cifar10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import models\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import optimizers\n",
    "import tensorflow as tf\n",
    "from keras.utils import np_utils\n",
    "from keras.models import load_model\n",
    "from keras.datasets import cifar10\n",
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values :  [0 1 2 3 4 5 6 7 8 9]\n",
      "Indices of Unique Values :  [1000 1000 1000 1000 1000 1000 1000 1000 1000 1000]\n",
      "(50000, 32, 32, 3)\n",
      "(10000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "x_train = x_train / 255.0\n",
    "x_test = x_test / 255.0\n",
    "\n",
    "uniqueValues, indicesList = np.unique(y_test, return_counts=True)\n",
    " \n",
    "print('Unique Values : ', uniqueValues)\n",
    "print('Indices of Unique Values : ', indicesList)\n",
    "\n",
    "y_train = np_utils.to_categorical(y_train, 10)\n",
    "y_test = np_utils.to_categorical(y_test, 10)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\n",
      "[8]\n",
      "[8]\n",
      "[0]\n",
      "[6]\n",
      "[6]\n",
      "[1]\n",
      "[6]\n",
      "[3]\n",
      "[1]\n",
      "[0]\n"
     ]
    }
   ],
   "source": [
    "for index, y in enumerate(y_test):\n",
    "  print(y)\n",
    "  if index == 10:\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values :  [0. 1.]\n",
      "Indices of Unique Values :  [0 3]\n"
     ]
    }
   ],
   "source": [
    "uniqueValues, indicesList = np.unique(y_test, return_index=True)\n",
    " \n",
    "print('Unique Values : ', uniqueValues)\n",
    "print('Indices of Unique Values : ', indicesList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'conv_base' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-2df3e415a7e9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUpSampling2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUpSampling2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconv_base\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFlatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBatchNormalization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'conv_base' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\clc87\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "A local file was found, but it seems to be incomplete or outdated because the auto file hash does not match the original value of 4d473c1dd8becc155b73f8504c6f6626 so we will re-download the data.\n",
      "Downloading data from https://github.com/keras-team/keras-applications/releases/download/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94773248/94765736 [==============================] - 74s 1us/step\n"
     ]
    }
   ],
   "source": [
    "conv_base = ResNet50(weights='imagenet', include_top=False, input_shape=(200, 200, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "50000/50000 [==============================] - 575s 11ms/sample - loss: 0.6643 - acc: 0.6390 - val_loss: 0.4553 - val_acc: 0.8774\n",
      "Epoch 2/5\n",
      "50000/50000 [==============================] - 551s 11ms/sample - loss: 0.5442 - acc: 0.7290 - val_loss: 0.3593 - val_acc: 0.9662\n",
      "Epoch 3/5\n",
      "50000/50000 [==============================] - 546s 11ms/sample - loss: 0.4314 - acc: 0.8205 - val_loss: 0.2644 - val_acc: 0.9843\n",
      "Epoch 4/5\n",
      "50000/50000 [==============================] - 549s 11ms/sample - loss: 0.3160 - acc: 0.8997 - val_loss: 0.1786 - val_acc: 0.9858\n",
      "Epoch 5/5\n",
      "50000/50000 [==============================] - 549s 11ms/sample - loss: 0.2197 - acc: 0.9466 - val_loss: 0.1108 - val_acc: 0.9870\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.UpSampling2D((2,2)))\n",
    "model.add(layers.UpSampling2D((2,2)))\n",
    "model.add(layers.UpSampling2D((2,2)))\n",
    "model.add(conv_base)\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.BatchNormalization())\n",
    "model.add(layers.Dense(128, activation='relu'))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.BatchNormalization())\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.BatchNormalization())\n",
    "model.add(layers.Dense(10, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer=optimizers.RMSprop(lr=2e-5), loss='binary_crossentropy', metrics=['acc'])\n",
    "\n",
    "history = model.fit(x_train, y_train, epochs=5, batch_size=20, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = keras.losses.binary_crossentropy(y_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'strided_slice_2:0' shape=() dtype=float32>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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