CNN/mnist_cnn_tensorflow.ipynb

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  "metadata": {
    "colab": {
      "name": "mnist_cnn_tensorflow.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "sL5MhtIPJhbC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# CNN\n",
        "\n",
        "In this notebook, we will go through basics of CNN using MNIST dataset. \n",
        "\n",
        "Here we will use [Tensorflow](https://www.tensorflow.org/ \"Tensorflow Homepage\").\n",
        "\n",
        "Hey yo, but what is CNN? \n",
        "\n",
        "Everything is explained in-detail in [blog post](https://dudeperf3ct.github.io/cnn/mnist/2018/10/17/Force-of-Convolutional-Neural-Networks/). This is notebook which replicates the result of blog and runs in colab. Enjoy!\n",
        "\n",
        "\n",
        "#### Run in Colab\n",
        "\n",
        "You can run this notebook in google colab.\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dudeperf3ct/DL_notebooks/blob/master/CNN/mnist_cnn_tensorflow.ipynb)\n"
      ]
    },
    {
      "metadata": {
        "id": "GvQ7ssuQJhbY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# load all the required libraries\n",
        "\n",
        "import numpy as np                                    # package for computing\n",
        "from sklearn.model_selection import train_test_split  # split dataset\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "import tensorflow as tf                                          # import keras with tensorflow as backend\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt             # matplotlib library for plotting\n",
        "\n",
        "# display plots inline (in notebook itself)\n",
        "%matplotlib inline                          "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9N0oNUGOJhbn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## MNIST Dataset\n",
        "\n",
        "\n",
        " \n",
        "The [MNIST dataset](http://yann.lecun.com/exdb/mnist/) of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples each of size 28 x 28 pixels. The digits have been size-normalized and centered in a fixed-size image.\n",
        "\n",
        "It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. \n",
        "\n",
        "Here is one example from dataset\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "oqKdhoNuJhbs",
        "colab_type": "code",
        "outputId": "16571b6a-6725-47d4-ebd1-602ded4d6ea6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# load mnist data\n",
        "\n",
        "# the data, split between train and validation sets\n",
        "(train_x, train_y), (test_x, test_y) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "# As image is grayscale it has values from [0-255] which we will visualize below\n",
        "# convert dtype to float32 and scale the data from [0-255] to [0-1]\n",
        "train_x = train_x.astype('float32')\n",
        "test_x = test_x.astype('float32')\n",
        "train_x /= 255\n",
        "test_x /= 255\n",
        "\n",
        "print('Training samples and shape:', train_x.shape[0], train_x.shape)\n",
        "print('Test samples and shape:', test_x.shape[0], test_x.shape)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training samples and shape: 60000 (60000, 28, 28)\n",
            "Test samples and shape: 10000 (10000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "N-TKte-MJhb-",
        "colab_type": "code",
        "outputId": "5a49de4a-7dac-442a-a1fa-edfbee8e8d91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# we will split val into --> 20% val set and 80% test set \n",
        "# stratify ensures the distribution of classes is same in both the sets\n",
        "\n",
        "val_x, test_x, val_y, test_y = train_test_split(test_x, test_y, test_size=0.8, stratify=test_y)\n",
        "\n",
        "print ('Validation samples and shape', val_x.shape[0], val_x.shape)\n",
        "print ('Test samples and shape', test_x.shape[0], test_x.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation samples and shape 2000 (2000, 28, 28)\n",
            "Test samples and shape 8000 (8000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "m1I9Ja9xJhcJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualization of data\n",
        "\n",
        "Enough talk, show me the data!"
      ]
    },
    {
      "metadata": {
        "id": "Q_2VqZ68JhcP",
        "colab_type": "code",
        "outputId": "26ebb90e-a69f-4746-ffeb-e3869e4666ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "cell_type": "code",
      "source": [
        "# plot the images in the batch, along with the corresponding labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
        "    ax.imshow(train_x[idx], cmap='gray')\n",
        "    # print out the correct label for each image\n",
        "    ax.set_title(str(train_y[idx]))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f05a743b048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "bYC8OBu3Jhch",
        "colab_type": "code",
        "outputId": "d5bfbc92-13ba-4892-fd74-5b7ac9986c5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        }
      },
      "cell_type": "code",
      "source": [
        "img = train_x[1]\n",
        "\n",
        "fig = plt.figure(figsize = (12,12)) \n",
        "ax = fig.add_subplot(111)\n",
        "ax.imshow(img, cmap='gray')\n",
        "width, height = img.shape\n",
        "thresh = img.max()/2.5\n",
        "for x in range(width):\n",
        "    for y in range(height):\n",
        "        val = round(img[x][y],2) if img[x][y] !=0 else 0\n",
        "        ax.annotate(str(val), xy=(y,x),\n",
        "                    horizontalalignment='center',\n",
        "                    verticalalignment='center',\n",
        "                    color='white' if img[x][y]<thresh else 'black')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tla1d+5e4GoJz1Py0lGMqhKjzk2zSGdbMzMwyAMPvaURERERERA2gqLe1IiIi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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f05a472c588>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fQp88rkRJhdF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Model"
      ]
    },
    {
      "metadata": {
        "id": "Esj_SCOkJhdJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# The MNIST dataset has 10 classes, representing the digits 0 through 9.\n",
        "NUM_CLASSES = 10\n",
        "NUM_HIDDEN = 16\n",
        "BATCH_SIZE = 128\n",
        "EPOCHS = 2000\n",
        "\n",
        "# The MNIST images size = 28x28 pixels.\n",
        "IMAGE_ROWS = 28\n",
        "IMAGE_COLS = 28\n",
        "INPUT_SHAPE = (IMAGE_ROWS, IMAGE_COLS, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oTHcu-05JhdT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def model(x):\n",
        "\n",
        "  with tf.name_scope('reshape'):\n",
        "    x = tf.reshape(x, [-1, 28, 28, 1])\n",
        "\n",
        "  # First CONV layer\n",
        "  with tf.variable_scope('conv1', reuse=tf.AUTO_REUSE):\n",
        "    conv1 = tf.layers.conv2d(x, 32, 3, activation=tf.nn.relu)\n",
        "    \n",
        "  # Second fully connected net\n",
        "  with tf.variable_scope('conv2', reuse=tf.AUTO_REUSE):\n",
        "    conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n",
        "\n",
        "  with tf.variable_scope('pool1', reuse=tf.AUTO_REUSE):\n",
        "    conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n",
        "  \n",
        "  with tf.variable_scope('dropout', reuse=tf.AUTO_REUSE):\n",
        "    h_drop1 = tf.layers.dropout(conv2, rate=0.25)\n",
        "    \n",
        "  with tf.variable_scope('flatten', reuse=tf.AUTO_REUSE):\n",
        "    h_flat = tf.layers.flatten(h_drop1)\n",
        "    \n",
        "  with tf.variable_scope('fc1', reuse=tf.AUTO_REUSE):\n",
        "    h_fc1 = tf.layers.dense(h_flat, 128)\n",
        "    \n",
        "  with tf.variable_scope('dropout', reuse=tf.AUTO_REUSE):\n",
        "    h_drop2 = tf.layers.dropout(h_fc1, rate=0.5)  \n",
        "\n",
        "  with tf.variable_scope('fc2', reuse=tf.AUTO_REUSE):\n",
        "    logits = tf.nn.softmax(tf.layers.dense(h_drop2, NUM_CLASSES))\n",
        "  \n",
        "  return logits "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uxhmm6bGJqY5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = tf.placeholder(tf.float32, [None, 784])\n",
        "y_labels = tf.placeholder(tf.int64, [None])\n",
        "\n",
        "y = model(x)\n",
        "\n",
        "# Loss, optimizer and trainer\n",
        "with tf.name_scope('loss'):\n",
        "  cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_labels, logits=y)\n",
        "cross_entropy = tf.reduce_mean(cross_entropy)\n",
        "\n",
        "with tf.name_scope('adam_optimizer'):\n",
        "  train_step = tf.train.AdamOptimizer(1e-3).minimize(cross_entropy)\n",
        "\n",
        "# Accuracy \n",
        "with tf.name_scope('accuracy'):\n",
        "  correct_prediction = tf.equal(tf.argmax(y, 1), y_labels)\n",
        "  correct_prediction = tf.cast(correct_prediction, tf.float32)\n",
        "accuracy = tf.reduce_mean(correct_prediction)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JyIUzZMIJqrB",
        "colab_type": "code",
        "outputId": "4abe14cb-7bb3-448f-abb4-1bb48cd0b776",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772
        }
      },
      "cell_type": "code",
      "source": [
        "mnist = input_data.read_data_sets('.') \n",
        "sess = tf.Session()\n",
        "tf.global_variables_initializer().run(session=sess)\n",
        "for i in range(EPOCHS):\n",
        "  batch = mnist.train.next_batch(BATCH_SIZE)\n",
        "  sess.run([train_step], feed_dict={x: batch[0], y_labels: batch[1]})\n",
        "\n",
        "  if i % 100 == 0:\n",
        "    train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_labels: batch[1]}, session=sess)\n",
        "    print('step %d, training accuracy %g' % (i, train_accuracy))\n",
        "\n",
        "print('test accuracy %g' % accuracy.eval(feed_dict={\n",
        "    x: mnist.test.images, y_labels: mnist.test.labels}, session=sess))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-11-09915f256c7a>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please write your own downloading logic.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting ./train-images-idx3-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting ./train-labels-idx1-ubyte.gz\n",
            "Extracting ./t10k-images-idx3-ubyte.gz\n",
            "Extracting ./t10k-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "step 0, training accuracy 0.289062\n",
            "step 100, training accuracy 0.882812\n",
            "step 200, training accuracy 0.976562\n",
            "step 300, training accuracy 0.984375\n",
            "step 400, training accuracy 0.960938\n",
            "step 500, training accuracy 0.945312\n",
            "step 600, training accuracy 0.976562\n",
            "step 700, training accuracy 0.96875\n",
            "step 800, training accuracy 0.984375\n",
            "step 900, training accuracy 0.992188\n",
            "step 1000, training accuracy 0.992188\n",
            "step 1100, training accuracy 1\n",
            "step 1200, training accuracy 0.96875\n",
            "step 1300, training accuracy 1\n",
            "step 1400, training accuracy 0.992188\n",
            "step 1500, training accuracy 1\n",
            "step 1600, training accuracy 1\n",
            "step 1700, training accuracy 0.992188\n",
            "step 1800, training accuracy 0.992188\n",
            "step 1900, training accuracy 1\n",
            "test accuracy 0.9829\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EgJtjCWPohoi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Result"
      ]
    },
    {
      "metadata": {
        "id": "k5EQ8RXNJheN",
        "colab_type": "code",
        "outputId": "17695384-6591-448f-b5e2-fee63a466ea0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "cell_type": "code",
      "source": [
        "# obtain one batch of test images\n",
        "images, labels = test_x[:32], test_y[:32]\n",
        "\n",
        "# get sample outputs\n",
        "predict = model(images)\n",
        "# convert output probabilities to predicted class\n",
        "pred = predict.eval(session=sess)\n",
        "preds = np.argmax(pred, axis=1)\n",
        "\n",
        "# plot the images in the batch, along with predicted and true labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
        "    ax.imshow(images[idx].reshape(28, 28), cmap='gray')\n",
        "    ax.set_title(\"{} ({})\".format(str(preds[idx]), str(labels[idx])),\n",
        "                 color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f05a6fb9908>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rD3RFyX5Ok2S",
        "colab_type": "code",
        "outputId": "5b65aa5c-3522-4a68-9d91-d31ec1e32c53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "random_idx = random.randint(0, len(test_x))\n",
        "\n",
        "# get sample outputs\n",
        "predict = model(test_x[None, random_idx])\n",
        "# convert output probabilities to predicted class\n",
        "pred = predict.eval(session=sess)\n",
        "preds = np.argmax(pred, axis=1)\n",
        "\n",
        "\n",
        "# plot the images one example, along with predicted and true labels and probabilities\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "ax = fig.add_subplot(1, 1, 1, xticks=[], yticks=[])\n",
        "ax.imshow(test_x[random_idx].reshape(28, 28), cmap='gray')\n",
        "ax.set_title(\"{} ({})\".format(str(preds[0]), str(test_y[random_idx])),\n",
        "             color=(\"green\" if preds[0]==test_y[random_idx] else \"red\"))\n",
        "ax = fig.add_subplot(1, 2, 2, xticks=[], yticks=[])\n",
        "x_labels = np.arange(10)\n",
        "ax.bar(np.arange(10), height=np.squeeze(pred), tick_label=x_labels)\n",
        "ax.set_title(\"{:.2f} % accuracy\".format(float(np.max(pred, axis=1)*100)))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5,1,'100.00 % accuracy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f05a01a1320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KoJN-uYCzP_p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}