CatvsDog.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np \n",
    "import os \n",
    "from random import shuffle\n",
    "from tqdm import tqdm\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train_dir = r'D:\\dogs-vs-cats-redux-kernels-edition\\train\\train'\n",
    "Test_dir = r'D:\\dogs-vs-cats-redux-kernels-edition\\test\\test'\n",
    "IMG_SIZE = 50\n",
    "LR = 0.0003\n",
    "MODEL_NAME ='dogsvscats-{}-{}.model'.format(LR, '2conv-basic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_img(img):\n",
    "    #dog.93.png for labelling\n",
    "    word_label = img.split('.')[-3]\n",
    "    if word_label == 'cat': return [1,0]\n",
    "    elif word_label == 'dog': return [0,1]\n",
    "\n",
    "def create_train_data():\n",
    "    training_data = []\n",
    "    for img in tqdm(os.listdir(Train_dir)):\n",
    "        label = label_img(img)\n",
    "        path = os.path.join(Train_dir,img)\n",
    "        img = cv2.resize(cv2.imread(path,cv2.IMREAD_GRAYSCALE), (IMG_SIZE,IMG_SIZE))\n",
    "        training_data.append([np.array(img), np.array(label)])\n",
    "        \n",
    "        \n",
    "    shuffle(training_data)\n",
    "    np.save('train_data.npy', training_data)\n",
    "    return training_data\n",
    "\n",
    "def process_test_data():\n",
    "    testing_data = []\n",
    "    for img in tqdm(os.listdir(Test_dir)):\n",
    "        path = os.path.join(Test_dir, img)\n",
    "        img_num = img.split('.')[0]\n",
    "        img = cv2.resize(cv2.imread(path,cv2.IMREAD_GRAYSCALE),(IMG_SIZE,IMG_SIZE))\n",
    "        testing_data.append([np.array(img), img_num])\n",
    "        \n",
    "    np.save('test_data.npy', testing_data)\n",
    "    return testing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████| 25000/25000 [03:00<00:00, 138.43it/s]\n"
     ]
    }
   ],
   "source": [
    "train_data = create_train_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "curses is not supported on this machine (please install/reinstall curses for an optimal experience)\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\helpers\\summarizer.py:9: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\helpers\\trainer.py:25: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\collections.py:13: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\config.py:123: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\config.py:129: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\config.py:131: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import tflearn\n",
    "from tflearn.layers.conv import conv_2d, max_pool_2d\n",
    "from tflearn.layers.core import input_data, dropout, fully_connected\n",
    "from tflearn.layers.estimator import regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\layers\\core.py:81: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\layers\\conv.py:73: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\initializations.py:119: calling UniformUnitScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tensorflow_core\\python\\util\\deprecation.py:507: UniformUnitScaling.__init__ (from tensorflow.python.ops.init_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.initializers.variance_scaling instead with distribution=uniform to get equivalent behavior.\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\layers\\conv.py:552: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\initializations.py:174: calling TruncatedNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\layers\\core.py:239: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\optimizers.py:238: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\objectives.py:66: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\objectives.py:70: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\layers\\estimator.py:189: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\helpers\\trainer.py:571: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\helpers\\trainer.py:115: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\summaries.py:46: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tensorflow_core\\python\\ops\\math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\helpers\\trainer.py:134: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\helpers\\trainer.py:164: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\helpers\\trainer.py:165: The name tf.local_variables_initializer is deprecated. Please use tf.compat.v1.local_variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\helpers\\trainer.py:166: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Devesh sangwan\\.conda\\envs\\tf-gpu\\lib\\site-packages\\tflearn\\helpers\\trainer.py:167: The name tf.get_collection_ref is deprecated. Please use tf.compat.v1.get_collection_ref instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')\n",
    "\n",
    "convnet = conv_2d(convnet, 32, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "convnet = conv_2d(convnet, 64, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "convnet = conv_2d(convnet, 32, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "convnet = conv_2d(convnet, 64, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "convnet = conv_2d(convnet, 32, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "convnet = conv_2d(convnet, 64, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "convnet = conv_2d(convnet, 32, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "convnet = conv_2d(convnet, 64, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "\n",
    "convnet = conv_2d(convnet, 32, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "convnet = conv_2d(convnet, 64, 2, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 2)\n",
    "\n",
    "convnet = fully_connected(convnet, 1024, activation='relu')\n",
    "convnet = dropout(convnet, 0.8)\n",
    "\n",
    "convnet = fully_connected(convnet, 2, activation='softmax')\n",
    "convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')\n",
    "\n",
    "model = tflearn.DNN(convnet, tensorboard_dir ='log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.exists('{}.meta'.format(MODEL_NAME)):\n",
    "    model.load(MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train_data[:-500]\n",
    "test = train_data[-500:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
    "Y = [i[1] for i in train]\n",
    "\n",
    "test_x = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
    "test_y = [i[1] for i in test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 38299  | total loss: \u001b[1m\u001b[32m0.03397\u001b[0m\u001b[0m | time: 8.732s\n",
      "| Adam | epoch: 100 | loss: 0.03397 - acc: 0.9905 -- iter: 24448/24500\n",
      "Training Step: 38300  | total loss: \u001b[1m\u001b[32m0.03116\u001b[0m\u001b[0m | time: 9.758s\n",
      "| Adam | epoch: 100 | loss: 0.03116 - acc: 0.9915 | val_loss: 1.56847 - val_acc: 0.7920 -- iter: 24500/24500\n",
      "--\n"
     ]
    }
   ],
   "source": [
    "model.fit({'input': X}, {'targets': Y}, n_epoch=100, validation_set=({'input': test_x}, {'targets': test_y}), snapshot_step=500, show_metric=True, run_id=MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:D:\\Machine learning\\ml_projects\\Cat and dog classifier\\dogsvscats-0.0003-2conv-basic.model is not in all_model_checkpoint_paths. Manually adding it.\n"
     ]
    }
   ],
   "source": [
    "model.save(MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████| 12500/12500 [01:55<00:00, 107.90it/s]"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "test_data = process_test_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "for num, data in enumerate(test_data[36:48]):\n",
    "    # cat is 1:0, dog is 0:1\n",
    "    img_num = data[1]\n",
    "    img_data = data[0]\n",
    "    \n",
    "    y = fig.add_subplot(3,4, num + 1)\n",
    "    orig = img_data\n",
    "    data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)\n",
    "    \n",
    "    model_out = model.predict([data])[0]\n",
    "    \n",
    "    if np.argmax(model_out) == 1: str_label='Dog'\n",
    "    else: str_label = 'Cat'\n",
    "        \n",
    "    y.imshow(orig, cmap='gray')\n",
    "    plt.title(str_label)\n",
    "    y.axes.get_xaxis().set_visible(False)\n",
    "    y.axes.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  }
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