nasnet.py 文件源码

def _separable_conv_block(ip, filters, kernel_size=(3, 3), strides=(1, 1), weight_decay=5e-5, id=None):
    '''Adds 2 blocks of [relu-separable conv-batchnorm]

    # Arguments:
        ip: input tensor
        filters: number of output filters per layer
        kernel_size: kernel size of separable convolutions
        strides: strided convolution for downsampling
        weight_decay: l2 regularization weight
        id: string id

    # Returns:
        a Keras tensor
    '''
    channel_dim = 1 if K.image_data_format() == 'channels_first' else -1

    with K.name_scope('separable_conv_block_%s' % id):
        x = Activation('relu')(ip)
        x = SeparableConv2D(filters, kernel_size, strides=strides, name='separable_conv_1_%s' % id,
                            padding='same', use_bias=False, kernel_initializer='he_normal',
                            kernel_regularizer=l2(weight_decay))(x)
        x = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
                               name="separable_conv_1_bn_%s" % (id))(x)
        x = Activation('relu')(x)
        x = SeparableConv2D(filters, kernel_size, name='separable_conv_2_%s' % id,
                            padding='same', use_bias=False, kernel_initializer='he_normal',
                            kernel_regularizer=l2(weight_decay))(x)
        x = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
                               name="separable_conv_2_bn_%s" % (id))(x)
    return x