Assignment_3_1st_DNN_9924.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Assignment 3 - 1st DNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Abhinavl3v3l/AUT/blob/master/Assignment_3_1st_DNN_9924.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNyZv-Ec52ot",
        "colab_type": "text"
      },
      "source": [
        "# **Import Libraries and modules**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3m3w1Cw49Zkt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://keras.io/\n",
        "!pip install -q keras\n",
        "import keras"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eso6UHE080D4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten, Add\n",
        "from keras.layers import Convolution2D, MaxPooling2D\n",
        "from keras.utils import np_utils\n",
        "\n",
        "from keras.datasets import mnist                                                # Importing Necessary Libraries and dataset(in keras)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zByEi95J86RD",
        "colab_type": "text"
      },
      "source": [
        "### Load pre-shuffled MNIST data into train and test sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eRM0QWN83PV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()                        # mnist data divided into Training and Test Data\n",
        "                                                                                # Where X_train are 60K 28x28 gray scale training images of numbers from [0-9] \n",
        "                                                                                # y_train are label of numbers from [0-9]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJQpLXHL20uf",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4a4Be72j8-ZC",
        "colab_type": "code",
        "outputId": "2280a6d8-5f66-4891-9c7b-aba8c15eb3dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "print (X_train.shape)\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.imshow(X_train[0])                                                           # Displaying what one of the images look like."
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6ec233f5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C+/mdZpq5ebyZpdUEx66ZZrwuIvyPSFpkZgvMbLqkT0vaUUAf72Bms7I3YmRmsyQtV/PN\nPrxD0prs9hpJ9xbYy+9plpmby80srYKPXdPNeO3uDf+RdIlG3/F/QdLfFtFDmb4+JOmx7OfJonuT\ndLtGnwYOa/S9kaskvU9St6TnJP23pPYm6u27kh6XtFujQessqLdlGn1Kv1vSruznkqKPXaKvQo4b\nn/ADguINPyAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQf0/sEWOix6VKakAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkmprriw9AnZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.reshape(X_train.shape[0], 28, 28,1)                           # (60000, 28, 28) ->  (60000, 28, 28, 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], 28, 28,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2m4YS4E9CRh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.astype('float32')                                             # Convert to float type\n",
        "X_test = X_test.astype('float32') \n",
        "X_train /= 255\n",
        "X_test /= 255                                                                   # Floating Value ranges from [0,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Mn0vAYD9DvB",
        "colab_type": "code",
        "outputId": "f7f3eb46-82d4-4301-f088-2c0901f70e9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_train[:10]"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9, 2, 1, 3, 1, 4], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZG8JiXR39FHC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert 1-dimensional class arrays to 10-dimensional class matrices\n",
        "Y_train = np_utils.to_categorical(y_train, 10)                                  \n",
        "Y_test = np_utils.to_categorical(y_test, 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYlFRvKS9HMB",
        "colab_type": "code",
        "outputId": "ff436545-5b56-4cab-b4e9-7db58c2b298b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "Y_train[:10]                                                                    ## y_train to Y_train  - example,5 value is converted to an array where 5th element of array is 1(lit) and rest all is 0 \n",
        "                                                                                \n",
        "                                                                                # OR \n",
        "                                                                                \n",
        "                                                                                # 1 Hot Encoded  \n"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osKqT73Q9JJB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Building the layers for maximum accuracy\n",
        "from keras.layers import Activation\n",
        "model = Sequential()\n",
        "\n",
        "\n",
        "model.add(Convolution2D(16, (3, 3), activation='relu', input_shape=(28,28,1)))  # Input Image size 28x28x1, 3x3 convolution with 32 channels,     GRF = 3, Parameters = 3x3x32x1  + 32        = 320           Output Image = 26x26x32\n",
        "model.add(Convolution2D(20, (3, 3), activation='relu'))  #24\n",
        "model.add(Convolution2D(24, (3, 3), activation='relu'))  #22\n",
        "model.add(Convolution2D(8, 1, activation='relu'))                              \n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2, 2))) #11\n",
        "\n",
        "model.add(Convolution2D(16, (3, 3), activation='relu'))  #9\n",
        "model.add(Convolution2D(20, (3, 3), activation='relu'))  #7\n",
        "model.add(Convolution2D(24, (3, 3), activation='relu'))  #5\n",
        "\n",
        "model.add(Convolution2D(25, 1, activation='relu')) \n",
        "model.add(Convolution2D(10, 5))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Activation('softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzdAYg1k9K7Z",
        "colab_type": "code",
        "outputId": "a4b1f818-b5d3-438e-bdb2-998bb76a5f1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_20\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_154 (Conv2D)          (None, 26, 26, 16)        160       \n",
            "_________________________________________________________________\n",
            "conv2d_155 (Conv2D)          (None, 24, 24, 20)        2900      \n",
            "_________________________________________________________________\n",
            "conv2d_156 (Conv2D)          (None, 22, 22, 24)        4344      \n",
            "_________________________________________________________________\n",
            "conv2d_157 (Conv2D)          (None, 22, 22, 8)         200       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 11, 11, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_158 (Conv2D)          (None, 9, 9, 16)          1168      \n",
            "_________________________________________________________________\n",
            "conv2d_159 (Conv2D)          (None, 7, 7, 20)          2900      \n",
            "_________________________________________________________________\n",
            "conv2d_160 (Conv2D)          (None, 5, 5, 24)          4344      \n",
            "_________________________________________________________________\n",
            "conv2d_161 (Conv2D)          (None, 5, 5, 25)          625       \n",
            "_________________________________________________________________\n",
            "conv2d_162 (Conv2D)          (None, 1, 1, 10)          6260      \n",
            "_________________________________________________________________\n",
            "flatten_17 (Flatten)         (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "activation_17 (Activation)   (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 22,901\n",
            "Trainable params: 22,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zp6SuGrL9M3h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',                              \n",
        "             optimizer='adam',\n",
        "             metrics=['accuracy'])                                              # Compiler or function which calculate and adjust the loss using a loss function and measure in accuracy. A function which will optimize all the features generated. \n",
        "                                                                                # Optimizer."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xWoKhPY9Of5",
        "colab_type": "code",
        "outputId": "eecd7f5b-f19b-4e24-aed0-47f2b79a9e19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        }
      },
      "source": [
        "model.fit(X_train, Y_train, batch_size=64, nb_epoch=25, verbose=1,validation_data=(X_test, Y_test))  # Learn Learn Learn"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/25\n",
            "60000/60000 [==============================] - 16s 273us/step - loss: 0.2284 - acc: 0.9298 - val_loss: 0.0634 - val_acc: 0.9803\n",
            "Epoch 2/25\n",
            "60000/60000 [==============================] - 13s 219us/step - loss: 0.0677 - acc: 0.9792 - val_loss: 0.0419 - val_acc: 0.9872\n",
            "Epoch 3/25\n",
            "60000/60000 [==============================] - 13s 218us/step - loss: 0.0513 - acc: 0.9839 - val_loss: 0.0508 - val_acc: 0.9849\n",
            "Epoch 4/25\n",
            "60000/60000 [==============================] - 13s 222us/step - loss: 0.0402 - acc: 0.9875 - val_loss: 0.0348 - val_acc: 0.9888\n",
            "Epoch 5/25\n",
            "60000/60000 [==============================] - 14s 227us/step - loss: 0.0357 - acc: 0.9889 - val_loss: 0.0339 - val_acc: 0.9888\n",
            "Epoch 6/25\n",
            "60000/60000 [==============================] - 14s 227us/step - loss: 0.0314 - acc: 0.9898 - val_loss: 0.0337 - val_acc: 0.9904\n",
            "Epoch 7/25\n",
            "60000/60000 [==============================] - 14s 226us/step - loss: 0.0270 - acc: 0.9916 - val_loss: 0.0362 - val_acc: 0.9875\n",
            "Epoch 8/25\n",
            "60000/60000 [==============================] - 14s 227us/step - loss: 0.0248 - acc: 0.9920 - val_loss: 0.0370 - val_acc: 0.9876\n",
            "Epoch 9/25\n",
            "60000/60000 [==============================] - 14s 226us/step - loss: 0.0225 - acc: 0.9923 - val_loss: 0.0301 - val_acc: 0.9905\n",
            "Epoch 10/25\n",
            "60000/60000 [==============================] - 14s 228us/step - loss: 0.0205 - acc: 0.9931 - val_loss: 0.0309 - val_acc: 0.9906\n",
            "Epoch 11/25\n",
            "60000/60000 [==============================] - 14s 225us/step - loss: 0.0188 - acc: 0.9940 - val_loss: 0.0349 - val_acc: 0.9897\n",
            "Epoch 12/25\n",
            "60000/60000 [==============================] - 14s 239us/step - loss: 0.0163 - acc: 0.9945 - val_loss: 0.0441 - val_acc: 0.9874\n",
            "Epoch 13/25\n",
            "60000/60000 [==============================] - 14s 235us/step - loss: 0.0145 - acc: 0.9951 - val_loss: 0.0416 - val_acc: 0.9885\n",
            "Epoch 14/25\n",
            "60000/60000 [==============================] - 14s 237us/step - loss: 0.0156 - acc: 0.9948 - val_loss: 0.0312 - val_acc: 0.9907\n",
            "Epoch 15/25\n",
            "60000/60000 [==============================] - 14s 238us/step - loss: 0.0127 - acc: 0.9959 - val_loss: 0.0398 - val_acc: 0.9882\n",
            "Epoch 16/25\n",
            "60000/60000 [==============================] - 14s 238us/step - loss: 0.0121 - acc: 0.9957 - val_loss: 0.0408 - val_acc: 0.9893\n",
            "Epoch 17/25\n",
            "60000/60000 [==============================] - 14s 238us/step - loss: 0.0122 - acc: 0.9960 - val_loss: 0.0355 - val_acc: 0.9902\n",
            "Epoch 18/25\n",
            "60000/60000 [==============================] - 14s 238us/step - loss: 0.0114 - acc: 0.9962 - val_loss: 0.0391 - val_acc: 0.9906\n",
            "Epoch 19/25\n",
            "60000/60000 [==============================] - 14s 234us/step - loss: 0.0096 - acc: 0.9968 - val_loss: 0.0351 - val_acc: 0.9909\n",
            "Epoch 20/25\n",
            "60000/60000 [==============================] - 14s 235us/step - loss: 0.0096 - acc: 0.9969 - val_loss: 0.0353 - val_acc: 0.9909\n",
            "Epoch 21/25\n",
            "60000/60000 [==============================] - 14s 233us/step - loss: 0.0093 - acc: 0.9968 - val_loss: 0.0399 - val_acc: 0.9902\n",
            "Epoch 22/25\n",
            "60000/60000 [==============================] - 14s 234us/step - loss: 0.0088 - acc: 0.9970 - val_loss: 0.0410 - val_acc: 0.9916\n",
            "Epoch 23/25\n",
            "60000/60000 [==============================] - 14s 233us/step - loss: 0.0081 - acc: 0.9973 - val_loss: 0.0425 - val_acc: 0.9896\n",
            "Epoch 24/25\n",
            "60000/60000 [==============================] - 14s 234us/step - loss: 0.0085 - acc: 0.9971 - val_loss: 0.0442 - val_acc: 0.9898\n",
            "Epoch 25/25\n",
            "60000/60000 [==============================] - 14s 233us/step - loss: 0.0072 - acc: 0.9975 - val_loss: 0.0560 - val_acc: 0.9872\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6ec2271a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnpyJzt0Uvhl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d1bbdb14-d853-450e-e84f-c15e89928552"
      },
      "source": [
        "model.fit(X_train, Y_train, batch_size=512, nb_epoch=50, verbose=1,validation_data=(X_test, Y_test))  # Learn Learn Learn"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/50\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 91us/step - loss: 0.0033 - acc: 0.9991 - val_loss: 0.0426 - val_acc: 0.9913\n",
            "Epoch 2/50\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 7.5116e-04 - acc: 1.0000 - val_loss: 0.0428 - val_acc: 0.9917\n",
            "Epoch 3/50\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 5.3927e-04 - acc: 1.0000 - val_loss: 0.0438 - val_acc: 0.9916\n",
            "Epoch 4/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 4.5959e-04 - acc: 1.0000 - val_loss: 0.0447 - val_acc: 0.9913\n",
            "Epoch 5/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 4.1482e-04 - acc: 1.0000 - val_loss: 0.0456 - val_acc: 0.9918\n",
            "Epoch 6/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 3.8478e-04 - acc: 1.0000 - val_loss: 0.0465 - val_acc: 0.9919\n",
            "Epoch 7/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 3.6444e-04 - acc: 1.0000 - val_loss: 0.0472 - val_acc: 0.9919\n",
            "Epoch 8/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 3.4978e-04 - acc: 1.0000 - val_loss: 0.0479 - val_acc: 0.9920\n",
            "Epoch 9/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 3.3641e-04 - acc: 1.0000 - val_loss: 0.0485 - val_acc: 0.9921\n",
            "Epoch 10/50\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 3.2686e-04 - acc: 1.0000 - val_loss: 0.0491 - val_acc: 0.9921\n",
            "Epoch 11/50\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 3.1906e-04 - acc: 1.0000 - val_loss: 0.0496 - val_acc: 0.9921\n",
            "Epoch 12/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 3.1254e-04 - acc: 1.0000 - val_loss: 0.0500 - val_acc: 0.9921\n",
            "Epoch 13/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 3.0727e-04 - acc: 1.0000 - val_loss: 0.0505 - val_acc: 0.9921\n",
            "Epoch 14/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 3.0255e-04 - acc: 1.0000 - val_loss: 0.0509 - val_acc: 0.9921\n",
            "Epoch 15/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.9890e-04 - acc: 1.0000 - val_loss: 0.0513 - val_acc: 0.9921\n",
            "Epoch 16/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.9567e-04 - acc: 1.0000 - val_loss: 0.0517 - val_acc: 0.9921\n",
            "Epoch 17/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.9291e-04 - acc: 1.0000 - val_loss: 0.0521 - val_acc: 0.9922\n",
            "Epoch 18/50\n",
            "60000/60000 [==============================] - 5s 81us/step - loss: 2.9090e-04 - acc: 1.0000 - val_loss: 0.0525 - val_acc: 0.9922\n",
            "Epoch 19/50\n",
            "60000/60000 [==============================] - 5s 81us/step - loss: 2.8808e-04 - acc: 1.0000 - val_loss: 0.0528 - val_acc: 0.9921\n",
            "Epoch 20/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.8627e-04 - acc: 1.0000 - val_loss: 0.0532 - val_acc: 0.9923\n",
            "Epoch 21/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.8464e-04 - acc: 1.0000 - val_loss: 0.0535 - val_acc: 0.9924\n",
            "Epoch 22/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.8305e-04 - acc: 1.0000 - val_loss: 0.0539 - val_acc: 0.9923\n",
            "Epoch 23/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.8181e-04 - acc: 1.0000 - val_loss: 0.0543 - val_acc: 0.9923\n",
            "Epoch 24/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.8061e-04 - acc: 1.0000 - val_loss: 0.0546 - val_acc: 0.9923\n",
            "Epoch 25/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7953e-04 - acc: 1.0000 - val_loss: 0.0549 - val_acc: 0.9923\n",
            "Epoch 26/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7857e-04 - acc: 1.0000 - val_loss: 0.0552 - val_acc: 0.9923\n",
            "Epoch 27/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7771e-04 - acc: 1.0000 - val_loss: 0.0556 - val_acc: 0.9923\n",
            "Epoch 28/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7694e-04 - acc: 1.0000 - val_loss: 0.0559 - val_acc: 0.9923\n",
            "Epoch 29/50\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 2.7628e-04 - acc: 1.0000 - val_loss: 0.0562 - val_acc: 0.9923\n",
            "Epoch 30/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7566e-04 - acc: 1.0000 - val_loss: 0.0565 - val_acc: 0.9923\n",
            "Epoch 31/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7507e-04 - acc: 1.0000 - val_loss: 0.0568 - val_acc: 0.9923\n",
            "Epoch 32/50\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 2.7454e-04 - acc: 1.0000 - val_loss: 0.0571 - val_acc: 0.9923\n",
            "Epoch 33/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7408e-04 - acc: 1.0000 - val_loss: 0.0574 - val_acc: 0.9923\n",
            "Epoch 34/50\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 2.7365e-04 - acc: 1.0000 - val_loss: 0.0576 - val_acc: 0.9923\n",
            "Epoch 35/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7324e-04 - acc: 1.0000 - val_loss: 0.0579 - val_acc: 0.9923\n",
            "Epoch 36/50\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 2.7289e-04 - acc: 1.0000 - val_loss: 0.0582 - val_acc: 0.9923\n",
            "Epoch 37/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7258e-04 - acc: 1.0000 - val_loss: 0.0584 - val_acc: 0.9923\n",
            "Epoch 38/50\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 2.7226e-04 - acc: 1.0000 - val_loss: 0.0587 - val_acc: 0.9923\n",
            "Epoch 39/50\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 2.7198e-04 - acc: 1.0000 - val_loss: 0.0590 - val_acc: 0.9923\n",
            "Epoch 40/50\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 2.7171e-04 - acc: 1.0000 - val_loss: 0.0592 - val_acc: 0.9923\n",
            "Epoch 41/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 2.7150e-04 - acc: 1.0000 - val_loss: 0.0594 - val_acc: 0.9923\n",
            "Epoch 42/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 2.7128e-04 - acc: 1.0000 - val_loss: 0.0597 - val_acc: 0.9923\n",
            "Epoch 43/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 2.7108e-04 - acc: 1.0000 - val_loss: 0.0599 - val_acc: 0.9923\n",
            "Epoch 44/50\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 2.7091e-04 - acc: 1.0000 - val_loss: 0.0601 - val_acc: 0.9923\n",
            "Epoch 45/50\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 2.7073e-04 - acc: 1.0000 - val_loss: 0.0604 - val_acc: 0.9923\n",
            "Epoch 46/50\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 2.7058e-04 - acc: 1.0000 - val_loss: 0.0606 - val_acc: 0.9923\n",
            "Epoch 47/50\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 2.7045e-04 - acc: 1.0000 - val_loss: 0.0608 - val_acc: 0.9923\n",
            "Epoch 48/50\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 2.7031e-04 - acc: 1.0000 - val_loss: 0.0612 - val_acc: 0.9923\n",
            "Epoch 49/50\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 2.7021e-04 - acc: 1.0000 - val_loss: 0.0613 - val_acc: 0.9923\n",
            "Epoch 50/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 2.7010e-04 - acc: 1.0000 - val_loss: 0.0616 - val_acc: 0.9923\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6ec2348160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtsH-lLk-eLb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(X_test, Y_test, verbose=0)                               # Evaluate the Model over test data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkX8JMv79q9r",
        "colab_type": "code",
        "outputId": "a2360013-2a94-4515-8dc4-af9d56ab417f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(score)                                                                    # Accuracy over test data."
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.06157461060819521, 0.9923]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCWoJkwE9suh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict(X_test)                                                  # predicting the values with validation accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ym7iCFBm9uBs",
        "colab_type": "code",
        "outputId": "142899b0-4942-4a2e-9adf-597e89312766",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "print(y_pred[:9])                                                               \n",
        "print(y_test[:9])"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.0000000e+00 1.9386244e-32 1.6728899e-31 4.2605639e-23 0.0000000e+00\n",
            "  6.2872232e-31 0.0000000e+00 1.0000000e+00 8.7543266e-30 2.2728110e-24]\n",
            " [9.2291015e-24 1.1109745e-27 1.0000000e+00 0.0000000e+00 1.9485941e-27\n",
            "  0.0000000e+00 5.4920004e-23 0.0000000e+00 3.8678312e-33 0.0000000e+00]\n",
            " [1.0299885e-32 1.0000000e+00 1.5063366e-20 3.5693614e-26 6.5576646e-22\n",
            "  8.3333915e-21 3.6167932e-29 3.6058734e-19 5.0218836e-20 3.0575253e-25]\n",
            " [1.0000000e+00 0.0000000e+00 8.1508207e-36 4.0630463e-35 2.6095860e-35\n",
            "  3.8153299e-23 9.8812194e-19 0.0000000e+00 9.8119283e-32 3.1140234e-34]\n",
            " [0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.0000000e+00\n",
            "  0.0000000e+00 8.0578804e-38 2.4131093e-31 6.4627028e-36 7.0429625e-25]\n",
            " [2.5655938e-34 1.0000000e+00 1.5141777e-20 8.4622088e-27 3.4507459e-22\n",
            "  3.0010777e-24 7.1416935e-30 1.7221578e-17 3.4644898e-22 2.1525581e-26]\n",
            " [0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.0000000e+00\n",
            "  0.0000000e+00 0.0000000e+00 6.4172393e-26 1.2520246e-30 2.4422645e-29]\n",
            " [0.0000000e+00 1.6566308e-33 1.8221994e-30 4.8340461e-37 3.2305295e-14\n",
            "  1.0463813e-22 0.0000000e+00 1.3861504e-30 4.4113707e-20 1.0000000e+00]\n",
            " [0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
            "  1.0000000e+00 3.4103842e-09 0.0000000e+00 3.5502370e-21 2.8373883e-24]]\n",
            "[7 2 1 0 4 1 4 9 5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CT--y98_dr2T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layer_dict = dict([(layer.name, layer) for layer in model.layers])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GY4Upv4dsUR",
        "colab_type": "code",
        "outputId": "ce213d29-8095-4252-83d5-600d0bd97340",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        }
      },
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from keras import backend as K\n",
        "%matplotlib inline\n",
        "# util function to convert a tensor into a valid image\n",
        "def deprocess_image(x):\n",
        "    # normalize tensor: center on 0., ensure std is 0.1\n",
        "    x -= x.mean()\n",
        "    x /= (x.std() + 1e-5)\n",
        "    x *= 0.1\n",
        "\n",
        "    # clip to [0, 1]\n",
        "    x += 0.5\n",
        "    x = np.clip(x, 0, 1)\n",
        "\n",
        "    # convert to RGB array\n",
        "    x *= 255\n",
        "    #x = x.transpose((1, 2, 0))\n",
        "    x = np.clip(x, 0, 255).astype('uint8')\n",
        "    return x\n",
        "\n",
        "def vis_img_in_filter(img = np.array(X_train[2]).reshape((1, 28, 28, 1)).astype(np.float64), \n",
        "                      layer_name = 'conv2d_14'):\n",
        "    layer_output = layer_dict[layer_name].output\n",
        "    img_ascs = list()\n",
        "    for filter_index in range(layer_output.shape[3]):\n",
        "        # build a loss function that maximizes the activation\n",
        "        # of the nth filter of the layer considered\n",
        "        loss = K.mean(layer_output[:, :, :, filter_index])\n",
        "\n",
        "        # compute the gradient of the input picture wrt this loss\n",
        "        grads = K.gradients(loss, model.input)[0]\n",
        "\n",
        "        # normalization trick: we normalize the gradient\n",
        "        grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)\n",
        "\n",
        "        # this function returns the loss and grads given the input picture\n",
        "        iterate = K.function([model.input], [loss, grads])\n",
        "\n",
        "        # step size for gradient ascent\n",
        "        step = 5.\n",
        "\n",
        "        img_asc = np.array(img)\n",
        "        # run gradient ascent for 20 steps\n",
        "        for i in range(20):\n",
        "            loss_value, grads_value = iterate([img_asc])\n",
        "            img_asc += grads_value * step\n",
        "\n",
        "        img_asc = img_asc[0]\n",
        "        img_ascs.append(deprocess_image(img_asc).reshape((28, 28)))\n",
        "        \n",
        "    if layer_output.shape[3] >= 35:\n",
        "        plot_x, plot_y = 6, 6\n",
        "    elif layer_output.shape[3] >= 23:\n",
        "        plot_x, plot_y = 4, 6\n",
        "    elif layer_output.shape[3] >= 11:\n",
        "        plot_x, plot_y = 2, 6\n",
        "    else:\n",
        "        plot_x, plot_y = 1, 2\n",
        "    fig, ax = plt.subplots(plot_x, plot_y, figsize = (12, 12))\n",
        "    ax[0, 0].imshow(img.reshape((28, 28)), cmap = 'gray')\n",
        "    ax[0, 0].set_title('Input image')\n",
        "    fig.suptitle('Input image and %s filters' % (layer_name,))\n",
        "    fig.tight_layout(pad = 0.3, rect = [0, 0, 0.9, 0.9])\n",
        "    for (x, y) in [(i, j) for i in range(plot_x) for j in range(plot_y)]:\n",
        "        if x == 0 and y == 0:\n",
        "            continue\n",
        "        ax[x, y].imshow(img_ascs[x * plot_y + y - 1], cmap = 'gray')\n",
        "        ax[x, y].set_title('filter %d' % (x * plot_y + y - 1))\n",
        "\n",
        "vis_img_in_filter()"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-170-19229f66b51a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'filter %d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mplot_y\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m \u001b[0mvis_img_in_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-170-19229f66b51a>\u001b[0m in \u001b[0;36mvis_img_in_filter\u001b[0;34m(img, layer_name)\u001b[0m\n\u001b[1;32m     22\u001b[0m def vis_img_in_filter(img = np.array(X_train[2]).reshape((1, 28, 28, 1)).astype(np.float64), \n\u001b[1;32m     23\u001b[0m                       layer_name = 'conv2d_14'):\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mlayer_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlayer_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m     \u001b[0mimg_ascs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mfilter_index\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'conv2d_14'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tvptcn8dxvp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}