Tensorflow保护程序：内核似乎已经死亡答案

【问题标题】：Tensorflow saver: Kernel appears to have diedTensorflow保护程序：内核似乎已经死亡
【发布时间】：2017-05-14 04:44:22
【问题描述】：

我在保存/恢复 tensorflow 模型时遇到了很多麻烦，要么我的“内核似乎已经死了”，要么我收到错误（“变量...已经退出”）。

当我的内核死机时，我会在控制台中看到此错误日志：

[I 21:13:41.505 NotebookApp] Saving file at /Nanodegree_MachineLearning/06_Capstone/capstone.ipynb
terminate called after throwing an instance of 'std::bad_alloc'
  what():  std::bad_alloc
[I 21:17:05.416 NotebookApp] KernelRestarter: restarting kernel (1/5)
WARNING:root:kernel 81679b46-ec9b-4ce6-b5be-ae2d9cf01210 restarted
[I 21:17:41.778 NotebookApp] Saving file at /Nanodegree_MachineLearning/06_Capstone/capstone.ipynb
[19324:20881:1229/212110:ERROR:object_proxy.cc(583)] Failed to call method: org.freedesktop.UPower.GetDisplayDevice: object_path= /org/freedesktop/UPower: org.freedesktop.DBus.Error.UnknownMethod: Method "GetDisplayDevice" with signature "" on interface "org.freedesktop.UPower" doesn't exist

我的代码如下：

if __name__ == '__main__':
    if LEARN_MODUS:
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            steps_per_epoch = len(X_train) // BATCH_SIZE
            num_examples = steps_per_epoch * BATCH_SIZE

            # Train model
            for i in range(EPOCHS):
                for step in range(steps_per_epoch):
                    #Calculate next Batch
                    batch_start = step * BATCH_SIZE
                    batch_end = (step + 1) * BATCH_SIZE
                    batch_x = X_train[batch_start:batch_end] 
                    batch_y = y_train[batch_start:batch_end]

                    #Run Training
                    loss = sess.run(train_op, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})

            try:
                saver
            except NameError:
                saver = tf.train.Saver()
            saver.save(sess, 'foo')
            print("Model saved")

为了恢复模型，我使用：

predicions = tf.argmax(fc2,1)
predicted_classes = []

try:
    saver
except NameError:
    saver = tf.train.Saver()

with tf.Session() as sess:   
    saver = tf.train.import_meta_graph('foo.meta')
    saver.restore(sess, tf.train.latest_checkpoint('./'))

    predicted_classes = sess.run(predicions, feed_dict={x: X_test, keep_prob: 1.0})

我尝试了很多不同的方法，有时它有效（但并非总是如此！？），有时它崩溃，有时我得到变量错误。我必须以其他方式使用保存/恢复吗？

我正在使用： Ubuntu 14.04 蟒蛇 3 Python 3.5.2 TensorFlow 0.12

在 jupyter 笔记本内

谢谢！

【问题讨论】：

标签： python tensorflow ubuntu-14.04 jupyter-notebook

【解决方案1】：

内存不足时可能会发生这种情况，解决方案是尝试更小的批量大小。我看到您将测试集输入到单个 run 调用中，这需要足够的内存来一次执行所有示例。您可以执行eval_in_batches 之类的操作来汇总几个较小的运行调用的准确性

【讨论】：