import tensorflow as tf
# 在不同的变量域中调用conv_relu，并且声明我们想创建新的变量
def my_image_filter(input_images):
with tf.variable_scope("conv1"):
# Variables created here will be named "conv1/weights" ,"conv1/biases"
relu1 = conv_relu(input_images, [5, 5, 32, 32], [32])
with tf.variable_scope("conv2"):
# Variables created here will be named "conv2/weights" , "conv2/biases"
return conv_relu(relu1, [5, 5, 32, 32], [32])
# 如果你想分享变量，你有两个选择，第一你可以创建一个有相同名字的变量域，使用reuse=True
with tf.variable_scope("model"):
output1 = my_image_filter(input1)
with tf.variable_scope("model", reuse=True): output2 = my_image_filter(input2)
# 你也可以调用scope.reuse_variables()来触发一个重用：
with tf.variable_scope("model") as scope:
output1 = my_image_filter(input1)
scope.reuse_variables()
output2 = my_image_filter(input2)
# 因为解析一个变量域的名字是有危险的
# 通过一个变量来初始化另一个变量也是可行的
with tf.variable_scope("model") as scope:
output1 = my_image_filter(input1)
with tf.variable_scope(scope, reuse=True):
output2 = my_image_filter(input2)
