在 Tensorflow 中更改变量的初始值设定项 [英] Change initializer of Variable in Tensorflow

问题描述

我有一个预定义的代码来创建一个 Tensorflow 图.变量包含在变量作用域中，每个变量都有一个预定义的初始值设定项.有没有办法改变变量的初始值设定项?

I have a predefined code that creates a Tensorflow graph. The variables are contained in variable scopes and each has a predefined initializer. Is there any way to change the initializer of the variables?

示例:第一个图定义

with tf.variable_scope('conv1')
    w = tf.get_variable('weights')

稍后我想修改变量并将初始值设定项更改为 Xavier:

Later on I would like to modify variable and change the initializer to Xavier:

 with tf.variable_scope('conv1')
     tf.get_variable_scope().reuse_variable()
     w = tf.get_variable('weights',initializer=tf.contrib.layers.xavier_initializer(uniform=False))

然而，当我重用一个变量时，初始化器不会改变.稍后当我执行 initialize_all_variables() 我得到默认值而不是 Xavier如何更改变量的初始值设定项?谢谢

However, when I reuse a variable, the initializer doesn't change. later on when I do initialize_all_variables() I get the default values and not Xavier How can I change the initializer of a variable? Thanks

推荐答案

问题是无法在设置重用时更改初始化(初始化在第一个块中设置).

The problem is that initialization can't be changed on setting up reuse (the initialization is set during the first block).

因此，只需在第一个变量作用域调用期间使用 xavier 初始化定义它.所以第一次调用将是，然后所有变量的初始化都是正确的:

So, just define it with xavier intialization during the first variable scope call. So the first call would be, then initialization of all variables with be correct:

with tf.variable_scope(name) as scope:
    kernel = tf.get_variable("W",
                             shape=kernel_shape, initializer=tf.contrib.layers.xavier_initializer_conv2d())
    # you could also just define your network layer 'now' using this kernel
    # ....
    # Which would need give you a model (rather just weights)

如果您需要重复使用权重集，第二次调用可以为您提供一份副本.

If you need to re-use the set of weights, the second call can get you a copy of it.

with tf.variable_scope(name, reuse=True) as scope:
    kernel = tf.get_variable("W")
    # you can now reuse the xavier initialized variable
    # ....

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