Tensorflow教程： tf.Variable() 和tf.get_variable()

1tf.Variable(initial_value=None, trainable=True,    collections=None, validate_shape=True, 
2caching_device=None, name=None, variable_def=None, dtype=None, expected_shape=None, 
3import_scope=None)

1tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, 
2trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, 
3custom_getter=None)

1import tensorflow as tf
2w_1 = tf.Variable(3,name="w_1")
3w_2 = tf.Variable(1,name="w_1")
4print w_1.name
5print w_2.name
6#输出
7#w_1:0
8#w_1_1:0

1import tensorflow as tf
2
3w_1 = tf.get_variable(name="w_1",initializer=1)
4w_2 = tf.get_variable(name="w_1",initializer=2)
5#错误信息
6#ValueError: Variable w_1 already exists, disallowed. Did
7#you mean to set reuse=True in VarScope?

 1 import tensorflow as tf
 2
 3 with tf.variable_scope("scope1"):
 4 w1 = tf.get_variable("w1", shape=[])
 5 w2 = tf.Variable(0.0, name="w2")
 6 with tf.variable_scope("scope1", reuse=True):
 7 w1_p = tf.get_variable("w1", shape=[])
 8 w2_p = tf.Variable(1.0, name="w2")
 9
10 print(w1 is w1_p, w2 is w2_p)
11 #输出
12 #True  False

 1import os
 2os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 3
 4import tensorflow as tf
 5
 6x1 = tf.truncated_normal([200, 100], name='x1')
 7x2 = tf.truncated_normal([200, 100], name='x2')
 8
 9def two_hidden_layers_1(x):
10assert x.shape.as_list() == [200, 100]
11w1 = tf.Variable(tf.random_normal([100, 50]), name='h1_weights')
12b1 = tf.Variable(tf.zeros([50]), name='h1_biases')
13h1 = tf.matmul(x, w1) + b1
14assert h1.shape.as_list() == [200, 50]
15w2 = tf.Variable(tf.random_normal([50, 10]), name='h2_weights')
16b2 = tf.Variable(tf.zeros([10]), name='2_biases')
17logits = tf.matmul(h1, w2) + b2
18return logits
19
20def two_hidden_layers_2(x):
21assert x.shape.as_list() == [200, 100]
22w1 = tf.get_variable('h1_weights', [100, 50],  initializer=tf.random_normal_initializer())
23b1 = tf.get_variable('h1_biases', [50], initializer=tf.constant_initializer(0.0))
24h1 = tf.matmul(x, w1) + b1
25assert h1.shape.as_list() == [200, 50]
26w2 = tf.get_variable('h2_weights', [50, 10], initializer=tf.random_normal_initializer())
27b2 = tf.get_variable('h2_biases', [10], initializer=tf.constant_initializer(0.0))
28logits = tf.matmul(h1, w2) + b2
29return logits
30
31
32def fully_connected(x, output_dim, scope):
33with tf.variable_scope(scope, reuse=tf.AUTO_REUSE) as scope:
34w = tf.get_variable('weights', [x.shape[1], output_dim], initializer=tf.random_normal_initializer())
35b = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
36return tf.matmul(x, w) + b
37
38def two_hidden_layers_3(x):
39h1 = fully_connected(x, 50, 'h1')
40h2 = fully_connected(h1, 10, 'h2')
41return h2
42# with tf.variable_scope('two_layers') as scope:
43#     logits1 = two_hidden_layers_1(x1) 
44#     # scope.reuse_variables()
45#     logits2 = two_hidden_layers_1(x2)
46# 不会报错
47# ---------------
48
49# with tf.variable_scope('two_layers') as scope:
50#     logits1 = two_hidden_layers_2(x1)
51#     # scope.reuse_variables()
52#     logits2 = two_hidden_layers_2(x2)
53# 会报错
54# ---------------
55
56with tf.variable_scope('two_layers') as scope:
57logits1 = two_hidden_layers_3(x1)
58# scope.reuse_variables()
59logits2 = two_hidden_layers_3(x2)
60# 不会报错
61# -------
62writer = tf.summary.FileWriter('./graphs/cool_variables', tf.get_default_graph())
63writer.close()