linear_regression_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled17.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/eshnil2000/google-colab/blob/master/linear_regression_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irsYTy61CLpr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Install and import all required modules\n",
        "#%%capture\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "#Install required modules\n",
        "!pip install tensorflow numpy matplotlib pandas sklearn\n",
        "import tensorflow as tf\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "import numpy as np\n",
        "import csv\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjT6F-sIyxcO",
        "colab_type": "code",
        "outputId": "e7d3ca6a-5bda-4cd8-9273-d80211ad885a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Plot a few sample images, check Labels of image\n",
        "DATA_DIR = '/tmp/data'\n",
        "\n",
        "data = input_data.read_data_sets(DATA_DIR, one_hot=True)\n",
        "\n",
        "max_images=5\n",
        "start_image=0\n",
        "\n",
        "for image in range(max_images):\n",
        "  pixels= data.train.images[start_image + image]\n",
        "  # Reshape the array into 28 x 28 array (2-dimensional array)\n",
        "  pixels = pixels.reshape((28, 28))\n",
        "  label= data.train.labels[start_image + image]\n",
        "  # Plot\n",
        "  plt.title('Label is {label}'.format(label=label))\n",
        "  plt.imshow(pixels,cmap='gray_r')\n",
        "  plt.show()\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
            "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
            "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
            "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sAdyW3zazLUTD8EfEuoi4O7/+LLAK2Bk4Frg0v9ulwHHdatLMOq+p9/ySZgP7Ar8BZkbE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\nB4EikKj11jxrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IH9SWtFBSv6T+qs/Xm9n4aSj8kg6jCP6dEbG6XLxb0pSyPgXYM9LYiFgeEX0R0Xcwn9DS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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ENcOv7KtXXwcei4jLKppuBZbk15cAt5Rfnpm1Sz1f6X0n8GFgg6SH8mkXAJcA35G0FHga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\nzRLl8JslyuE3S9T/A1cTU/irB8f4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8Zply+M0y5fCbZcrhN8uUw2+WKZ/n382deOKJLdvPOOOMPlVig8Z7frNMOfxmmXL4zTLl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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEZ1qK7KI5FZ",
        "colab_type": "code",
        "outputId": "2eb1e489-eef2-498c-df3c-bdb211321728",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "#Train the model\n",
        "NUM_STEPS = 1000\n",
        "MINIBATCH_SIZE = 100\n",
        "\n",
        "\n",
        "data = input_data.read_data_sets(DATA_DIR, one_hot=True)\n",
        "\n",
        "x = tf.placeholder(tf.float32, [None, 784])\n",
        "W = tf.Variable(tf.zeros([784, 10]),name=\"weights\")\n",
        "\n",
        "y_true = tf.placeholder(tf.float32, [None, 10])\n",
        "y_pred = tf.matmul(x, W)\n",
        "y_final = tf.nn.softmax(tf.matmul(x,W))\n",
        "\n",
        "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n",
        "    logits=y_pred, labels=y_true))\n",
        "\n",
        "gd_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
        "\n",
        "correct_mask = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))\n",
        "accuracy = tf.reduce_mean(tf.cast(correct_mask, tf.float32))\n",
        "classification=[]\n",
        "with tf.Session() as sess:\n",
        "\n",
        "    # Train\n",
        "    sess.run(tf.global_variables_initializer())\n",
        "    for _ in range(NUM_STEPS):\n",
        "        batch_xs, batch_ys = data.train.next_batch(MINIBATCH_SIZE)\n",
        "        sess.run(gd_step, feed_dict={x: batch_xs, y_true: batch_ys})\n",
        "        \n",
        "    # Test\n",
        "    ans = sess.run(accuracy, feed_dict={x: data.test.images, y_true: data.test.labels})\n",
        "    classification= sess.run(y_final, feed_dict={x: data.test.images})\n",
        "    \n",
        "    #print(tf.trainable_variables())\n",
        "    var = [v for v in tf.trainable_variables() ]\n",
        "    \n",
        "print(\"Accuracy: {:.4}%\".format(ans*100))\n",
        "#print(len(classification))\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
            "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
            "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
            "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n",
            "Accuracy: 91.74%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7qcjcKL9lk-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "197b1cfe-d1df-41fc-b37e-edf32f7c655f"
      },
      "source": [
        "#Test the predictions\n",
        "for i in range(5):\n",
        "  plt.imshow(data.test.images[i].reshape(28,28),cmap='gray_r')\n",
        "  plt.show()\n",
        "  print(\"Predicted value is:\", np.argmax(classification[i]))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADV5JREFUeJzt3X+oXPWZx/HPUzeNYKrmNtMYbext\nc0UJwabLEFYra1dtuAmB6D+SICUFaQoqrlB0xaKr+E9YbYqgVG80NC6tbTGVBAmubqhooJaMJv6K\nu+uvG5twzZ0YoSkIadJn/5iTcqv3fGecc2bO3DzvF1xm5jznzHlyyOeemfmeO19zdwGI5wtVNwCg\nGoQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQ/9DPnc2bN8+Hh4f7uUsglPHxcR0+fNg6WbdQ\n+M1sVNIDkk6T9Ki7b0itPzw8rEajUWSXABLq9XrH63b9st/MTpP0kKQVkhZLWmtmi7t9PgD9VeQ9\n/zJJ77j7e+5+TNKvJK0upy0AvVYk/OdJ+uOUxweyZX/HzNabWcPMGs1ms8DuAJSp55/2u/uYu9fd\nvV6r1Xq9OwAdKhL+g5IWTnn81WwZgBmgSPh3S7rAzL5uZl+UtEbS9nLaAtBrXQ/1uftxM7tJ0n+p\nNdS32d3fLK0zAD1VaJzf3XdI2lFSLwD6iMt7gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiB\noAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4\ngaAIPxAU4QeCIvxAUIQfCKrQLL1mNi7pqKQTko67e72MpgD0XqHwZ/7F3Q+X8DwA+oiX/UBQRcPv\nkp41s5fNbH0ZDQHoj6Iv+y9z94Nm9hVJz5nZ/7j7C1NXyH4prJek888/v+DuAJSl0Jnf3Q9mt5OS\nnpK0bJp1xty97u71Wq1WZHcAStR1+M3sDDP70sn7kpZLeqOsxgD0VpGX/fMlPWVmJ5/nl+7+TCld\nAei5rsPv7u9J+maJvQDoI4b6gKAIPxAU4QeCIvxAUIQfCIrwA0GV8Vd9ITz55JO5tU2bNiW3Pffc\nc5P1008/PVm/7rrrkvVzzjkntzYyMpLcFnFx5geCIvxAUIQfCIrwA0ERfiAowg8ERfiBoBjn79Ct\nt96aWxsfH+/pvh9++OFk/cwzz8ytLV68uOx2ZoyFCxfm1m677bbktvX6qf8t9Jz5gaAIPxAU4QeC\nIvxAUIQfCIrwA0ERfiAoxvk79Oijj+bWXn311eS27cba9+3bl6zv2bMnWX/++edzay+99FJy23ZT\nqH3wwQfJehGzZs1K1ufNm5esT0xMJOupf3vqGgCJcX4ApzDCDwRF+IGgCD8QFOEHgiL8QFCEHwiq\n7Ti/mW2WtErSpLsvyZYNSfq1pGFJ45KudfePe9dm9a688squap0YHR0ttP3HH+cf+nbXCLQbz969\ne3dXPXVi9uzZyfqFF16YrF900UXJ+pEjR3JrixYtSm4bQSdn/p9L+vT/ztsl7XT3CyTtzB4DmEHa\nht/dX5D06V+hqyVtye5vkXR1yX0B6LFu3/PPd/eT11Z+KGl+Sf0A6JPCH/i5u0vyvLqZrTezhpk1\nms1m0d0BKEm34T9kZgskKbudzFvR3cfcve7u9Vqt1uXuAJSt2/Bvl7Quu79O0rZy2gHQL23Db2ZP\nSPq9pAvN7ICZXS9pg6Tvmtnbkq7KHgOYQdqO87v72pxSscFtlGbu3Lm5tSuuuKLQcxe9hqGIrVu3\nJuup6xsk6eKLL86trVmzpqueTiVc4QcERfiBoAg/EBThB4Ii/EBQhB8Iiq/uRmUmJ3MvDJUk3XDD\nDcl668ryfHfddVdubWhoKLltBJz5gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAoxvlRmYceeihZb3cd\nwNlnn52st/vq7+g48wNBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIzzo6d27dqVW9uwodh0D9u2peeK\nWbJkSaHnP9Vx5geCIvxAUIQfCIrwA0ERfiAowg8ERfiBoNqO85vZZkmrJE26+5Js2d2SfiCpma12\nh7vv6FWTmLl27Mj/b3Hs2LHktldddVWyfskll3TVE1o6OfP/XNLoNMt/6u5Lsx+CD8wwbcPv7i9I\nOtKHXgD0UZH3/DeZ2WtmttnM5pbWEYC+6Db8P5O0SNJSSROSfpK3opmtN7OGmTWazWbeagD6rKvw\nu/shdz/h7n+VtEnSssS6Y+5ed/d6rVbrtk8AJesq/Ga2YMrDayS9UU47APqlk6G+JyR9R9I8Mzsg\n6d8lfcfMlkpySeOSftjDHgH0QNvwu/vaaRY/1oNeMAN98sknyfozzzyTW5s9e3Zy23vuuSdZnzVr\nVrKONK7wA4Ii/EBQhB8IivADQRF+ICjCDwTFV3ejkPvuuy9Z37NnT25txYoVyW0vvfTSrnpCZzjz\nA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQjPMj6emnn07W77333mT9rLPOyq3deeedXfWEcnDmB4Ii\n/EBQhB8IivADQRF+ICjCDwRF+IGgGOcP7qOPPkrWb7755mT9+PHjyfrKlStza0yxXS3O/EBQhB8I\nivADQRF+ICjCDwRF+IGgCD8QVNtxfjNbKOlxSfMluaQxd3/AzIYk/VrSsKRxSde6+8e9axXdOHHi\nRLI+OjqarL///vvJ+sjISLLe7u/9UZ1OzvzHJf3I3RdL+idJN5rZYkm3S9rp7hdI2pk9BjBDtA2/\nu0+4+yvZ/aOS3pJ0nqTVkrZkq22RdHWvmgRQvs/1nt/MhiV9S9IfJM1394ms9KFabwsAzBAdh9/M\n5kjaKukWd//T1Jq7u1qfB0y33Xoza5hZo9lsFmoWQHk6Cr+ZzVIr+L9w999miw+Z2YKsvkDS5HTb\nuvuYu9fdvV6r1croGUAJ2obfzEzSY5LecveNU0rbJa3L7q+TtK389gD0Sid/0vttSd+T9LqZ7c2W\n3SFpg6TfmNn1kvZLurY3LaKId999N1lvNBqFnn/jxo3J+qJFiwo9P3qnbfjdfZckyylfWW47APqF\nK/yAoAg/EBThB4Ii/EBQhB8IivADQfHV3aeA/fv359aWL19e6Lnvv//+ZH3VqlWFnh/V4cwPBEX4\ngaAIPxAU4QeCIvxAUIQfCIrwA0Exzn8KeOSRR3JrqWsAOnH55Zcn663vesFMxJkfCIrwA0ERfiAo\nwg8ERfiBoAg/EBThB4JinH8GePHFF5P1Bx98sE+d4FTCmR8IivADQRF+ICjCDwRF+IGgCD8QFOEH\ngmo7zm9mCyU9Lmm+JJc05u4PmNndkn4gqZmteoe77+hVo5Ht2rUrWT969GjXzz0yMpKsz5kzp+vn\nxmDr5CKf45J+5O6vmNmXJL1sZs9ltZ+6e3pWBwADqW343X1C0kR2/6iZvSXpvF43BqC3Ptd7fjMb\nlvQtSX/IFt1kZq+Z2WYzm5uzzXoza5hZo9lsTrcKgAp0HH4zmyNpq6Rb3P1Pkn4maZGkpWq9MvjJ\ndNu5+5i71929XqvVSmgZQBk6Cr+ZzVIr+L9w999KkrsfcvcT7v5XSZskLetdmwDK1jb81vp61sck\nveXuG6csXzBltWskvVF+ewB6pZNP+78t6XuSXjezvdmyOyStNbOlag3/jUv6YU86RCFLly5N1nfu\n3JmsDw0NldkOBkgnn/bvkjTdl7Mzpg/MYFzhBwRF+IGgCD8QFOEHgiL8QFCEHwjK3L1vO6vX695o\nNPq2PyCaer2uRqPR0bzpnPmBoAg/EBThB4Ii/EBQhB8IivADQRF+IKi+jvObWVPS/imL5kk63LcG\nPp9B7W1Q+5LorVtl9vY1d+/o+/L6Gv7P7Nys4e71yhpIGNTeBrUvid66VVVvvOwHgiL8QFBVh3+s\n4v2nDGpvg9qXRG/dqqS3St/zA6hO1Wd+ABWpJPxmNmpm/2tm75jZ7VX0kMfMxs3sdTPba2aV/v1x\nNg3apJm9MWXZkJk9Z2ZvZ7fTTpNWUW93m9nB7NjtNbOVFfW20Mx+Z2b7zOxNM/vXbHmlxy7RVyXH\nre8v+83sNEn/J+m7kg5I2i1prbvv62sjOcxsXFLd3SsfEzazf5b0Z0mPu/uSbNl/SDri7huyX5xz\n3f3fBqS3uyX9ueqZm7MJZRZMnVla0tWSvq8Kj12ir2tVwXGr4sy/TNI77v6eux+T9CtJqyvoY+C5\n+wuSjnxq8WpJW7L7W9T6z9N3Ob0NBHefcPdXsvtHJZ2cWbrSY5foqxJVhP88SX+c8viABmvKb5f0\nrJm9bGbrq25mGvOzadMl6UNJ86tsZhptZ27up0/NLD0wx66bGa/Lxgd+n3WZu/+jpBWSbsxe3g4k\nb71nG6Thmo5mbu6XaWaW/psqj123M16XrYrwH5S0cMrjr2bLBoK7H8xuJyU9pcGbffjQyUlSs9vJ\nivv5m0GauXm6maU1AMdukGa8riL8uyVdYGZfN7MvSlojaXsFfXyGmZ2RfRAjMztD0nIN3uzD2yWt\ny+6vk7Stwl7+zqDM3Jw3s7QqPnYDN+O1u/f9R9JKtT7xf1fSj6voIaevb0h6Nft5s+reJD2h1svA\nv6j12cj1kr4saaektyX9t6ShAertPyW9Luk1tYK2oKLeLlPrJf1rkvZmPyurPnaJvio5blzhBwTF\nB35AUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4L6f6yMEem39pFEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted value is: 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted value is: 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted value is: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8WLOfjdwg6a8k7Zb0tqT/lHReF/X2r5Jel/SaZoM2UFBvl2v2Jf1rkl7NfkaLfu4SfRXy\nvHGGHxAUH/gBQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwjq/wGm5U836B5Q0wAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted value is: 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted value is: 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZTHtUyV93nk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}