未找到:FeedInputs:无法找到提要输出 TensorFlow

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问题描述

我在这个网站上尝试了这个在 C++ 中使用 Tensorflow 保存模型的例子:https://medium.com/jim-fleming/loading-a-tensorflow-graph-with-the-c-api-4caaff88463f#.ji310n4zo

I was trying this example of using Tensorflow saved model in c++ in this website:https://medium.com/jim-fleming/loading-a-tensorflow-graph-with-the-c-api-4caaff88463f#.ji310n4zo

效果很好.但它不保存变量 a 和 b 的值，因为它只保存图形而不保存变量.我试图替换以下行:

It works well. But it does not save the values of the variables a and b as it only saves the graph not the variables. I tried to replace the following line:

tf.train.write_graph(sess.graph_def, 'models/', 'graph.pb', as_text=False)

与

saver.save(sess, 'models/graph', global_step=0)

当然是在创建保护程序对象之后.它不起作用，它输出:

of course after creating the saver object. It does not work and it outputs:

未找到:FeedInputs:无法找到提要输出

我检查了加载的节点，它们只是:

I checked the nodes the Nodes that are loaded and they are only:

_源

_SINK

在 write_graph 函数中，然后在 C++ 中加载模型时，我加载了以下节点:

while in the write_graph function and then load the model in C++, I got the following nodes loaded:

_源

_SINK

save/restore_slice_1/shape_and_slice

save/restore_slice_1/tensor_name

save/restore_slice/shape_and_slice

save/restore_slice/tensor_name

save/save/shapes_and_slices

save/save/tensor_names

保存/常量

save/restore_slice_1

save/restore_slice

保存/分配_1

读/读

b/initial_value

b/分配

一个

保存/分配

保存/恢复所有

保存/保存

save/control_dependency

读/读

a/initial_value

a/分配

初始化

张量

甚至saver.save()创建的图形文件比write_graph创建的1.9KB小得多，165B.

and even the graph file that is created by saver.save() is much smaller, 165B, compared to the one created by write_graph, 1.9KB.

推荐答案

我不确定这是否是解决问题的最佳方式，但至少可以解决问题.

I'm not sure if that is the best way of solving the problem but at least it solves it.

由于 write_graph 也可以存储常量的值，所以在使用 write_graph 函数编写图形之前，我在 python 中添加了以下代码:

As write_graph can also store the values of the constants, I added the following code to the python just before writing the graph with write_graph function:

for v in tf.trainable_variables():
    vc = tf.constant(v.eval())
    tf.assign(v, vc, name="assign_variables")

这会创建在训练后存储变量值的常量，然后创建张量assign_variables"以将它们分配给变量.现在，当您调用 write_graph 时，它会将变量的值存储在文件中.

This creates constants that store variables' values after being trained and then create tensors "assign_variables" to assign them to the variables. Now, when you call write_graph, it will store the variables' values in the file.

剩下的唯一部分是在 c 代码中调用这些张量assign_variables"，以确保为您的变量分配了存储在文件.这是一种方法:

The only remaining part is to call these tensors "assign_variables" in the c code to make sure that your variables are assigned with the constants values that are stored in the file. Here is a one way to do it:

      Status status = NewSession(SessionOptions(), &session);
      std::vector<tensorflow::Tensor> outputs;
      for(int i = 0;status.ok(); i++) {
        char name[100];
        if (i==0)
            sprintf(name, "assign_variables");
        else
            sprintf(name, "assign_variables_%d", i);

        status = session->Run({}, {name}, {}, &outputs);
      }

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