问题描述
我安装了Theano(TH)，Tensorflow(TF)和Keras.基本测试似乎表明它们可以与GPU(GTX 1070)，Cuda 8.0，cuDNN5.1一起使用.
I installed Theano (TH), Tensorflow (TF) and Keras.Basic testing seem to indicate that they work with the GPU (GTX 1070), Cuda 8.0, cuDNN5.1 .
如果我运行cifar10_cnn.py Keras示例以TH为后端，似乎可以正常工作，大约需要18s/epoch.如果我使用TF来运行，那么几乎所有时间都 (它偶尔起作用，无法重现)，在每个时期之后，优化将以acc = 0.1停止.好像权重没有更新.
If I run the cifar10_cnn.py Keras example with TH as backend, it seems to work ok, taking ~18s/epoch.If I run it with TF then,almost all the times (it has worked occasionally, can't reproduce it), the optimization stalls with acc=0.1 after every epoch. It is as if weights were not updated.
这很可惜，因为TF后端以大约10s/epoch的速度运行(即使它工作的次数很少).我正在使用Conda，对Python还是很陌生.如果有帮助，"conda list"似乎显示某些软件包的两个版本.
This is a shame because TF backend was taking ~10s/epoch (even the very few times it worked). I'm using Conda and I am very new to Python. If that helps, "conda list" seems to show two versions for some of the packages.
如果您有任何线索，请告诉我.谢谢.下面的屏幕截图:
If you have any clues, please let me know. Thanks. Screenshot below :
python cifar10_cnn.py
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
X_train shape: (50000, 32, 32, 3)
50000 train samples
10000 test samples
Using real-time data augmentation.
Epoch 1/200
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: GeForce GTX 1070
major: 6 minor: 1 memoryClockRate (GHz) 1.7845
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.60GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
50000/50000 [==============================] - 11s - loss: 2.3029 - acc: 0.0999 - val_loss: 2.3026 - val_acc: 0.1000
Epoch 2/200
50000/50000 [==============================] - 10s - loss: 2.3028 - acc: 0.0980 - val_loss: 2.3026 - val_acc: 0.1000
Epoch 3/200
50000/50000 [==============================] - 10s - loss: 2.3028 - acc: 0.0992 - val_loss: 2.3026 - val_acc: 0.1000
Epoch 4/200
50000/50000 [==============================] - 10s - loss: 2.3028 - acc: 0.0980 - val_loss: 2.3026 - val_acc: 0.1000
Epoch 5/200
13184/50000 [======>.......................] - ETA: 7s - loss: 2.3026 - acc: 0.1044^CTraceback (most recent call last):
推荐答案
在我看来，这只是随机猜测，因为有10种可能性，而且有10％的时间是正确的.我唯一能想到的是您的学习率太高了.我已经看到学习率很高的模型有时会收敛，有时会不收敛.现在，在后端，我认为theano会执行更多优化，因此这可能会稍微影响某些方面.尝试将学习率降低10倍，看看它是否收敛.
It look to me like it is just random guessing since there are 10 possibilities and it is right 10% of the time. The only thing I can think of is that you learning rate is a bit too high. I have seen with a high learning rate models will sometimes converge and sometimes not converge. On the backend right now I think theano performs more optimizations so maybe this is slightly affecting something. Try lowering the learning rate by a factor of 10 and see if it converges.
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