python之Tensorflow:在 GPU 上运行训练阶段，在 CPU 上运行测试阶段

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

我已经创建了这段代码(我已经放了其中的一部分，仅供引用，否则它会很大，我知道规则将包含一个功能齐全的代码，对此我深表歉意)。

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，我的模型已经成功训练并保存了相关数据，所以我希望加载文件并在模型中进行最后的训练和测试部分，但这次使用我的 CPU。原因是 GPU 无法处理 mnist.train.images 和 mnist.train.labels 的整个数据集。

因此，我手动选择这部分并运行它:

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}) 

但是我得到了一个 OMM GPU 内存错误，这对我来说没有意义，因为我认为我已经强制程序依赖 CPU。我没有在第一个(批处理训练)代码中放置命令 sess.close() ，但我不确定这是否真的是它背后的原因。我实际上关注了这篇文章for the CPU 有什么建议如何只在 CPU 上运行最后一部分吗？

请您参考如下方法:

with tf.device() 语句仅适用于图构建，不适用于执行，因此在设备 block 内执行 sess.run 相当于没有设备完全没有。

要完成您想做的事，您需要构建单独的训练图和测试图，它们共享变量。