def misconception_with_bypass(input,
window_size,
stride,
depth,
is_training,
scope=None):
with tf.name_scope(scope):
with slim.arg_scope(
[slim.conv2d],
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params={'is_training': is_training}):
residual = misconception_layer(input, window_size, stride, depth,
is_training, scope)
if stride > 1:
input = slim.avg_pool2d(
input, [1, stride], stride=[1, stride], padding='SAME')
input = zero_pad_features(input, depth)
return input + residual
layers.py 文件源码
python
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