Tensorflow 开启训练后卡死

毕设做深度学习的课题，使用到了TensorFlow，但训练时出现了问题：

跑脚本开启训练之后，跑完不到100次就会卡死，然后显示python已停止工作

图片描述

这是我的训练的代码

# 导入数据集
import load_record
# 导入TensorFlow并创建Session
import tensorflow as tf
sess = tf.InteractiveSession()
# # 构建计算图
x = tf.placeholder("float", shape = [None, 224, 224, 1])
y_ = tf.placeholder("float", shape = [None, 2])
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_2x2(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, 224, 224, 1])
# 第一层卷积
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_2x2(h_conv1)
# 第二层卷积
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_2x2(h_conv2)
# 第一层全连接层
W_fc1 = weight_variable([56 * 56 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 56*56*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# Dropout层防止过拟合
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# 第二层全连接层输出
W_fc2 = weight_variable([1024, 2])
b_fc2 = bias_variable([2])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
# 训练模型
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.global_variables_initializer())
print('ready...')
for i in range(500):
  images, labels = load_record.inputs(data_set = 'train', batch_size = 50, num_epochs = None)
  tf.train.start_queue_runners(sess = sess)
  _images, _labels = sess.run([images, labels])
  if i % 100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:_images, y_: _labels, keep_prob: 1.0})
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: _images, y_: _labels, keep_prob: 0.5})

数据集我是用tfrecord读进去的

def inputs(data_set,batch_size,num_epochs):
    if not num_epochs:
        num_epochs = None
    if data_set == 'train':
        file = TRAIN_FILE
    else:
        file = VALIDATION_FILE
    with tf.name_scope('input') as scope:
        filename_queue = tf.train.string_input_producer([file], num_epochs=num_epochs)
    image,label = read_and_decode(filename_queue)
    #随机获得batch_size大小的图像和label
    images,labels = tf.train.shuffle_batch([image, label], 
        batch_size=batch_size,
        num_threads=64,
        capacity=1000 + 3 * batch_size,
        min_after_dequeue=1000
    )
    return images,labels

求教，如需补充描述请@

只迭代了第一次， 第二次就不行了

回答：

看上去是TenserFlow要求CPU具备AVX和AVX2特性,但你的CPU不支持.

AVX指令集是Sandy Bridge和Larrabee架构下的新指令集.
Sandy Bridge是英特2011年后出的CPU.

如果可以的话找台新机器试试你的代码.

Linux上用下面的代码可以检查是否CPU支持avx技术

 cat /proc/cpuinfo |grep avx

回答：

请问楼主解决了吗？谢谢

回答：

是tf.train.string_input_producer的原因
https://blog.csdn.net/lujiand...

回答：

请问解决了吗？怎么解决的？

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