概述

我之前已经问了一个类似的问题
(Training multiple Sequential models in a row slows down)

并看到其他类似问题(Keras: Out of memory when doing hyper parameter grid search)

并且解决方案始终是在完成模型使用后将K.clear_session()添加到代码中.所以我在上一个问题中做到了这一点,我仍在泄露记忆

这是重现问题的代码.

import random
import time
from keras.models import Sequential
from keras.layers import Dense
from keras import backend as K
import tracemalloc


def run():
    tracemalloc.start()
    num_input_nodes = 12
    num_hidden_nodes = 8
    num_output_nodes = 1

    random_numbers = random.sample(range(1000),50)
    train_x,train_y = create_training_dataset(random_numbers,num_input_nodes)

    for i in range(100):
        snapshot = tracemalloc.take_snapshot()
        for j in range(10):
            start_time = time.time()
            nn = Sequential()
            nn.add(Dense(num_hidden_nodes,input_dim=num_input_nodes,activation='relu'))
            nn.add(Dense(num_output_nodes))
            nn.compile(loss='mean_squared_error',optimizer='adam')
            nn.fit(train_x,train_y,nb_epoch=300,batch_size=2,verbose=0)
            K.clear_session()
            print("Iteration {iter}. Current time {t}. Took {elapsed} seconds".
                  format(iter=i*10 + j + 1,t=time.strftime('%H:%M:%s'),elapsed=int(time.time() - start_time)))

        top_stats = tracemalloc.take_snapshot().compare_to(snapshot,'lineno')

        print("[ Top 5 differences ]")
        for stat in top_stats[:5]:
            print(stat)


def create_training_dataset(dataset,input_nodes):
    """
    Outputs a training dataset (train_x,train_y) as numpy arrays.
    Each item in train_x has 'input_nodes' number of items while train_y items are of size 1
    :param dataset: list of ints
    :param input_nodes:
    :return: (numpy array,numpy array),train_x,train_y
    """
    data_x,data_y = [],[]
    for i in range(len(dataset) - input_nodes - 1):
        a = dataset[i:(i + input_nodes)]
        data_x.append(a)
        data_y.append(dataset[i + input_nodes])
    return numpy.array(data_x),numpy.array(data_y)

run()

这是我从第一个内存调试打印得到的输出

/tensorflow/python/framework/ops.py:121：size = 3485 KiB(3485 KiB),count = 42343(42343)
/tensorflow/python/framework/ops.py:1400：size = 998 KiB(998 KiB),count = 8413(8413)
/tensorflow/python/framework/ops.py:116：size = 888 KiB(888 KiB),count = 32468(32468)
/tensorflow/python/framework/ops.py:1185：size = 795 KiB(795 KiB),count = 3179(3179)
/tensorflow/python/framework/ops.py:2354：size = 599 KiB(599 KiB),count = 5886(5886)

系统信息：

> python 3.5
> keras(1.2.2)
> tensorflow(1.0.0)

调用K.clear_session()释放了迭代之间与默认图关联的一些(后端)状态,但需要额外调用tf.reset_default_graph()来清除Python状态.

请注意,可能存在更有效的解决方案：由于nn不依赖于任何一个循环变量,因此可以在循环外定义它,并在循环内重用相同的实例.如果这样做,则无需清除会话或重置默认图表,并且性能会提高,因为您可以从迭代之间的缓存中受益.

总结

以上是编程之家为你收集整理的python-3.x – Keras(TensorFlow,CPU)：训练循环中的顺序模型吃掉内存全部内容，希望文章能够帮你解决python-3.x – Keras(TensorFlow,CPU)：训练循环中的顺序模型吃掉内存所遇到的程序开发问题。

如果您也喜欢它,动动您的小指点个赞吧