dog-breed-classifier - keras.ipynb

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  {
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   "source": [
    "## Introduction to our first task: 'Dogs vs Cats'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"data/dogscats/\"\n",
    "sz=224\n",
    "batch_size=64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.preprocessing import image\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.applications import ResNet50\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling2D\n",
    "from keras import backend as K\n",
    "from keras.applications.resnet50 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_dir = f'{PATH}train'\n",
    "validation_data_dir = f'{PATH}valid'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 23000 images belonging to 2 classes.\n",
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,\n",
    "    shear_range=0.2, zoom_range=0.2, horizontal_flip=True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(train_data_dir,\n",
    "    target_size=(sz, sz),\n",
    "    batch_size=batch_size, class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(validation_data_dir,\n",
    "    shuffle=False,\n",
    "    target_size=(sz, sz),\n",
    "    batch_size=batch_size, class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = ResNet50(weights='imagenet', include_top=False)\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "predictions = Dense(1, activation='sigmoid')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "for layer in base_model.layers: layer.trainable = False\n",
    "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "359/359 [==============================] - 1954s 5s/step - loss: 0.2013 - acc: 0.9466 - val_loss: 0.0677 - val_acc: 0.9829\n",
      "Epoch 2/3\n",
      "359/359 [==============================] - 1937s 5s/step - loss: 0.0833 - acc: 0.9709 - val_loss: 0.0615 - val_acc: 0.9840\n",
      "Epoch 3/3\n",
      "182/359 [==============>...............] - ETA: 14:20 - loss: 0.0638 - acc: 0.9779"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model.fit_generator(train_generator, train_generator.n // batch_size, epochs=3, workers=4,\n",
    "        validation_data=validation_generator, validation_steps=validation_generator.n // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_at = 140\n",
    "for layer in model.layers[:split_at]: layer.trainable = False\n",
    "for layer in model.layers[split_at:]: layer.trainable = True\n",
    "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "359/359 [==============================] - 217s 603ms/step - loss: 0.0762 - acc: 0.9741 - val_loss: 7.9436 - val_acc: 0.5055\n",
      "CPU times: user 4min 47s, sys: 14.1 s, total: 5min 1s\n",
      "Wall time: 3min 38s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f22d8428f28>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit_generator(train_generator, train_generator.n // batch_size, epochs=1, workers=3,\n",
    "        validation_data=validation_generator, validation_steps=validation_generator.n // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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