tf.variable_scope & tf.varaible_scope

# -*- coding:utf-8 -*-
import tensorflow as tf
# 设置GPU按需增长
#config = tf.ConfigProto()
#config.gpu_options.allow_growth = True
#sess = tf.Session(config=config)
sess = tf.Session()

# 1.placeholder
v1 = tf.placeholder(tf.float32, shape=[2,3,4])
print v1.name
v1 = tf.placeholder(tf.float32, shape=[2,3,4], name='ph')
print v1.name
v1 = tf.placeholder(tf.float32, shape=[2,3,4], name='ph')
print v1.name
print type(v1)
print v1

# 2. tf.Variable()
v2 = tf.Variable([1,2], dtype=tf.float32)
print v2.name
v2 = tf.Variable([1,2], dtype=tf.float32, name='V')
print v2.name
v2 = tf.Variable([1,2], dtype=tf.float32, name='V')
print v2.name
print type(v2)
print v2

with tf.name_scope('nsc1'):
v1 = tf.Variable([1], name='v1')
with tf.variable_scope('vsc1'):
v2 = tf.Variable([1], name='v2')
v3 = tf.get_variable(name='v3', shape=[])
print 'v1.name: ', v1.name
print 'v2.name: ', v2.name
print 'v3.name: ', v3.name

'''
第一组，用 tf.Variable() 的方式来定义。
'''
# 拿官方的例子改动一下
def my_image_filter():
conv1_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
name="conv1_weights")
conv1_biases = tf.Variable(tf.zeros([32]), name="conv1_biases")
conv2_weights = tf.Variable(tf.random_normal([5, 5, 32, 32]),
name="conv2_weights")
conv2_biases = tf.Variable(tf.zeros([32]), name="conv2_biases")
return None

# First call creates one set of 4 variables.
result1 = my_image_filter()
# Another set of 4 variables is created in the second call.
result2 = my_image_filter()
# 获取所有的可训练变量
vs = tf.trainable_variables()
print 'There are %d train_able_variables in the Graph: ' % len(vs)
for v in vs:
print v

'''
2.第二种方式，用 tf.get_variable() 的方式
'''
# 下面是定义一个卷积层的通用方式
def conv_relu(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_initializer(0.0))
return None

def my_image_filter():
# 按照下面的方式定义卷积层，非常直观，而且富有层次感
with tf.variable_scope("conv1"):
# Variables created here will be named "conv1/weights", "conv1/biases".
relu1 = conv_relu([5, 5, 32, 32], [32])
with tf.variable_scope("conv2"):
# Variables created here will be named "conv2/weights", "conv2/biases".
return conv_relu( [5, 5, 32, 32], [32])

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()
scope.reuse_variables()
result2 = my_image_filter()

# 看看下面，完美地实现了变量共享！！！
vs = tf.trainable_variables()
print 'There are %d train_able_variables in the Graph: ' % len(vs)
for v in vs:
print v