featureDropout_ResNet50V2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import cv2\n",
    "import keras\n",
    "from keras import layers\n",
    "from keras import optimizers\n",
    "from keras.layers import Input, Lambda, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, AlphaDropout\n",
    "from keras.layers import Conv2D, SpatialDropout2D, AveragePooling2D, MaxPooling2D, GlobalAveragePooling2D, GlobalMaxPooling2D, GlobalMaxPooling2D, Dropout\n",
    "from keras.models import Model, load_model\n",
    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, ReduceLROnPlateau\n",
    "from keras.utils import plot_model\n",
    "import keras.backend as K\n",
    "from keras.applications import resnet, resnet_v2, vgg16, vgg19\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_tr, y_tr), (x_test_norm, y_test_norm) = mnist.load_data()\n",
    "x_tr = np.asarray([cv2.resize(a, (56, 56), interpolation = cv2.INTER_LINEAR) for a in x_tr])\n",
    "x_test_norm = np.asarray([cv2.resize(a, (56, 56), interpolation = cv2.INTER_LINEAR) for a in x_test_norm])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_tr = x_tr.reshape(60000, 56, 56, 1)\n",
    "x_test_norm = x_test_norm.reshape(10000, 56, 56, 1)\n",
    "y_tr = to_categorical(y_tr)\n",
    "y_test_norm = to_categorical(y_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model=resnet_v2.ResNet50V2(include_top=False, input_shape = (56, 56, 1), weights=None, pooling='avg')\n",
    "\n",
    "last = base_model.layers[-1]\n",
    "second = base_model.layers[-2]\n",
    "\n",
    "# x = SpatialDropout2D(0.5)(second.output)\n",
    "# x = last(x)\n",
    "x = Dense(10, activation = 'softmax')(last.output)\n",
    "\n",
    "new_model = Model(inputs=base_model.input, outputs=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.SGD(lr=0.001, momentum = 0.9, nesterov = True)\n",
    "new_model.compile(optimizer, loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model.fit(x_tr, y_tr, validation_data = (x_test_norm, y_test_norm), batch_size=32, epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 8s 813us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.019084783305926247, 0.9930999875068665]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_model.evaluate(x_test_norm, y_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_model.save('models/resnetv2_99.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model = load_model('models/resnetv2_99.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 9s 900us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.022550889069284312, 0.9922000169754028]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "attacktest = pd.read_csv('whitetoblack.csv', header=None)\n",
    "attacktest = attacktest.to_numpy()\n",
    "attacktest = attacktest.reshape(10000, 56, 56, 1)\n",
    "new_model.evaluate(attacktest, y_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tfGPU",
   "language": "python",
   "name": "tfgpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}