neuralnets.py 文件源码

def conv_block_td(input_tensor, kernel_size, filters, stage, block, input_shape, strides=(2, 2), trainable=True):

    # conv block time distributed

    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = TimeDistributed(Convolution2D(nb_filter1, (1, 1), strides=strides, trainable=trainable, kernel_initializer='normal'), input_shape=input_shape, name=conv_name_base + '2a')(input_tensor)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter2, (1, kernel_size), padding='same', trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '2b')(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter3, (1, 1), kernel_initializer='normal'), name=conv_name_base + '2c', trainable=trainable)(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2c')(x)

    shortcut = TimeDistributed(Convolution2D(nb_filter3, (1, 1), strides=strides, trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '1')(input_tensor)
    shortcut = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '1')(shortcut)

    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x