Question

我正在尝试遵循this教程，以学习有关使用keras进行深度学习的知识，但是我不断遇到MemoryError。您能指出是什么原因造成的，以及如何护理它吗？

代码如下：

import numpy as np
from keras import models, regularizers, layers
from keras.datasets import imdb

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)

def vectorize_sequences(sequences, dimension=10000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results


x_train = vectorize_sequences(train_data)

这是回溯（行号与上述代码中的行号不匹配）

Traceback (most recent call last):
  File "<input>", line 1, in <module>
  File "/home/uttam/pycharm-2018.2.4/helpers/pydev/_pydev_bundle/pydev_umd.py", line 197, in runfile
    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
  File "/home/uttam/pycharm-2018.2.4/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "/home/uttam/PycharmProjects/IMDB/imdb.py", line 33, in <module>
    x_train = vectorize_sequences(train_data)
  File "/home/uttam/PycharmProjects/IMDB/imdb.py", line 27, in vectorize_sequences
    results = np.zeros((len(sequences), dimension))
MemoryError

Answer 1

是的，您是正确的。问题确实来自vectorize_sequences。

您应该分批执行此逻辑（使用像partial_x_train这样的切片数据）或使用生成器（here是一个很好的解释和示例）。

我希望这会有所帮助：）

带有TensorFlow后端的Keras --- MemoryError

1 个答案: