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我希望在我的GPU上運行我的tensorflow代碼的訓練階段，而在完成並存儲結果以加載我創建的模型並在CPU上運行其測試階段。Tensorflow：在GPU上運行訓練階段並在CPU上測試階段
我已經創建了這個代碼（我已經把它的一部分，僅供參考，因爲它是巨大的，否則，我知道規則將包括一個功能完整的代碼，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)
在那之後，我的模型已經成功培訓並保存相關數據，所以我希望加載該文件，並做模型的最終訓練和測試的一部分，但用我的CPU這一次。原因是GPU無法處理mnist.train.images和mnist.train.labels的整個數據集。
所以，我手動選擇這個部分，我運行它：
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內存錯誤，它沒有任何意義，我因爲我覺得我已經迫使程序依靠CPU。我沒有在第一個（批量培訓）代碼中使用sess.close（）命令，但我不確定這是否是它背後的原因。我跟着這個帖子實際上for the CPU 任何建議如何運行CPU上的最後一部分？