Tensorflow：在GPU上运行训练阶段并在CPU上测试阶段

问题描述：

我希望在我的GPU上运行我的tensorflow代码的训练阶段，而在完成并存储结果以加载我创建的模型并在CPU上运行其测试阶段。Tensorflow：在GPU上运行训练阶段并在CPU上测试阶段

我已经创建了这个代码（我已经把它的一部分，仅供参考，因为它是巨大的，否则，我知道规则将包括一个功能完整的代码，我很抱歉）。

import pandas as pd 
import matplotlib.pyplot as plt 
import numpy as np 
import tensorflow as tf 
from tensorflow.contrib.rnn.python.ops import rnn_cell, rnn 

# Import MNIST data http://yann.lecun.com/exdb/mnist/ 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 
x_train = mnist.train.images 
# Check that the dataset contains 55,000 rows and 784 columns 
N,D = x_train.shape 

tf.reset_default_graph() 
sess = tf.InteractiveSession() 

x = tf.placeholder("float", [None, n_steps,n_input]) 
y_true = tf.placeholder("float", [None, n_classes]) 
keep_prob = tf.placeholder(tf.float32,shape=[]) 
learning_rate = tf.placeholder(tf.float32,shape=[]) 

#[............Build the RNN graph model.............] 

sess.run(tf.global_variables_initializer()) 
# Because I am using my GPU for the training, I avoid allocating the whole 
# mnist.validation set because of memory error, so I gragment it to 
# small batches (100) 
x_validation_bin, y_validation_bin = mnist.validation.next_batch(batch_size) 
x_validation_bin = binarize(x_validation_bin, threshold=0.1) 
x_validation_bin = x_validation_bin.reshape((-1,n_steps,n_input)) 

for k in range(epochs): 

    steps = 0 

    for i in range(training_iters): 
     #Stochastic descent 
     batch_x, batch_y = mnist.train.next_batch(batch_size) 
     batch_x = binarize(batch_x, threshold=0.1) 
     batch_x = batch_x.reshape((-1,n_steps,n_input)) 
     sess.run(train_step, feed_dict={x: batch_x, y_true: batch_y,keep_prob: keep_prob,eta:learning_rate}) 

     if do_report_err == 1: 
      if steps % display_step == 0: 
       # Calculate batch accuracy 
       acc = sess.run(accuracy, feed_dict={x: batch_x, y_true: batch_y,keep_prob: 1.0}) 
       # Calculate batch loss 
       loss = sess.run(total_loss, feed_dict={x: batch_x, y_true: batch_y,keep_prob: 1.0}) 
       print("Iter " + str(i) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy = " + "{:.5f}".format(acc)) 
     steps += 1 




    # Validation Accuracy and Cost 
    validation_accuracy = sess.run(accuracy,feed_dict={x:x_validation_bin, y_true:y_validation_bin, keep_prob:1.0}) 
    validation_cost = sess.run(total_loss,feed_dict={x:x_validation_bin, y_true:y_validation_bin, keep_prob:1.0}) 

    validation_loss_array.append(final_validation_cost) 
    validation_accuracy_array.append(final_validation_accuracy) 
    saver.save(sess, savefilename) 
    total_epochs = total_epochs + 1 

    np.savez(datasavefilename,epochs_saved = total_epochs,learning_rate_saved = learning_rate,keep_prob_saved = best_keep_prob, validation_loss_array_saved = validation_loss_array,validation_accuracy_array_saved = validation_accuracy_array,modelsavefilename = savefilename)

在那之后，我的模型已经成功培训并保存相关数据，所以我希望加载该文件，并做模型的最终训练和测试的一部分，但用我的CPU这一次。原因是GPU无法处理mnist.train.images和mnist.train.labels的整个数据集。

所以，我手动选择这个部分，我运行它：

with tf.device('/cpu:0'): 
# Initialise variables 
    sess.run(tf.global_variables_initializer()) 

    # Accuracy and Cost 
    saver.restore(sess, savefilename) 
    x_train_bin = binarize(mnist.train.images, threshold=0.1) 
    x_train_bin = x_train_bin.reshape((-1,n_steps,n_input)) 
    final_train_accuracy = sess.run(accuracy,feed_dict={x:x_train_bin, y_true:mnist.train.labels, keep_prob:1.0}) 
    final_train_cost = sess.run(total_loss,feed_dict={x:x_train_bin, y_true:mnist.train.labels, keep_prob:1.0}) 

    x_test_bin = binarize(mnist.test.images, threshold=0.1) 
    x_test_bin = x_test_bin.reshape((-1,n_steps,n_input)) 
    final_test_accuracy = sess.run(accuracy,feed_dict={x:x_test_bin, y_true:mnist.test.labels, keep_prob:1.0}) 
    final_test_cost = sess.run(total_loss,feed_dict={x:x_test_bin, y_true:mnist.test.labels, keep_prob:1.0})

，但我得到的OMM GPU内存错误，它没有任何意义，我因为我觉得我已经迫使程序依靠CPU。我没有在第一个（批量培训）代码中使用sess.close（）命令，但我不确定这是否是它背后的原因。我跟着这个帖子实际上for the CPU 任何建议如何运行CPU上的最后一部分？

答

with tf.device()陈述仅适用于图形构建，而不是执行，因此在设备块内部执行sess.run等同于根本没有设备。

要做你想做的事你需要建立单独的训练和测试图，它们共享变量。

Tensorflow：在GPU上运行训练阶段并在CPU上测试阶段

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