Tensorflow:terminate called after throwing an instance of 'std::bad_alloc' what(): std::bad_alloc

在tensorflow教程深入mnist这一部分，如果照搬CNN代码，会出现terminate called after throwing an instance of 'std::bad_alloc'   what():  std::bad_alloc  Process finished with exit code 134 (interrupted by signal 6: SIGABRT)这个错误，这是因为一次测试10000幅mnist图像会导致电脑内存不足甚至死机，对此我们可以减少测试的数据集。

可以添加如下代码：

            test_batch = mnist.test.next_batch(1000)
            acc_forone=compute_accuracy(test_batch[0], test_batch[1])

完整代码如下：

#coding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre=sess.run(prediction,feed_dict={xs:v_xs,keep_prob:1}) #这里的keep_prob是保留概率，即我们要保留的RELU的结果所占比例
    correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:1})
    return result

def weight_variable(shape):
    inital=tf.truncated_normal(shape,stddev=0.1)     #stddev爲標準差
    return tf.Variable(inital)

def bias_variable(shape):
    inital=tf.constant(0.1,shape=shape)
    return tf.Variable(inital)

def conv2d(x,W):    #x爲像素值，W爲權值
    #strides[1,x_movement,y_movement,1]
    #must have strides[0]=strides[3]=1
    #padding=????
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')#

def max_pool_2x2(x):
    # strides[1,x_movement,y_movement,1]
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')#ksize二三维为池化窗口

#define placeholder for inputs to network
xs=tf.placeholder(tf.float32,[None,784])/255
ys=tf.placeholder(tf.float32,[None,10])
keep_prob=tf.placeholder(tf.float32)
x_image=tf.reshape(xs, [-1,28,28,1]) #-1为这个维度不确定,变成一个4维的矩阵，最后为最里面的维数
#print x_image.shape                 #最后这个1理解为输入的channel，因为为黑白色所以为1

##conv1 layer##
W_conv1=weight_variable([5,5,1,32]) #patch 5x5,in size 1 是image的厚度,outsize 32 是提取的特征的维数
b_conv1=bias_variable([32])
h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)# output size 28x28x32 因为padding='SAME'
h_pool1=max_pool_2x2(h_conv1)      #output size 14x14x32

##conv2 layer##
W_conv2=weight_variable([5,5,32,64]) #patch 5x5,in size 32 是conv1的厚度,outsize 64 是提取的特征的维数
b_conv2=bias_variable([64])
h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)# output size 14x14x64 因为padding='SAME'
h_pool2=max_pool_2x2(h_conv2)      #output size 7x7x64

##func1 layer##
W_fc1=weight_variable([7*7*64,1024])
b_fc1=bias_variable([1024])
#[n_samples,7,7,64]->>[n_samples,7*7*64]
h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)  #防止过拟合

##func2 layer##
W_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])
#prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
prediction=tf.matmul(h_fc1_drop,W_fc2)+b_fc2
#h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)  #防止过拟合

#the errro between prediction and real data

#cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=ys, logits=prediction))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess=tf.Session()
sess.run(tf.global_variables_initializer())

for i in range(1000):
    batch_xs,batch_ys=mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:0.5})
    if i%50 ==0:
        accuracy = 0
        for j in range(10):
            test_batch = mnist.test.next_batch(1000)
            acc_forone=compute_accuracy(test_batch[0], test_batch[1])
            #print 'once=%f' %(acc_forone)
            accuracy=acc_forone+accuracy
        print '测试结果:batch:%g,准确率:%f' %(i,accuracy/10)