# 下面是定义一个卷积层的通用方式
def conv_relu(input, kernel_shape, bias_shape):
  # Create variable named "weights".
  weights = tf.get_variable("weights", kernel_shape,
    initializer=tf.random_normal_initializer())
  # Create variable named "biases".
  biases = tf.get_variable("biases", bias_shape,
    initializer=tf.constant_intializer(0.0))
  conv = tf.nn.conv2d(input, weights,
    strides=[1, 1, 1, 1], padding='SAME')
  return tf.nn.relu(conv + biases)


# 定义一个图片过滤器
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])


# 实验一：调用 my_image_filter() 两次
result1 = my_image_filter(image1)
result2 = my_image_filter(image2)
> Raises ValueError(... conv1/weights already exists ...), tf.get_variable()会检测已经存在的变量是否已经共享


# 解决方法一, 可以在设计网络时加上一个布尔型的 reuse 参数 
with tf.variable_scope("image_filters"):
  result1 = my_image_filter(image1)
with tf.variable_scope("image_filters", reuse=True):
  result2 = my_image_filter(image2)


# 解决方法二
with tf.variable_scope("image_filters") as scope:
  # 下面我们两次调用 my_image_filter 函数，但是由于引入了变量共享机制
  # 可以看到我们只是创建了一遍网络结构。
  result1 = my_image_filter(image1)
  scope.reuse_variables()
  result2 = my_image_filter(image2)


# 解决方法三
with tf.variable_scope("image_filters") as scope:
  result1 = my_image_filter(image1)
with tf.variable_scope(scope, reuse=True):
  result2 = my_image_filter(image2)


# 打印出所有的可训练参变量
vs = tf.trainable_variables()
print('There are %d trainable_variables in the Graph: ' % len(vs))
for v in vs:
  print(v)


# 输出结果证明确实：参变量共享，因为只有四个变量，没有创建新的变量。
There are 4 trainable_variables in the Graph: 
Tensor("image_filters/conv1/weights/read:0", shape=(5, 5, 32, 32), dtype=float32)
Tensor("image_filters/conv1/biases/read:0", shape=(32,), dtype=float32)
Tensor("image_filters/conv2/weights/read:0", shape=(5, 5, 32, 32), dtype=float32)
Tensor("image_filters/conv2/biases/read:0", shape=(32,), dtype=float32)

# Returns all variables created with trainable=True in a var_list
var_list = tf.trainable_variables()

init = tf.global_variables_initializer()
sess.run(init)

for var in var_list:
  sess.run(var)