cow_vgg16model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5eb1ca60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e652893",
   "metadata": {},
   "outputs": [],
   "source": [
    "# resizing and converting to RGB\n",
    "#we have to preprocess the image to fit the VGG16 model. The model expects a 224 by 224 RGB image.\n",
    "def load_and_preprocess_image(path):\n",
    "    image = cv2.imread(path)\n",
    "    image = cv2.resize(image, (224,224))\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47aa32db",
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_SHAPE = (224, 224) # (height, width) in no. of pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7076d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = (r\"C:\\Users\\Dell\\Dropbox\\My PC (DESKTOP-Q65MJBI)\\Downloads\\cows_pics_test\\cows_test\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d8a5b3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAINING_DATA_DIR = str(data_root)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92ef2f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen_kwargs = dict(rescale=1./255, validation_split=.20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0171d9f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from shutil import copy2\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0b93318a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "try:\n",
    "  # The %tensorflow_version magic only works in colab.\n",
    "  %tensorflow_version 2.x\n",
    "except Exception:\n",
    "    pass\n",
    "import tensorflow as tf\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bbacf753",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow_hub in c:\\users\\dell\\anaconda3\\lib\\site-packages (0.9.0)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow_hub) (1.15.0)\n",
      "Requirement already satisfied: numpy>=1.12.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow_hub) (1.19.5)\n",
      "Requirement already satisfied: protobuf>=3.8.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow_hub) (3.17.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow_hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b992a4a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_hub as hub\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b52a069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.4.1'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33ccb77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Increase precision of presented data for better side-by-side comparison\n",
    "pd.set_option(\"display.precision\", 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f2696f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "954884f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 164 images belonging to 3 classes.\n",
      "Found 660 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**datagen_kwargs)\n",
    "valid_generator = valid_datagen.flow_from_directory(\n",
    "TRAINING_DATA_DIR,\n",
    "subset=\"validation\",\n",
    "shuffle=True,\n",
    "target_size=IMAGE_SHAPE\n",
    ")\n",
    "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**datagen_kwargs)\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "TRAINING_DATA_DIR,\n",
    "subset=\"training\",\n",
    "shuffle=True,\n",
    "target_size=IMAGE_SHAPE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6bfce2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((32, 224, 224, 3), (32, 3))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for image_batch, label_batch in train_generator:\n",
    "    break\n",
    "image_batch.shape, label_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00bd3b5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1': 0, '2': 1, '3': 2}\n"
     ]
    }
   ],
   "source": [
    "print (train_generator.class_indices)\n",
    "\n",
    "labels = '\\n'.join(sorted(train_generator.class_indices.keys()))\n",
    "\n",
    "with open('labels.txt', 'w') as f:\n",
    "    f.write(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1701461",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(64,(3,3),activation=\"relu\", padding=\"same\"))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(64,(3,3),activation=\"relu\", padding=\"same\"))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(64,(3,3),activation=\"relu\", padding=\"same\"))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(128,(3,3),activation=\"relu\", padding=\"same\"))\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "model.add(Conv2D(128,(3,3),activation=\"relu\", padding=\"same\"))\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(1024,activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(256,activation=\"relu\"))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(len(labels),activation=\"softmax\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "104fb1b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, None, None, 3)]   0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, None, None, 64)    1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, None, None, 64)    36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, None, None, 128)   73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, None, None, 128)   147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, None, None, 256)   295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 512)               524800    \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 5)                 2565      \n",
      "=================================================================\n",
      "Total params: 16,816,965\n",
      "Trainable params: 2,102,277\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "base_model=VGG16(weights='imagenet',include_top=False)\n",
    "\n",
    "x=base_model.output\n",
    "x=GlobalAveragePooling2D()(x)\n",
    "x=Dense(1024,activation='relu')(x)\n",
    "x=Dense(1024,activation='relu')(x)\n",
    "x=Dropout(0.5)(x)\n",
    "x=Dense(512,activation='relu')(x)\n",
    "preds=Dense(len(labels),activation='softmax')(x)\n",
    "\n",
    "model=Model(inputs=base_model.input,outputs=preds)\n",
    "\n",
    "for layer in model.layers[:-5]:\n",
    "    layer.trainable=False\n",
    "for layer in model.layers[-5:]:\n",
    "    layer.trainable=True\n",
    "    \n",
    "model.compile(\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1dc33117",
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(patience=5, verbose=1,restore_best_weights=True)\n",
    "reduce_lr = ReduceLROnPlateau(factor=0.1, patience=3,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9df49de3",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "nb_epochs = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3778abb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "ename": "UnknownError",
     "evalue": " Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\n\t [[node model/block1_conv1/Relu (defined at <ipython-input-20-5cbd8fbd1047>:1) ]] [Op:__inference_train_function_1530]\n\nFunction call stack:\ntrain_function\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnknownError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-5cbd8fbd1047>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m model.fit_generator(\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[0mtrain_generator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0msteps_per_epoch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_generator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamples\u001b[0m \u001b[1;33m//\u001b[0m \u001b[0mbatch_size\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[0mvalidation_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalid_generator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mvalidation_steps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalid_generator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamples\u001b[0m \u001b[1;33m//\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   1845\u001b[0m                   \u001b[1;34m'will be removed in a future version. '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1846\u001b[0m                   'Please use `Model.fit`, which supports generators.')\n\u001b[1;32m-> 1847\u001b[1;33m     return self.fit(\n\u001b[0m\u001b[0;32m   1848\u001b[0m         \u001b[0mgenerator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1849\u001b[0m         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1098\u001b[0m                 _r=1):\n\u001b[0;32m   1099\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1100\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\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   1101\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1102\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\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~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\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    827\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 828\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\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    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"xla\"\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\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~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    886\u001b[0m         \u001b[1;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    887\u001b[0m         \u001b[1;31m# stateless function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 888\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\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    889\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    890\u001b[0m       \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfiltered_flat_args\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2940\u001b[0m       (graph_function,\n\u001b[0;32m   2941\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2942\u001b[1;33m     return graph_function._call_flat(\n\u001b[0m\u001b[0;32m   2943\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0;32m   2944\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1916\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1917\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1918\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1919\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    553\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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    554\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 555\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    556\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\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    557\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\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---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnknownError\u001b[0m:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\n\t [[node model/block1_conv1/Relu (defined at <ipython-input-20-5cbd8fbd1047>:1) ]] [Op:__inference_train_function_1530]\n\nFunction call stack:\ntrain_function\n"
     ]
    }
   ],
   "source": [
    "model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch = train_generator.samples // batch_size,\n",
    "    validation_data = valid_generator, \n",
    "    validation_steps = valid_generator.samples // batch_size,\n",
    "    epochs = nb_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d5d1441",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.ylabel(\"Loss (training and validation)\")\n",
    "plt.xlabel(\"Training Steps\")\n",
    "plt.ylim([0,50])\n",
    "plt.plot(hist[\"loss\"])\n",
    "plt.plot(hist[\"val_loss\"])\n",
    "\n",
    "plt.figure()\n",
    "plt.ylabel(\"Accuracy (training and validation)\")\n",
    "plt.xlabel(\"Training Steps\")\n",
    "plt.ylim([0,1])\n",
    "plt.plot(hist[\"acc\"])\n",
    "plt.plot(hist[\"val_acc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18c27304",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_image_batch, val_label_batch = next(iter(valid_generator))\n",
    "true_label_ids = np.argmax(val_label_batch, axis=-1)\n",
    "print(\"Validation batch shape:\", val_image_batch.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de590c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_labels = sorted(train_generator.class_indices.items(), key=lambda pair:pair[1])\n",
    "dataset_labels = np.array([key.title() for key, value in dataset_labels])\n",
    "print(dataset_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d18b9b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_model_predictions = model.predict(val_image_batch)\n",
    "print(\"Prediction results shape:\", tf_model_predictions.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b8adb51",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_ids = np.argmax(tf_model_predictions, axis=-1)\n",
    "predicted_labels = dataset_labels[predicted_ids]\n",
    "print(predicted_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd4d5aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,9))\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "for n in range((len(predicted_labels)-2)):\n",
    "    plt.subplot(6,5,n+1)\n",
    "    plt.imshow(val_image_batch[n])\n",
    "    color = \"green\" if predicted_ids[n] == true_label_ids[n] else \"red\"\n",
    "    plt.title(predicted_labels[n].title(), color=color)\n",
    "    plt.axis('off')\n",
    "_ = plt.suptitle(\"Model predictions (green: correct, red: incorrect)\")"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}