is 1.679563\n", - "Loss after epoch 50000 is 1.4087588\n", - "Loss after epoch 55000 is 1.226228\n", - "Loss after epoch 60000 is 1.1032239\n", - "Loss after epoch 65000 is 1.0203019\n", - "Loss after epoch 70000 is 0.9644026\n", - "Loss after epoch 75000 is 0.92674285\n", - "Loss after epoch 80000 is 0.90138215\n", - "Loss after epoch 85000 is 0.8842748\n", - "Loss after epoch 90000 is 0.87272763\n", - "Loss after epoch 95000 is 0.864965\n", - "Loss after epoch 100000 is 0.85972536\n", - "Loss after epoch 105000 is 0.85620254\n", - "Loss after epoch 110000 is 0.85381925\n", - "Loss after epoch 115000 is 0.85222006\n", - "Loss after epoch 120000 is 0.8511251\n", - "Loss after epoch 125000 is 0.85038906\n", - "Loss after epoch 130000 is 0.8499146\n", - "Loss after epoch 135000 is 0.8495802\n", - "Loss after epoch 140000 is 0.8493226\n", - "Loss after epoch 145000 is 0.84920985\n", - "Loss after epoch 150000 is 0.84911305\n", - "Loss after epoch 155000 is 0.8490328\n", - "Loss after epoch 160000 is 0.8489686\n", - "Loss after epoch 165000 is 0.8489226\n", - "Loss after epoch 170000 is 0.8489226\n", - "Loss after epoch 175000 is 0.8489226\n", - "Loss after epoch 180000 is 0.8489226\n", - "Loss after epoch 185000 is 0.8489226\n", - "Loss after epoch 190000 is 0.8489226\n", - "Loss after epoch 195000 is 0.8489226\n", - "Now testing the model in the test set\n", - "The final loss is: 0.95510364\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "image/png": 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TX52ap4Ft8fMh7Md4obl/j6TadVM+d12rdp5tX+RvcjNaIYT4j68urGHfX3t4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\nEDkmnbYoFPTJetbsmUh0u1cYs+EKaLS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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - } - ] - } - ] -} \ No newline at end of file From 4a879d45ff59f6459e380c9141e897eda45d06d8 Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Fri, 28 Dec 2018 01:36:31 +0530 Subject: [PATCH 10/12] Delete Assignment_2.ipynb --- Assignment_2.ipynb | 654 --------------------------------------------- 1 file changed, 654 deletions(-) delete mode 100644 Assignment_2.ipynb diff --git a/Assignment_2.ipynb b/Assignment_2.ipynb deleted file mode 100644 index 530a016..0000000 --- a/Assignment_2.ipynb +++ /dev/null @@ -1,654 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Assignment_2.ipynb", - "version": "0.3.2", - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "metadata": { - "id": "A5gF9gMFq3Ck", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "dummy_list=[1, 2, 3, 4, 5, 3]" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "QkJs_byK_qPg", - "colab_type": "code", - "outputId": "65790928-5ac4-4cac-9033-5cd5c6b6e3f4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - } - }, - "cell_type": "code", - "source": [ - "print(dummy_list)" - ], - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[1, 2, 3, 4, 5, 3]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "F2QMHeQAB4Ja", - "colab_type": "code", - "outputId": "ffd98b19-842c-4173-fd00-86956c639f63", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - } - }, - "cell_type": "code", - "source": [ - "dummy_list.reverse()\n", - "print(dummy_list)\n", - "#for o in reversed(dummy_list):\n", - "# print(o)" - ], - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[3, 5, 4, 3, 2, 1]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "7XeXBwK6CD1i", - "colab_type": "code", - "outputId": "b18002de-a2a2-4855-e2da-82dc314d6f59", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - } - }, - "cell_type": "code", - "source": [ - "dummy_list_2 = [2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", - "dummy_list.extend(dummy_list_2)\n", - "print(dummy_list)" - ], - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[3, 5, 4, 3, 2, 1, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "ujmdan92Du6g", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "dummy_dict={}" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "TPaUVjDSEqyv", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "i=0\n", - "while i!= len(dummy_list):\n", - " if dummy_list[i] not in dummy_dict:\n", - " dummy_dict[dummy_list[i]]=1\n", - " else:\n", - " for key,value in dummy_dict.items():\n", - " if key == dummy_list[i]:\n", - " dummy_dict[key] = value + 1\n", - " break\n", - " #print(\"Key: \" + str(key))\n", - " #print (\"Value: \" + str(value))\n", - " i = i + 1\n", - " #print(i)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "TY6jC_gyQbPP", - "colab_type": "code", - "outputId": "c6e40202-7c20-4caf-93dc-12e0c76ad1f2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 886 - } - }, - "cell_type": "code", - "source": [ - "for key, value in dummy_dict.items():\n", - " print(\"Key: \" + str(key))\n", - " print (\"Value: \" + str(value))\n", - " print('\\n')" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Key: 3\n", - "Value: 2\n", - "\n", - "\n", - "Key: 5\n", - "Value: 1\n", - "\n", - "\n", - "Key: 4\n", - "Value: 2\n", - "\n", - "\n", - "Key: 2\n", - "Value: 2\n", - "\n", - "\n", - "Key: 1\n", - "Value: 2\n", - "\n", - "\n", - "Key: 200\n", - "Value: 1\n", - "\n", - "\n", - "Key: 16\n", - "Value: 1\n", - "\n", - "\n", - "Key: 0\n", - "Value: 1\n", - "\n", - "\n", - "Key: 9.45\n", - "Value: 1\n", - "\n", - "\n", - "Key: 45.67\n", - "Value: 1\n", - "\n", - "\n", - "Key: 90\n", - "Value: 1\n", - "\n", - "\n", - "Key: 12.01\n", - "Value: 1\n", - "\n", - "\n", - "Key: 12.02\n", - "Value: 1\n", - "\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "G0Ft30gsucvR", - "colab_type": "code", - "outputId": "8f9a521d-8ceb-475c-fd71-cd4a4b193d64", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 84 - } - }, - "cell_type": "code", - "source": [ - "'''for key in sorted(dummy_dict.keys()):\n", - " print (\"Key :\" + str(key) + '\\t\\t\\t' + \"Value :\" + str(dummy_dict[key]))\n", - "\n", - "print(\"\\n\\n\")\n", - "for key in sorted(dummy_dict.keys(), reverse=True):\n", - " print (\"Key :\" + str(key) + '\\t\\t\\t' + \"Value :\" + str(dummy_dict[key]))'''\n", - "\n", - "dummy_list.sort()\n", - "print(dummy_list)\n", - "print('\\n')\n", - "dummy_list.sort(reverse=True)\n", - "print(dummy_list)\n", - "#print(sorted(dummy_list, reversed=True))" - ], - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 9.45, 12.01, 12.02, 16, 45.67, 90, 200]\n", - "\n", - "\n", - "[200, 90, 45.67, 16, 12.02, 12.01, 9.45, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "DtOAyHUtvm8Z", - "colab_type": "code", - "outputId": "23b3a649-a150-40d2-909a-09506e32c10f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - } - }, - "cell_type": "code", - "source": [ - "x=200\n", - "dummy_list.remove(x)\n", - "print(dummy_list)" - ], - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[90, 45.67, 16, 12.02, 12.01, 9.45, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "Adi6fYUBwnFG", - "colab_type": "code", - "outputId": "6fa54ac4-c156-4ce3-a0cf-03ffbb5b6d15", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - } - }, - "cell_type": "code", - "source": [ - "del dummy_list[5]\n", - "print (dummy_list)" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[90, 45.67, 16, 12.02, 12.01, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "OTEasC7uxmOX", - "colab_type": "code", - "outputId": "fa25c22f-b308-4b2a-ac63-90d0dbf4faaa", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - } - }, - "cell_type": "code", - "source": [ - "dummy_list.clear()\n", - "print(dummy_list)" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "5Tw-3o-kwdZS", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "# Now the Numpy part" - ] - }, - { - "metadata": { - "id": "XI0_hACCQlA_", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "import numpy as np" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "VV7zXbLnYp2t", - "colab_type": "code", - "outputId": "7a0112f4-dc8d-4621-9c9f-49f64395f5d2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 201 - } - }, - "cell_type": "code", - "source": [ - "b = np.linspace(1.3,2.5, num=64)\n", - "print (b)" - ], - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[1.3 1.31904762 1.33809524 1.35714286 1.37619048 1.3952381\n", - " 1.41428571 1.43333333 1.45238095 1.47142857 1.49047619 1.50952381\n", - " 1.52857143 1.54761905 1.56666667 1.58571429 1.6047619 1.62380952\n", - " 1.64285714 1.66190476 1.68095238 1.7 1.71904762 1.73809524\n", - " 1.75714286 1.77619048 1.7952381 1.81428571 1.83333333 1.85238095\n", - " 1.87142857 1.89047619 1.90952381 1.92857143 1.94761905 1.96666667\n", - " 1.98571429 2.0047619 2.02380952 2.04285714 2.06190476 2.08095238\n", - " 2.1 2.11904762 2.13809524 2.15714286 2.17619048 2.1952381\n", - " 2.21428571 2.23333333 2.25238095 2.27142857 2.29047619 2.30952381\n", - " 2.32857143 2.34761905 2.36666667 2.38571429 2.4047619 2.42380952\n", - " 2.44285714 2.46190476 2.48095238 2.5 ]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "JYYLSWaQRKJC", - "colab_type": "code", - "outputId": "b3b3c270-f675-4506-e784-beee8ca1e2f7", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 50 - } - }, - "cell_type": "code", - "source": [ - "n = int(input())\n", - "x = np.array([1, 2, 3])\n", - "y = np.tile(x,(3*n))\n", - "print (y)" - ], - "execution_count": 14, - "outputs": [ - { - "output_type": "stream", - "text": [ - "3\n", - "[1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "FGwwjf_pTMRs", - "colab_type": "code", - "outputId": "6cb8a5f9-16c3-4d19-c99f-709cdb74f778", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - } - }, - "cell_type": "code", - "source": [ - "b = np.arange(20)\n", - "\n", - "#print(b)\n", - "\n", - "mask = b%2!=0 #perform computations on the list \n", - "\n", - "#print(mask)\n", - "\n", - "print(b[mask]) #applying the mask on the numpy array" - ], - "execution_count": 15, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[ 1 3 5 7 9 11 13 15 17 19]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "ZRDXe1q4Yw6C", - "colab_type": "code", - "outputId": "1a88b34c-7a90-4b40-c9d7-7e16fbe9b027", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - } - }, - "cell_type": "code", - "source": [ - "#expected output array([2, 4])\n", - "a = np.array([1,2,3,2,3,4,3,4,5,6])\n", - "b = np.array([7,2,10,2,7,4,9,4,9,8])\n", - "np.intersect1d(a,b)" - ], - "execution_count": 16, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([2, 4])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 16 - } - ] - }, - { - "metadata": { - "id": "vrIl330LZDO3", - "colab_type": "code", - "outputId": "25c61d75-1de8-4e4e-e596-40453739cba6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 100 - } - }, - "cell_type": "code", - "source": [ - "a = np.arange(10)\n", - "print (a)\n", - "print('\\n')\n", - "a.shape = a.size//-1, 5\n", - "print(a)" - ], - "execution_count": 17, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[0 1 2 3 4 5 6 7 8 9]\n", - "\n", - "\n", - "[[0 1 2 3 4]\n", - " [5 6 7 8 9]]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "atGd8_9_Zjc-", - "colab_type": "code", - "outputId": "67882dcf-e6ff-4a67-b531-0195b7d8177d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 84 - } - }, - "cell_type": "code", - "source": [ - "a = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n", - "np.array(a).tolist()\n", - "print(a)\n", - "print('\\n')\n", - "np.asarray(a)" - ], - "execution_count": 18, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n", - "\n", - "\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5, 6, 7, 8, 9])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 18 - } - ] - }, - { - "metadata": { - "id": "L8JI_PAmbcbp", - "colab_type": "code", - "outputId": "dac71dd3-e9f4-4a36-be66-04a92d7ecb3f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 100 - } - }, - "cell_type": "code", - "source": [ - "d = np.ones((5,5))\n", - "d[1:-1,1:-1] = 0\n", - "print(d)" - ], - "execution_count": 19, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[[1. 1. 1. 1. 1.]\n", - " [1. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 1.]\n", - " [1. 1. 1. 1. 1.]]\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "SY2a-sGxU4Q8", - "colab_type": "code", - "outputId": "96dfeeb7-b231-47f2-be76-0bcd1696699a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 150 - } - }, - "cell_type": "code", - "source": [ - "x = np.zeros((8,8),dtype=int)\n", - "x[1::2,::2] = 1\n", - "x[::2,1::2] = 1\n", - "print(x)" - ], - "execution_count": 20, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[[0 1 0 1 0 1 0 1]\n", - " [1 0 1 0 1 0 1 0]\n", - " [0 1 0 1 0 1 0 1]\n", - " [1 0 1 0 1 0 1 0]\n", - " [0 1 0 1 0 1 0 1]\n", - " [1 0 1 0 1 0 1 0]\n", - " [0 1 0 1 0 1 0 1]\n", - " [1 0 1 0 1 0 1 0]]\n" - ], - "name": "stdout" - } - ] - } - ] -} \ No newline at end of file From 5b2128968084a7bc7d860c28e56bb71a2d5f9473 Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Mon, 21 Jan 2019 16:39:00 +0530 Subject: [PATCH 11/12] Assignment 5 Part 1 (Pandas) Solved. --- Intro_to_pandas.ipynb | 1703 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1703 insertions(+) create mode 100644 Intro_to_pandas.ipynb diff --git a/Intro_to_pandas.ipynb b/Intro_to_pandas.ipynb new file mode 100644 index 0000000..e968a90 --- /dev/null +++ b/Intro_to_pandas.ipynb @@ -0,0 +1,1703 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of intro_to_pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "YHIWvc9Ms-Ll", + "TJffr5_Jwqvd" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "JndnmDMp66FL" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hMqWDc_m6rUC", + "cellView": "both", + "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": { + "colab_type": "text", + "id": "rHLcriKWLRe4" + }, + "cell_type": "markdown", + "source": [ + "# Intro to pandas" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "QvJBqX8_Bctk" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n", + " * Access and manipulate data within a `DataFrame` and `Series`\n", + " * Import CSV data into a *pandas* `DataFrame`\n", + " * Reindex a `DataFrame` to shuffle data" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TIFJ83ZTBctl" + }, + "cell_type": "markdown", + "source": [ + "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n", + "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "s_JOISVgmn9v" + }, + "cell_type": "markdown", + "source": [ + "## Basic Concepts\n", + "\n", + "The following line imports the *pandas* API and prints the API version:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "aSRYu62xUi3g", + "outputId": "4c052cad-dfa4-4fae-c502-c2e62f3fb85e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + } + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "u'0.22.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "daQreKXIUslr" + }, + "cell_type": "markdown", + "source": [ + "The primary data structures in *pandas* are implemented as two classes:\n", + "\n", + " * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n", + " * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n", + "\n", + "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fjnAk1xcU0yc" + }, + "cell_type": "markdown", + "source": [ + "One way to create a `Series` is to construct a `Series` object. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "DFZ42Uq7UFDj", + "outputId": "7f768471-8f34-4680-9fbc-0d4900311739", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 3, + "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": 3 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "U5ouUp1cU6pC" + }, + "cell_type": "markdown", + "source": [ + "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "avgr6GfiUh8t", + "outputId": "24e4cefe-1dcd-402a-f3e9-f99414807ceb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 137 + } + }, + "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": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulation
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785\n", + "2 Sacramento 485199" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "oa5wfZT7VHJl" + }, + "cell_type": "markdown", + "source": [ + "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "av6RYOraVG1V", + "outputId": "2168747e-41c4-4e0a-9ac5-bd2c212906e0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "california_housing_dataframe.describe()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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mean-119.56210835.62522528.5893532643.664412539.4108241429.573941501.2219413.883578207300.912353
std2.0051662.13734012.5869372179.947071421.4994521147.852959384.5208411.908157115983.764387
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max-114.31000041.95000052.00000037937.0000006445.00000035682.0000006082.00000015.000100500001.000000
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean -119.562108 35.625225 28.589353 2643.664412 \n", + "std 2.005166 2.137340 12.586937 2179.947071 \n", + "min -124.350000 32.540000 1.000000 2.000000 \n", + "25% -121.790000 33.930000 18.000000 1462.000000 \n", + "50% -118.490000 34.250000 29.000000 2127.000000 \n", + "75% -118.000000 37.720000 37.000000 3151.250000 \n", + "max -114.310000 41.950000 52.000000 37937.000000 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean 539.410824 1429.573941 501.221941 3.883578 \n", + "std 421.499452 1147.852959 384.520841 1.908157 \n", + "min 1.000000 3.000000 1.000000 0.499900 \n", + "25% 297.000000 790.000000 282.000000 2.566375 \n", + "50% 434.000000 1167.000000 409.000000 3.544600 \n", + "75% 648.250000 1721.000000 605.250000 4.767000 \n", + "max 6445.000000 35682.000000 6082.000000 15.000100 \n", + "\n", + " median_house_value \n", + "count 17000.000000 \n", + "mean 207300.912353 \n", + "std 115983.764387 \n", + "min 14999.000000 \n", + "25% 119400.000000 \n", + "50% 180400.000000 \n", + "75% 265000.000000 \n", + "max 500001.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "WrkBjfz5kEQu" + }, + "cell_type": "markdown", + "source": [ + "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "s3ND3bgOkB5k", + "outputId": "f81980c5-2ce6-4494-b958-1647dc15d8e0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 196 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.head()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
0-114.3134.1915.05612.01283.01015.0472.01.493666900.0
1-114.4734.4019.07650.01901.01129.0463.01.820080100.0
2-114.5633.6917.0720.0174.0333.0117.01.650985700.0
3-114.5733.6414.01501.0337.0515.0226.03.191773400.0
4-114.5733.5720.01454.0326.0624.0262.01.925065500.0
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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": 6 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "w9-Es5Y6laGd" + }, + "cell_type": "markdown", + "source": [ + "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "nqndFVXVlbPN", + "outputId": "425c7814-fb16-441b-b529-912371ca3f57", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 395 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "XtYZ7114n3b-" + }, + "cell_type": "markdown", + "source": [ + "## Accessing Data\n", + "\n", + "You can access `DataFrame` data using familiar Python dict/list operations:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "_TFm7-looBFF", + "outputId": "d7ab8f7b-e1e3-480c-a6f5-be3cf05dfc37", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 100 + } + }, + "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": 8, + "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": 8 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "V5L6xacLoxyv", + "outputId": "1319d6e2-f41e-4a35-a0ad-7ead83684577", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "gcYX1tBPugZl", + "outputId": "927153f2-e74a-4256-ad8d-b6211ad065b9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 124 + } + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 10, + "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": 10 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "65g1ZdGVjXsQ" + }, + "cell_type": "markdown", + "source": [ + "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "RM1iaD-ka3Y1" + }, + "cell_type": "markdown", + "source": [ + "## Manipulating Data\n", + "\n", + "You may apply Python's basic arithmetic operations to `Series`. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XWmyCFJ5bOv-", + "outputId": "91280582-c473-48cd-c7f5-36a8a73df7e9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 11, + "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": 11 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TQzIVnbnmWGM" + }, + "cell_type": "markdown", + "source": [ + "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ko6pLK6JmkYP", + "outputId": "92b5b7fd-95be-4fc5-8033-5b8da8b2a95b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 12, + "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": 12 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "xmxFuQmurr6d" + }, + "cell_type": "markdown", + "source": [ + "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n", + "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n", + "\n", + "The example below creates a new `Series` that indicates whether `population` is over one million:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Fc1DvPAbstjI", + "outputId": "e447f694-479c-4964-fa8e-4e5bfebad9cc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZeYYLoV9b9fB" + }, + "cell_type": "markdown", + "source": [ + "\n", + "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "0gCEX99Hb8LR", + "outputId": "7d96cf32-a00a-4c43-81d1-88953ab65041", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 137 + } + }, + "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": 14, + "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": 14 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "6qh63m-ayb-c" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #1\n", + "\n", + "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n", + "\n", + " * The city is named after a saint.\n", + " * The city has an area greater than 50 square miles.\n", + "\n", + "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n", + "\n", + "**Hint:** \"San\" in Spanish means \"saint.\"" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "zCOn8ftSyddH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 137 + }, + "outputId": "508be6c9-95f2-432c-be0a-e2f2d24e8886" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities['Wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 15, + "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
2Sacramento48519997.924955.055147False
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" + ], + "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 \n", + "\n", + " Wide and has saint name \n", + "0 False \n", + "1 True \n", + "2 False " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "YHIWvc9Ms-Ll" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5OlrqtdtCIb", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-xAOJeMiXFB" + }, + "cell_type": "markdown", + "source": [ + "## Indexes\n", + "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n", + "\n", + "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2684gsWNinq9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "outputId": "11071fbe-39ca-482f-b66f-e3a7e7e28996" + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F_qPe2TBjfWd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "outputId": "42bb63a5-5a3c-494a-8d99-b324a91dea7c" + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "hp2oWY9Slo_h" + }, + "cell_type": "markdown", + "source": [ + "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "sN0zUzSAj-U1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 137 + }, + "outputId": "6f05c626-0057-43e2-d444-e6c50622ac44" + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityWide and has saint name
2Sacramento48519997.924955.055147False
0San Francisco85246946.8718187.945381False
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "\n", + " Wide and has saint name \n", + "2 False \n", + "0 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "-GQFz8NZuS06" + }, + "cell_type": "markdown", + "source": [ + "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n", + "Try running the following cell multiple times!" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mF8GC0k8uYhz", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 137 + }, + "outputId": "5496dd16-e56e-4b2d-a68c-039690cb4a57" + }, + "cell_type": "code", + "source": [ + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densityWide and has saint name
2Sacramento48519997.924955.055147False
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "\n", + " Wide and has saint name \n", + "2 False \n", + "0 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 24 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fSso35fQmGKb" + }, + "cell_type": "markdown", + "source": [ + "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8UngIdVhz8C0" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #2\n", + "\n", + "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "PN55GrDX0jzO", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 166 + }, + "outputId": "37aab3dd-e4b4-4afe-8bdb-52da45962509" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities.reindex([2, 0, 1, 4])" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "2 Sacramento 485199.0 97.92 4955.055147 \n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "1 San Jose 1015785.0 176.53 5754.177760 \n", + "4 NaN NaN NaN NaN \n", + "\n", + " Wide and has saint name \n", + "2 False \n", + "0 False \n", + "1 True \n", + "4 NaN " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TJffr5_Jwqvd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8oSvi2QWwuDH" + }, + "cell_type": "markdown", + "source": [ + "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yBdkucKCwy4x", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cities.reindex([0, 4, 5, 2])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "2l82PhPbwz7g" + }, + "cell_type": "markdown", + "source": [ + "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n", + "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n", + "in which the index values are browser names).\n", + "\n", + "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n", + "sanitizing the input." + ] + } + ] +} \ No newline at end of file From f36ff1941bce5a23e92fd8d545a62935bc7d5cda Mon Sep 17 00:00:00 2001 From: Anubhab-Bob <43186348+Anubhab-Bob@users.noreply.github.com> Date: Mon, 21 Jan 2019 16:39:34 +0530 Subject: [PATCH 12/12] Delete Intro_to_pandas.ipynb --- Intro_to_pandas.ipynb | 1703 ----------------------------------------- 1 file changed, 1703 deletions(-) delete mode 100644 Intro_to_pandas.ipynb diff --git a/Intro_to_pandas.ipynb b/Intro_to_pandas.ipynb deleted file mode 100644 index e968a90..0000000 --- a/Intro_to_pandas.ipynb +++ /dev/null @@ -1,1703 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Copy of intro_to_pandas.ipynb", - "version": "0.3.2", - "provenance": [], - "collapsed_sections": [ - "JndnmDMp66FL", - "YHIWvc9Ms-Ll", - "TJffr5_Jwqvd" - ], - "include_colab_link": true - }, - "kernelspec": { - "name": "python2", - "display_name": "Python 2" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "JndnmDMp66FL" - }, - "cell_type": "markdown", - "source": [ - "#### Copyright 2017 Google LLC." - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "hMqWDc_m6rUC", - "cellView": "both", - "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": { - "colab_type": "text", - "id": "rHLcriKWLRe4" - }, - "cell_type": "markdown", - "source": [ - "# Intro to pandas" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "QvJBqX8_Bctk" - }, - "cell_type": "markdown", - "source": [ - "**Learning Objectives:**\n", - " * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n", - " * Access and manipulate data within a `DataFrame` and `Series`\n", - " * Import CSV data into a *pandas* `DataFrame`\n", - " * Reindex a `DataFrame` to shuffle data" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "TIFJ83ZTBctl" - }, - "cell_type": "markdown", - "source": [ - "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n", - "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials." - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "s_JOISVgmn9v" - }, - "cell_type": "markdown", - "source": [ - "## Basic Concepts\n", - "\n", - "The following line imports the *pandas* API and prints the API version:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "aSRYu62xUi3g", - "outputId": "4c052cad-dfa4-4fae-c502-c2e62f3fb85e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - } - }, - "cell_type": "code", - "source": [ - "from __future__ import print_function\n", - "\n", - "import pandas as pd\n", - "pd.__version__" - ], - "execution_count": 2, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "u'0.22.0'" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 2 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "daQreKXIUslr" - }, - "cell_type": "markdown", - "source": [ - "The primary data structures in *pandas* are implemented as two classes:\n", - "\n", - " * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n", - " * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n", - "\n", - "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)." - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "fjnAk1xcU0yc" - }, - "cell_type": "markdown", - "source": [ - "One way to create a `Series` is to construct a `Series` object. For example:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "DFZ42Uq7UFDj", - "outputId": "7f768471-8f34-4680-9fbc-0d4900311739", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 84 - } - }, - "cell_type": "code", - "source": [ - "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" - ], - "execution_count": 3, - "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": 3 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "U5ouUp1cU6pC" - }, - "cell_type": "markdown", - "source": [ - "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "avgr6GfiUh8t", - "outputId": "24e4cefe-1dcd-402a-f3e9-f99414807ceb", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 137 - } - }, - "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": 4, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " City name Population\n", - "0 San Francisco 852469\n", - "1 San Jose 1015785\n", - "2 Sacramento 485199" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 4 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "oa5wfZT7VHJl" - }, - "cell_type": "markdown", - "source": [ - "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "av6RYOraVG1V", - "outputId": "2168747e-41c4-4e0a-9ac5-bd2c212906e0", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 286 - } - }, - "cell_type": "code", - "source": [ - "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", - "california_housing_dataframe.describe()" - ], - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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1-114.4734.4019.07650.01901.01129.0463.01.820080100.0
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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": 6 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "w9-Es5Y6laGd" - }, - "cell_type": "markdown", - "source": [ - "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "nqndFVXVlbPN", - "outputId": "425c7814-fb16-441b-b529-912371ca3f57", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 395 - } - }, - "cell_type": "code", - "source": [ - "california_housing_dataframe.hist('housing_median_age')" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[]],\n", - " dtype=object)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 7 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "XtYZ7114n3b-" - }, - "cell_type": "markdown", - "source": [ - "## Accessing Data\n", - "\n", - "You can access `DataFrame` data using familiar Python dict/list operations:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "_TFm7-looBFF", - "outputId": "d7ab8f7b-e1e3-480c-a6f5-be3cf05dfc37", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 100 - } - }, - "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": 8, - "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": 8 - } - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "V5L6xacLoxyv", - "outputId": "1319d6e2-f41e-4a35-a0ad-7ead83684577", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 50 - } - }, - "cell_type": "code", - "source": [ - "print(type(cities['City name'][1]))\n", - "cities['City name'][1]" - ], - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'San Jose'" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 9 - } - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "gcYX1tBPugZl", - "outputId": "927153f2-e74a-4256-ad8d-b6211ad065b9", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 124 - } - }, - "cell_type": "code", - "source": [ - "print(type(cities[0:2]))\n", - "cities[0:2]" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
City namePopulation
0San Francisco852469
1San Jose1015785
\n", - "
" - ], - "text/plain": [ - " City name Population\n", - "0 San Francisco 852469\n", - "1 San Jose 1015785" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 10 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "65g1ZdGVjXsQ" - }, - "cell_type": "markdown", - "source": [ - "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here." - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "RM1iaD-ka3Y1" - }, - "cell_type": "markdown", - "source": [ - "## Manipulating Data\n", - "\n", - "You may apply Python's basic arithmetic operations to `Series`. For example:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "XWmyCFJ5bOv-", - "outputId": "91280582-c473-48cd-c7f5-36a8a73df7e9", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 84 - } - }, - "cell_type": "code", - "source": [ - "population / 1000." - ], - "execution_count": 11, - "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": 11 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "TQzIVnbnmWGM" - }, - "cell_type": "markdown", - "source": [ - "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "ko6pLK6JmkYP", - "outputId": "92b5b7fd-95be-4fc5-8033-5b8da8b2a95b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 84 - } - }, - "cell_type": "code", - "source": [ - "import numpy as np\n", - "\n", - "np.log(population)" - ], - "execution_count": 12, - "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": 12 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "xmxFuQmurr6d" - }, - "cell_type": "markdown", - "source": [ - "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n", - "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n", - "\n", - "The example below creates a new `Series` that indicates whether `population` is over one million:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "Fc1DvPAbstjI", - "outputId": "e447f694-479c-4964-fa8e-4e5bfebad9cc", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 84 - } - }, - "cell_type": "code", - "source": [ - "population.apply(lambda val: val > 1000000)" - ], - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0 False\n", - "1 True\n", - "2 False\n", - "dtype: bool" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 13 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "ZeYYLoV9b9fB" - }, - "cell_type": "markdown", - "source": [ - "\n", - "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "0gCEX99Hb8LR", - "outputId": "7d96cf32-a00a-4c43-81d1-88953ab65041", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 137 - } - }, - "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": 14, - "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": 14 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "6qh63m-ayb-c" - }, - "cell_type": "markdown", - "source": [ - "## Exercise #1\n", - "\n", - "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n", - "\n", - " * The city is named after a saint.\n", - " * The city has an area greater than 50 square miles.\n", - "\n", - "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n", - "\n", - "**Hint:** \"San\" in Spanish means \"saint.\"" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "zCOn8ftSyddH", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 137 - }, - "outputId": "508be6c9-95f2-432c-be0a-e2f2d24e8886" - }, - "cell_type": "code", - "source": [ - "# Your code here\n", - "cities['Wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", - "cities" - ], - "execution_count": 15, - "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
2Sacramento48519997.924955.055147False
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" - ], - "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 \n", - "\n", - " Wide and has saint name \n", - "0 False \n", - "1 True \n", - "2 False " - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 15 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "YHIWvc9Ms-Ll" - }, - "cell_type": "markdown", - "source": [ - "### Solution\n", - "\n", - "Click below for a solution." - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "T5OlrqtdtCIb", - "colab": {} - }, - "cell_type": "code", - "source": [ - "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", - "cities" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "f-xAOJeMiXFB" - }, - "cell_type": "markdown", - "source": [ - "## Indexes\n", - "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n", - "\n", - "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered." - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "2684gsWNinq9", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - }, - "outputId": "11071fbe-39ca-482f-b66f-e3a7e7e28996" - }, - "cell_type": "code", - "source": [ - "city_names.index" - ], - "execution_count": 16, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "RangeIndex(start=0, stop=3, step=1)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 16 - } - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "F_qPe2TBjfWd", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - }, - "outputId": "42bb63a5-5a3c-494a-8d99-b324a91dea7c" - }, - "cell_type": "code", - "source": [ - "cities.index" - ], - "execution_count": 17, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "RangeIndex(start=0, stop=3, step=1)" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 17 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "hp2oWY9Slo_h" - }, - "cell_type": "markdown", - "source": [ - "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "sN0zUzSAj-U1", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 137 - }, - "outputId": "6f05c626-0057-43e2-d444-e6c50622ac44" - }, - "cell_type": "code", - "source": [ - "cities.reindex([2, 0, 1])" - ], - "execution_count": 18, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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City namePopulationArea square milesPopulation densityWide and has saint name
2Sacramento48519997.924955.055147False
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
\n", - "
" - ], - "text/plain": [ - " City name Population Area square miles Population density \\\n", - "2 Sacramento 485199 97.92 4955.055147 \n", - "0 San Francisco 852469 46.87 18187.945381 \n", - "1 San Jose 1015785 176.53 5754.177760 \n", - "\n", - " Wide and has saint name \n", - "2 False \n", - "0 False \n", - "1 True " - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 18 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "-GQFz8NZuS06" - }, - "cell_type": "markdown", - "source": [ - "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n", - "Try running the following cell multiple times!" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "mF8GC0k8uYhz", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 137 - }, - "outputId": "5496dd16-e56e-4b2d-a68c-039690cb4a57" - }, - "cell_type": "code", - "source": [ - "cities.reindex(np.random.permutation(cities.index))" - ], - "execution_count": 24, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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City namePopulationArea square milesPopulation densityWide and has saint name
2Sacramento48519997.924955.055147False
0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
\n", - "
" - ], - "text/plain": [ - " City name Population Area square miles Population density \\\n", - "2 Sacramento 485199 97.92 4955.055147 \n", - "0 San Francisco 852469 46.87 18187.945381 \n", - "1 San Jose 1015785 176.53 5754.177760 \n", - "\n", - " Wide and has saint name \n", - "2 False \n", - "0 False \n", - "1 True " - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 24 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "fSso35fQmGKb" - }, - "cell_type": "markdown", - "source": [ - "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)." - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "8UngIdVhz8C0" - }, - "cell_type": "markdown", - "source": [ - "## Exercise #2\n", - "\n", - "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "PN55GrDX0jzO", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 166 - }, - "outputId": "37aab3dd-e4b4-4afe-8bdb-52da45962509" - }, - "cell_type": "code", - "source": [ - "# Your code here\n", - "cities.reindex([2, 0, 1, 4])" - ], - "execution_count": 25, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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City namePopulationArea square milesPopulation densityWide and has saint name
2Sacramento485199.097.924955.055147False
0San Francisco852469.046.8718187.945381False
1San Jose1015785.0176.535754.177760True
4NaNNaNNaNNaNNaN
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" - ], - "text/plain": [ - " City name Population Area square miles Population density \\\n", - "2 Sacramento 485199.0 97.92 4955.055147 \n", - "0 San Francisco 852469.0 46.87 18187.945381 \n", - "1 San Jose 1015785.0 176.53 5754.177760 \n", - "4 NaN NaN NaN NaN \n", - "\n", - " Wide and has saint name \n", - "2 False \n", - "0 False \n", - "1 True \n", - "4 NaN " - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 25 - } - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "TJffr5_Jwqvd" - }, - "cell_type": "markdown", - "source": [ - "### Solution\n", - "\n", - "Click below for the solution." - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "8oSvi2QWwuDH" - }, - "cell_type": "markdown", - "source": [ - "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "yBdkucKCwy4x", - "colab": {} - }, - "cell_type": "code", - "source": [ - "cities.reindex([0, 4, 5, 2])" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "2l82PhPbwz7g" - }, - "cell_type": "markdown", - "source": [ - "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n", - "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n", - "in which the index values are browser names).\n", - "\n", - "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n", - "sanitizing the input." - ] - } - ] -} \ No newline at end of file