dogvscat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "handmade-bahrain",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing necessary libraries\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.preprocessing.image import load_img, ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aware-chapel",
   "metadata": {},
   "source": [
    "# Training the model on shuffled training data and predicting the trash bucket"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dependent-offering",
   "metadata": {},
   "source": [
    "Data Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "numeric-tracker",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    12500\n",
      "1    12500\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# extracting and preprocessing data\n",
    "filenames = os.listdir('dogvscat_traindata/train')\n",
    "labels = []\n",
    "for filename in filenames:\n",
    "    category = filename.split('.')[0]\n",
    "    if category == 'dog':\n",
    "        labels.append(str(1))\n",
    "    else:\n",
    "        labels.append(str(0))\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'filename': filenames,\n",
    "    'label': labels\n",
    "})\n",
    "\n",
    "print(df['label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "small-education",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dog.2509.jpg\n"
     ]
    }
   ],
   "source": [
    "#visualizing a random data\n",
    "sample = random.choice(filenames)\n",
    "image = load_img(\"dogvscat_traindata/train/\"+sample)\n",
    "plt.imshow(image)\n",
    "plt.show()\n",
    "print(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "objective-yacht",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    10015\n",
      "0     9985\n",
      "Name: label, dtype: int64\n",
      "0    2515\n",
      "1    2485\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# splitting the training data into training and validation ( 20% of total data i.e 5000 examples)\n",
    "\n",
    "train_data, val_data = train_test_split(df, test_size=0.2, random_state = 42)\n",
    "train_data = train_data.reset_index(drop=True)   # 20000 examples\n",
    "val_data = val_data.reset_index(drop=True)       # 5000 examples\n",
    "\n",
    "print(train_data['label'].value_counts())\n",
    "print(val_data['label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "hindu-xerox",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 validated image filenames belonging to 2 classes.\n",
      "Found 5000 validated image filenames belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# generating and augmentating training and validation data through ImageDataGenerator\n",
    "\n",
    "train_data_gen = ImageDataGenerator(\n",
    "    rotation_range=15,\n",
    "    rescale=1./255,\n",
    "    shear_range=0.1,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1\n",
    ")\n",
    "\n",
    "train_generator = train_data_gen.flow_from_dataframe(\n",
    "    train_data, \n",
    "    \"dogvscat_traindata/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='label',\n",
    "    target_size=(128,128),\n",
    "    class_mode='binary',\n",
    "    batch_size=20\n",
    ")\n",
    "\n",
    "val_data_gen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "val_generator = val_data_gen.flow_from_dataframe(val_data, \n",
    "    \"dogvscat_traindata/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='label',\n",
    "    target_size=(128,128),\n",
    "    class_mode='binary',\n",
    "    batch_size=20\n",
    ")\n",
    "\n",
    "STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
    "STEP_SIZE_VALID=val_generator.n//val_generator.batch_size\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-hungary",
   "metadata": {},
   "source": [
    "Model Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "female-designation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 128, 128, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 64, 64, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                524352    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 560,385\n",
      "Trainable params: 560,385\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# defining the model\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=3, input_shape = (128,128,3), padding= 'same', activation = 'relu'))\n",
    "model.add(MaxPooling2D(pool_size= (2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=3, padding= 'same', activation = 'relu'))\n",
    "model.add(MaxPooling2D(pool_size= (2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=3, padding= 'same', activation = 'relu'))\n",
    "model.add(MaxPooling2D(pool_size= (2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64, activation ='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Dense(128, activation ='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Dense(64, activation ='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Dense(1, activation ='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alleged-apartment",
   "metadata": {},
   "source": [
    "Training the dogvscat model with (train_data, val_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "interested-trading",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "1000/1000 [==============================] - 407s 406ms/step - loss: 0.6950 - binary_accuracy: 0.5132 - val_loss: 0.6767 - val_binary_accuracy: 0.6018\n",
      "Epoch 2/6\n",
      "1000/1000 [==============================] - 400s 400ms/step - loss: 0.6669 - binary_accuracy: 0.5968 - val_loss: 0.6186 - val_binary_accuracy: 0.6692\n",
      "Epoch 3/6\n",
      "1000/1000 [==============================] - 398s 398ms/step - loss: 0.6157 - binary_accuracy: 0.6576 - val_loss: 0.5678 - val_binary_accuracy: 0.7000\n",
      "Epoch 4/6\n",
      "1000/1000 [==============================] - 402s 402ms/step - loss: 0.5817 - binary_accuracy: 0.6968 - val_loss: 0.5140 - val_binary_accuracy: 0.7476\n",
      "Epoch 5/6\n",
      "1000/1000 [==============================] - 402s 401ms/step - loss: 0.5564 - binary_accuracy: 0.7127 - val_loss: 0.5132 - val_binary_accuracy: 0.7548\n",
      "Epoch 6/6\n",
      "1000/1000 [==============================] - 400s 400ms/step - loss: 0.5441 - binary_accuracy: 0.7201 - val_loss: 0.5085 - val_binary_accuracy: 0.7548\n"
     ]
    }
   ],
   "source": [
    "hist_model = model.fit(train_generator, steps_per_epoch=STEP_SIZE_TRAIN, epochs=6, verbose=1, validation_data=val_generator, validation_steps=STEP_SIZE_VALID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ongoing-supervisor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code to show the training-vs-validation accuracy vs loss for animal model.\n",
    "\n",
    "accuracy = hist_model.history['binary_accuracy']\n",
    "val_accuracy = hist_model.history['val_binary_accuracy']\n",
    "loss = hist_model.history['loss']\n",
    "val_loss = hist_model.history['val_loss']\n",
    "epochs = range(len(accuracy))\n",
    "\n",
    "fig = plt.figure(figsize=(10,8)) \n",
    "plt.plot(epochs, accuracy, 'b', label = 'Training accuracy')\n",
    "plt.plot(epochs,val_accuracy,'r', label= 'Validation accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "# txt = \"Fig 1.b : \"               # captioning the figure\n",
    "# fig.text(.5, .05, txt, ha='center')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "plt.plot(epochs, loss, 'b', label = 'Training accuracy')\n",
    "plt.plot(epochs,val_loss,'r', label= 'Validation accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "confident-remove",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info:: {'Cat' = 0, 'Dog'= 1}\n"
     ]
    }
   ],
   "source": [
    "# model.save('dogvscat/dogvscat.model')\n",
    "model = load_model('dogvscat/dogvscat.model')\n",
    "print(\"Info:: {'Cat' = 0, 'Dog'= 1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sensitive-accident",
   "metadata": {},
   "source": [
    "Testing the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "oriental-madagascar",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 500 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# code to test DogVsCat classfication model on testing dataset\n",
    "\n",
    "# creating test generator to test the model\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    'dogvscat/test_data/test',\n",
    "    target_size=(128, 128),\n",
    "    class_mode=None,\n",
    "    shuffle=False,\n",
    "    batch_size=1,\n",
    ")\n",
    "\n",
    "STEP_SIZE_TEST=test_generator.n//test_generator.batch_size\n",
    "# test_generator.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dramatic-parent",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500/500 [==============================] - 4s 7ms/step\n",
      "{0: 315, 1: 185}\n",
      "Accuracy of dogvsCat Model: 76.2 %.\n"
     ]
    }
   ],
   "source": [
    "#predicting output label for test dataset\n",
    "pred = model.predict(test_generator, steps=STEP_SIZE_TEST, verbose=1)\n",
    "\n",
    "# converting the predicted output data and getting desired ouput\n",
    "animal_pred = [ (1 if y[0] > 0.5 else 0) for y in pred]\n",
    "animal_desired = test_generator.classes\n",
    "# animal_desired = test_generator.class_indices\n",
    "# # labels = (train_generator.class_indices)\n",
    "# # labels = dict((v,k) for k,v in labels.items())\n",
    "# # predictions = [labels[k] for k in y_pred]\n",
    "\n",
    "# saving the predicted output in a dataframe file\n",
    "examplenames = test_generator.filenames\n",
    "results = pd.DataFrame({\"Filenames\":examplenames, \"Desired Output\": animal_desired, \"Predicted Output\": animal_pred})\n",
    "# results.to_csv(\"animal_prediction.txt\", header= False, index= False, sep =' ')\n",
    "\n",
    "# counting the number of prediction for each label\n",
    "unique, counts = np.unique(animal_pred, return_counts=True)\n",
    "result = dict(zip(unique, counts))\n",
    "print(result)\n",
    "\n",
    "#getting the accuracy of the model\n",
    "accuracy = accuracy_score(animal_desired,animal_pred)   \n",
    "print(\"Accuracy of dogvsCat Model: \" + str((accuracy *100))+ \" %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "sealed-batch",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[223  92]\n",
      " [ 27 158]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code block to print the Gender Confusion Matrix.\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "animal_confusion_matrix = confusion_matrix(animal_pred, animal_desired)\n",
    "print(animal_confusion_matrix)\n",
    "\n",
    "plt.imshow(animal_confusion_matrix, cmap=plt.cm.Blues)\n",
    "plt.xlabel(\"Predicted labels\")\n",
    "plt.ylabel(\"True labels\")\n",
    "plt.xticks([0, 1], ['Cat', 'Dog'])\n",
    "plt.yticks([0, 1], ['Cat', 'Dog'])\n",
    "plt.title('Animal Confusion Matrix')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "figured-reproduction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4738 validated image filenames.\n",
      "4738/4738 [==============================] - 35s 7ms/step\n",
      "{0: 1899, 1: 2839}\n"
     ]
    }
   ],
   "source": [
    "# code to test DogVsCat classfication model with another dataset (consists images of random wild animals)\n",
    "\n",
    "#creating dataframe for the random test\n",
    "random_filenames = os.listdir('dogvscat_traindata/another_dataset/train/wild')\n",
    "random_df = pd.DataFrame({ 'filename': random_filenames })\n",
    "# print(random_df)\n",
    "\n",
    "# creating test generator to test the model\n",
    "random_test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "random_test_generator = random_test_datagen.flow_from_dataframe(\n",
    "    random_df,\n",
    "    'dogvscat_traindata/another_dataset/train/wild',\n",
    "    x_col='filename',\n",
    "    target_size=(128, 128),\n",
    "    class_mode=None,\n",
    "    shuffle=False,\n",
    "    batch_size=1,\n",
    ")\n",
    "\n",
    "STEP_SIZE_TEST=random_test_generator.n//random_test_generator.batch_size\n",
    "# random_test_generator.reset()\n",
    "\n",
    "#predicting output label for random test dataset\n",
    "pred = model.predict(random_test_generator, steps=STEP_SIZE_TEST, verbose=1)\n",
    "\n",
    "# converting the predicted output data and getting desired ouput\n",
    "random_animal_pred = [ (1 if y[0] > 0.5 else 0) for y in pred]\n",
    "\n",
    "# saving the predicted and desired output in a dataframe\n",
    "examplenames = random_test_generator.filenames\n",
    "predictions = pd.DataFrame({\"Filenames\":examplenames, \"Predicted Output\": random_animal_pred})\n",
    "\n",
    "# counting the number of prediction for each label\n",
    "unique, counts = np.unique(random_animal_pred, return_counts=True)\n",
    "result = dict(zip(unique, counts))\n",
    "print(result)\n",
    "# print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "unlikely-tragedy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 128, 128, 3)\n"
     ]
    }
   ],
   "source": [
    "# creating rand_test array that contains images built from random generator numbers as pixels\n",
    "\n",
    "rand_test = [ ]\n",
    "for _ in range(5000):\n",
    "    example = np.random.rand(128,128,3)\n",
    "    rand_test.append(example)\n",
    "\n",
    "rand_test = np.array(rand_test)\n",
    "\n",
    "print(rand_test.shape)\n",
    "# print(rand_test[1])\n",
    "# plt.imshow(rand_test[1])\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "reliable-leeds",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 18s 176ms/step\n",
      "{0: 4880, 1: 120}\n"
     ]
    }
   ],
   "source": [
    "# prediciting on rand_test array\n",
    "\n",
    "rand_pred = model.predict(rand_test, steps = len(rand_test)//50, verbose=1)\n",
    "random_pred = [ (1 if y[0] > 0.5 else 0) for y in rand_pred]\n",
    "\n",
    "unique, counts = np.unique(random_pred, return_counts = True)\n",
    "result = dict(zip(unique, counts))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "three-mauritius",
   "metadata": {},
   "source": [
    "<!-- Inconclusive trash bucket, Prediction split- Cat: 55-60 & Dog: 45-40 -->\n",
    "Label 0 i.e Cats seems to be the trash bucket"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "instructional-paragraph",
   "metadata": {},
   "source": [
    "# Training the model on unshuffled dataset and predicting the trash bucket"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "younger-decision",
   "metadata": {},
   "source": [
    "Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "banner-breed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# extracting and preprocessing data\n",
    "filenames = os.listdir('dogvscat_traindata/train')\n",
    "\n",
    "cat_train_df = pd.DataFrame( columns= ['filename', 'label'])\n",
    "dog_train_df = pd.DataFrame( columns= ['filename', 'label'])\n",
    "\n",
    "cat_val_df = pd.DataFrame( columns= ['filename', 'label'])\n",
    "dog_val_df = pd.DataFrame( columns= ['filename', 'label'])\n",
    "\n",
    "dog_train = 0\n",
    "cat_train = 0\n",
    "\n",
    "for filename in filenames:\n",
    "    category = filename.split('.')[0]\n",
    "    \n",
    "    if category == 'dog' and dog_train < 10000:\n",
    "        dog_train+=1\n",
    "        dog_train_df= dog_train_df.append({'filename': filename, 'label' : '1'}, ignore_index=True)\n",
    "    elif category == 'dog' and dog_train >= 10000:\n",
    "        dog_val_df= dog_val_df.append({'filename': filename, 'label' : '1'}, ignore_index=True)\n",
    "        \n",
    "    if category == 'cat' and cat_train < 10000:\n",
    "        cat_train+=1\n",
    "        cat_train_df= cat_train_df.append({'filename': filename, 'label' : '0'}, ignore_index=True)\n",
    "    elif category == 'cat' and cat_train >= 10000:\n",
    "        cat_val_df= cat_val_df.append({'filename': filename, 'label' : '0'}, ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "august-instrument",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    10000\n",
      "Name: label, dtype: int64\n",
      "1    2500\n",
      "Name: label, dtype: int64\n",
      "0    10000\n",
      "Name: label, dtype: int64\n",
      "0    2500\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# print(dog_train_df)\n",
    "print(dog_train_df['label'].value_counts())\n",
    "\n",
    "# print(dog_val_df)\n",
    "print(dog_val_df['label'].value_counts())\n",
    "\n",
    "# print(cat_train_df)\n",
    "print(cat_train_df['label'].value_counts())\n",
    "\n",
    "# print(cat_val_df)\n",
    "print(cat_val_df['label'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "smaller-footwear",
   "metadata": {},
   "source": [
    "# Part 1: Cat images first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "champion-mongolia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    10000\n",
      "1    10000\n",
      "Name: label, dtype: int64\n",
      "0    2500\n",
      "1    2500\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# creating a new dataframe with cats images first and then dog images\n",
    "\n",
    "train1_df = pd.concat([cat_train_df, dog_train_df], ignore_index = True) # 20000 examples\n",
    "valid1_df = pd.concat([cat_val_df, dog_val_df], ignore_index = True) # 5000 exmaples\n",
    "\n",
    "# print(train1_df)\n",
    "print(train1_df['label'].value_counts())\n",
    "\n",
    "# print(valid1_df)\n",
    "print(valid1_df['label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "touched-welcome",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 validated image filenames belonging to 2 classes.\n",
      "Found 5000 validated image filenames belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# generating and augmentating training and validation data through ImageDataGenerator\n",
    "\n",
    "train_data_gen = ImageDataGenerator(\n",
    "    rotation_range=15,\n",
    "    rescale=1./255,\n",
    "    shear_range=0.1,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1\n",
    ")\n",
    "\n",
    "train1_generator = train_data_gen.flow_from_dataframe(\n",
    "    train1_df, \n",
    "    \"dogvscat_traindata/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='label',\n",
    "    target_size=(128,128),\n",
    "    class_mode='binary',\n",
    "    shuffle = False,\n",
    "    batch_size=20\n",
    ")\n",
    "\n",
    "val_data_gen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "valid1_generator = val_data_gen.flow_from_dataframe(valid1_df, \n",
    "    \"dogvscat_traindata/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='label',\n",
    "    target_size=(128,128),\n",
    "    class_mode='binary',\n",
    "    shuffle = False,                                           \n",
    "    batch_size=20\n",
    ")\n",
    "\n",
    "STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
    "STEP_SIZE_VALID=val_generator.n//val_generator.batch_size\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wrong-comfort",
   "metadata": {},
   "source": [
    "Model Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "empty-pointer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_15 (Conv2D)           (None, 128, 128, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_30 (Dropout)         (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 64, 64, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_16 (MaxPooling (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_31 (Dropout)         (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_17 (MaxPooling (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_32 (Dropout)         (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 64)                524352    \n",
      "_________________________________________________________________\n",
      "dropout_33 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "dropout_34 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_35 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 560,385\n",
      "Trainable params: 560,385\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# defining and compling the model\n",
    "\n",
    "catfirst_model = Sequential()\n",
    "\n",
    "catfirst_model.add(Conv2D(filters=32, kernel_size=3, input_shape = (128,128,3), padding= 'same', activation = 'relu'))\n",
    "catfirst_model.add(MaxPooling2D(pool_size= (2,2)))\n",
    "catfirst_model.add(Dropout(0.25))\n",
    "\n",
    "catfirst_model.add(Conv2D(filters=32, kernel_size=3, padding= 'same', activation = 'relu'))\n",
    "catfirst_model.add(MaxPooling2D(pool_size= (2,2)))\n",
    "catfirst_model.add(Dropout(0.25))\n",
    "\n",
    "catfirst_model.add(Conv2D(filters=32, kernel_size=3, padding= 'same', activation = 'relu'))\n",
    "catfirst_model.add(MaxPooling2D(pool_size= (2,2)))\n",
    "catfirst_model.add(Dropout(0.25))\n",
    "\n",
    "catfirst_model.add(Flatten())\n",
    "catfirst_model.add(Dense(64, activation ='relu'))\n",
    "catfirst_model.add(Dropout(0.25))\n",
    "\n",
    "catfirst_model.add(Dense(128, activation ='relu'))\n",
    "catfirst_model.add(Dropout(0.25))\n",
    "\n",
    "catfirst_model.add(Dense(64, activation ='relu'))\n",
    "catfirst_model.add(Dropout(0.25))\n",
    "\n",
    "catfirst_model.add(Dense(1, activation ='sigmoid'))\n",
    "\n",
    "catfirst_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy'])\n",
    "\n",
    "catfirst_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "early-links",
   "metadata": {},
   "source": [
    "Training the dogvscat model with (train1_df, valid1_df) which contain cats images first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "junior-display",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "1000/1000 [==============================] - 380s 379ms/step - loss: 0.2337 - binary_accuracy: 0.9942 - val_loss: 569.2219 - val_binary_accuracy: 0.5000\n",
      "Epoch 2/6\n",
      "1000/1000 [==============================] - 377s 377ms/step - loss: 25.6298 - binary_accuracy: 0.8678 - val_loss: 12.6720 - val_binary_accuracy: 0.5000\n",
      "Epoch 3/6\n",
      "1000/1000 [==============================] - 375s 375ms/step - loss: 1.0364 - binary_accuracy: 0.8505 - val_loss: 29.2379 - val_binary_accuracy: 0.5000\n",
      "Epoch 4/6\n",
      "1000/1000 [==============================] - 3617s 4s/step - loss: 1.5358 - binary_accuracy: 0.9026 - val_loss: 35.9968 - val_binary_accuracy: 0.5000\n",
      "Epoch 5/6\n",
      "1000/1000 [==============================] - 377s 376ms/step - loss: 2.0625 - binary_accuracy: 0.9172 - val_loss: 3.6855 - val_binary_accuracy: 0.5000\n",
      "Epoch 6/6\n",
      "1000/1000 [==============================] - 377s 377ms/step - loss: 0.8453 - binary_accuracy: 0.7737 - val_loss: 2.5662 - val_binary_accuracy: 0.5000\n"
     ]
    }
   ],
   "source": [
    "hist1_model = catfirst_model.fit(train1_generator, steps_per_epoch=STEP_SIZE_TRAIN, epochs=6, verbose=1, validation_data=valid1_generator, validation_steps=STEP_SIZE_VALID, shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "outdoor-likelihood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code to show the training and validation output for animal model.\n",
    "\n",
    "accuracy = hist1_model.history['binary_accuracy']\n",
    "val_accuracy = hist1_model.history['val_binary_accuracy']\n",
    "loss = hist1_model.history['loss']\n",
    "val_loss = hist1_model.history['val_loss']\n",
    "epochs = range(len(accuracy))\n",
    "\n",
    "fig = plt.figure(figsize=(10,8)) \n",
    "plt.plot(epochs, accuracy, 'b', label = 'Training accuracy')\n",
    "plt.plot(epochs,val_accuracy,'r', label= 'Validation accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "# txt = \"Fig 1.b : \"               # captioning the figure\n",
    "# fig.text(.5, .05, txt, ha='center')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "plt.plot(epochs, loss, 'b', label = 'Training accuracy')\n",
    "plt.plot(epochs,val_loss,'r', label= 'Validation accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "nominated-number",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info:: {'Cat' = 0, 'Dog'= 1}\n"
     ]
    }
   ],
   "source": [
    "# catfirst_model.save('dogvscat/catfirst_dogvscat.model')\n",
    "catfirst_model = load_model('dogvscat/catfirst_dogvscat.model')\n",
    "print(\"Info:: {'Cat' = 0, 'Dog'= 1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "facial-practitioner",
   "metadata": {},
   "source": [
    "Testing the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "small-hollywood",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 500 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# code to test DogVsCat classfication model on testing dataset\n",
    "\n",
    "# creating test generator to test the model\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "test1_generator = test_datagen.flow_from_directory(\n",
    "    'dogvscat/test_data/test/',\n",
    "    target_size=(128, 128),\n",
    "    class_mode=None,\n",
    "    shuffle=False,\n",
    "    batch_size=1,\n",
    ")\n",
    "\n",
    "STEP_SIZE_TEST=test1_generator.n//test1_generator.batch_size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "graphic-firmware",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500/500 [==============================] - 4s 8ms/step\n",
      "Accuracy of dogvscat Model: 50.0 %.\n"
     ]
    }
   ],
   "source": [
    "#predicting output label for test1 dataset\n",
    "pred1 = catfirst_model.predict(test1_generator, steps=STEP_SIZE_TEST, verbose=1)\n",
    "\n",
    "# converting the predicted output data and getting desired ouput\n",
    "animal1_pred = [ (1 if y[0] > 0.5 else 0) for y in pred1]\n",
    "animal1_desired = test1_generator.classes\n",
    "\n",
    "# saving the predicted output in a text file\n",
    "examplenames = test1_generator.filenames\n",
    "results1 = pd.DataFrame({\"Filenames\":examplenames, \"Desired Output\": animal1_desired, \"Predicted Output\": animal1_pred})\n",
    "\n",
    "accuracy = accuracy_score(animal1_desired,animal1_pred)   \n",
    "print(\"Accuracy of dogvscat Model: \" + str((accuracy *100))+ \" %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "charged-cemetery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0]\n",
      " [250 250]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code block to print the Gender Confusion Matrix.\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "animal1_confusion_matrix = confusion_matrix(animal1_pred, animal1_desired)\n",
    "print(animal1_confusion_matrix)\n",
    "\n",
    "plt.imshow(animal1_confusion_matrix, cmap=plt.cm.Blues)\n",
    "plt.xlabel(\"Predicted labels\")\n",
    "plt.ylabel(\"True labels\")\n",
    "plt.xticks([0, 1], ['Cat', 'Dog'])\n",
    "plt.yticks([0, 1], ['Cat', 'Dog'])\n",
    "plt.title('Animal Confusion Matrix')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "classified-cabinet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 128, 128, 3)\n"
     ]
    }
   ],
   "source": [
    "# creating rand_test array that contains images built from random generator numbers as pixels\n",
    "rand1_test = [ ]\n",
    "\n",
    "for _ in range(5000):\n",
    "    example = np.random.rand(128,128,3)\n",
    "    rand1_test.append(example)\n",
    "\n",
    "rand1_test = np.array(rand1_test)\n",
    "\n",
    "print(rand1_test.shape)\n",
    "# plt.imshow(rand_test[1])\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "sudden-script",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 20s 197ms/step\n",
      "{1: 5000}\n"
     ]
    }
   ],
   "source": [
    "# prediciting on rand_test array\n",
    "\n",
    "rand1_pred = catfirst_model.predict(rand1_test, steps = len(rand1_test)//50, verbose=1)\n",
    "\n",
    "random1_pred = [ (1 if y[0] > 0.5 else 0) for y in rand1_pred]\n",
    "\n",
    "unique, counts = np.unique(random1_pred, return_counts = True)\n",
    "result = dict(zip(unique, counts))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "retained-belief",
   "metadata": {},
   "source": [
    "Since the model was trained with dogs at the end it will predict any image as a dog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "historical-hacker",
   "metadata": {},
   "source": [
    "# Part 2: Dog images first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "forced-queens",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    10000\n",
      "1    10000\n",
      "Name: label, dtype: int64\n",
      "0    2500\n",
      "1    2500\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train2_df = pd.concat([dog_train_df, cat_train_df], ignore_index = True) # 20000 examples\n",
    "valid2_df = pd.concat([dog_val_df, cat_val_df], ignore_index = True) # 5000 exmaples\n",
    "\n",
    "# print(train2_df)\n",
    "print(train2_df['label'].value_counts())\n",
    "\n",
    "# print(valid2_df)\n",
    "print(valid2_df['label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "statewide-designation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 validated image filenames belonging to 2 classes.\n",
      "Found 5000 validated image filenames belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# generating and augmentating training and validation data through ImageDataGenerator\n",
    "\n",
    "train_data_gen = ImageDataGenerator(\n",
    "    rotation_range=15,\n",
    "    rescale=1./255,\n",
    "    shear_range=0.1,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1\n",
    ")\n",
    "\n",
    "train2_generator = train_data_gen.flow_from_dataframe(\n",
    "    train2_df, \n",
    "    \"dogvscat_traindata/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='label',\n",
    "    target_size=(128,128),\n",
    "    class_mode='binary',\n",
    "    shuffle = False,\n",
    "    batch_size=20\n",
    ")\n",
    "\n",
    "val_data_gen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "valid2_generator = val_data_gen.flow_from_dataframe(valid2_df, \n",
    "    \"dogvscat_traindata/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='label',\n",
    "    target_size=(128,128),\n",
    "    class_mode='binary',\n",
    "    shuffle = False,                                           \n",
    "    batch_size=20\n",
    ")\n",
    "\n",
    "STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
    "STEP_SIZE_VALID=val_generator.n//val_generator.batch_size\n",
    "\n",
    "# print(train2_generator.filenames)\n",
    "# print(valid2_generator.filenames)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollow-packet",
   "metadata": {},
   "source": [
    "Model Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "minute-community",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_18 (Conv2D)           (None, 128, 128, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_18 (MaxPooling (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_36 (Dropout)         (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 64, 64, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_19 (MaxPooling (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_37 (Dropout)         (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_20 (MaxPooling (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_38 (Dropout)         (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_6 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 64)                524352    \n",
      "_________________________________________________________________\n",
      "dropout_39 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "dropout_40 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_41 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 560,385\n",
      "Trainable params: 560,385\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# defining and compling the model\n",
    "\n",
    "dogfirst_model = Sequential()\n",
    "\n",
    "dogfirst_model.add(Conv2D(filters=32, kernel_size=3, input_shape = (128,128,3), padding= 'same', activation = 'relu'))\n",
    "dogfirst_model.add(MaxPooling2D(pool_size= (2,2)))\n",
    "dogfirst_model.add(Dropout(0.25))\n",
    "\n",
    "dogfirst_model.add(Conv2D(filters=32, kernel_size=3, padding= 'same', activation = 'relu'))\n",
    "dogfirst_model.add(MaxPooling2D(pool_size= (2,2)))\n",
    "dogfirst_model.add(Dropout(0.25))\n",
    "\n",
    "dogfirst_model.add(Conv2D(filters=32, kernel_size=3, padding= 'same', activation = 'relu'))\n",
    "dogfirst_model.add(MaxPooling2D(pool_size= (2,2)))\n",
    "dogfirst_model.add(Dropout(0.25))\n",
    "\n",
    "dogfirst_model.add(Flatten())\n",
    "dogfirst_model.add(Dense(64, activation ='relu'))\n",
    "dogfirst_model.add(Dropout(0.25))\n",
    "\n",
    "dogfirst_model.add(Dense(128, activation ='relu'))\n",
    "dogfirst_model.add(Dropout(0.25))\n",
    "\n",
    "dogfirst_model.add(Dense(64, activation ='relu'))\n",
    "dogfirst_model.add(Dropout(0.25))\n",
    "\n",
    "dogfirst_model.add(Dense(1, activation ='sigmoid'))\n",
    "\n",
    "dogfirst_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy'])\n",
    "\n",
    "dogfirst_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fiscal-johnson",
   "metadata": {},
   "source": [
    "Training the dogvscat model with (train2_df, valid2_df) with dog images first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "controlled-array",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "1000/1000 [==============================] - 440s 439ms/step - loss: 0.1990 - binary_accuracy: 0.9909 - val_loss: 70.5050 - val_binary_accuracy: 0.5000\n",
      "Epoch 2/6\n",
      "1000/1000 [==============================] - 415s 415ms/step - loss: 4.0073 - binary_accuracy: 0.8882 - val_loss: 13.3986 - val_binary_accuracy: 0.5000\n",
      "Epoch 3/6\n",
      "1000/1000 [==============================] - 397s 397ms/step - loss: 0.9345 - binary_accuracy: 0.9112 - val_loss: 4.4361 - val_binary_accuracy: 0.5000\n",
      "Epoch 4/6\n",
      "1000/1000 [==============================] - 392s 392ms/step - loss: 0.7205 - binary_accuracy: 0.8277 - val_loss: 3.6722 - val_binary_accuracy: 0.5000\n",
      "Epoch 5/6\n",
      "1000/1000 [==============================] - 388s 388ms/step - loss: 0.9375 - binary_accuracy: 0.7126 - val_loss: 2.8636 - val_binary_accuracy: 0.5000\n",
      "Epoch 6/6\n",
      "1000/1000 [==============================] - 389s 389ms/step - loss: 0.8004 - binary_accuracy: 0.7355 - val_loss: 2.2515 - val_binary_accuracy: 0.5000\n"
     ]
    }
   ],
   "source": [
    "hist2_model = dogfirst_model.fit(train2_generator, steps_per_epoch=STEP_SIZE_TRAIN, epochs=6, verbose=1, validation_data=valid2_generator, validation_steps=STEP_SIZE_VALID, shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "available-actress",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wFYqG/MtTSjuv6JiVlZWpqqpqdZcqSVKjvfjii2y77ba5y8huzpw5dO7cmZQS3/nOd+jbty+nn3567rJWSnV1dc2ZlX379v1Mx2ro70VEPJVSanBNkHIuUXEz8DiwdURMi4hvRsQJEXFCaZdxwOvAFOAa4NvlqmVlzJsHH3yQuwpJkpq+a665hv79+7P99tsza9YsvvWtb+UuaaVMnjyZLbfckr333vszB7BVUdaRsHIo90jYnXfCsGHQrx8MGVLc9twT1luvbG8pSWpmHAlTQ5rMSFhzte22cOGFsOGGcM01MGIEdO8OAwbAd78Lf/kLlHr5JEmSVpkhrJ4ttoAf/hDuuw8++ggefRT+8z+LkbArr4Thw6FbN9hpJzjzTBg3DmbPzl21JElqbrx25HJ06AC77Vbcfvzjol/siSfgwQeL2+WXw8UXQ9u2UFlZTF3utVexf50lSyRJkj7FkbCV0LFj0R92/vnw8MPFSNn48fCDH0C7dkUgGzoU1l0XBg+Gc84pni8tgSJJklTDEPYZdOoEe+8NP/kJPPYYzJwJ99wDZ5wB1dXw85/DvvsWU5l77AHnnQcPPVSMqEmStKqGDBnCPffcs9S2Sy+9lBNPPHGZr9lrr71YcmLbV77yFWbOnPmpfc4///ya9bqW5fbbb69ZYwvg3HPPZfz48StRvZYwhK1G66wDX/4y/Oxn8PjjxUjZuHFw6qnwySfw058WU5brrlv8ecEFRc/Z/Pm5K5ckNSejRo1i7NixS20bO3bsMi+iXd+4ceNYd911V+m964ewCy64gH322WeVjpXLklX7czOElVGXLrD//vCLX8DEifDhh8XZld/5TnGG5fnnFyNk660H++xTnJU5YQIs4xqrkiQBcPDBB3PnnXeyYMECAKZOnco777zD7rvvzoknnkhlZSXbb7895513XoOv7927Nx+UFsW88MIL2Wqrrdhtt914+eWXa/a55ppr2GmnnaioqOBrX/sac+fOZcKECdxxxx2ceeaZ9O/fn9dee43Ro0fzxz/+EYD777+fAQMG0K9fP4455hjml0YZevfuzXnnncfAgQPp168fL7300qdqmjp1KrvvvjsDBw5k4MCBTJgwoea5iy66iH79+lFRUcHZZ58NwJQpU9hnn32oqKhg4MCBvPbaazz00EMMGzas5nUnnXRSzSWbevfuzfe///2ahVkb+nwA7733HiNGjKCiooKKigomTJjAueeey6V1LhL6ox/9iMsuu2ylfrOG2Ji/BnXtWqxBtuTvx4cfFiNhSxr9zzmn2L7OOkVP2ZJ1ygYNKnrOJElN0GmnwaRJq/eY/fsv98rg3bp1Y+edd+auu+7iwAMPZOzYsYwcOZKI4MILL6Rbt24sXryYvffem+eee44dd9yxweM89dRTjB07lkmTJrFo0SIGDhzIoEGDADjooIM47rjjADjnnHO49tprOfnkkxk+fDjDhg3j4IMPXupY8+bNY/To0dx///1stdVWfOMb3+Cqq67itNNOA2D99dfn6aef5sorr+Tiiy/mt7/97VKv32CDDbjvvvvo2LEjr776KqNGjaKqqoq77rqLP//5zzzxxBN06tSJDz/8EIAjjjiCs88+mxEjRjBv3jyqq6t56623lvu1du/enaeffhqAGTNmNPj5TjnlFPbcc09uu+02Fi9ezJw5c9hkk0046KCDOO2006iurmbs2LE8+eSTy32vxnAkLKNu3eDAA4v/zp59FqZPhz/9CY4+Gt5+u2j4/8IXiv0OOAB++UuoqoImMooqScqo7pRk3anIW2+9lYEDBzJgwABeeOGFpaYO63v00UcZMWIEnTp14nOf+xzDhw+vee75559n9913p1+/ftx000288MILy63n5Zdfpk+fPmy11VYAHHXUUTzyyCM1zx900EEADBo0qOai33UtXLiQ4447jn79+nHIIYfU1D1+/HiOPvpoOnXqBBQBdPbs2bz99tuMGDECgI4dO9Y8vzyHHnroCj/fAw88UNNb17ZtW7p27Urv3r3p3r07zzzzDPfeey8DBgyge/fuK3y/FXF8pQlZf3046KDiBvD++8VZmEtGysaNK7Z37VpMY+61VzFSVlEBbYzTkpTHckasyunAAw/k9NNP5+mnn2bu3LkMGjSIN954g4svvpiJEyey3nrrMXr0aOat4tlgo0eP5vbbb6eiooLrr7+ehx566DPV26FDB6AINg1dUPySSy5hww035Nlnn6W6upqOHTuu9Hu0a9eO6urqmsf1P/s6ddaPWtnPd+yxx3L99dfz7rvvcswxx6x0bQ3xn+4mbIMN4JBDikViX3wR3nkH/vAHOPRQeOkl+N73YODAIryNGFGsW/bcc8WZmZKklq1z584MGTKEY445pmYU7OOPP2adddaha9euvPfee9x1113LPcYee+zB7bffzieffMLs2bP5y1/+UvPc7Nmz2XjjjVm4cCE33XRTzfYuXbowu4FVyrfeemumTp3KlClTABgzZgx77rlnoz/PrFmz2HjjjWnTpg1jxoypaZ7fd999ue6662p6tj788EO6dOlCz549uf322wGYP38+c+fOZfPNN2fy5MnMnz+fmTNncv/99y/z/Zb1+fbee2+uuuoqoGjgn1W6TM6IESO4++67mThxIvvtt1+jP9fyGMKakY03hlGj4De/gVdegbfegjFjigD23HPFWZgVFcUllw4+GK64AiZPhmZ2eVBJUiONGjWKZ599tiaEVVRUMGDAALbZZhsOP/xwBg8evNzXDxw4kEMPPZSKigr2339/dtppp5rnfvKTn7DLLrswePBgttlmm5rthx12GL/85S8ZMGAAr732Ws32jh07ct1113HIIYfQr18/2rRpwwknnNDoz/Ltb3+bG264gYqKCl566aWaUauhQ4cyfPhwKisr6d+/f80SGmPGjOHyyy9nxx13ZNddd+Xdd9+lV69ejBw5kh122IGRI0cyYMCAZb7fsj7fZZddxoMPPki/fv0YNGhQzbRo+/btGTJkCCNHjqRt27aN/lzL4wW8W5A33yzWIVsyffnPfxbbN9igdupyyBDYaiuIyFmpJDVvXsC79amurq45s7Jv374N7rOyF/C2J6wF2XxzOOqo4pYSTJ1aG8gefBBuvbXYb+ONlw5ln/+8oUySpGWZPHkyw4YNY8SIEcsMYKvCENZCRUCfPsXtmGOKUPbaa0uHsptvLvbt2XPpUNanT9bSJUlqUrbbbjtef/311X5cQ1grEQFbblncjjuuCGUvv1w7fXnvvfD73xf7br557cXIhwyBzTbLWbkkSS2TIayVioBttiluJ5xQhLLJk4tA9tBDxcr+pUWG2WKLpUPZpptmLFySmoiUEmEvh0pWpcfeECagCGXbb1/cTjqpWObi+edrQ9n//R9ce22xb9++tVOXe+0FG22Us3JJWvM6duzIjBkz6N69u0FMpJSYMWPGSq9t5tmRapTFi4tlMJaEsocfho8/Lp7bZpulQ1mPHjkrlaTyW7hwIdOmTVvlhVDV8nTs2JGePXuy1lprLbV9eWdHGsK0ShYvhmeeqW3yf/RRmDOneG777WtD2Z57wmq4soMkSc2SIUxlt3AhPP10bSh77DEoLW7MjjvWhrI99oD11stbqyRJa4ohTGvcggXFxcaXhLK//Q3mzSt6zwYMqG3y33334lqYkiS1RIYwZTd/PjzxRO2SGI8/Xmxr0wYGDartJ9ttN+jSJXe1kiStHoYwNTnz5hVBbEmj/9//Xkxptm0LO+1UG8oGD4Y6F72XJKlZMYSpyZs7FyZMqA1lTz4JixbBWmvBzjsXoWzLLaFjx8bf2rf3ckySpLwMYWp25swp+siWhLKqquKMzJW1MqGtsbcOHRq/nyFQklo3L+CtZqdzZ9hvv+IGMHs2TJ9eTGPOn1/8uTpuH3647OcWLPjsn6Oxga0cgbBDh6LnTpLUNBnC1Cx06bLmG/arqxsX+D5LKJw5c/nH/azaty9vCOzWDfr1M+xJ0qowhEnL0KYNrL12ccuhuroYjVuZULeygfDjj+H995f9fGP06AH77w8HHABf/jKsu25ZvxZJajEMYVIT1aZN7YhTDimtOAROmwZ33QV//SvceGNxdutuuxWB7IADYNtt7YuTpGWxMV/SZ7Z4cbEO3J13Frdnny229+5dG8j22ivfqKIk5eLZkZLWqGnTYNy4IpCNH18sQbL22rD33kUg+8pXYLPNclcpSeVnCJOUzbx58PDDtaNkr79ebO/Xr3aU7AtfgHY2R0hqgQxhkpqElODll2sD2aOPFovyrrceDB1aBLL99oP1189dqSStHoYwSU3SrFlw331FIBs3rjhTs00b2GWX2lGyigqb+yU1X4YwSU1edTU89VTtKNmS/8w33bToITvggKKnrHPnvHVK0sowhElqdt59t1j+4s474d57i6smtG9fnGW5ZJTs85/PXaUkLZ8hTFKztmABPPZY7SjZyy8X27feujaQ7bZbEdIkqSkxhElqUV57rTaQPfRQEdK6dClW7D/ggGIF/402yl2lJBnCJLVgc+bA/ffXhrJ33im2V1bWjpINGuT1LSXlYQiT1CqkVKzWvySQ/f3vxbYNN6y9vuW++0LXrrkrldRaGMIktUoffAB3310Esrvvhpkzi0Vhd9+9dpRs661dAkNS+RjCJLV6ixbB44/XjpI9/3yxfYstagPZnnvmu2C6pJbJECZJ9bz5Zu31LR94AD75BDp1gn32qb2+Zc+euauU1NwZwiRpOT75BB58sHaU7M03i+0VFbWjZLvsAm3b5q1TUvNjCJOkRkoJJk+uDWR/+xssXgzduy99fctu3XJXKqk5MIRJ0ir66KNixf477yxW8P/gg2K5i113rZ227NfP5n5JDTOESdJqsHgxTJxYO0r2zDPF9l69lr6+ZadOeeuU1HQYwiSpDN55p7a5/7774N//hg4dYMiQ2l6yPn1yVykpJ0OYJJXZ/PnwyCO1o2RTphTbt922NpANHgxrrZW3TklrliFMktawV1+tDWQPPwwLFxYr9de9vuUGG+SuUlK5GcIkKaPZs2H8+CKQjRsH//pX0ci/0061o2QDBnh9S6klMoRJUhNRXQ2TJtWOkj35ZLEsxkYb1Tb377svdOmSu1JJq4MhTJKaqPffr72+5T33wKxZRd/YHnvUjpJttVXuKiWtKkOYJDUDCxfChAm1o2STJxfbt9yyNpDtsUdxBqak5sEQJknN0BtvLH19y/nzoXPnpa9vuckmuauUtDyGMElq5ubOLYLYklGyt94qtg8YUDtKttNOXt9SamoMYZLUgqQEzz9fG8gmTCga/tdfv1j64sgji9EyL6Uk5be8EOYJ0ZLUzEQU16s8+2x49FGYPh3+8IfiwuJ33lmsRda/P4wZAwsW5K5W0rIYwiSpmevWDUaNgt//vriU0rXXFk3+3/gGbLEFXHxxcdalpKbFECZJLUiHDnDMMbXTlVttBWeeWVxk/IwzanvJJOVnCJOkFqhNm+LsyQcegKqqonH/0kuLkbEjjywWjJWUlyFMklq4QYPg5puLi4qfdBLcdltxVuW++8K99xaN/pLWPEOYJLUSvXvDJZcUU5I/+xm88ELRzF9RATfeaBO/tKYZwiSplVlvveLMyjfegOuuK5a3OOoo6NMHfvELm/ilNcUQJkmtVIcOMHo0/OMfxcr822wD3/9+0cT/ve/BP/+Zu0KpZTOESVIrF1Es8nr//UUT/7BhcNllRRP/178OzzyTu0KpZTKESZJqDBpULPz62mtwyinw5z/DwIHFCvx3320Tv7Q6GcIkSZ+y+ebw618XTfw//zlMnlyMlu24I9xwg0380upgCJMkLdO66xZ9YlOnwvXXF9tGjy6a+C+6CGbOzFaa1OwZwiRJK9S+fXEG5XPPwV13wbbbFmdY9uoF3/0uvPlm7gql5qesISwihkbEyxExJSLObuD5zSPi/oh4LiIeioie5axHkvTZRMDQoTB+PDz9NBx4IFx+OXz+83D44cU2SY1TthAWEW2BK4D9ge2AURGxXb3dLgZuTCntCFwA/Kxc9UiSVq8BA4qLhr/+Opx6KvzlL0Vj/957F6NlNvFLy1fOkbCdgSkppddTSguAscCB9fbZDnigdP/BBp6XJDVxm20Gv/pV0cR/0UXw0kvFdSv79Sv6yObPz12h1DSVM4RtCrxV5/G00ra6ngUOKt0fAXSJiO5lrEmSVCbrrgtnnVWsxH/DDcVFxI8+umji//nPbeKX6svdmH8GsGdEPAPsCbwNLK6/U0QcHxFVEVE1ffr0NV2jJGkltG8P3/gGPPss3HMPbL89/OAHRRP/6afbxC8tUc4Q9jbQq87jnqVtNVJK76SUDkopDQB+VNo2s/6BUkpXp5QqU0qVPXr0KGPJkqTVJQK+/GW4775i1f2vfhX++7+LJv5Ro+Cpp3JXKOVVzhA2EegbEX0ioj1wGHBH3R0iYv2IWFLDD4DflbEeSVIm/fvDmDFFE/9pp8Gdd0JlJXzpS8V1K6urc1corXllC2EppUXAScA9wIvArSmlFyLigogYXtptL+DliHgF2BC4sFz1SJLy69ULLr64aOL/5S/hlVfggAOKJv7rrrOJX61LpGZ2DnFlZWWqqqrKXYYkaTVYsABuuaUIZs89BxttVFyz8oQTYL31clcnfXYR8VRKqbKh53I35kuSWrH27eHII2HSJLj33mJE7Ic/LEbMTjutuFyS1FIZwiRJ2UXAvvsWQWzSJDjoILjiiqKJ/7DDwAkQtUSGMElSk1JRATfeWKw39r3vFavv77QTDBlSNPTbxK+WwhAmSWqSevaEX/wC/vnPomdsyhQYNgx22AF+9zub+NX8GcIkSU1a167FiNjrrxfLXLRvD9/8JvTuDf/1X/Dhh7krlFaNIUyS1CystRZ8/evFwq/33VdMW/7oR8W1K089tZi+lJoTQ5gkqVmJgH32gbvvLi6N9LWvwZVXwpZbwqGHwsSJuSuUGscQJklqtnbcsbhY+BtvwBlnFMFs551hzz3hr3+1iV9NmyFMktTs9ewJF11UrMT/q18Voew//qO4ePhvfwvz5uWuUPo0Q5gkqcX43Ofgu9+F116Dm26Cjh3huOOKJv4LL4QZM3JXKNUyhEmSWpy11oLDD4enn4bx42HAADjnnKKJ/+STizMtpdwMYZKkFisC9t67WPD1uefgkEPgN7+Bvn1h5Eh48sncFao1M4RJklqFfv3g+uuLfrEzzywukbTLLrDHHvCXv9jErzXPECZJalU23RR+/vOiif/Xv4Y334Thw2G77eCaa2zi15pjCJMktUpdusDppxeXQ/rDH6BTJzj+eNh8c/jpT23iV/kZwiRJrdpaa8GoUfDUU3D//TBoEPz4x9CrF5x0UnGmpVQOhjBJkiia+L/0JRg3Dv7xDzjsMLj6athqq6Kh/4kncleolsYQJklSPTvsAL/7HUydCmedVVyr8gtfKJr477jDJn6tHoYwSZKWYZNN4Gc/K5r4L7mkaOI/8EDYdttilOyTT3JXqObMECZJ0gp06QKnnVb0h918M3TuDN/6VtHE/5OfwAcf5K5QzZEhTJKkRmrXrugVq6qCBx6AnXaCc88tVuL/zneKMy2lxoqUUu4aVkplZWWqqqrKXYYkSQC88EKx3tjvfw8LF8JBB8EZZxQ9ZFpadTUsWND42/z5K7f/yh7nu9+FY48t72eOiKdSSpUNPdeuvG8tSVLLtv32cO21xdpi/+//wVVXwZ/+BIMHF2Fs+HBoU+Z5p8WLV19oKWfwWby4PJ+/ffvG3Tp0KKaWl9zv0aM89TSWI2GSJK1Gc+YUZ1ZecklxdmXfvnDkkcV6ZOUKPuU4WzOiNqw0NuSsKACV4zjt2hW1NlXLGwkzhEmSVAaLFhUjYr/8ZbEQ7BJt2+YLLCtza9u2aYeb5sLpSEmS1rB27eDQQ2HkyGJ0bK21ilvbtrkrU1NhCJMkqYwiij4kqT6XqJAkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQMDGGSJEkZGMIkSZIyMIRJkiRlYAiTJEnKwBAmSZKUgSFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJIkKQNDmCRJUgaGMEmSpAwMYZIkSRkYwiRJkjIwhEmSJGVgCJMkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQMDGGSJEkZGMIkSZIyMIRJkiRlYAiTJEnKwBAmSZKUgSFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJIkKQNDmCRJUgZlDWERMTQiXo6IKRFxdgPPbxYRD0bEMxHxXER8pZz1SJIkNRVlC2ER0Ra4Atgf2A4YFRHb1dvtHODWlNIA4DDgynLVI0mS1JSUcyRsZ2BKSun1lNICYCxwYL19EvC50v2uwDtlrEeSJKnJaFfGY28KvFXn8TRgl3r7nA/cGxEnA+sA+5SxHkmSpCYjd2P+KOD6lFJP4CvAmIj4VE0RcXxEVEVE1fTp09d4kZIkSatbOUPY20CvOo97lrbV9U3gVoCU0uNAR2D9+gdKKV2dUqpMKVX26NGjTOVKkiStOeUMYROBvhHRJyLaUzTe31Fvn38CewNExLYUIcyhLkmS1OKVLYSllBYBJwH3AC9SnAX5QkRcEBHDS7t9DzguIp4FbgZGp5RSuWqSJElqKsrZmE9KaRwwrt62c+vcnwwMLmcNkiRJTVHuxnxJkqRWyRAmSZKUgSFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJIkKQNDmCRJUgaGMEmSpAwMYZIkSRkYwiRJkjIwhEmSJGVgCJMkScqgUSEsIv4vIg6ICEObJEnSatDYUHUlcDjwakT8PCK2LmNNkiRJLV6jQlhKaXxK6QhgIDAVGB8REyLi6IhYq5wFSpIktUSNnl6MiO7AaOBY4BngMopQdl9ZKpMkSWrB2jVmp4i4DdgaGAP8R0rpX6WnbomIqnIVJ0mS1FI1KoQBl6eUHmzoiZRS5WqsR5IkqVVo7HTkdhGx7pIHEbFeRHy7PCVJkiS1fI0NYcellGYueZBS+gg4riwVSZIktQKNDWFtIyKWPIiItkD78pQkSZLU8jW2J+xuiib835Qef6u0TZIkSaugsSHs+xTB68TS4/uA35alIkmSpFagUSEspVQNXFW6SZIk6TNq7DphfYGfAdsBHZdsTyltUaa6JEmSWrTGNuZfRzEKtggYAtwI/L5cRUmSJLV0jQ1ha6eU7gcipfRmSul84IDylSVJktSyNbYxf35EtAFejYiTgLeBzuUrS5IkqWVr7EjYqUAn4BRgEPB14KhyFSVJktTSrXAkrLQw66EppTOAOcDRZa9KkiSphVvhSFhKaTGw2xqoRZIkqdVobE/YMxFxB/C/wL+XbEwp/V9ZqpIkSWrhGhvCOgIzgC/V2ZYAQ5gkSdIqaOyK+faBSZIkrUaNXTH/OoqRr6WklI5Z7RVJkiS1Ao2djvxrnfsdgRHAO6u/HEmSpNahsdORf6r7OCJuBh4rS0WSJEmtQGMXa62vL7DB6ixEkiSpNWlsT9hslu4Jexf4flkqkiRJagUaOx3ZpdyFSJIktSaNmo6MiBER0bXO43Uj4qtlq0qSJKmFa2xP2HkppVlLHqSUZgLnlaUiSZKkVqCxIayh/Rq7vIUkSZLqaWwIq4qIX0fE50u3XwNPlbMwSZKklqyxIexkYAFwCzAWmAd8p1xFSZIktXSNPTvy38DZZa5FkiSp1Wjs2ZH3RcS6dR6vFxH3lK0qSZKkFq6x05Hrl86IBCCl9BGumC9JkrTKGhvCqiNisyUPIqI3S6+gL0mSpJXQ2GUmfgQ8FhEPAwHsDhxftqokSZJauMY25t8dEZUUwesZ4HbgkzLWJUmS1KI19gLexwKnAj2BScAXgMeBL5WtMkmSpBassT1hpwI7AW+mlIYAA4CZ5SpKkiSppWtsCJuXUpoHEBEdUkovAVuXryxJkqSWrbGN+dNK64TdDtwXER8Bb5arKEmSpJausY35I0p3z4+IB4GuwN1lq0qSJKmFa+xIWI2U0sPlKESSJKk1aWxPmCRJklYjQ5gkSVIGhjBJkqQMDGGSJEkZGMIkSZIyMIRJkiRlYAiTJEnKwBAmSZKUgSFMkiQpg7KGsIgYGhEvR8SUiDi7gecviYhJpdsrETGznPVIkiQ1FSt92aLGioi2wBXAvsA0YGJE3JFSmrxkn5TS6XX2PxkYUK56JEmSmpJyjoTtDExJKb2eUloAjAUOXM7+o4Cby1iPJElSk1HOELYp8Fadx9NK2z4lIjYH+gAPlLEeSZKkJqOpNOYfBvwxpbS4oScj4viIqIqIqunTp6/h0iRJkla/coawt4FedR73LG1ryGEsZyoypXR1SqkypVTZo0eP1ViiJElSHuUMYROBvhHRJyLaUwStO+rvFBHbAOsBj5exFkmSpCalbCEspbQIOAm4B3gRuDWl9EJEXBARw+vsehgwNqWUylWLJElSU1O2JSoAUkrjgHH1tp1b7/H55axBkiSpKWoqjfmSJEmtiiFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJIkKQNDmCRJUgaGMEmSpAwMYZIkSRkYwiRJkjIwhEmSJGVgCJMkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQMDGGSJEkZGMIkSZIyMIRJkiRlYAiTJEnKwBAmSZKUgSFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJIkKQNDmCRJUgaGMEmSpAwMYZIkSRkYwiRJkjIwhEmSJGVgCJMkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQMDGGSJEkZGMIkSZIyMIRJkiRlYAiTJEnKwBAmSZKUgSFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJIkKQNDmCRJUgaGMEmSpAwMYZIkSRkYwiRJkjIwhEmSJGVgCJMkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQMDGGSJEkZGMIkSZIyMIRJkiRlUNYQFhFDI+LliJgSEWcvY5+RETE5Il6IiD+Usx5JkqSmol25DhwRbYErgH2BacDEiLgjpTS5zj59gR8Ag1NKH0XEBuWqR5IkqSkp50jYzsCUlNLrKaUFwFjgwHr7HAdckVL6CCCl9H4Z65EkSWoyyhnCNgXeqvN4WmlbXVsBW0XE3yLi7xExtIz1SJIkNRllm45ciffvC+wF9AQeiYh+KaWZdXeKiOOB4wE222yzNVyiJEnS6lfOkbC3gV51HvcsbatrGnBHSmlhSukN4BWKULaUlNLVKaXKlFJljx49ylawJEnSmlLOEDYR6BsRfSKiPXAYcEe9fW6nGAUjItanmJ58vYw1SZIkNQllC2EppUXAScA9wIvArSmlFyLigogYXtrtHmBGREwGHgTOTCnNKFdNkiRJTUWklHLXsFIqKytTVVVV7jIkSZJWKCKeSilVNvScK+ZLkiRlYAiTJEnKwBAmSZKUgSFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJIkKQNDmCRJUgaGMEmSpAwMYZIkSRkYwiRJkjIwhEmSJGVgCJMkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQMDGGSJEkZGMIkSZIyMIRJkiRlYAiTJEnKwBAmSZKUgSFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJIkKQNDmCRJUgaGMEmSpAwMYZIkSRkYwiRJkjIwhEmSJGVgCJMkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQMDGGSJEkZGMIkSZIyMIRJkiRlYAiTJEnKwBAmSZKUgSFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJIkKQNDmCRJUgaGMEmSpAwMYZIkSRkYwiRJkjIwhEmSJGVgCJMkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQMDGGSJEkZlDWERcTQiHg5IqZExNkNPD86IqZHxKTS7dhy1iNJktRUtCvXgSOiLXAFsC8wDZgYEXeklCbX2/WWlNJJ5apDkiSpKSrnSNjOwJSU0usppQXAWODAMr6fJElSs1G2kTBgU+CtOo+nAbs0sN/XImIP4BXg9JTSWw3ss+acdhpMmpS1BEmStAb07w+XXprt7XM35v8F6J1S2hG4D7ihoZ0i4viIqIqIqunTp6/RAiVJksqhnCNhbwO96jzuWdpWI6U0o87D3wK/aOhAKaWrgasBKisr0+ots56MiViSJLUe5RwJmwj0jYg+EdEeOAy4o+4OEbFxnYfDgRfLWI8kSVKTUbaRsJTSoog4CbgHaAv8LqX0QkRcAFSllO4ATomI4cAi4ENgdLnqkSRJakoipfLO7q1ulZWVqaqqKncZkiRJKxQRT6WUKht6LndjviRJUqtkCJMkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQMDGGSJEkZGMIkSZIyMIRJkiRlYAiTJEnKwBAmSZKUgSFMkiQpA0OYJElSBoYwSZKkDAxhkiRJGURKKXcNKyUipgNvlvlt1gc+KPN7aOX5uzQ9/iZNk79L0+Nv0jStid9l85RSj4aeaHYhbE2IiKqUUmXuOrQ0f5emx9+kafJ3aXr8TZqm3L+L05GSJEkZGMIkSZIyMIQ17OrcBahB/i5Nj79J0+Tv0vT4mzRNWX8Xe8IkSZIycCRMkiQpA0NYPRExNCJejogpEXF27noEEfG7iHg/Ip7PXYsKEdErIh6MiMkR8UJEnJq7ptYuIjpGxJMR8WzpN/nP3DWpVkS0jYhnIuKvuWsRRMTUiPhHREyKiKpsdTgdWSsi2gKvAPsC04CJwKiU0uSshbVyEbEHMAe4MaW0Q+56BBGxMbBxSunpiOgCPAV81f9W8omIANZJKc2JiLWAx4BTU0p/z1yagIj4LlAJfC6lNCx3Pa1dREwFKlNKWdducyRsaTsDU1JKr6eUFgBjgQMz19TqpZQeAT7MXYdqpZT+lVJ6unR/NvAisGneqlq3VJhTerhW6eb/y24CIqIncADw29y1qGkxhC1tU+CtOo+n4T8s0nJFRG9gAPBE5lJavdKU1yTgfeC+lJK/SdNwKXAWUJ25DtVKwL0R8VREHJ+rCEOYpFUWEZ2BPwGnpZQ+zl1Pa5dSWpxS6g/0BHaOCKfvM4uIYcD7KaWncteipeyWUhoI7A98p9T2ssYZwpb2NtCrzuOepW2S6in1Hf0JuCml9H+561GtlNJM4EFgaOZSBIOB4aUepLHAlyLi93lLUkrp7dKf7wO3UbQjrXGGsKVNBPpGRJ+IaA8cBtyRuSapySk1gV8LvJhS+nXuegQR0SMi1i3dX5viBKOXshYlUko/SCn1TCn1pvg35YGU0tczl9WqRcQ6pROKiIh1gC8DWc6+N4TVkVJaBJwE3EPRaHxrSumFvFUpIm4GHge2johpEfHN3DWJwcCRFP+vflLp9pXcRbVyGwMPRsRzFP+H8r6UksshSJ+2IfBYRDwLPAncmVK6O0chLlEhSZKUgSNhkiRJGRjCJEmSMjCESZIkZWAIkyRJysAQJkmSlIEhTJKWIyL2igiXepC02hnCJEmSMjCESWoRIuLrEfFkaeHY35QuZj0nIi6JiBci4v6I6FHat39E/D0inouI2yJivdL2LSNifEQ8GxFPR8TnS4fvHBF/jIiXIuKm0hUDiIifR8Tk0nEuzvTRJTVThjBJzV5EbAscCgwuXcB6MXAEsA5QlVLaHngYOK/0khuB76eUdgT+UWf7TcAVKaUKYFfgX6XtA4DTgO2ALYDBEdEdGAFsXzrOT8v5GSW1PIYwSS3B3sAgYGJETCo93gKoBm4p7fN7YLeI6Aqsm1J6uLT9BmCP0rXkNk0p3QaQUpqXUppb2ufJlNK0lFI1MAnoDcwC5gHXRsRBwJJ9JalRDGGSWoIAbkgp9S/dtk4pnd/Afqt6nbb5de4vBtqVrjW7M/BHYBiQ5dpzkpovQ5ikluB+4OCI2AAgIrpFxOYU/xt3cGmfw4HHUkqzgI8iYvfS9iOBh1NKs4FpEfHV0jE6RESnZb1hRHQGuqaUxgGnAxVl+FySWrB2uQuQpM8qpTQ5Is4B7o2INsBC4DvAv4GdS8+9T9E3BnAU8D+lkPU6cHRp+5HAbyLigtIxDlnO23YB/hwRHSlG4r67mj+WpBYuUlrV0XlJatoiYk5KqXPuOiSpIU5HSpIkZeBImCRJUgaOhEmSJGVgCJMkScrAECZJkpSBIUySJCkDQ5gkSVIGhjBJkqQM/j/cex+OfkUjhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code to show the training and validation output for animal model.\n",
    "\n",
    "accuracy = hist2_model.history['binary_accuracy']\n",
    "val_accuracy = hist2_model.history['val_binary_accuracy']\n",
    "loss = hist2_model.history['loss']\n",
    "val_loss = hist2_model.history['val_loss']\n",
    "epochs = range(len(accuracy))\n",
    "\n",
    "fig = plt.figure(figsize=(10,8)) \n",
    "plt.plot(epochs, accuracy, 'b', label = 'Training accuracy')\n",
    "plt.plot(epochs,val_accuracy,'r', label= 'Validation accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "# txt = \"Fig 1.b : \"               # captioning the figure\n",
    "# fig.text(.5, .05, txt, ha='center')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "plt.plot(epochs, loss, 'b', label = 'Training accuracy')\n",
    "plt.plot(epochs,val_loss,'r', label= 'Validation accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cognitive-potential",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info:: {'Cat' = 0, 'Dog'= 1}\n"
     ]
    }
   ],
   "source": [
    "# dogfirst_model.save('dogvscat/dogfirst_dogvscat.model')\n",
    "dogfirst_model = load_model('dogvscat/dogfirst_dogvscat.model')\n",
    "print(\"Info:: {'Cat' = 0, 'Dog'= 1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incomplete-complaint",
   "metadata": {},
   "source": [
    "Testing the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "strange-dinner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 500 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# code to test DogVsCat classfication model on testing dataset\n",
    "\n",
    "# creating test generator to test the model\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "test2_generator = test_datagen.flow_from_directory(\n",
    "    'dogvscat/test_data/test/',\n",
    "    target_size=(128, 128),\n",
    "    class_mode=None,\n",
    "    shuffle=False,\n",
    "    batch_size=1,\n",
    ")\n",
    "\n",
    "STEP_SIZE_TEST=test2_generator.n//test2_generator.batch_size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "sporting-paraguay",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500/500 [==============================] - 4s 7ms/step\n",
      "Accuracy of dogvscat Model: 50.0 %.\n"
     ]
    }
   ],
   "source": [
    "#predicting output label for test1 dataset\n",
    "pred2 = dogfirst_model.predict(test2_generator, steps=STEP_SIZE_TEST, verbose=1)\n",
    "\n",
    "# converting the predicted output data and getting desired ouput\n",
    "animal2_pred = [ (1 if y[0] > 0.5 else 0) for y in pred2]\n",
    "animal2_desired = test2_generator.classes\n",
    "\n",
    "# saving the predicted output in a text file\n",
    "examplenames = test2_generator.filenames\n",
    "results2 = pd.DataFrame({\"Filenames\":examplenames, \"Desired Output\": animal2_desired, \"Predicted Output\": animal2_pred})\n",
    "\n",
    "accuracy = accuracy_score(animal2_desired,animal2_pred)   \n",
    "print(\"Accuracy of dogvscat Model: \" + str((accuracy *100))+ \" %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "liberal-israeli",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[250 250]\n",
      " [  0   0]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code block to print the Gender Confusion Matrix.\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "animal2_confusion_matrix = confusion_matrix(animal2_pred, animal2_desired)\n",
    "print(animal2_confusion_matrix)\n",
    "\n",
    "plt.imshow(animal2_confusion_matrix, cmap=plt.cm.Blues)\n",
    "plt.xlabel(\"Predicted labels\")\n",
    "plt.ylabel(\"True labels\")\n",
    "plt.xticks([0, 1], ['Cat', 'Dog'])\n",
    "plt.yticks([0, 1], ['Cat', 'Dog'])\n",
    "plt.title('Animal Confusion Matrix')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "compound-seeker",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 128, 128, 3)\n"
     ]
    }
   ],
   "source": [
    "# creating rand_test array that contains images built from random generator numbers as pixels\n",
    "rand2_test = [ ]\n",
    "\n",
    "for _ in range(5000):\n",
    "    example = np.random.rand(128,128,3)\n",
    "    rand2_test.append(example)\n",
    "\n",
    "rand2_test = np.array(rand2_test)\n",
    "\n",
    "print(rand2_test.shape)\n",
    "# plt.imshow(rand_test[1])\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "blond-hypothesis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 18s 182ms/step\n",
      "{0: 5000}\n"
     ]
    }
   ],
   "source": [
    "# prediciting on rand_test array\n",
    "\n",
    "rand2_pred = dogfirst_model.predict(rand2_test, steps = len(rand2_test)//50, verbose=1)\n",
    "\n",
    "random2_pred = [ (1 if y[0] > 0.5 else 0) for y in rand2_pred]\n",
    "\n",
    "unique, counts = np.unique(random2_pred, return_counts = True)\n",
    "result = dict(zip(unique, counts))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bearing-collector",
   "metadata": {},
   "source": [
    "Since the model was trained with cats at the end it will predict any image as a cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "digital-advertiser",
   "metadata": {},
   "source": [
    "# Not shuffling the data in binary case doesn't help with training. Hence, no conclusive inference about trash bucket can be made."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}