代码拉取完成，页面将自动刷新
Simple Tensorflow implementation of Squeeze Excitation Networks using Cifar10
I implemented the following SENet
If you want to see the original author's code, please refer to this link
input_x = tf.pad(input_x, [[0, 0], [32, 32], [32, 32], [0, 0]]) # size 32x32 -> 96x96
with tf.Session() as sess : NO
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess : OK
def Squeeze_excitation_layer(self, input_x, out_dim, ratio, layer_name):
with tf.name_scope(layer_name) :
squeeze = Global_Average_Pooling(input_x)
excitation = Fully_connected(squeeze, units=out_dim / ratio, layer_name=layer_name+'_fully_connected1')
excitation = Relu(excitation)
excitation = Fully_connected(excitation, units=out_dim, layer_name=layer_name+'_fully_connected2')
excitation = Sigmoid(excitation)
excitation = tf.reshape(excitation, [-1,1,1,out_dim])
scale = input_x * excitation
return scale
Will be soon
Junho Kim
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