错误Theano+Keras

Question

我试图在AWS实例上运行Keras脚本。虽然该脚本在我自己的计算机上运行良好（Python2.7-没有CPU），但它在启用了GPU的AWS实例上导致了一些错误。我已经安装了最新版本的Theano-和其他脚本（例如mnisttutoral）不会出错。在

导致该问题的脚本是标准的Keras tutoral脚本（https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py）。根据堆栈溢出指导，我已经将边界模式移到“有效”模式，这似乎消除了一个问题。但是-我立即遇到了以下问题（下面是错误堆栈）。我已经在bash运行了下面的一行 “THEANO_FLAGS=optimizer=fast_compile，device=gpu，floatX=float32 cifar10.py”，但这并没有提供更多信息。也许我应该换成nolearn/千层面套餐-但请告诉我是否有简单的方法来解决这个问题。在

Using Theano backend.
Using gpu device 0: GRID K520 (CNMeM is disabled)
X_train shape: (50000, 3, 32, 32)
50000 train samples
10000 test samples
Using real time data augmentation
----------------------------------------
Epoch 0
----------------------------------------
Training...
Testing...
Traceback (most recent call last):
  File "keras_python_4.py", line 152, in <module>
    score = model.test_on_batch(X_batch, Y_batch)
  File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 445, in test_on_batch
    return self._test(ins)
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/theano_backend.py", line 357, in __call__
    return self.function(*inputs)
  File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 871, in __call__
    storage_map=getattr(self.fn, 'storage_map', None))
  File "/usr/local/lib/python2.7/dist-packages/theano/gof/link.py", line 314, in raise_with_op
    reraise(exc_type, exc_value, exc_trace)
  File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 859, in __call__
    outputs = self.fn()
ValueError: GpuElemwise. Input dimension mis-match. Input 1 (indices start at 0) has shape[0] == 32, but the output's size on that axis is 16.
Apply node that caused the error: GpuElemwise{Composite{(i0 * log(clip((i1 / i2), i3, i4)))}}[(0, 0)](GpuFromHost.0, GpuSoftmaxWithBias.0, GpuDimShuffle{0,x}.0, CudaNdarrayConstant{[[  1.00000001e-07]]}, CudaNdarrayConstant{[[ 0.99999988]]})
Toposort index: 95
Inputs types: [CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, col), CudaNdarrayType(float32, (True, True)), CudaNdarrayType(float32, (True, True))]
Inputs shapes: [(16, 10), (32, 10), (32, 1), (1, 1), (1, 1)]
Inputs strides: [(10, 1), (10, 1), (1, 0), (0, 0), (0, 0)]
Inputs values: ['not shown', 'not shown', 'not shown', <CudaNdarray object at 0x7f2165bb7730>, <CudaNdarray object at 0x7f2165bb7970>]
Outputs clients: [[GpuCAReduce{add}{0,1}(GpuElemwise{Composite{(i0 * log(clip((i1 / i2), i3, i4)))}}[(0, 0)].0)]]

HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

Answer 1

当您将border_模式从“same”更改为“valid”时，您的特征地图的维度将逐渐缩小。在某种程度上，一个层的维度变得如此之小（在你的例子中看起来已经是1x1了），以至于你不能再在上面添加额外的层了。在

如果指定层的某些跨距，则过滤器的大小和输入的大小也需要与步幅长度兼容。在