VGG11-l2-regularization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-mymq0_iPw2g"
   },
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "from keras.optimizers import SGD\n",
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0i037-1TP3p1"
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import keras\n",
    "import numpy\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "from PIL import Image\n",
    "import cv2\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 32, 32\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "X_train = []\n",
    "X_test = []\n",
    "for i in range(len(x_train)):\n",
    "  X_train.append(cv2.resize(x_train[i], (32,32)))\n",
    "\n",
    "for i in range(len(x_test)):\n",
    "  X_test.append(cv2.resize(x_test[i], (32,32)))\n",
    "\n",
    "X_train = numpy.asarray(X_train) \n",
    "X_test = numpy.asarray(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "RVesKJ0QP5wd",
    "outputId": "350e09b3-8476-4e84-814a-7e003f1c70f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 32, 32, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "#Convert to float and then normalize the images by dividing by 255(max value of a pixel)\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ug94SQhJP8NL"
   },
   "outputs": [],
   "source": [
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 753
    },
    "colab_type": "code",
    "id": "No5GtUMtP-aA",
    "outputId": "dd2fb0e3-2b14-4ba3-a409-1403c962a35c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_33 (Conv2D)           (None, 32, 32, 64)        640       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_21 (MaxPooling (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_34 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_22 (MaxPooling (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_35 (Conv2D)           (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "conv2d_36 (Conv2D)           (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_23 (MaxPooling (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_37 (Conv2D)           (None, 4, 4, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "conv2d_38 (Conv2D)           (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_24 (MaxPooling (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_39 (Conv2D)           (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "conv2d_40 (Conv2D)           (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_25 (MaxPooling (None, 1, 1, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 4096)              2101248   \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 512)               2097664   \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 30,204,682\n",
      "Trainable params: 30,204,682\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#We add regularization constant of 0.001  (L2 regularization)\n",
    "model = Sequential()\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', input_shape=input_shape,kernel_regularizer=regularizers.l2(0.01), padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2)))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu',kernel_regularizer=regularizers.l2(0.001), padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2)))\n",
    "model.add(Conv2D(256, (3, 3), activation='relu',kernel_regularizer=regularizers.l2(0.001 ), padding='same'))\n",
    "model.add(Conv2D(256, (3, 3), activation='relu',kernel_regularizer=regularizers.l2(0.001 ), padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2)))\n",
    "model.add(Conv2D(512, (3, 3), activation='relu',kernel_regularizer=regularizers.l2(0.001 ), padding='same'))\n",
    "model.add(Conv2D(512, (3, 3), activation='relu',kernel_regularizer=regularizers.l2(0.001 ), padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2)))\n",
    "model.add(Conv2D(512, (3, 3), activation='relu',kernel_regularizer=regularizers.l2(0.001), padding='same'))\n",
    "model.add(Conv2D(512, (3, 3), activation='relu',kernel_regularizer=regularizers.l2(0.001), padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2))) \n",
    "model.add(Flatten()) \n",
    "model.add(Dense(4096, activation='relu',kernel_regularizer=regularizers.l2(0.001 )))\n",
    "model.add(Dense(4096, activation='relu',kernel_regularizer=regularizers.l2(0.001 )))\n",
    "model.add(Dense(512, activation='relu',kernel_regularizer=regularizers.l2(0.001)))\n",
    "model.add(Dense(10, activation='softmax', kernel_regularizer=regularizers.l2(0.001)))\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IgNC4-4uQt4Z"
   },
   "outputs": [],
   "source": [
    "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "colab_type": "code",
    "id": "k9y44iBXRQ_6",
    "outputId": "3befb4bb-9559-4b75-ebe8-65bf82238b6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 135s 2ms/step - loss: 6.9398 - acc: 0.6261 - val_loss: 4.1431 - val_acc: 0.9732\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 130s 2ms/step - loss: 2.9901 - acc: 0.9717 - val_loss: 2.0578 - val_acc: 0.9798\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 130s 2ms/step - loss: 1.5153 - acc: 0.9794 - val_loss: 1.0861 - val_acc: 0.9792\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 130s 2ms/step - loss: 0.8191 - acc: 0.9816 - val_loss: 0.6045 - val_acc: 0.9841\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 130s 2ms/step - loss: 0.4879 - acc: 0.9834 - val_loss: 0.3798 - val_acc: 0.9861\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 131s 2ms/step - loss: 0.3286 - acc: 0.9838 - val_loss: 0.2838 - val_acc: 0.9833\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 131s 2ms/step - loss: 0.2519 - acc: 0.9847 - val_loss: 0.2740 - val_acc: 0.9749\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 131s 2ms/step - loss: 0.2149 - acc: 0.9851 - val_loss: 0.2287 - val_acc: 0.9763\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 131s 2ms/step - loss: 0.1963 - acc: 0.9852 - val_loss: 0.1964 - val_acc: 0.9844\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 131s 2ms/step - loss: 0.1832 - acc: 0.9871 - val_loss: 0.1815 - val_acc: 0.9861\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, batch_size=32, validation_data=(x_test,y_test), epochs=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "MebAkN4-RTGu",
    "outputId": "e727b526-5863-41d0-91ac-afd75a3dc898"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 5s 529us/step\n",
      "[0.18145463039875032, 0.9861]\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, batch_size=32)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "E2liad3jZUNe",
    "outputId": "cff71e53-3041-4132-88c8-3aeec1675f28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "History {'val_loss': [4.143069383239746, 2.0578493068695067, 1.0861302642822266, 0.6045008671760559, 0.37981229286193846, 0.28376878037452696, 0.2740239696264267, 0.22868434433937074, 0.19635534541606903, 0.18145463039875032], 'val_acc': [0.9732, 0.9798, 0.9792, 0.9841, 0.9861, 0.9833, 0.9749, 0.9763, 0.9844, 0.9861], 'loss': [6.939784062194824, 2.9900779248555502, 1.5152657278060913, 0.8190619910558065, 0.48787217200597127, 0.3285701893011729, 0.25193846286932625, 0.21493756291866303, 0.19628630491892496, 0.1831802503824234], 'acc': [0.6260833333333333, 0.9717166666666667, 0.9794333333333334, 0.9816333333333334, 0.98345, 0.9838333333333333, 0.9847333333333333, 0.9850666666666666, 0.9852333333333333, 0.98715]}\n",
      "history.history.keys(): dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    }
   ],
   "source": [
    "print(\"History\", history.history)\n",
    "print(\"history.history.keys():\", history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "4pmtddqhZXA4",
    "outputId": "5ab2d155-9022-435f-c791-a9df0942a6dd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SfVNRQ4PPJCNNk+RERKR3dTpR7qGHHmLu3LkcPXqUhoYG7HY7TzzxRF/UFpHK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\nqIuIiEQIhbqIiEiE+P828rksnUQhVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff970d81b38>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.title('Train_Accuracy vs Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "A0h89xI3ZY8t",
    "outputId": "1f27e476-46a7-46a4-dc9d-f07e50a23706"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RRx8BUF1dzbx585g1axYzZszg+++/P+cxn3zyCWPGjHFkZCEuy4ETxfz900NY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff970d968d0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('Test(Validation)_Accuracy vs Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "w9fDhbwJZbBr",
    "outputId": "1f36ce7a-e91f-41c6-bc4a-2015c0ebe364"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U6k0H5fWyQQYAWM3UcP7kk0+0cuVKvfTSSwoLCzOzKXTAnMlDNKhXmLZnntGn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\nAQDwMYQzAAA+hnAGAMDHEM4AAPiY/x+TF3DdJ4VuJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff970d96860>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.title('Train_loss vs Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "R1YHwq1tZeQ-",
    "outputId": "4f11a0c3-2a3a-4783-dbf9-cc84b01a850b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ejNdee+2m9fn5+dBoNMZljUaD/Hz5hq2vXXV+JJVD50REZB4kKe/Nmzdj2LBh6NWrlxS7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\nDpDMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff970c90518>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Test(Validation)_Loss vs Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 652
    },
    "colab_type": "code",
    "id": "sqrhd491Zhpb",
    "outputId": "a4c3b7f9-6938-42cb-9f07-fa5180ad2f32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 5s 536us/step\n",
      "Rotation degree: -45 Score:[8.439601834869384, 0.4814]\n",
      "10000/10000 [==============================] - 5s 522us/step\n",
      "Rotation degree: -40 Score:[6.714005543899536, 0.589]\n",
      "10000/10000 [==============================] - 5s 525us/step\n",
      "Rotation degree: -35 Score:[5.110840104818344, 0.6889]\n",
      "10000/10000 [==============================] - 5s 518us/step\n",
      "Rotation degree: -30 Score:[3.5404354447841646, 0.7866]\n",
      "10000/10000 [==============================] - 5s 519us/step\n",
      "Rotation degree: -25 Score:[2.244192848205566, 0.8678]\n",
      "10000/10000 [==============================] - 5s 518us/step\n",
      "Rotation degree: -20 Score:[1.3582411518096924, 0.9232]\n",
      "10000/10000 [==============================] - 5s 519us/step\n",
      "Rotation degree: -15 Score:[0.8178639618635177, 0.9572]\n",
      "10000/10000 [==============================] - 5s 521us/step\n",
      "Rotation degree: -10 Score:[0.5553975246191025, 0.9734]\n",
      "10000/10000 [==============================] - 5s 519us/step\n",
      "Rotation degree: -5 Score:[0.4584701637506485, 0.9797]\n",
      "10000/10000 [==============================] - 5s 521us/step\n",
      "Rotation degree: 0 Score:[0.39120862691402436, 0.9839]\n",
      "10000/10000 [==============================] - 5s 527us/step\n",
      "Rotation degree: 5 Score:[0.47080748364925384, 0.9792]\n",
      "10000/10000 [==============================] - 5s 519us/step\n",
      "Rotation degree: 10 Score:[0.5828770918130874, 0.9721]\n",
      "10000/10000 [==============================] - 5s 519us/step\n",
      "Rotation degree: 15 Score:[0.7753211725950241, 0.9597]\n",
      "10000/10000 [==============================] - 5s 518us/step\n",
      "Rotation degree: 20 Score:[1.1575856167793275, 0.9361]\n",
      "10000/10000 [==============================] - 5s 521us/step\n",
      "Rotation degree: 25 Score:[1.7821538168430329, 0.8962]\n",
      "10000/10000 [==============================] - 5s 524us/step\n",
      "Rotation degree: 30 Score:[2.70450921125412, 0.8389]\n",
      "10000/10000 [==============================] - 5s 523us/step\n",
      "Rotation degree: 35 Score:[4.016278053951264, 0.7577]\n",
      "10000/10000 [==============================] - 5s 522us/step\n",
      "Rotation degree: 40 Score:[5.466768901395798, 0.6661]\n",
      "10000/10000 [==============================] - 5s 520us/step\n",
      "Rotation degree: 45 Score:[6.93717942314148, 0.5757]\n"
     ]
    }
   ],
   "source": [
    "s_loss = []\n",
    "s_acc = []\n",
    "for j in range(-45,50,5):\n",
    "  rotate_test_x = []\n",
    "  for i in range(len(X_test)):\n",
    "    X_test_rotate = Image.fromarray(X_test[i])\n",
    "    new_image = X_test_rotate.rotate(j)\n",
    "    img_array = numpy.array(new_image)\n",
    "    rotate_test_x.append(img_array)\n",
    "  X_test_rotate = numpy.asarray(rotate_test_x)\n",
    "  X_test_rotate = X_test_rotate.reshape(X_test_rotate.shape[0], img_rows, img_cols, 1)\n",
    "  score = model.evaluate(X_test_rotate, y_test, batch_size=32)\n",
    "  print(\"Rotation degree: \" +str(j) + \" Score:\" + str(score))\n",
    "  s_loss.append(score[0])\n",
    "  s_acc.append(score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "colab_type": "code",
    "id": "rkJVCPVHZj_S",
    "outputId": "07c9e2ef-36b0-4f1e-e68e-4679c608da2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8.439601834869384,\n",
       " 6.714005543899536,\n",
       " 5.110840104818344,\n",
       " 3.5404354447841646,\n",
       " 2.244192848205566,\n",
       " 1.3582411518096924,\n",
       " 0.8178639618635177,\n",
       " 0.5553975246191025,\n",
       " 0.4584701637506485,\n",
       " 0.39120862691402436,\n",
       " 0.47080748364925384,\n",
       " 0.5828770918130874,\n",
       " 0.7753211725950241,\n",
       " 1.1575856167793275,\n",
       " 1.7821538168430329,\n",
       " 2.70450921125412,\n",
       " 4.016278053951264,\n",
       " 5.466768901395798,\n",
       " 6.93717942314148]"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "colab_type": "code",
    "id": "Ct8g9ZzdaGVT",
    "outputId": "5470aba7-248d-46e5-c921-21064ce4664f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4814,\n",
       " 0.589,\n",
       " 0.6889,\n",
       " 0.7866,\n",
       " 0.8678,\n",
       " 0.9232,\n",
       " 0.9572,\n",
       " 0.9734,\n",
       " 0.9797,\n",
       " 0.9839,\n",
       " 0.9792,\n",
       " 0.9721,\n",
       " 0.9597,\n",
       " 0.9361,\n",
       " 0.8962,\n",
       " 0.8389,\n",
       " 0.7577,\n",
       " 0.6661,\n",
       " 0.5757]"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "NCgN4EeEaIGi",
    "outputId": "07f4d7f4-b459-47b6-ab75-b9b79efddd02"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1nMvLy+Hl5SXmIUTn5qxCQk8fFJQ3IK/EvK4oJyIi8W07egUAMCYhqNuOKWo5x8bG4tNP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\n5UxERGRm/j+DxOoRWAByHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff970c2d860>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( range(-45,50,5), s_loss)\n",
    "plt.title('Test_Loss vs degree')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('degree')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "65zdNZUnaJyD",
    "outputId": "bd259430-829d-4ee6-d2e0-d030b3770644"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fKuf4VLGxvOmefHWoCIIALJoaCalEInYcIrKwEH8PLE3k+FSxsbzprpXVapF1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\nEZGYUXiLiIjEjMJbREQkZhTeIiIiMfMLAWqHVdzMgOcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff970b1bac8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( range(-45,50,5), s_acc)\n",
    "plt.title('Test_Accuracy vs degree')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('degree')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 251
    },
    "colab_type": "code",
    "id": "tZIHTAsoaLuz",
    "outputId": "d3477000-667f-4139-b28f-4a1befc30ab1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 5s 536us/step\n",
      "Filter radius: 0 Score:[0.39120862691402436, 0.9839]\n",
      "10000/10000 [==============================] - 5s 519us/step\n",
      "Filter radius: 1 Score:[0.4019737101793289, 0.9835]\n",
      "10000/10000 [==============================] - 5s 523us/step\n",
      "Filter radius: 2 Score:[0.6802522034883499, 0.965]\n",
      "10000/10000 [==============================] - 5s 521us/step\n",
      "Filter radius: 3 Score:[2.888186405754089, 0.82]\n",
      "10000/10000 [==============================] - 5s 519us/step\n",
      "Filter radius: 4 Score:[7.416354692077637, 0.529]\n",
      "10000/10000 [==============================] - 5s 521us/step\n",
      "Filter radius: 5 Score:[10.558487956237792, 0.3178]\n",
      "10000/10000 [==============================] - 5s 519us/step\n",
      "Filter radius: 6 Score:[12.295349296569825, 0.1799]\n"
     ]
    }
   ],
   "source": [
    "from PIL import ImageFilter\n",
    "s_loss = []\n",
    "s_acc = []\n",
    "for j in range(0,7):\n",
    "  filter_test_x = []\n",
    "  for i in range(len(X_test)):\n",
    "    X_test_filter = Image.fromarray(X_test[i])\n",
    "    new_image = X_test_filter.filter(ImageFilter.GaussianBlur(radius=j))\n",
    "    img_array = numpy.array(new_image)\n",
    "    filter_test_x.append(img_array)\n",
    "  X_test_filter = numpy.asarray(filter_test_x)\n",
    "  X_test_filter = X_test_filter.reshape(X_test_filter.shape[0], img_rows, img_cols, 1)\n",
    "  score = model.evaluate(X_test_filter, y_test, batch_size=32)\n",
    "  print(\"Filter radius: \" +str(j) + \" Score:\" + str(score))\n",
    "  s_loss.append(score[0])\n",
    "  s_acc.append(score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "AQehbou-aNkM",
    "outputId": "500755ee-1376-4948-8630-743163c1282a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c2Axvr9p7NLPqoNIWERGXyc4rYeGGg+zcn4UF6NWpKUN6XkeQv83saB5BpS0iIjWuvMLB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\nGUPfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff970a998d0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( range(0,7), s_loss)\n",
    "plt.title('Test_Loss vs Filter_radius')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Filter_radius')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "AxZR7XGNaoW2",
    "outputId": "a7d834f0-8891-4f5d-a1f3-cbacc0c71512"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Lm+gRZeepsWp7Cq6rStHWwwF/ebYn2rRwEDsWEVkQljdRA1RqtIj++RIOnroOa6kEU5/p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\ny5uIiMjMsLyJiIjMzP8H0eZkV9gI3ekAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9709448d0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( range(0,7), s_acc)\n",
    "plt.title('Test_Accuracy vs Filter_radius')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Filter_radius')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tTQKfMkYaqG_"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Q1.4.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}