在pycham中使用for循环时，纪元时间增加 [英] Epoch time increases when using for loop in pycham
问题描述
网络大小增加不是原因(问题)
The increase in network size is not the cause(problem)
这是我的代码
for i in [32, 64, 128, 256, 512]:
for j in [32, 64, 128, 256, 512]:
for k in [32, 64, 128, 256, 512]:
for l in [0.1, 0.2, 0.3, 0.4, 0.5]:
model = Sequential()
model.add(Dense(i))
model.add(Dropout(l))
model.add(Dense(j))
model.add(Dropout(l))
model.add(Dense(k))
model.add(Dropout(l))
model.compile(~)
hist = model.fit(~)
plt.savefig(str(count) + '.png')
plt.clf()
f = open(str(count) + '.csv', 'w')
text = ~
f.write(text)
f.close()
count+=1
print()
print("count :" + str(count))
print()
我开始将count设置为0
count为460〜479的时间是
when count is 460~ 479 the epoch time is
Train on 7228 samples, validate on 433 samples
Epoch 1/10
- 2254s - loss: 0.0045 - acc: 1.3835e-04 - val_loss: 0.0019 - val_acc: 0.0000e+00
Epoch 2/10
- 86s - loss: 0.0020 - acc: 1.3835e-04 - val_loss: 0.0030 - val_acc: 0.0000e+00
Epoch 3/10
- 85s - loss: 0.0017 - acc: 1.3835e-04 - val_loss: 0.0016 - val_acc: 0.0000e+00
Epoch 4/10
- 86s - loss: 0.0015 - acc: 1.3835e-04 - val_loss: 1.6094e-04 - val_acc: 0.0000e+00
Epoch 5/10
- 86s - loss: 0.0014 - acc: 1.3835e-04 - val_loss: 1.4120e-04 - val_acc: 0.0000e+00
Epoch 6/10
- 85s - loss: 0.0013 - acc: 1.3835e-04 - val_loss: 3.8155e-04 - val_acc: 0.0000e+00
Epoch 7/10
- 85s - loss: 0.0012 - acc: 1.3835e-04 - val_loss: 4.1694e-04 - val_acc: 0.0000e+00
Epoch 8/10
- 85s - loss: 0.0012 - acc: 1.3835e-04 - val_loss: 4.8163e-04 - val_acc: 0.0000e+00
Epoch 9/10
- 86s - loss: 0.0011 - acc: 1.3835e-04 - val_loss: 3.8670e-04 - val_acc: 0.0000e+00
Epoch 10/10
- 85s - loss: 9.9018e-04 - acc: 1.3835e-04 - val_loss: 0.0016 - val_acc: 0.0000e+00
但是当我重新启动pycharm时，count是480
but when I restart pycharm and count is 480
时期是
Train on 7228 samples, validate on 433 samples
Epoch 1/10
- 151s - loss: 0.0071 - acc: 1.3835e-04 - val_loss: 0.0018 - val_acc: 0.0000e+00
Epoch 2/10
- 31s - loss: 0.0038 - acc: 1.3835e-04 - val_loss: 0.0014 - val_acc: 0.0000e+00
Epoch 3/10
- 32s - loss: 0.0031 - acc: 1.3835e-04 - val_loss: 2.0248e-04 - val_acc: 0.0000e+00
Epoch 4/10
- 32s - loss: 0.0026 - acc: 1.3835e-04 - val_loss: 3.7600e-04 - val_acc: 0.0000e+00
Epoch 5/10
- 32s - loss: 0.0021 - acc: 1.3835e-04 - val_loss: 4.3882e-04 - val_acc: 0.0000e+00
Epoch 6/10
- 32s - loss: 0.0020 - acc: 1.3835e-04 - val_loss: 0.0037 - val_acc: 0.0000e+00
Epoch 7/10
- 32s - loss: 0.0021 - acc: 1.3835e-04 - val_loss: 1.2072e-04 - val_acc: 0.0000e+00
Epoch 8/10
- 32s - loss: 0.0019 - acc: 1.3835e-04 - val_loss: 0.0031 - val_acc: 0.0000e+00
Epoch 9/10
- 33s - loss: 0.0018 - acc: 1.3835e-04 - val_loss: 0.0051 - val_acc: 0.0000e+00
Epoch 10/10
- 33s - loss: 0.0018 - acc: 1.3835e-04 - val_loss: 3.2728e-04 - val_acc: 0.0000e+00
我刚刚再次开始，但是纪元时间更快了.
I just started it again, but the epoch time was faster.
我不知道为什么会这样.
I don't know why this happened.
在Python 3.6版本中， 我使用tensorflow-gpu 1.13.1版本，并且 Cuda使用10.0版本. 操作系统是Windows 10 1903专业版，并且 操作系统内部版本使用18362.239 Pycharm使用2019.1.1社区版本.
In the Python 3.6 version, I use tensorflow-gpu 1.13.1 version, and Cuda uses 10.0 version. OS is a Windows 10 1903 pro version and OS build uses 18362.239 Pycharm uses a 2019.1.1 community version.
我只是使用了for循环，我想知道为什么会这样.
I just used the for loop, and I wonder why this happened.
我更改了for循环中的单位数.
I changed the number of units in the for loop.
我还用plt.savefig保存了图形，并以.csv格式保存了数据.
I also saved the figure with a plt.savefig, and saved the data in .csv format.
我还问如何解决.
推荐答案
您应使用:
from keras import backend as K`
K.clear_session()
在创建模型之前(即model=Sequential()).那是因为:
before creating the model (i.e. model=Sequential()). That's because:
操作不是TF垃圾收集的，因此您总是向图添加更多节点.
Ops are not garbage collected by TF so you always add more node to the graph.
因此，如果我们不使用K.clear_session，则会发生内存泄漏.
So if we don't use K.clear_session, then memory leak occurs.
感谢Slack中 keras.io 的@ dref360.
Thanks to @dref360 at keras.io in Slack.
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