在 Tensorflow 中，如果元图使用 TFRecord 输入(没有占位符)，如何使用恢复的元图 [英] In Tensorflow, how to use a restored meta-graph if the meta graph was feeding with TFRecord input (without placeholders)

问题描述

我用 TFRecord 输入管道训练了一个网络.换句话说，没有占位符.简单的例子是:

I trained a network with TFRecord input pipeline. In other words, there was no placeholders. Simple example would be:

input, truth = _get_next_batch()  # TFRecord. `input` is not a tf.placeholder
net = Model(input)
net.set_loss(truth)
optimizer = tf...(net.loss)

假设，我获得了三个文件，ckpt-20000.meta、ckpt-20000.data-0000-of-0001、ckpt-20000.索引.我明白，以后可以使用 .meta 文件导入元图并访问张量，例如:

Let's say, I acquired three files, ckpt-20000.meta, ckpt-20000.data-0000-of-0001, ckpt-20000.index. I understood that, later one can import the meta-graph using the .meta file and access tensors such as:

new_saver = tf.train.import_meta_graph('ckpt-20000.meta')
new_saver.restore(sess, 'ckpt-20000')
logits = tf.get_collection("logits")[0]

然而，元图在管道中从一开始就没有占位符.有没有一种方法可以使用元图和输入的查询推理?

However, the meta-graph does not have a placeholder from the beginning in the pipeline. Is there a way that I can use meta-graph and query inference of an input?

有关信息，在查询应用程序(或脚本)中，我曾经使用占位符和恢复的模型权重定义模型(见下文).我想知道我是否可以直接使用元图而不重新定义，因为它会更简单.

For information, in a query application (or a script), I used to define a model with a placeholder and restored model weights (see below). I am wondering if I can just utilize the meta-graph without re-definition since it would be much more simple.

input = tf.placeholder(...)
net = Model(input)
tf.restore(sess, 'ckpt-2000')
lgt = sess.run(net.logits, feed_dict = {input:img})

推荐答案

您可以构建一个使用 placeholder_with_default() 作为输入的图，因此可以同时使用 TFRecord 输入管道 以及 feed_dict{}.

You can build a graph that uses placeholder_with_default() for the inputs, so can use both TFRecord input pipeline as well as feed_dict{}.

示例:

input, truth = _get_next_batch()
_x = tf.placeholder_with_default(input, shape=[...], name='input')
_y = tf.placeholder_with_default(truth, shape-[...], name='label')

net = Model(_x)
net.set_loss(_y)
optimizer = tf...(net.loss)

然后在推理过程中，

loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
  new_saver = tf.train.import_meta_graph('ckpt-20000.meta')
  new_saver.restore(sess, 'ckpt-20000')

  # Get the tensors by their variable name
  input = loaded_graph.get_tensor_by_name('input:0')
  logits = loaded_graph.get_tensor_by_name(...)

  # Now you can feed the inputs to your tensors
  lgt = sess.run(logits, feed_dict = {input:img})

在上面的例子中，如果你不提供输入，那么输入将从 TFRecord 输入管道读取.

In the above example, if you don't feed input, then the input will be read from the TFRecord input pipeline.

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