GoogleNet.py 文件源码

def conv2d_bn(x, nb_filter,num_row, num_col, strides=(1,1), padding='same', name=None):
    if name is not None:
        bn_name = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name = None
        conv_name = None

    if K.image_data_format() == 'channels_first':
        bn_axis = 1
    else:
        bn_axis = 3

    x = Convolution2D(nb_filter,[num_row, num_col],padding=padding,strides=strides,activation='relu',name=conv_name)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name)(x)
    return x