f.Variable()与tf.get_variable()这两个的区别

Variable
tensorflow中有两个关于variable的op，tf.Variable()与tf.get_variable()下面介绍这两个的区别

tf.Variable与tf.get_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(name, shape=None, dtype=None, initializer=None, regularizer=None, 
        trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, 
        custom_getter=None)

区别
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?

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
get_variable()与Variable的实质区别
来看下面一段代码：

    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

看到这，就可以明白官网上说的参数复用的真面目了。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。

random Tensor
可用于赋值给tf.Variable()的第一个参数

    tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)
    
    tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)
    
    tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)
    
    tf.random_shuffle(value, seed=None, name=None)
    
    tf.random_crop(value, size, seed=None, name=None)
    
    tf.multinomial(logits, num_samples, seed=None, name=None)
    
    tf.random_gamma(shape, alpha, beta=None, dtype=tf.float32, seed=None, name=None)
    
    tf.set_random_seed(seed)

constant value tensor

    tf.zeros(shape, dtype=tf.float32, name=None)
    
    tf.zeros_like(tensor, dtype=None, name=None)
    
    tf.ones(shape, dtype=tf.float32, name=None)
    
    tf.ones_like(tensor, dtype=None, name=None)
    
    tf.fill(dims, value, name=None)

    tf.constant(value, dtype=None, shape=None, name='Const')
    

initializer

    tf.constant_initializer(value=0, dtype=tf.float32)
    tf.random_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)
    tf.truncated_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)
    tf.random_uniform_initializer(minval=0, maxval=None, seed=None, dtype=tf.float32)
    tf.uniform_unit_scaling_initializer(factor=1.0, seed=None, dtype=tf.float32)
    tf.zeros_initializer(shape, dtype=tf.float32, partition_info=None)
    tf.ones_initializer(dtype=tf.float32, partition_info=None)
    tf.orthogonal_initializer(gain=1.0, dtype=tf.float32, seed=None)

参考资料
https://www.tensorflow.org/api_docs/python/state_ops/variables#Variable
https://www.tensorflow.org/api_docs/python/state_ops/sharing_variables#get_variable
https://www.tensorflow.org/versions/r0.10/api_docs/python/constant_op/
https://www.tensorflow.org/api_docs/python/state_ops/

作者：ke1th
来源：CSDN
原文：https://blog.csdn.net/u012436149/article/details/53696970
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