dog_breed_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important liprary imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm\n",
    "from keras.preprocessing import image\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the labels data into dataframe and viewing the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10222, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>breed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000bec180eb18c7604dcecc8fe0dba07</td>\n",
       "      <td>boston_bull</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001513dfcb2ffafc82cccf4d8bbaba97</td>\n",
       "      <td>dingo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>001cdf01b096e06d78e9e5112d419397</td>\n",
       "      <td>pekinese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00214f311d5d2247d5dfe4fe24b2303d</td>\n",
       "      <td>bluetick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0021f9ceb3235effd7fcde7f7538ed62</td>\n",
       "      <td>golden_retriever</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id             breed\n",
       "0  000bec180eb18c7604dcecc8fe0dba07       boston_bull\n",
       "1  001513dfcb2ffafc82cccf4d8bbaba97             dingo\n",
       "2  001cdf01b096e06d78e9e5112d419397          pekinese\n",
       "3  00214f311d5d2247d5dfe4fe24b2303d          bluetick\n",
       "4  0021f9ceb3235effd7fcde7f7538ed62  golden_retriever"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the labels.csv file and check shape and records\n",
    "labels_all = pd.read_csv('./dogbreedidfromcomp/labels.csv')\n",
    "print(labels_all.shape)\n",
    "labels_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scottish_deerhound      126\n",
       "maltese_dog             117\n",
       "afghan_hound            116\n",
       "entlebucher             115\n",
       "bernese_mountain_dog    114\n",
       "Name: breed, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading number or each breed\n",
    "breed_all = labels_all['breed']\n",
    "breed_count = breed_all.value_counts()\n",
    "breed_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id</th>\n",
       "      <th>breed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>0042188c895a2f14ef64a918ed9c7b64</td>\n",
       "      <td>scottish_deerhound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>00693b8bc2470375cc744a6391d397ec</td>\n",
       "      <td>maltese_dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>01e787576c003930f96c966f9c3e1d44</td>\n",
       "      <td>scottish_deerhound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>01ee3c7ff9bcaba9874183135877670e</td>\n",
       "      <td>entlebucher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88</td>\n",
       "      <td>021b5a49189665c0442c19b5b33e8cf1</td>\n",
       "      <td>entlebucher</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                id               breed\n",
       "0      9  0042188c895a2f14ef64a918ed9c7b64  scottish_deerhound\n",
       "1     12  00693b8bc2470375cc744a6391d397ec         maltese_dog\n",
       "2     79  01e787576c003930f96c966f9c3e1d44  scottish_deerhound\n",
       "3     80  01ee3c7ff9bcaba9874183135877670e         entlebucher\n",
       "4     88  021b5a49189665c0442c19b5b33e8cf1         entlebucher"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting all breeds because i have high computation power\n",
    "CLASS_NAME = ['scottish_deerhound', 'maltese_dog', 'afghan_hound', 'entlebucher', 'bernese_mountain_dog']\n",
    "labels = labels_all[(labels_all['breed'].isin(CLASS_NAME))]\n",
    "labels = labels.reset_index()\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 588/588 [00:03<00:00, 169.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train Images shape:  (588, 224, 224, 3)  size: 88,510,464\n",
      "One-hot encoded output shape:  (588, 5)  size: 2,940\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Creating numpy matrix with zeros\n",
    "X_data = np.zeros((len(labels), 224, 224, 3), dtype='float32')\n",
    "# One hot encoding\n",
    "Y_data = label_binarize(labels['breed'], classes = CLASS_NAME)\n",
    "\n",
    "# Reading and converting image to numpy array and normalizing dataset\n",
    "for i in tqdm(range(len(labels))):\n",
    "    img = image.load_img('./dogbreedidfromcomp/train/%s.jpg' % labels['id'][i], target_size=(224, 224))\n",
    "    img = image.img_to_array(img)\n",
    "    x = np.expand_dims(img.copy(), axis=0)\n",
    "    X_data[i] = x / 255.0\n",
    "\n",
    "# Printing train image and one hot encode shape & size\n",
    "print('\\nTrain Images shape: ',X_data.shape,' size: {:,}'.format(X_data.size))\n",
    "print('One-hot encoded output shape: ',Y_data.shape,' size: {:,}'.format(Y_data.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next we will create a network architecture for the model. We have used different types of layers according to their features namely Conv_2d (It is used to create a convolutional kernel that is convolved with the input layer to produce the output tensor), max_pooling2d (It is a downsampling technique which takes out the maximum value over the window defined by poolsize), flatten (It flattens the input and creates a 1D output), Dense (Dense layer produce the output as the dot product of input and kernel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 220, 220, 64)      4864      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 110, 110, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 108, 108, 32)      18464     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 54, 54, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 48, 48, 16)        25104     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 24, 24, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 20, 20, 8)         3208      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 10, 10, 8)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               102528    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 5)                 325       \n",
      "=================================================================\n",
      "Total params: 162,749\n",
      "Trainable params: 162,749\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Building the Model\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters = 64, kernel_size = (5,5), activation ='relu', input_shape = (224,224,3)))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Conv2D(filters = 32, kernel_size = (3,3), activation ='relu', kernel_regularizer = 'l2'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Conv2D(filters = 16, kernel_size = (7,7), activation ='relu', kernel_regularizer = 'l2'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Conv2D(filters = 8, kernel_size = (5,5), activation ='relu', kernel_regularizer = 'l2'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation = \"relu\", kernel_regularizer = 'l2'))\n",
    "model.add(Dense(64, activation = \"relu\", kernel_regularizer = 'l2'))\n",
    "model.add(Dense(len(CLASS_NAME), activation = \"softmax\"))\n",
    "\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer = Adam(0.0001),metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After defining the network architecture we found out the total parameters as 162,619."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## After defining the network architecture we will start with splitting the test and train data then dividing train data in train and validation data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data set into training and testing data sets\n",
    "X_train_and_val, X_test, Y_train_and_val, Y_test = train_test_split(X_data, Y_data, test_size = 0.1)\n",
    "# Splitting the training data set into training and validation data sets\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_train_and_val, Y_train_and_val, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "4/4 [==============================] - 14s 3s/step - loss: 5.4002 - accuracy: 0.1820 - val_loss: 5.3620 - val_accuracy: 0.2264\n",
      "Epoch 2/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 5.3482 - accuracy: 0.2317 - val_loss: 5.3152 - val_accuracy: 0.1887\n",
      "Epoch 3/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 5.3004 - accuracy: 0.2671 - val_loss: 5.2685 - val_accuracy: 0.1981\n",
      "Epoch 4/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 5.2535 - accuracy: 0.2577 - val_loss: 5.2219 - val_accuracy: 0.2170\n",
      "Epoch 5/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 5.2060 - accuracy: 0.2742 - val_loss: 5.1752 - val_accuracy: 0.2075\n",
      "Epoch 6/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 5.1590 - accuracy: 0.3191 - val_loss: 5.1297 - val_accuracy: 0.2170\n",
      "Epoch 7/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 5.1119 - accuracy: 0.2577 - val_loss: 5.0878 - val_accuracy: 0.2075\n",
      "Epoch 8/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 5.0658 - accuracy: 0.2364 - val_loss: 5.0436 - val_accuracy: 0.2075\n",
      "Epoch 9/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 5.0194 - accuracy: 0.2364 - val_loss: 4.9988 - val_accuracy: 0.2075\n",
      "Epoch 10/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.9739 - accuracy: 0.2837 - val_loss: 4.9527 - val_accuracy: 0.2830\n",
      "Epoch 11/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.9263 - accuracy: 0.3073 - val_loss: 4.9129 - val_accuracy: 0.2264\n",
      "Epoch 12/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.8787 - accuracy: 0.3144 - val_loss: 4.8664 - val_accuracy: 0.2830\n",
      "Epoch 13/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 4.8306 - accuracy: 0.3428 - val_loss: 4.8197 - val_accuracy: 0.2642\n",
      "Epoch 14/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 4.7806 - accuracy: 0.3593 - val_loss: 4.7762 - val_accuracy: 0.2642\n",
      "Epoch 15/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.7283 - accuracy: 0.3381 - val_loss: 4.7180 - val_accuracy: 0.3113\n",
      "Epoch 16/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.6755 - accuracy: 0.3593 - val_loss: 4.6568 - val_accuracy: 0.3302\n",
      "Epoch 17/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 4.6091 - accuracy: 0.3901 - val_loss: 4.6165 - val_accuracy: 0.3019\n",
      "Epoch 18/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.5533 - accuracy: 0.4255 - val_loss: 4.5221 - val_accuracy: 0.3679\n",
      "Epoch 19/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.4802 - accuracy: 0.4232 - val_loss: 4.4684 - val_accuracy: 0.3679\n",
      "Epoch 20/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 4.4286 - accuracy: 0.4397 - val_loss: 4.3929 - val_accuracy: 0.3868\n",
      "Epoch 21/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.3663 - accuracy: 0.4397 - val_loss: 4.3431 - val_accuracy: 0.4623\n",
      "Epoch 22/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.2817 - accuracy: 0.4917 - val_loss: 4.2840 - val_accuracy: 0.4717\n",
      "Epoch 23/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.2441 - accuracy: 0.4563 - val_loss: 4.2103 - val_accuracy: 0.4528\n",
      "Epoch 24/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.1939 - accuracy: 0.4988 - val_loss: 4.1895 - val_accuracy: 0.4528\n",
      "Epoch 25/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.1270 - accuracy: 0.5035 - val_loss: 4.1390 - val_accuracy: 0.4528\n",
      "Epoch 26/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.0735 - accuracy: 0.5154 - val_loss: 4.0954 - val_accuracy: 0.4623\n",
      "Epoch 27/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 4.0227 - accuracy: 0.5201 - val_loss: 4.0544 - val_accuracy: 0.4717\n",
      "Epoch 28/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 3.9889 - accuracy: 0.5225 - val_loss: 4.0310 - val_accuracy: 0.4623\n",
      "Epoch 29/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 3.9455 - accuracy: 0.5390 - val_loss: 3.9880 - val_accuracy: 0.4528\n",
      "Epoch 30/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 3.8986 - accuracy: 0.5556 - val_loss: 3.9746 - val_accuracy: 0.4623\n",
      "Epoch 31/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.8653 - accuracy: 0.5414 - val_loss: 3.9091 - val_accuracy: 0.4528\n",
      "Epoch 32/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.8045 - accuracy: 0.5674 - val_loss: 3.8863 - val_accuracy: 0.4623\n",
      "Epoch 33/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.7706 - accuracy: 0.5697 - val_loss: 3.8699 - val_accuracy: 0.4623\n",
      "Epoch 34/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.7393 - accuracy: 0.5863 - val_loss: 3.8311 - val_accuracy: 0.4623\n",
      "Epoch 35/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.6918 - accuracy: 0.5981 - val_loss: 3.8209 - val_accuracy: 0.4717\n",
      "Epoch 36/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.6551 - accuracy: 0.6076 - val_loss: 3.8011 - val_accuracy: 0.4811\n",
      "Epoch 37/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.6250 - accuracy: 0.6312 - val_loss: 3.7678 - val_accuracy: 0.4906\n",
      "Epoch 38/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.5799 - accuracy: 0.6430 - val_loss: 3.7603 - val_accuracy: 0.4906\n",
      "Epoch 39/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.5719 - accuracy: 0.6194 - val_loss: 3.7886 - val_accuracy: 0.4623\n",
      "Epoch 40/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.5856 - accuracy: 0.6501 - val_loss: 3.7515 - val_accuracy: 0.5094\n",
      "Epoch 41/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.5127 - accuracy: 0.6572 - val_loss: 3.7295 - val_accuracy: 0.4717\n",
      "Epoch 42/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.4867 - accuracy: 0.6548 - val_loss: 3.6771 - val_accuracy: 0.5000\n",
      "Epoch 43/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.4377 - accuracy: 0.6785 - val_loss: 3.6510 - val_accuracy: 0.5283\n",
      "Epoch 44/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.4157 - accuracy: 0.6478 - val_loss: 3.6823 - val_accuracy: 0.5189\n",
      "Epoch 45/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.3979 - accuracy: 0.6974 - val_loss: 3.6133 - val_accuracy: 0.5660\n",
      "Epoch 46/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.3516 - accuracy: 0.6761 - val_loss: 3.6109 - val_accuracy: 0.4906\n",
      "Epoch 47/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.3524 - accuracy: 0.6690 - val_loss: 3.6389 - val_accuracy: 0.5189\n",
      "Epoch 48/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 3.3508 - accuracy: 0.6856 - val_loss: 3.7187 - val_accuracy: 0.4623\n",
      "Epoch 49/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.3118 - accuracy: 0.6998 - val_loss: 3.6145 - val_accuracy: 0.5283\n",
      "Epoch 50/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.2780 - accuracy: 0.6950 - val_loss: 3.5596 - val_accuracy: 0.5283\n",
      "Epoch 51/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.2462 - accuracy: 0.6832 - val_loss: 3.5568 - val_accuracy: 0.5094\n",
      "Epoch 52/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.2210 - accuracy: 0.7376 - val_loss: 3.5354 - val_accuracy: 0.5283\n",
      "Epoch 53/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.2411 - accuracy: 0.6785 - val_loss: 3.6600 - val_accuracy: 0.4717\n",
      "Epoch 54/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.2740 - accuracy: 0.6879 - val_loss: 3.5986 - val_accuracy: 0.5283\n",
      "Epoch 55/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.2298 - accuracy: 0.6856 - val_loss: 3.5963 - val_accuracy: 0.5000\n",
      "Epoch 56/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 3.1483 - accuracy: 0.7494 - val_loss: 3.5096 - val_accuracy: 0.5189\n",
      "Epoch 57/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.1530 - accuracy: 0.7210 - val_loss: 3.5104 - val_accuracy: 0.5283\n",
      "Epoch 58/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.0804 - accuracy: 0.7565 - val_loss: 3.4639 - val_accuracy: 0.5566\n",
      "Epoch 59/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 3.0406 - accuracy: 0.7707 - val_loss: 3.4634 - val_accuracy: 0.5283\n",
      "Epoch 60/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.0289 - accuracy: 0.7872 - val_loss: 3.4432 - val_accuracy: 0.5189\n",
      "Epoch 61/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 3.0041 - accuracy: 0.7825 - val_loss: 3.4394 - val_accuracy: 0.5283\n",
      "Epoch 62/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.9789 - accuracy: 0.7849 - val_loss: 3.4250 - val_accuracy: 0.5377\n",
      "Epoch 63/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.9453 - accuracy: 0.8061 - val_loss: 3.4228 - val_accuracy: 0.5755\n",
      "Epoch 64/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.9287 - accuracy: 0.8038 - val_loss: 3.4297 - val_accuracy: 0.5377\n",
      "Epoch 65/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.9285 - accuracy: 0.7896 - val_loss: 3.4039 - val_accuracy: 0.5377\n",
      "Epoch 66/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.9073 - accuracy: 0.7825 - val_loss: 3.4794 - val_accuracy: 0.5566\n",
      "Epoch 67/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.8829 - accuracy: 0.8061 - val_loss: 3.4491 - val_accuracy: 0.5566\n",
      "Epoch 68/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.8765 - accuracy: 0.7896 - val_loss: 3.5654 - val_accuracy: 0.4906\n",
      "Epoch 69/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.8764 - accuracy: 0.8085 - val_loss: 3.4269 - val_accuracy: 0.5189\n",
      "Epoch 70/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.8112 - accuracy: 0.8298 - val_loss: 3.4867 - val_accuracy: 0.5094\n",
      "Epoch 71/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.8119 - accuracy: 0.8416 - val_loss: 3.4141 - val_accuracy: 0.5283\n",
      "Epoch 72/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.7762 - accuracy: 0.8227 - val_loss: 3.4941 - val_accuracy: 0.5189\n",
      "Epoch 73/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.7460 - accuracy: 0.8582 - val_loss: 3.4377 - val_accuracy: 0.5283\n",
      "Epoch 74/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.7577 - accuracy: 0.8274 - val_loss: 3.5169 - val_accuracy: 0.5189\n",
      "Epoch 75/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.7408 - accuracy: 0.8298 - val_loss: 3.4018 - val_accuracy: 0.5377\n",
      "Epoch 76/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.7228 - accuracy: 0.8487 - val_loss: 3.3748 - val_accuracy: 0.5755\n",
      "Epoch 77/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.6835 - accuracy: 0.8582 - val_loss: 3.3695 - val_accuracy: 0.5660\n",
      "Epoch 78/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.6567 - accuracy: 0.8842 - val_loss: 3.3700 - val_accuracy: 0.5660\n",
      "Epoch 79/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.6286 - accuracy: 0.8652 - val_loss: 3.3738 - val_accuracy: 0.5755\n",
      "Epoch 80/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.6028 - accuracy: 0.8865 - val_loss: 3.3576 - val_accuracy: 0.5377\n",
      "Epoch 81/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.5874 - accuracy: 0.8889 - val_loss: 3.3809 - val_accuracy: 0.5660\n",
      "Epoch 82/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.5681 - accuracy: 0.9031 - val_loss: 3.3767 - val_accuracy: 0.5566\n",
      "Epoch 83/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.5879 - accuracy: 0.8652 - val_loss: 3.4766 - val_accuracy: 0.5189\n",
      "Epoch 84/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.5548 - accuracy: 0.9078 - val_loss: 3.3906 - val_accuracy: 0.5377\n",
      "Epoch 85/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.5424 - accuracy: 0.8889 - val_loss: 3.4985 - val_accuracy: 0.5094\n",
      "Epoch 86/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.5550 - accuracy: 0.8865 - val_loss: 3.3639 - val_accuracy: 0.5660\n",
      "Epoch 87/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.5120 - accuracy: 0.9125 - val_loss: 3.3809 - val_accuracy: 0.5660\n",
      "Epoch 88/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.4737 - accuracy: 0.9220 - val_loss: 3.3978 - val_accuracy: 0.5660\n",
      "Epoch 89/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.4522 - accuracy: 0.9338 - val_loss: 3.3430 - val_accuracy: 0.5566\n",
      "Epoch 90/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.4259 - accuracy: 0.9504 - val_loss: 3.3874 - val_accuracy: 0.5660\n",
      "Epoch 91/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.4192 - accuracy: 0.9314 - val_loss: 3.4103 - val_accuracy: 0.5755\n",
      "Epoch 92/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.3986 - accuracy: 0.9504 - val_loss: 3.4067 - val_accuracy: 0.5660\n",
      "Epoch 93/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.4084 - accuracy: 0.9243 - val_loss: 3.4785 - val_accuracy: 0.5283\n",
      "Epoch 94/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.3986 - accuracy: 0.9291 - val_loss: 3.4121 - val_accuracy: 0.5849\n",
      "Epoch 95/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.3758 - accuracy: 0.9314 - val_loss: 3.3729 - val_accuracy: 0.5943\n",
      "Epoch 96/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 2.3691 - accuracy: 0.9291 - val_loss: 3.5682 - val_accuracy: 0.5566\n",
      "Epoch 97/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.3980 - accuracy: 0.9220 - val_loss: 3.3890 - val_accuracy: 0.5566\n",
      "Epoch 98/100\n",
      "4/4 [==============================] - 13s 3s/step - loss: 2.3404 - accuracy: 0.9409 - val_loss: 3.3838 - val_accuracy: 0.5849\n",
      "Epoch 99/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.3159 - accuracy: 0.9409 - val_loss: 3.4029 - val_accuracy: 0.5849\n",
      "Epoch 100/100\n",
      "4/4 [==============================] - 12s 3s/step - loss: 2.2830 - accuracy: 0.9693 - val_loss: 3.4040 - val_accuracy: 0.5660\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "epochs = 100\n",
    "batch_size = 128\n",
    "\n",
    "history = model.fit(X_train, Y_train, batch_size = batch_size, epochs = epochs, validation_data = (X_val, Y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here we analyse how the model is learning with each epoch in terms of accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the training history\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(history.history['accuracy'], color='r')\n",
    "plt.plot(history.history['val_accuracy'], color='b')\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['train', 'val'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will use predict function to make predictions using this model also we are finding out the accuracy on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 102ms/step - loss: 3.8915 - accuracy: 0.5424\n",
      "Accuracy over the test set: \n",
      "  54.24 %\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model.predict(X_test)\n",
    "score = model.evaluate(X_test, Y_test)\n",
    "print('Accuracy over the test set: \\n ', round((score[1]*100), 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Originally :  entlebucher\n",
      "Predicted :  entlebucher\n"
     ]
    }
   ],
   "source": [
    "# Plotting image to compare\n",
    "plt.imshow(X_test[1,:,:,:])\n",
    "plt.show()\n",
    "\n",
    "# Finding max value from predition list and comaparing original value vs predicted\n",
    "print(\"Originally : \",labels['breed'][np.argmax(Y_test[1])])\n",
    "print(\"Predicted : \",labels['breed'][np.argmax(Y_pred[1])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We started with downloading the dataset creating the model and finding out the predictions using the model. We can optimize different hyper parameters in order to tune this model for a higher accuracy. This model can be used to predict different breeds of dogs which can be further used by different NGO's working on saving animals and for educational purposes also."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "7c3458bb701476ea2acb41cd3819a608b09cbdda51706af7937ba7dfb7d47c62"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit ('base': conda)",
   "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}