8. train_custom_preprocess.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This tutorial demostrate how to design a custom image preprocessing function for training.\n",
    "\n",
    "`Terrance, DeVries, et al. \"Improved regularization of convolutional neural networks with cutout\" 2017.`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# import matplotlib for visualization\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# set to plot in the notebook\n",
    "%matplotlib inline \n",
    "\n",
    "# import keras (using tensorflow backend)\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.1\n",
    "config.gpu_options.visible_device_list= '0'\n",
    "set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import cifar10\n",
    "\n",
    "# set batch size and number of classes\n",
    "input_shape = (32,32,3)\n",
    "batch_size = 500\n",
    "num_classes = 10\n",
    "epochs = 10\n",
    "\n",
    "# load cifar10\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "\n",
    "# convert class vectors to one-hot binary class matrices.\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "# normalize data to [-1, 1]\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 127.5\n",
    "x_test /= 127.5\n",
    "x_train -= 1.\n",
    "x_test -= 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 30, 30, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 30, 30, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 13, 13, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 120)               276600    \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 120)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1210      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 297,202\n",
      "Trainable params: 297,202\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "\n",
    "from keras.layers import Dense, Activation, Flatten, Conv2D, MaxPooling2D\n",
    "\n",
    "def cnn_model(input_shape, num_classes):    \n",
    "    model = Sequential()\n",
    "\n",
    "    # conv1\n",
    "    model.add(Conv2D(32, (3, 3), padding='valid', input_shape=input_shape))\n",
    "    model.add(Activation('relu'))\n",
    "    # pool1\n",
    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "    model.add(Activation('relu'))\n",
    "\n",
    "    # conv2\n",
    "    model.add(Conv2D(64, (3, 3), padding='valid'))\n",
    "    model.add(Activation('relu'))\n",
    "    # pool2\n",
    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "    model.add(Activation('relu'))\n",
    "\n",
    "    # fc1\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(120))\n",
    "    model.add(Activation('relu'))\n",
    "\n",
    "    # fc2\n",
    "    model.add(Dense(num_classes))\n",
    "    model.add(Activation('softmax'))\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = cnn_model(input_shape, num_classes)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     },
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     "metadata": {},
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   ],
   "source": [
    "# you can save model diagram by following code\n",
    "#from keras.utils import plot_model\n",
    "#plot_model(model, to_file='cnn_model.png')\n",
    "\n",
    "# show model\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 2.2603 - categorical_accuracy: 0.1608 - val_loss: 2.2053 - val_categorical_accuracy: 0.2082\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 2.1541 - categorical_accuracy: 0.2353 - val_loss: 2.0861 - val_categorical_accuracy: 0.2638\n",
      "Epoch 3/10\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 2.0481 - categorical_accuracy: 0.2748 - val_loss: 1.9844 - val_categorical_accuracy: 0.3027\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 1.9604 - categorical_accuracy: 0.3086 - val_loss: 1.9027 - val_categorical_accuracy: 0.3331\n",
      "Epoch 5/10\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 1.8892 - categorical_accuracy: 0.3330 - val_loss: 1.8391 - val_categorical_accuracy: 0.3532\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 1.8317 - categorical_accuracy: 0.3523 - val_loss: 1.7857 - val_categorical_accuracy: 0.3693\n",
      "Epoch 7/10\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 1.7835 - categorical_accuracy: 0.3690 - val_loss: 1.7383 - val_categorical_accuracy: 0.3872\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 1.7413 - categorical_accuracy: 0.3842 - val_loss: 1.6972 - val_categorical_accuracy: 0.3943\n",
      "Epoch 9/10\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 1.7006 - categorical_accuracy: 0.3974 - val_loss: 1.6543 - val_categorical_accuracy: 0.4118\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 1.6613 - categorical_accuracy: 0.4098 - val_loss: 1.6221 - val_categorical_accuracy: 0.4269\n"
     ]
    }
   ],
   "source": [
    "# compile model\n",
    "optimizer = keras.optimizers.sgd()\n",
    "loss = keras.losses.categorical_crossentropy\n",
    "metric = keras.metrics.categorical_accuracy\n",
    "model.compile(loss=loss,optimizer=optimizer,metrics=[metric])\n",
    "\n",
    "import numpy as np\n",
    "def get_random_eraser(p=0.2, s_l=0.02, s_h=0.2, r_1=0.3, r_2=1/0.3, v_l=-1., v_h=1., pixel_level=False):\n",
    "    def eraser(input_img):\n",
    "        img_h, img_w, img_c = input_img.shape\n",
    "        p_1 = np.random.rand()\n",
    "\n",
    "        if p_1 > p:\n",
    "            return input_img\n",
    "\n",
    "        while True:\n",
    "            s = np.random.uniform(s_l, s_h) * img_h * img_w\n",
    "            r = np.random.uniform(r_1, r_2)\n",
    "            w = int(np.sqrt(s / r))\n",
    "            h = int(np.sqrt(s * r))\n",
    "            left = np.random.randint(0, img_w)\n",
    "            top = np.random.randint(0, img_h)\n",
    "\n",
    "            if left + w <= img_w and top + h <= img_h:\n",
    "                break\n",
    "\n",
    "        if pixel_level:\n",
    "            c = np.random.uniform(v_l, v_h, (h, w, img_c))\n",
    "        else:\n",
    "            c = np.random.uniform(v_l, v_h)\n",
    "\n",
    "        input_img[top:top + h, left:left + w, :] = c\n",
    "\n",
    "        return input_img\n",
    "\n",
    "    return eraser\n",
    "\n",
    "preprocessing_function=get_random_eraser()\n",
    "\n",
    "# import image data generator\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "datagen = ImageDataGenerator(preprocessing_function=preprocessing_function)\n",
    "\n",
    "# Compute quantities required for feature-wise normalization\n",
    "datagen.fit(x_train)\n",
    "\n",
    "# Fit the model on the batches generated by datagen.flow().\n",
    "history = model.fit_generator(\n",
    "    datagen.flow(x_train, y_train, batch_size=batch_size),\n",
    "    steps_per_epoch=x_train.shape[0] // batch_size,\n",
    "    epochs=epochs,\n",
    "    validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:42.27%.\n"
     ]
    }
   ],
   "source": [
    "[train_loss, train_acc] = history.model.evaluate(x=x_train, y=y_train, batch_size=batch_size, verbose=0)\n",
    "print(\"Train accuracy:{0:.2f}%.\".format(train_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:42.69%.\n"
     ]
    }
   ],
   "source": [
    "[test_loss, test_acc] = history.model.evaluate(x=x_test, y=y_test, batch_size=batch_size, verbose=0)\n",
    "print(\"Test accuracy:{0:.2f}%.\".format(test_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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