def _bn_relu_conv(nb_filter, nb_row, nb_col, subsample=False, upsample=False,
batch_norm=True, weight_decay=None):
def f(input):
processed = input
if batch_norm:
processed = BatchNormalization(mode=0, axis=1)(processed)
processed = Activation('relu')(processed)
stride = (1, 1)
if subsample:
stride = (2, 2)
if upsample:
processed = UpSampling2D(size=(2, 2))(processed)
return Convolution2D(nb_filter=nb_filter, nb_row=nb_row, nb_col=nb_col,
subsample=stride, init='he_normal',
border_mode='same',
W_regularizer=_l2(weight_decay))(processed)
return f
# Adds a shortcut between input and residual block and merges them with 'sum'
fcn_resunet_blocks.py 文件源码
python
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