# Trains a ResNet on the CIFAR10 dataset.

ResNet v1: Deep Residual Learning for Image Recognition

ResNet v2: Identity Mappings in Deep Residual Networks

Model | n | 200-epoch accuracy | Original paper accuracy | sec/epoch GTX1080Ti
--- | --- | --- | --- | ---
ResNet20 v1 | 3 | 92.16 % | 91.25 % | 35
ResNet32 v1 | 5 | 92.46 % | 92.49 % | 50
ResNet44 v1 | 7 | 92.50 % | 92.83 % | 70
ResNet56 v1 | 9 | 92.71 % | 93.03 % | 90
ResNet110 v1 | 18 | 92.65 % | 93.39+-.16 % | 165
ResNet164 v1 | 27 | - % | 94.07 % | -
ResNet1001 v1 | N/A | - % | 92.39 % | -

Model | n | 200-epoch accuracy | Original paper accuracy | sec/epoch GTX1080Ti
--- | --- | --- | --- | ---
ResNet20 v2 | 2 | - % | - % | ---
ResNet32 v2 | N/A | NA % | NA % | NA
ResNet44 v2 | N/A | NA % | NA % | NA
ResNet56 v2 | 6 | 93.01 % | NA % | 100
ResNet110 v2 | 12 | 93.15 % | 93.63 % | 180
ResNet164 v2 | 18 | - % | 94.54 % | -
ResNet1001 v2 | 111 | - % | 95.08+-.14 % | -

    from __future__ import print_function
    import keras
    from keras.layers import Dense, Conv2D, BatchNormalization, Activation
    from keras.layers import AveragePooling2D, Input, Flatten
    from keras.optimizers import Adam
    from keras.callbacks import ModelCheckpoint, LearningRateScheduler
    from keras.callbacks import ReduceLROnPlateau
    from keras.preprocessing.image import ImageDataGenerator
    from keras.regularizers import l2
    from keras import backend as K
    from keras.models import Model
    from keras.datasets import cifar10
    import numpy as np
    import os

    # Training parameters
    batch_size = 32  # orig paper trained all networks with batch_size=128
    epochs = 200
    data_augmentation = True
    num_classes = 10

    # Subtracting pixel mean improves accuracy
    subtract_pixel_mean = True

    # Model parameter
    # ----------------------------------------------------------------------------
    #           |      | 200-epoch | Orig Paper| 200-epoch | Orig Paper| sec/epoch
    # Model     |  n   | ResNet v1 | ResNet v1 | ResNet v2 | ResNet v2 | GTX1080Ti
    #           |v1(v2)| %Accuracy | %Accuracy | %Accuracy | %Accuracy | v1 (v2)
    # ----------------------------------------------------------------------------
    # ResNet20  | 3 (2)| 92.16     | 91.25     | -----     | -----     | 35 (---)
    # ResNet32  | 5(NA)| 92.46     | 92.49     | NA        | NA        | 50 ( NA)
    # ResNet44  | 7(NA)| 92.50     | 92.83     | NA        | NA        | 70 ( NA)
    # ResNet56  | 9 (6)| 92.71     | 93.03     | 93.01     | NA        | 90 (100)
    # ResNet110 |18(12)| 92.65     | 93.39+-.16| 93.15     | 93.63     | 165(180)
    # ResNet164 |27(18)| -----     | 94.07     | -----     | 94.54     | ---(---)
    # ResNet1001| (111)| -----     | 92.39     | -----     | 95.08+-.14| ---(---)
    # ---------------------------------------------------------------------------
    n = 3

    # Model version
    # Orig paper: version = 1 (ResNet v1), Improved ResNet: version = 2 (ResNet v2)
    version = 1

    # Computed depth from supplied model parameter n
    if version == 1:
        depth = n * 6 + 2
    elif version == 2:
        depth = n * 9 + 2

    # Model name, depth and version
    model_type = 'ResNet%dv%d' % (depth, version)

    # Load the CIFAR10 data.
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()

    # Input image dimensions.
    input_shape = x_train.shape[1:]

    # Normalize data.
    x_train = x_train.astype('float32') / 255
    x_test = x_test.astype('float32') / 255

    # If subtract pixel mean is enabled
    if subtract_pixel_mean:
        x_train_mean = np.mean(x_train, axis=0)
        x_train -= x_train_mean
        x_test -= x_train_mean

    print('x_train shape:', x_train.shape)
    print(x_train.shape[0], 'train samples')
    print(x_test.shape[0], 'test samples')
    print('y_train shape:', y_train.shape)

    # Convert class vectors to binary class matrices.
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    def lr_schedule(epoch):
        """Learning Rate Schedule

        Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
        Called automatically every epoch as part of callbacks during training.

        # Arguments
            epoch (int): The number of epochs

        # Returns
            lr (float32): learning rate
        """
        lr = 1e-3
        if epoch > 180:
            lr *= 0.5e-3
        elif epoch > 160:
            lr *= 1e-3
        elif epoch > 120:
            lr *= 1e-2
        elif epoch > 80:
            lr *= 1e-1
        print('Learning rate: ', lr)
        return lr

    def resnet_layer(inputs,
                     num_filters=16,
                     kernel_size=3,
                     strides=1,
                     activation='relu',
                     batch_normalization=True,
                     conv_first=True):
        """2D Convolution-Batch Normalization-Activation stack builder

        # Arguments
            inputs (tensor): input tensor from input image or previous layer
            num_filters (int): Conv2D number of filters
            kernel_size (int): Conv2D square kernel dimensions
            strides (int): Conv2D square stride dimensions
            activation (string): activation name
            batch_normalization (bool): whether to include batch normalization
            conv_first (bool): conv-bn-activation (True) or
                bn-activation-conv (False)

        # Returns
            x (tensor): tensor as input to the next layer
        """
        conv = Conv2D(num_filters,
                      kernel_size=kernel_size,
                      strides=strides,
                      padding='same',
                      kernel_initializer='he_normal',
                      kernel_regularizer=l2(1e-4))

        x = inputs
        if conv_first:
            x = conv(x)
            if batch_normalization:
                x = BatchNormalization()(x)
            if activation is not None:
                x = Activation(activation)(x)
        else:
            if batch_normalization:
                x = BatchNormalization()(x)
            if activation is not None:
                x = Activation(activation)(x)
            x = conv(x)
        return x

    def resnet_v1(input_shape, depth, num_classes=10):
        """ResNet Version 1 Model builder [a]

        Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
        Last ReLU is after the shortcut connection.
        At the beginning of each stage, the feature map size is halved (downsampled)
        by a convolutional layer with strides=2, while the number of filters is
        doubled. Within each stage, the layers have the same number filters and the
        same number of filters.
        Features maps sizes:
        stage 0: 32x32, 16
        stage 1: 16x16, 32
        stage 2:  8x8,  64
        The Number of parameters is approx the same as Table 6 of [a]:
        ResNet20 0.27M
        ResNet32 0.46M
        ResNet44 0.66M
        ResNet56 0.85M
        ResNet110 1.7M

        # Arguments
            input_shape (tensor): shape of input image tensor
            depth (int): number of core convolutional layers
            num_classes (int): number of classes (CIFAR10 has 10)

        # Returns
            model (Model): Keras model instance
        """
        if (depth - 2) % 6 != 0:
            raise ValueError('depth should be 6n+2 (eg 20, 32, 44 in [a])')
        # Start model definition.
        num_filters = 16
        num_res_blocks = int((depth - 2) / 6)

        inputs = Input(shape=input_shape)
        x = resnet_layer(inputs=inputs)
        # Instantiate the stack of residual units
        for stack in range(3):
            for res_block in range(num_res_blocks):
                strides = 1
                if stack > 0 and res_block == 0:  # first layer but not first stack
                    strides = 2  # downsample
                y = resnet_layer(inputs=x,
                                 num_filters=num_filters,
                                 strides=strides)
                y = resnet_layer(inputs=y,
                                 num_filters=num_filters,
                                 activation=None)
                if stack > 0 and res_block == 0:  # first layer but not first stack
                    # linear projection residual shortcut connection to match
                    # changed dims
                    x = resnet_layer(inputs=x,
                                     num_filters=num_filters,
                                     kernel_size=1,
                                     strides=strides,
                                     activation=None,
                                     batch_normalization=False)
                x = keras.layers.add([x, y])
                x = Activation('relu')(x)
            num_filters *= 2

        # Add classifier on top.
        # v1 does not use BN after last shortcut connection-ReLU
        x = AveragePooling2D(pool_size=8)(x)
        y = Flatten()(x)
        outputs = Dense(num_classes,
                        activation='softmax',
                        kernel_initializer='he_normal')(y)

        # Instantiate model.
        model = Model(inputs=inputs, outputs=outputs)
        return model

    def resnet_v2(input_shape, depth, num_classes=10):
        """ResNet Version 2 Model builder [b]

        Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as
        bottleneck layer
        First shortcut connection per layer is 1 x 1 Conv2D.
        Second and onwards shortcut connection is identity.
        At the beginning of each stage, the feature map size is halved (downsampled)
        by a convolutional layer with strides=2, while the number of filter maps is
        doubled. Within each stage, the layers have the same number filters and the
        same filter map sizes.
        Features maps sizes:
        conv1  : 32x32,  16
        stage 0: 32x32,  64
        stage 1: 16x16, 128
        stage 2:  8x8,  256

        # Arguments
            input_shape (tensor): shape of input image tensor
            depth (int): number of core convolutional layers
            num_classes (int): number of classes (CIFAR10 has 10)

        # Returns
            model (Model): Keras model instance
        """
        if (depth - 2) % 9 != 0:
            raise ValueError('depth should be 9n+2 (eg 56 or 110 in [b])')
        # Start model definition.
        num_filters_in = 16
        num_res_blocks = int((depth - 2) / 9)

        inputs = Input(shape=input_shape)
        # v2 performs Conv2D with BN-ReLU on input before splitting into 2 paths
        x = resnet_layer(inputs=inputs,
                         num_filters=num_filters_in,
                         conv_first=True)

        # Instantiate the stack of residual units
        for stage in range(3):
            for res_block in range(num_res_blocks):
                activation = 'relu'
                batch_normalization = True
                strides = 1
                if stage == 0:
                    num_filters_out = num_filters_in * 4
                    if res_block == 0:  # first layer and first stage
                        activation = None
                        batch_normalization = False
                else:
                    num_filters_out = num_filters_in * 2
                    if res_block == 0:  # first layer but not first stage
                        strides = 2    # downsample

                # bottleneck residual unit
                y = resnet_layer(inputs=x,
                                 num_filters=num_filters_in,
                                 kernel_size=1,
                                 strides=strides,
                                 activation=activation,
                                 batch_normalization=batch_normalization,
                                 conv_first=False)
                y = resnet_layer(inputs=y,
                                 num_filters=num_filters_in,
                                 conv_first=False)
                y = resnet_layer(inputs=y,
                                 num_filters=num_filters_out,
                                 kernel_size=1,
                                 conv_first=False)
                if res_block == 0:
                    # linear projection residual shortcut connection to match
                    # changed dims
                    x = resnet_layer(inputs=x,
                                     num_filters=num_filters_out,
                                     kernel_size=1,
                                     strides=strides,
                                     activation=None,
                                     batch_normalization=False)
                x = keras.layers.add([x, y])

            num_filters_in = num_filters_out

        # Add classifier on top.
        # v2 has BN-ReLU before Pooling
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = AveragePooling2D(pool_size=8)(x)
        y = Flatten()(x)
        outputs = Dense(num_classes,
                        activation='softmax',
                        kernel_initializer='he_normal')(y)

        # Instantiate model.
        model = Model(inputs=inputs, outputs=outputs)
        return model

    if version == 2:
        model = resnet_v2(input_shape=input_shape, depth=depth)
    else:
        model = resnet_v1(input_shape=input_shape, depth=depth)

    model.compile(loss='categorical_crossentropy',
                  optimizer=Adam(learning_rate=lr_schedule(0)),
                  metrics=['accuracy'])
    model.summary()
    print(model_type)

    # Prepare model model saving directory.
    save_dir = os.path.join(os.getcwd(), 'saved_models')
    model_name = 'cifar10_%s_model.{epoch:03d}.h5' % model_type
    if not os.path.isdir(save_dir):
        os.makedirs(save_dir)
    filepath = os.path.join(save_dir, model_name)

    # Prepare callbacks for model saving and for learning rate adjustment.
    checkpoint = ModelCheckpoint(filepath=filepath,
                                 monitor='val_acc',
                                 verbose=1,
                                 save_best_only=True)

    lr_scheduler = LearningRateScheduler(lr_schedule)

    lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
                                   cooldown=0,
                                   patience=5,
                                   min_lr=0.5e-6)

    callbacks = [checkpoint, lr_reducer, lr_scheduler]

    # Run training, with or without data augmentation.
    if not data_augmentation:
        print('Not using data augmentation.')
        model.fit(x_train, y_train,
                  batch_size=batch_size,
                  epochs=epochs,
                  validation_data=(x_test, y_test),
                  shuffle=True,
                  callbacks=callbacks)
    else:
        print('Using real-time data augmentation.')
        # This will do preprocessing and realtime data augmentation:
        datagen = ImageDataGenerator(
            # set input mean to 0 over the dataset
            featurewise_center=False,
            # set each sample mean to 0
            samplewise_center=False,
            # divide inputs by std of dataset
            featurewise_std_normalization=False,
            # divide each input by its std
            samplewise_std_normalization=False,
            # apply ZCA whitening
            zca_whitening=False,
            # epsilon for ZCA whitening
            zca_epsilon=1e-06,
            # randomly rotate images in the range (deg 0 to 180)
            rotation_range=0,
            # randomly shift images horizontally
            width_shift_range=0.1,
            # randomly shift images vertically
            height_shift_range=0.1,
            # set range for random shear
            shear_range=0.,
            # set range for random zoom
            zoom_range=0.,
            # set range for random channel shifts
            channel_shift_range=0.,
            # set mode for filling points outside the input boundaries
            fill_mode='nearest',
            # value used for fill_mode = "constant"
            cval=0.,
            # randomly flip images
            horizontal_flip=True,
            # randomly flip images
            vertical_flip=False,
            # set rescaling factor (applied before any other transformation)
            rescale=None,
            # set function that will be applied on each input
            preprocessing_function=None,
            # image data format, either "channels_first" or "channels_last"
            data_format=None,
            # fraction of images reserved for validation (strictly between 0 and 1)
            validation_split=0.0)

        # Compute quantities required for featurewise normalization
        # (std, mean, and principal components if ZCA whitening is applied).
        datagen.fit(x_train)

        # Fit the model on the batches generated by datagen.flow().
        model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
                            validation_data=(x_test, y_test),
                            epochs=epochs, verbose=1, workers=4,
                            callbacks=callbacks)

    # Score trained model.
    scores = model.evaluate(x_test, y_test, verbose=1)
    print('Test loss:', scores[0])
    print('Test accuracy:', scores[1])

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