python - TensorFlow Python 脚本被杀死

Question

我是 TensorFlow 的初学者。我的 TensorFlow 脚本突然退出并显示 Killed。我的代码如下:

import tensorflow as tf
# Load data X_train, y_train and X_valid, y_valid

# An image augmentation pipeline
def augment(x):
    x = tf.image.random_brightness(x, max_delta=0.2)
    x = tf.image.random_contrast(x, 0.5, 2)
    return x

from sklearn.utils import shuffle
X_train, y_train = shuffle(X_train, y_train)

def LeNet(x):
    # Define LeNet architecture
    return logits

# Features:
x = tf.placeholder(tf.float32, (None, 32, 32, 3))
# Labels:
y = tf.placeholder(tf.int32, (None))
# Dropout probability
prob = tf.placeholder(tf.float32, (None))
# Learning rate
rate = tf.placeholder(tf.float32, (None))
rate_summary = tf.summary.scalar('learning rate', rate)

logits = LeNet(x)
accuracy_operation = # defined accuracy_operation

accuracy_summary = tf.summary.scalar('validation accuracy', accuracy_operation)
saver = tf.train.Saver()

summary = tf.summary.merge_all()
writer = tf.summary.FileWriter('./summary', tf.get_default_graph())

def evaluate(X_data, y_data):
    # Return accuracy with X_data, y_data
    return accuracy

with tf.Session() as sess:

    saver.restore(sess, './lenet')

    for i in range(EPOCHS):
        X_train, y_train = shuffle(X_train, y_train)
        for offset in range(0, len(X_train), BATCH_SIZE):
            end = offset + BATCH_SIZE
            batch_x, batch_y = X_train[offset:end], y_train[offset:end]
            batch_x = sess.run(augment(batch_x))

            # Run the training operation, update learning rate

        validation_accuracy = evaluate(X_valid, y_valid)
        writer.add_summary(sess.run(summary, feed_dict = {x: X_valid, y: y_valid, prob: 1., rate: alpha}))

我省略了我确信不会引起问题的部分。我知道哪些部分没问题，因为脚本之前没有遇到任何麻烦。添加某些部分(主要是摘要写入操作)后，脚本突然显示Killed，并在执行一定数量的训练操作后退出。我怀疑这是由于内存泄漏造成的，但我无法检测到它。

Answer 1

几天前我也遇到了类似的问题。就我而言，正如我后来了解到的那样，我进行了一些计算量非常大的操作。一旦我减小了张量的大小，消息就消失了并且我的代码开始运行。我无法准确说出您的情况中问题的原因是什么，但根据我的经验和您所说的(该错误仅在添加摘要时出现)，我建议您调整 X_valid、Y_valid 的大小。可能只是作者无法处理太多数据......