Tensorflow MNIST：拋出'std :: bad_alloc'實例後調用

Question

我想在Tensorflow上實現一個卷積神經網絡，使用它們的默認MNIST數據集。

from __future__ import print_function 
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}) 
    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): 
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial) 

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

def conv2d(x, W): 
    # stride [1, x_movement, y_movement, 1] 
    # Must have strides[0] = strides[3] = 1 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 

def max_pool_2x2(x): 
    # stride [1, x_movement, y_movement, 1] 
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') 

# define placeholder for inputs to network 
xs = tf.placeholder(tf.float32, [None, 784]) # 28x28 
ys = tf.placeholder(tf.float32, [None, 10]) 
keep_prob = tf.placeholder(tf.float32) 
x_image = tf.reshape(xs, [-1, 28, 28, 1]) 
# print(x_image.shape) # [n_samples, 28,28,1] 

## conv1 layer ## 
W_conv1 = weight_variable([5,5, 1,32]) # patch 5x5, in size 1, out size 32 
b_conv1 = bias_variable([32]) 
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32 
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, out size 64 
b_conv2 = bias_variable([64]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64 
h_pool2 = max_pool_2x2(h_conv2)           # output size 7x7x64 

## fc1 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) 

## fc2 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) 

# the error between prediction and real data 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), 
               reduction_indices=[1]))  # loss 
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 

sess = tf.Session() 

sess.run(tf.global_variables_initializer()) 


for i in range(100): 
    batch_xs, batch_ys = mnist.train.next_batch(10) 
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5}) 
    if i % 10 == 0: 
     print(compute_accuracy(
     mnist.test.images, mnist.test.labels)) 

上執行，蟒蛇崩潰此消息： 拋 '的std :: bad_alloc的' 的一個實例是什麼（）終止後，被稱爲：標準:: bad_alloc的

我可以指出，這發生在我調用compute_accuracy函數時，或者一般情況下，當我加載整個mnist.test iamges和標籤時。 因爲我希望使用這些數據，所以可以做些什麼的建議。作爲一個整體，我能夠以不同的方式處理圖像。

Answer 1

我認爲你的內存不足。它可以在我的機器上運行（6GB顯卡）。嘗試減小批量大小，或使用較小的完全連接圖層。

Answer 2

我有同樣的問題。我通過減少測試圖像的數量來計算精確度來解決它，例如我換成

print(compute_accuracy(mnist.test.images, mnist.test.labels)) 

類似的東西

batch_test = mnist.test.next_batch(5000) 
print(compute_accuracy(batch_test[0], batch_test[1]) 

我希望這有助於。