ML4DM_Lab_6.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML4DM_Lab6.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "3F5WgeT4YFMT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.datasets import cifar10\n",
        "from keras.layers import Input, Flatten, Dense\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "import keras\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.models import Model\n",
        "from keras.layers import BatchNormalization\n",
        "from keras import layers\n",
        "from keras.utils import plot_model\n",
        "from keras.layers import Input, Flatten, Dense"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7K0suEvUYG5F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(trainX, trainY), (testX, testY) = cifar10.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "peMHsyI-YPB3",
        "colab_type": "code",
        "outputId": "64d097fd-dfb9-4ca9-ae9f-eec3b08d380c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "trainX.shape, testX.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((50000, 32, 32, 3), (10000, 32, 32, 3))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7Npx-43rmZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainX = trainX/255.0\n",
        "testX = testX/255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErsJH47kYPer",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_classes = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDVfOll_YRTw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainY = keras.utils.to_categorical(trainY, num_classes)\n",
        "testY = keras.utils.to_categorical(testY, num_classes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIz5HgYpZSVg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input = Input(shape=trainX.shape[1:],name = 'input')\n",
        "\n",
        "x = Conv2D(filters=32,kernel_size=(3,3))(input)\n",
        "x = Activation('relu')(x)\n",
        "\n",
        "x = Conv2D(filters=32,kernel_size=(3,3))(x)\n",
        "x1 = Activation('relu')(x)\n",
        "\n",
        "x = Conv2D(filters=32,kernel_size=(1,1))(x1)\n",
        "x = Activation('relu')(x)\n",
        "\n",
        "x = layers.add([x,x1])\n",
        "\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "\n",
        "x = Conv2D(filters=32,kernel_size=(3,3))(x)\n",
        "x1 = Activation('relu')(x)\n",
        "\n",
        "x = Conv2D(filters=32,kernel_size=(1,1))(x1)\n",
        "x = Activation('relu')(x)\n",
        "\n",
        "x = layers.add([x,x1])\n",
        "\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "\n",
        "x = Flatten()(x)\n",
        "x = Dense(2018)(x)\n",
        "x = Activation('relu')(x)\n",
        "x = Dense(512)(x)\n",
        "x = Activation('relu')(x)\n",
        "x = Dense(10)(x)\n",
        "x = Activation('sigmoid')(x)\n",
        "\n",
        "model = Model(inputs=input, outputs=x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhfDKgzxb373",
        "colab_type": "code",
        "outputId": "69dedcb3-5cb6-4f05-d88c-d15e41cf8aa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_39\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input (InputLayer)              (None, 32, 32, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_239 (Conv2D)             (None, 30, 30, 32)   896         input[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_260 (Activation)     (None, 30, 30, 32)   0           conv2d_239[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_240 (Conv2D)             (None, 28, 28, 32)   9248        activation_260[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_261 (Activation)     (None, 28, 28, 32)   0           conv2d_240[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_241 (Conv2D)             (None, 28, 28, 32)   1056        activation_261[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_262 (Activation)     (None, 28, 28, 32)   0           conv2d_241[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 28, 28, 32)   0           activation_262[0][0]             \n",
            "                                                                 activation_261[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_52 (MaxPooling2D) (None, 14, 14, 32)   0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_242 (Conv2D)             (None, 12, 12, 32)   9248        max_pooling2d_52[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "activation_263 (Activation)     (None, 12, 12, 32)   0           conv2d_242[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_243 (Conv2D)             (None, 12, 12, 32)   1056        activation_263[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_264 (Activation)     (None, 12, 12, 32)   0           conv2d_243[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 12, 12, 32)   0           activation_264[0][0]             \n",
            "                                                                 activation_263[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_53 (MaxPooling2D) (None, 6, 6, 32)     0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "flatten_33 (Flatten)            (None, 1152)         0           max_pooling2d_53[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_82 (Dense)                (None, 2018)         2326754     flatten_33[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_265 (Activation)     (None, 2018)         0           dense_82[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_83 (Dense)                (None, 512)          1033728     activation_265[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_266 (Activation)     (None, 512)          0           dense_83[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_84 (Dense)                (None, 10)           5130        activation_266[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_267 (Activation)     (None, 10)           0           dense_84[0][0]                   \n",
            "==================================================================================================\n",
            "Total params: 3,387,116\n",
            "Trainable params: 3,387,116\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ydx6vJuYhHR",
        "colab_type": "code",
        "outputId": "7a46f3b1-8eb3-48cd-9f64-01a9c104da6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "plot_model(model, to_file='model.png')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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nGI1GuLu7IygoCG+99RYAQETw7rvvonXr1jAYDPD29sbgwYNx7Ngxy/OXLl0Kd3d3uLm5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\nw5eISAGGLxGRAgxfIiIF/g9Tm7I8dExldgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPqDWWqkgOVD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "opt = keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53HEYQvGgaho",
        "colab_type": "code",
        "outputId": "b206e4b8-c031-4789-ff08-ec7fde4761ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "source": [
        "history = model.fit(trainX,trainY,\n",
        "              epochs=10,\n",
        "              validation_split=0.1,\n",
        "              shuffle=True,verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 45000 samples, validate on 5000 samples\n",
            "Epoch 1/10\n",
            "45000/45000 [==============================] - 30s 668us/step - loss: 1.5187 - acc: 0.4487 - val_loss: 1.2634 - val_acc: 0.5498\n",
            "Epoch 2/10\n",
            "45000/45000 [==============================] - 24s 544us/step - loss: 1.1007 - acc: 0.6077 - val_loss: 0.9703 - val_acc: 0.6576\n",
            "Epoch 3/10\n",
            "45000/45000 [==============================] - 24s 540us/step - loss: 0.8959 - acc: 0.6846 - val_loss: 0.9154 - val_acc: 0.6792\n",
            "Epoch 4/10\n",
            "45000/45000 [==============================] - 24s 536us/step - loss: 0.7356 - acc: 0.7401 - val_loss: 0.9237 - val_acc: 0.6826\n",
            "Epoch 5/10\n",
            "45000/45000 [==============================] - 24s 537us/step - loss: 0.5953 - acc: 0.7880 - val_loss: 0.9262 - val_acc: 0.6952\n",
            "Epoch 6/10\n",
            "45000/45000 [==============================] - 24s 540us/step - loss: 0.4672 - acc: 0.8320 - val_loss: 1.0045 - val_acc: 0.6900\n",
            "Epoch 7/10\n",
            "45000/45000 [==============================] - 24s 537us/step - loss: 0.3515 - acc: 0.8737 - val_loss: 1.0069 - val_acc: 0.7246\n",
            "Epoch 8/10\n",
            "45000/45000 [==============================] - 24s 538us/step - loss: 0.2698 - acc: 0.9019 - val_loss: 1.1334 - val_acc: 0.7078\n",
            "Epoch 9/10\n",
            "45000/45000 [==============================] - 24s 544us/step - loss: 0.2118 - acc: 0.9247 - val_loss: 1.2289 - val_acc: 0.7082\n",
            "Epoch 10/10\n",
            "45000/45000 [==============================] - 24s 539us/step - loss: 0.1724 - acc: 0.9389 - val_loss: 1.3825 - val_acc: 0.7180\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNcbT7d3tTYA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_graph(history):\n",
        "  plt.plot(history.history['loss'],color='blue',label='Train Loss')\n",
        "  plt.plot(history.history['val_loss'],color='red',label='Valid Loss')\n",
        "  plt.title('Loss')\n",
        "  plt.legend()\n",
        "  plt.xlabel('No. of epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.show()\n",
        "  plt.plot(history.history['acc'],color='blue',label='Train Accuracy')\n",
        "  plt.plot(history.history['val_acc'],color='red',label='Valid Accuracy')\n",
        "  plt.legend()\n",
        "  plt.title('Accuracy')\n",
        "  plt.xlabel('No. of epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zL25Qg8LtV7u",
        "colab_type": "code",
        "outputId": "f7d1a5d0-b97d-40e6-db29-880e71729809",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "show_graph(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YNBgtyURJQaQAmjb1Hd7efx/q1IGzz4Y2bXzNg0gyUFIQKYTOnWHGDJ+2+sMP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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SJOuLfeXKUKmSG+APkWAmhY+AP4GbgceAbsAKVb0nGIGeKEsK3lq71l1TvvsO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\nY0ya/wcY/jU4X+zNYQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikPgiWQHxEGl",
        "colab_type": "code",
        "outputId": "bd5f44f7-f5be-444f-d9aa-8e28be332c54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "test_loss, test_acc = model.evaluate(testX, testY)\n",
        "test_loss , test_acc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 2s 171us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.4626855607032776, 0.7066)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXJ-Fowy5dUu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input = Input(shape=trainX.shape[1:],name = 'input')\n",
        "\n",
        "x = Conv2D(filters=32,kernel_size=(3,3))(input)\n",
        "x = Activation('relu')(x)\n",
        "\n",
        "x = Conv2D(filters=32,kernel_size=(3,3))(x)\n",
        "x = Activation('relu')(x)\n",
        "\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "\n",
        "x = Conv2D(filters=32,kernel_size=(3,3))(x)\n",
        "x1 = Activation('relu')(x)\n",
        "\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "\n",
        "x = Flatten()(x)\n",
        "x = Dense(2018)(x)\n",
        "x = Activation('relu')(x)\n",
        "x = Dense(512)(x)\n",
        "x = Activation('relu')(x)\n",
        "x = Dense(10)(x)\n",
        "x = Activation('sigmoid')(x)\n",
        "\n",
        "model = Model(inputs=input, outputs=x)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}