TensorFlow:恢复多个图

问题描述

假设我们有两个TensorFlow计算图G1和G2，并且权重为W1和W2.假设我们仅通过构造G1和G2来构建新图G.我们如何为这个新图形G恢复W1和W2?

Suppose we have two TensorFlow computation graphs, G1 and G2, with saved weights W1 and W2. Assume we build a new graph G simply by constructing G1 and G2. How can we restore both W1 and W2 for this new graph G?

举一个简单的例子:

import tensorflow as tf

V1 = tf.Variable(tf.zeros([1]))
saver_1 = tf.train.Saver()
V2 = tf.Variable(tf.zeros([1]))
saver_2 = tf.train.Saver()

sess = tf.Session()
saver_1.restore(sess, 'W1')
saver_2.restore(sess, 'W2')

在此示例中，saver_1成功恢复了相应的V1，但是saver_2失败，并显示NotFoundError.

In this example, saver_1 succesfully restores the corresponding V1, but saver_2 fails with a NotFoundError.

推荐答案

您可能可以使用两个保护程序，其中每个保护程序仅查找变量之一.如果仅使用tf.train.Saver()，我认为它将查找您已定义的所有变量.您可以使用tf.train.Saver([v1, ...])为其提供要查找的变量列表.有关更多信息，您可以在此处阅读有关tf.train.Saver构造函数的信息: https://www.tensorflow.org/versions/r0.11/api_docs/python/state_ops.html#Saver

You can probably use two savers where each saver looks for just one of the variables. If you just use tf.train.Saver(), I think it will look for all variables you have defined. You can give it a list of variables to look for by using tf.train.Saver([v1, ...]). For more info, you can read about the tf.train.Saver constructor here: https://www.tensorflow.org/versions/r0.11/api_docs/python/state_ops.html#Saver

这是一个简单的工作示例.假设您在文件"save_vars.py"中进行计算，它具有以下代码:

Here's a simple working example. Suppose you do your computation in a file "save_vars.py" and it has the following code:

import tensorflow as tf

# Graph 1 - set v1 to have value [1.0]
g1 = tf.Graph()
with g1.as_default():
    v1 = tf.Variable(tf.zeros([1]), name="v1")
    assign1 = v1.assign(tf.constant([1.0]))
    init1 = tf.initialize_all_variables()
    save1 = tf.train.Saver()

# Graph 2 - set v2 to have value [2.0]
g2 = tf.Graph()
with g2.as_default():
    v2 = tf.Variable(tf.zeros([1]), name="v2")
    assign2 = v2.assign(tf.constant([2.0]))
    init2 = tf.initialize_all_variables()
    save2 = tf.train.Saver()

# Do the computation for graph 1 and save
sess1 = tf.Session(graph=g1)
sess1.run(init1)
print sess1.run(assign1)
save1.save(sess1, "tmp/v1.ckpt")

# Do the computation for graph 2 and save
sess2 = tf.Session(graph=g2)
sess2.run(init2)
print sess2.run(assign2)
save2.save(sess2, "tmp/v2.ckpt")

如果确保您具有tmp目录并运行python save_vars.py，则将获取已保存的检查点文件.

If you ensure that you have a tmp directory and run python save_vars.py, you'll get the saved checkpoint files.

现在，您可以使用名为"restore_vars.py"的文件通过以下代码进行还原:

Now, you can restore using a file named "restore_vars.py" with the following code:

import tensorflow as tf

# The variables v1 and v2 that we want to restore
v1 = tf.Variable(tf.zeros([1]), name="v1")
v2 = tf.Variable(tf.zeros([1]), name="v2")

# saver1 will only look for v1
saver1 = tf.train.Saver([v1])
# saver2 will only look for v2
saver2 = tf.train.Saver([v2])
with tf.Session() as sess:
    saver1.restore(sess, "tmp/v1.ckpt")
    saver2.restore(sess, "tmp/v2.ckpt")
    print sess.run(v1)
    print sess.run(v2)

，当您运行python restore_vars.py时，输出应为

and when you run python restore_vars.py, the output should be

[1.]
[2.]

(至少在我的计算机上是输出).如果有任何不清楚的地方，随时发表评论.

(at least on my computer that's the output). Feel free to post a comment if anything was unclear.

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