VGG11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WZfZS53P6R8W"
   },
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "from keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1pAz9wHn6mbR"
   },
   "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": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "bDTL_RaQyxt1",
    "outputId": "22214cbc-44ef-497e-94e0-e1d48f80449b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 32, 32)"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "cV4SehixoEyq",
    "outputId": "6a9997d1-e6e4-49d9-860c-1d95221cf286"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 32, 32, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yD9HtyQj0TYc"
   },
   "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": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 753
    },
    "colab_type": "code",
    "id": "K52GB5Sq1GTP",
    "outputId": "9c6d0eff-34ef-427c-f81f-37e7dda58ae5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 64)        640       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 4, 4, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 1, 1, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4096)              2101248   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 512)               2097664   \n",
      "_________________________________________________________________\n",
      "dense_4 (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": [
    "model = Sequential()\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', input_shape=input_shape, padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2)))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2)))\n",
    "model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2)))\n",
    "model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2)))\n",
    "model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = (2, 2))) \n",
    "model.add(Flatten()) \n",
    "model.add(Dense(4096, activation='relu'))\n",
    "model.add(Dense(4096, activation='relu'))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "91B-XN-o1XO8"
   },
   "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": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "colab_type": "code",
    "id": "N5u_TXO-1coI",
    "outputId": "20e82abc-d67c-4cc4-d17d-c1f8d998753e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 113s 2ms/step - loss: 0.3569 - acc: 0.8801 - val_loss: 0.0575 - val_acc: 0.9836\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 113s 2ms/step - loss: 0.0612 - acc: 0.9818 - val_loss: 0.0427 - val_acc: 0.9882\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 112s 2ms/step - loss: 0.0382 - acc: 0.9892 - val_loss: 0.0311 - val_acc: 0.9908\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 112s 2ms/step - loss: 0.0284 - acc: 0.9919 - val_loss: 0.0330 - val_acc: 0.9913\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 112s 2ms/step - loss: 0.0220 - acc: 0.9937 - val_loss: 0.0281 - val_acc: 0.9921\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 112s 2ms/step - loss: 0.0165 - acc: 0.9953 - val_loss: 0.0271 - val_acc: 0.9929\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 112s 2ms/step - loss: 0.0123 - acc: 0.9963 - val_loss: 0.0236 - val_acc: 0.9931\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 112s 2ms/step - loss: 0.0105 - acc: 0.9971 - val_loss: 0.0273 - val_acc: 0.9924\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 112s 2ms/step - loss: 0.0094 - acc: 0.9974 - val_loss: 0.0256 - val_acc: 0.9937\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 112s 2ms/step - loss: 0.0067 - acc: 0.9983 - val_loss: 0.0322 - val_acc: 0.9911\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": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "D3JqjYYF1oAa",
    "outputId": "779035d3-48e4-4e28-d107-732b4a2c3aab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 4s 392us/step\n",
      "[0.03222943743088908, 0.9911]\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, batch_size=32)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "e3w3dYBH6vmT",
    "outputId": "6187acdf-0549-4ca7-92a9-aaff01cd1663"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "History {'val_loss': [0.05752460888680071, 0.042716833085776305, 0.031086521799280308, 0.03301134239154344, 0.02807655531921191, 0.027149229627203748, 0.023572146921069362, 0.027258629364400985, 0.025609088702569716, 0.03222943743088908], 'val_acc': [0.9836, 0.9882, 0.9908, 0.9913, 0.9921, 0.9929, 0.9931, 0.9924, 0.9937, 0.9911], 'loss': [0.35689901853725314, 0.0612022745838544, 0.0381719230657133, 0.028397868129936978, 0.021956117314726, 0.016465884358258336, 0.012255517465585338, 0.010492771415949877, 0.009381190909165161, 0.006721448758822105], 'acc': [0.8800833333333333, 0.9818, 0.9891833333333333, 0.9919333333333333, 0.9936833333333334, 0.9952666666666666, 0.9963333333333333, 0.99715, 0.9973833333333333, 0.9983333333333333]}\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": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "ndPrnMTN7CMk",
    "outputId": "b7a07b64-f291-4083-c444-0651b5e8dd87"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZszjvvPO45ZZbGDVqVG/UF/F8Ph8lZU4ybAnExXT5zyEiItKpbo2pf/vb32bO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56605f6be0>"
      ]
     },
     "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": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "LCTfI64H7IBU",
    "outputId": "25924b51-de5a-4da7-d879-d8db5b071466"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jvtaqts5CTlEVXUO8dBoG+OlgPm8t20uvsA48c8dQp19/cq6w14WuInJG1bX1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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56605f6358>"
      ]
     },
     "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": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "yvvB4s-27g5m",
    "outputId": "376e66a0-50ce-4e9e-812a-c654a39d64c0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6FABABCHUwxB3bAMAjAShHoZ6763OuDoA4EwQ6mFocnqSbFYLl7UBAM4IoR6G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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56603f3940>"
      ]
     },
     "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": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "5DdFnoLE7wwL",
    "outputId": "22763cc0-2ce5-440c-9041-82a5496c4a7d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RETktlf05sFgsDO0Thb22jg07cs2OIyIicloq+3M0uHdr3CwWncoXEZFmz8OZO583bx5b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\nOgB8WnnQq1MofWPC6NM5jEC/5nfGpaq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TU3nuuefu+PjV1dWsXbuW4uLiOz6WiPw0KnsR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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f566030f828>"
      ]
     },
     "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": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 652
    },
    "colab_type": "code",
    "id": "1bxARCdI74PV",
    "outputId": "fdf14bc4-3c53-48f0-b62c-95b2e9d37d53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 4s 391us/step\n",
      "Rotation degree: -45 Score:[8.263532884597778, 0.4865]\n",
      "10000/10000 [==============================] - 4s 376us/step\n",
      "Rotation degree: -40 Score:[6.745380521774292, 0.5806]\n",
      "10000/10000 [==============================] - 4s 379us/step\n",
      "Rotation degree: -35 Score:[5.15834286441803, 0.6788]\n",
      "10000/10000 [==============================] - 4s 377us/step\n",
      "Rotation degree: -30 Score:[3.6693379108428954, 0.7718]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Rotation degree: -25 Score:[2.2955894186973573, 0.8571]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Rotation degree: -20 Score:[1.2492707360369744, 0.9217]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Rotation degree: -15 Score:[0.6673230911970408, 0.9584]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Rotation degree: -10 Score:[0.3747247033868657, 0.9766]\n",
      "10000/10000 [==============================] - 4s 377us/step\n",
      "Rotation degree: -5 Score:[0.2075486031479929, 0.9869]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Rotation degree: 0 Score:[0.12380297759616904, 0.9922]\n",
      "10000/10000 [==============================] - 4s 377us/step\n",
      "Rotation degree: 5 Score:[0.22805897455216745, 0.9858]\n",
      "10000/10000 [==============================] - 4s 375us/step\n",
      "Rotation degree: 10 Score:[0.2118638239026168, 0.9868]\n",
      "10000/10000 [==============================] - 4s 376us/step\n",
      "Rotation degree: 15 Score:[0.31229675275088165, 0.9804]\n",
      "10000/10000 [==============================] - 4s 384us/step\n",
      "Rotation degree: 20 Score:[0.5370197673923516, 0.9663]\n",
      "10000/10000 [==============================] - 4s 381us/step\n",
      "Rotation degree: 25 Score:[0.8850995954633283, 0.9448]\n",
      "10000/10000 [==============================] - 4s 377us/step\n",
      "Rotation degree: 30 Score:[1.5417911808073534, 0.9036]\n",
      "10000/10000 [==============================] - 4s 377us/step\n",
      "Rotation degree: 35 Score:[2.6017384689331053, 0.8376]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Rotation degree: 40 Score:[3.923830470657349, 0.755]\n",
      "10000/10000 [==============================] - 4s 380us/step\n",
      "Rotation degree: 45 Score:[5.32408429222107, 0.6677]\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])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "colab_type": "code",
    "id": "TWUZoUrWGA7V",
    "outputId": "6dcbe2bb-dab6-4af3-db92-49ef8e883419"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8.263532884597778,\n",
       " 6.745380521774292,\n",
       " 5.15834286441803,\n",
       " 3.6693379108428954,\n",
       " 2.2955894186973573,\n",
       " 1.2492707360369744,\n",
       " 0.6673230911970408,\n",
       " 0.3747247033868657,\n",
       " 0.2075486031479929,\n",
       " 0.12380297759616904,\n",
       " 0.22805897455216745,\n",
       " 0.2118638239026168,\n",
       " 0.31229675275088165,\n",
       " 0.5370197673923516,\n",
       " 0.8850995954633283,\n",
       " 1.5417911808073534,\n",
       " 2.6017384689331053,\n",
       " 3.923830470657349,\n",
       " 5.32408429222107]"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "colab_type": "code",
    "id": "HnzZ0PZ_Ja4T",
    "outputId": "21a138a0-6c19-4cf7-a9ce-ccbc28a8b5cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4865,\n",
       " 0.5806,\n",
       " 0.6788,\n",
       " 0.7718,\n",
       " 0.8571,\n",
       " 0.9217,\n",
       " 0.9584,\n",
       " 0.9766,\n",
       " 0.9869,\n",
       " 0.9922,\n",
       " 0.9858,\n",
       " 0.9868,\n",
       " 0.9804,\n",
       " 0.9663,\n",
       " 0.9448,\n",
       " 0.9036,\n",
       " 0.8376,\n",
       " 0.755,\n",
       " 0.6677]"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "qG1RN_fgJcPV",
    "outputId": "b813712b-67b4-4c84-ac95-350cb88dd1ee"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lcgvqkNjXX+pIRESiOV/WhBMX6hAf6Y0BdrBrBm5SzmFhYb2Vg3pY+ohI7D9R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56600dc9b0>"
      ]
     },
     "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": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "4mtGMJqYJrlo",
    "outputId": "efeb0f3b-6953-4945-c019-5db77250305d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vD3Pp1J5ieZNF7MkoRrvehHnTA6CQ88eMiK63+I4geLjYIuVYMYoquXRqT/C3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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56602c6400>"
      ]
     },
     "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": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 251
    },
    "colab_type": "code",
    "id": "EOeawANwJ_uV",
    "outputId": "c7580947-0af7-46b0-8cb5-c2c39471356c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 4s 388us/step\n",
      "Filter radius: 0 Score:[0.12380297759616904, 0.9922]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Filter radius: 1 Score:[0.1288113596051111, 0.9919]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Filter radius: 2 Score:[0.47054962616960294, 0.9696]\n",
      "10000/10000 [==============================] - 4s 380us/step\n",
      "Filter radius: 3 Score:[2.5986409624814986, 0.8293]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Filter radius: 4 Score:[5.613272819137573, 0.6116]\n",
      "10000/10000 [==============================] - 4s 378us/step\n",
      "Filter radius: 5 Score:[6.405314479827881, 0.4621]\n",
      "10000/10000 [==============================] - 4s 377us/step\n",
      "Filter radius: 6 Score:[7.127731807708741, 0.2879]\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": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "-ZIdBwFrN-b8",
    "outputId": "d1a05cc5-cb5e-41c3-b927-fc922d13def6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BDEYjDp6qwk/7C1FwbpNXmL8LEkcGYHBYX9jwUihZYzkTEVmQS23yShwZgBBu8jIbLGci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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5660183da0>"
      ]
     },
     "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": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "DL3g_S6yOeRS",
    "outputId": "7fa2c4fe-5a94-4535-8c76-ae8159c5390c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G9XVFoE83mEh5ahsRCYPlTdRIysJyfBOejCu5JXBra4P5/h6wszIVOhYRGSGWN9FDqKxW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\nGJY3ERGRgWF5ExERGRiWNxERkYH5f0tDQ+aJFxXFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56601275f8>"
      ]
     },
     "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": "ZtJIxIvgOtcv"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Q1.1-1.3.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
}