EVA_S2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EVA-S2.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZDbuXWn3tlr",
        "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": "FDhGi40K30jj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2f369dee-3a34-4e00-fa75-3a10b593e93b"
      },
      "source": [
        "# import requred librery \n",
        "import numpy as np\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Convolution2D\n",
        "from keras.utils import np_utils\n",
        "\n",
        "from keras.datasets import mnist"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUKuN2uL33Dq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "824aa49f-48c8-4f9c-99c9-64ef219882f7"
      },
      "source": [
        "# load data from mnist data-set\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPHJjRPz33A8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "8681a1a4-d1de-4c42-a55d-9c56abc8109b"
      },
      "source": [
        "#display one of image to check data is load correctly.\n",
        "print (X_train.shape)\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.imshow(X_train[0])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff3c60f44a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "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": "cY25ikk132-T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#reshape image in to 28 x 28 in size passing only one channel(RGB) \n",
        "X_train = X_train.reshape(X_train.shape[0], 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": "_63aZF3R326Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# changing data type to float 32 and dividing with 255 it nomalize from 0 to 1\n",
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "X_train /= 255\n",
        "X_test /= 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44sqvryj323J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "205bd77d-97b7-457b-f5e2-19c7e0e56a39"
      },
      "source": [
        "y_train[:10]"
      ],
      "execution_count": 9,
      "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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXuvhYP132zc",
        "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": "80axJ2th32vj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "d06e5773-8557-4f09-9aec-85b293fbf633"
      },
      "source": [
        "Y_train[:10]\n"
      ],
      "execution_count": 11,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1afUCuug32sD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 910
        },
        "outputId": "7d46bd94-1e5a-4759-fe6e-fa1418f144d7"
      },
      "source": [
        "from keras.layers import Activation, MaxPooling2D\n",
        "\n",
        "model = Sequential() \n",
        "model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(28,28,1))) #(28,28,1)*(3,3,32)=(26,26,32), receptive field=3\n",
        "model.add(Convolution2D(64, 3, 3, activation='relu')) #(26,26,32)*(3,3,64)=(24,24,64), receptive field=5\n",
        "model.add(Convolution2D(128, 3, 3, activation='relu'))#(24,24,64)*(3,3,128)=(22,22,128), receptive field=7\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))             #(22,22,128)*MP(2,2)=(11,11,128), receptive field=7\n",
        "\n",
        "model.add(Convolution2D(256, 3, 3, activation='relu')) #(11,11,128)*(3,3,256)=(9,9,256), receptive field=9\n",
        "model.add(Convolution2D(512, 3, 3, activation='relu')) #(9,9,256)*(3,3,512)=(7,7,512), receptive field=11\n",
        "model.add(Convolution2D(1024, 3, 3, activation='relu'))#(7,7,512)*(3,3,1024)=(5,5,1024), receptive field=13\n",
        "model.add(Convolution2D(2048, 3, 3, activation='relu'))#(5,5,1024)*(3,3,2048)=(3,3,2048), receptive field=15\n",
        "model.add(Convolution2D(10, 3, 3, activation='relu'))  #(3,3,2048)*(3,3,10)=(1,1,10), receptive field=16\n",
        "\n",
        "model.add(Flatten()) # total 10 parameter(output class) present\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", input_shape=(28, 28, 1...)`\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\")`\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\")`\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\")`\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation=\"relu\")`\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1024, (3, 3), activation=\"relu\")`\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(2048, (3, 3), activation=\"relu\")`\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(10, (3, 3), activation=\"relu\")`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 22, 22, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 11, 11, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 9, 9, 256)         295168    \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 7, 7, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 5, 5, 1024)        4719616   \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 3, 3, 2048)        18876416  \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 1, 1, 10)          184330    \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 25,348,362\n",
            "Trainable params: 25,348,362\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRUg5vtT4KWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "             optimizer='adam',\n",
        "             metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bq34J1sa4Nw_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "84dcc810-b0a1-4f87-bc3e-174391284118"
      },
      "source": [
        "model.fit(X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\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": [
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 116s 2ms/step - loss: 2.2976 - acc: 0.1016\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.0987\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.0987\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.0987\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.0987\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.0987\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.0987\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.0987\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.0987\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.0987\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff3c609e198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OX75HVt84Nsx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(X_test, Y_test, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEM8ufBG4Nop",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "71556585-56c2-4f33-c3b2-6402ce2c2a41"
      },
      "source": [
        "print(score)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.3025851249694824, 0.098]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFvHY0_a4UwO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CaPZ4lIF4W6z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "4ae78503-25f0-4882-8855-fba9514b2596"
      },
      "source": [
        "print(y_pred[:9])\n",
        "print(y_test[:9])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            " [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            " [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            " [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            " [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            " [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            " [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            " [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            " [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]]\n",
            "[7 2 1 0 4 1 4 9 5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mylm_Z3ZKUe9",
        "colab_type": "text"
      },
      "source": [
        "Due to large no of parameter, that reduce the learning rate of network.\n",
        "\n",
        "Receptive fild seen by network is less (2048, 3, 3) after this convolution parameter drastically increases and  after (10, 3, 3) convolution future suppression happens during this stage we loss important feature.\n",
        "\n",
        "Becouse of large no. of parameter and dimenssion reduction diring 3x3 convolution this network in not prerforming batter. "
      ]
    }
  ]
}