重新編譯CNN的python腳本以結果「殺死」運行

我正在運行一個python腳本，它將爲CNN識別手寫數字而運行。列車過程顯示預期結果。但測試過程顯示「已殺死」。我想知道是否因爲計算機內存太小。重新編譯CNN的python腳本以結果「殺死」運行

import tensorflow as tf 
import tensorflow.examples.tutorials.mnist.input_data as input_data 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  
x = tf.placeholder(tf.float32, [None, 784])       
y_actual = tf.placeholder(tf.float32, shape=[None, 10])   


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): 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 

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

x_image = tf.reshape(x, [-1,28,28,1])   
W_conv1 = weight_variable([5, 5, 1, 32])  
b_conv1 = bias_variable([32])  
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)  
h_pool1 = max_pool(h_conv1)         

W_conv2 = weight_variable([5, 5, 32, 64]) 
b_conv2 = bias_variable([64]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  
h_pool2 = max_pool(h_conv2)         

W_fc1 = weight_variable([7 * 7 * 64, 1024]) 
b_fc1 = bias_variable([1024]) 
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)  

keep_prob = tf.placeholder("float") 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)     

W_fc2 = weight_variable([1024, 10]) 
b_fc2 = bias_variable([10]) 
y_predict=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) 
cross_entropy = -tf.reduce_sum(y_actual*tf.log(y_predict))  
train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy)  
correct_prediction = tf.equal(tf.argmax(y_predict,1), tf.argmax(y_actual,1))  
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))     


sess=tf.InteractiveSession()       
sess.run(tf.initialize_all_variables()) 
for i in range(20000): 
    batch = mnist.train.next_batch(50) 
    if i%100 == 0:     
    train_acc = accuracy.eval(feed_dict={x:batch[0], y_actual: batch[1], keep_prob: 1.0}) 
    print('step',i,'training accuracy',train_acc) 
    train_step.run(feed_dict={x: batch[0], y_actual: batch[1], keep_prob: 0.5}) 

test_acc=accuracy.eval(feed_dict={x: mnist.test.images, y_actual: mnist.test.labels, keep_prob: 1.0}) 
print("test accuracy",test_acc)

來源

2017-03-17 HONG ZI

該進程因爲你的python程序內存不足而死亡。嘗試減少批量大小。要重新確認其內存錯誤，請執行以下操作：

cat /var/log/kern.log

來源

2017-05-12 04:15:27

重新編譯CNN的python腳本以結果「殺死」運行

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