wresnet.py 文件源码

def wide_basic(incoming, nb_in_filters, nb_out_filters, dropout=None, subsample=(2, 2)):
    nb_bottleneck_filter = nb_out_filters

    if nb_in_filters == nb_out_filters:
        # conv3x3
        y = BatchNormalization(mode=0, axis=1)(incoming)
        y = Activation('relu')(y)
        y = ZeroPadding2D((1, 1))(y)
        y = Convolution2D(nb_bottleneck_filter, nb_row=3, nb_col=3,
                          subsample=subsample, init='he_normal', border_mode='valid')(y)

        # conv3x3
        y = BatchNormalization(mode=0, axis=1)(y)
        y = Activation('relu')(y)
        if dropout is not None:
            y = Dropout(dropout)(y)
        y = ZeroPadding2D((1, 1))(y)
        y = Convolution2D(nb_bottleneck_filter, nb_row=3, nb_col=3,
                          subsample=(1, 1), init='he_normal', border_mode='valid')(y)

        return merge([incoming, y], mode='sum')

    else:  # Residual Units for increasing dimensions
        # common BN, ReLU
        shortcut = BatchNormalization(mode=0, axis=1)(incoming)
        shortcut = Activation('relu')(shortcut)

        # conv3x3
        y = ZeroPadding2D((1, 1))(shortcut)
        y = Convolution2D(nb_bottleneck_filter, nb_row=3, nb_col=3,
                          subsample=subsample, init='he_normal', border_mode='valid')(y)

        # conv3x3
        y = BatchNormalization(mode=0, axis=1)(y)
        y = Activation('relu')(y)
        if dropout is not None:
            y = Dropout(dropout)(y)
        y = ZeroPadding2D((1, 1))(y)
        y = Convolution2D(nb_out_filters, nb_row=3, nb_col=3,
                          subsample=(1, 1), init='he_normal', border_mode='valid')(y)

        # shortcut
        shortcut = Convolution2D(nb_out_filters, nb_row=1, nb_col=1,
                                 subsample=subsample, init='he_normal', border_mode='same')(shortcut)

        return merge([shortcut, y], mode='sum')