1 简介
tf.Variable()
tf.Variable(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None, dtype=None, expected_shape=None, import_scope=None)
tf.get_variable()
tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, custom_getter=None)
2 区别
1、使用tf.Variable时，如果检测到命名冲突，系统会自己处理。使用tf.get_variable()时，系统不会处理冲突，而会报错
import tensorflow as tf w_1 = tf.Variable(3,name="w_1") w_2 = tf.Variable(1,name="w_1") print w_1.name print w_2.name #输出 #w_1:0 #w_1_1:0
import tensorflow as tf w_1 = tf.get_variable(name="w_1",initializer=1) w_2 = tf.get_variable(name="w_1",initializer=2) #错误信息 #ValueError: Variable w_1 already exists, disallowed. Did #you mean to set reuse=True in VarScope?
2、基于这两个函数的特性，当我们需要共享变量的时候，需要使用tf.get_variable()。在其他情况下，这两个的用法是一样的
import tensorflow as tf with tf.variable_scope("scope1"): w1 = tf.get_variable("w1", shape=[]) w2 = tf.Variable(0.0, name="w2") with tf.variable_scope("scope1", reuse=True): w1_p = tf.get_variable("w1", shape=[]) w2_p = tf.Variable(1.0, name="w2") print(w1 is w1_p, w2 is w2_p) #输出 #True False
由于tf.Variable() 每次都在创建新对象，所有reuse=True 和它并没有什么关系。对于get_variable()，来说，如果已经创建的变量对象，就把那个对象返回，如果没有创建变量对象的话，就创建一个新的。
以上内容来自于：tensorflow学习笔记（二十三）：variable与get_variable
3 实例
import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' import tensorflow as tf x1 = tf.truncated_normal([200, 100], name='x1') x2 = tf.truncated_normal([200, 100], name='x2') def two_hidden_layers_1(x): assert x.shape.as_list() == [200, 100] w1 = tf.Variable(tf.random_normal([100, 50]), name='h1_weights') b1 = tf.Variable(tf.zeros([50]), name='h1_biases') h1 = tf.matmul(x, w1) + b1 assert h1.shape.as_list() == [200, 50] w2 = tf.Variable(tf.random_normal([50, 10]), name='h2_weights') b2 = tf.Variable(tf.zeros([10]), name='2_biases') logits = tf.matmul(h1, w2) + b2 return logits def two_hidden_layers_2(x): assert x.shape.as_list() == [200, 100] w1 = tf.get_variable('h1_weights', [100, 50], initializer=tf.random_normal_initializer()) b1 = tf.get_variable('h1_biases', [50], initializer=tf.constant_initializer(0.0)) h1 = tf.matmul(x, w1) + b1 assert h1.shape.as_list() == [200, 50] w2 = tf.get_variable('h2_weights', [50, 10], initializer=tf.random_normal_initializer()) b2 = tf.get_variable('h2_biases', [10], initializer=tf.constant_initializer(0.0)) logits = tf.matmul(h1, w2) + b2 return logits def fully_connected(x, output_dim, scope): with tf.variable_scope(scope, reuse=tf.AUTO_REUSE) as scope: w = tf.get_variable('weights', [x.shape[1], output_dim], initializer=tf.random_normal_initializer()) b = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0)) return tf.matmul(x, w) + b def two_hidden_layers_3(x): h1 = fully_connected(x, 50, 'h1') h2 = fully_connected(h1, 10, 'h2') return h2 # with tf.variable_scope('two_layers') as scope: # logits1 = two_hidden_layers_1(x1) # # scope.reuse_variables() # logits2 = two_hidden_layers_1(x2) # 不会报错 # --------------- # with tf.variable_scope('two_layers') as scope: # logits1 = two_hidden_layers_2(x1) # # scope.reuse_variables() # logits2 = two_hidden_layers_2(x2) # 会报错 # --------------- with tf.variable_scope('two_layers') as scope: logits1 = two_hidden_layers_3(x1) # scope.reuse_variables() logits2 = two_hidden_layers_3(x2) # 不会报错 # ------- writer = tf.summary.FileWriter('./graphs/cool_variables', tf.get_default_graph()) writer.close()
