如何替换已保存图形的输入，例如数据集迭代器的占位符?

本文介绍了如何替换已保存图形的输入，例如数据集迭代器的占位符?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

限时送ChatGPT账号..

我有一个已保存的 Tensorflow 图，它​​通过带有 feed_dict 参数的 placeholder 消耗输入.

I have a saved Tensorflow graph that consumes input through a placeholder with a feed_dict param.

sess.run(my_tensor, feed_dict={input_image: image})

因为使用 Dataset Iterator 提供数据是 更高效，我想加载保存的图形，用 Iterator 替换 input_image placeholder 并运行.我怎样才能做到这一点?有没有更好的方法来做到这一点?非常感谢提供代码示例的答案.

Because feeding data with a Dataset Iterator is more efficient, I want to load the saved graph, replace the input_image placeholder with an Iterator and run. How can I do that? Is there a better way to do it? An answer with code example would be highly appreciated.

推荐答案

您可以通过序列化您的图形并使用 tf.import_graph_def 重新导入它来实现这一点，它有一个 input_map> 用于在所需位置插入输入的参数.

You can achieve that by serializing your graph and reimport it using tf.import_graph_def, which has an input_map argument used to plug-in inputs at the desired places.

要做到这一点，您至少需要知道要替换的输入的名称以及要执行的输出的名称(在我的示例中分别是 x 和 y).

To do that you need at least to know the name of the inputs you replace and of the outputs you wish to execute (resp. x and y in my examples).

import tensorflow as tf

# restore graph (built from scratch here for the example)
x = tf.placeholder(tf.int64, shape=(), name='x')
y = tf.square(x, name='y')

# just for display -- you don't need to create a Session for serialization
with tf.Session() as sess:
  print("with placeholder:")
  for i in range(10):
    print(sess.run(y, {x: i}))

# serialize the graph
graph_def = tf.get_default_graph().as_graph_def()

tf.reset_default_graph()

# build new pipeline
batch = tf.data.Dataset.range(10).make_one_shot_iterator().get_next()
# plug in new pipeline
[y] = tf.import_graph_def(graph_def, input_map={'x:0': batch}, return_elements=['y:0'])

# enjoy Dataset inputs!
with tf.Session() as sess:
  print('with Dataset:')
  try:
    while True:
      print(sess.run(y))
  except tf.errors.OutOfRangeError:
    pass        

请注意，占位符节点仍然存在，因为我没有费心在这里解析 graph_def 以将其删除 - 您可以将其删除以作为改进，尽管我认为保留它也可以在这里.

Note that the placeholder node is still there as I did not bother here to parse graph_def to remove it -- you could remove it as an improvement, although I think it is also OK to leave it here.

根据您恢复图形的方式，输入替换可能已经内置在加载程序中，这使事情变得更简单(无需回到 GraphDef).例如，如果您从 .meta 文件加载图形，则可以使用接受相同 input_map 参数的 tf.train.import_meta_graph.

Depending on how you restore your graph, the input replacement may be already built-in in the loader, which makes things simpler (no need to go back to a GraphDef). For example, if you load your graph from a .meta file, you can use tf.train.import_meta_graph which accepts the same input_map argument.

import tensorflow as tf

# build new pipeline
batch = tf.data.Dataset.range(10).make_one_shot_iterator().get_next()
# load your net and plug in new pipeline
# you need to know the name of the tensor where to plug-in your input
restorer = tf.train.import_meta_graph(graph_filepath, input_map={'x:0': batch})
y = tf.get_default_graph().get_tensor_by_name('y:0')

# enjoy Dataset inputs!
with tf.Session() as sess:
  # not needed here, but in practice you would also need to restore weights
  # restorer.restore(sess, weights_filepath)
  print('with Dataset:')
  try:
    while True:
      print(sess.run(y))
  except tf.errors.OutOfRangeError:
    pass        

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