zhenxuan00 / triple-gan Goto Github PK

Hello,

I am wondering is there any other implementation in other languages like PyTorch or Tensorflow?

I try to run your code on the MNIST dataset, however I get a strange error as soon as the pre-training has finished. I really appreciate any guidance. Thanks!

The error is as follows:

Traceback (most recent call last):
File "x2y_yz2x_xy2p_ssl_mnist.py", line 462, in
cl_b = train_batch_cla(x_labelled[from_l_c:to_l_c], y_labelled[from_l_c:to_l_c], sample_y, p_u[from_u_c:to_u_c], alpha_cla_g, lr, alpha_unlabeled_entropy, alpha_average)
File "/home/mehdi/anaconda3/envs/python2/lib/python2.7/site-packages/theano/compile/function_module.py", line 871, in call
storage_map=getattr(self.fn, 'storage_map', None))
File "/home/mehdi/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/link.py", line 314, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "/home/mehdi/anaconda3/envs/python2/lib/python2.7/site-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] == 1, but the output's size on that axis is 100.
Apply node that caused the error: GpuElemwise{Composite{((((i0 * i1) + ((i2 * i3 * i4) / i5)) + ((i2 * i6 * i3 * i7) / (i5 * i8))) + i9)},no_inplace}(GpuElemwise{Composite{Cast{float32}(EQ(i0, i1))},no_inplace}.0, GpuDimShuffle{0,x}.0, CudaNdarrayConstant{[[-1.]]}, GpuDimShuffle{x,x}.0, GpuElemwise{log,no_inplace}.0, GpuDimShuffle{x,x}.0, GpuElemwise{Composite{Cast{float32}(AND(GE(i0, i1), LE(i0, i2)))},no_inplace}.0, GpuSoftmaxWithBias.0, GpuElemwise{clip,no_inplace}.0, GpuElemwise{Composite{((i0 * (i1 / i2)) / i3)},no_inplace}.0)
Toposort index: 1145
Inputs types: [CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, col), CudaNdarrayType(float32, (True, True)), CudaNdarrayType(float32, (True, True)), CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, (True, True)), CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, row)]
Inputs shapes: [(100, 10), (1, 1), (1, 1), (1, 1), (100, 10), (1, 1), (100, 10), (100, 10), (100, 10), (1, 10)]
Inputs strides: [(10, 1), (0, 0), (0, 0), (0, 0), (10, 1), (0, 0), (10, 1), (10, 1), (10, 1), (0, 1)]
Inputs values: ['not shown', CudaNdarray([[ 0.41114426]]), CudaNdarray([[-1.]]), CudaNdarray([[ 0.30000001]]), 'not shown', CudaNdarray([[ 100.]]), 'not shown', 'not shown', 'not shown', 'not shown']
Outputs clients: [[GpuDimShuffle{0,1,x,x}(GpuElemwise{Composite{((((i0 * i1) + ((i2 * i3 * i4) / i5)) + ((i2 * i6 * i3 * i7) / (i5 * i8))) + i9)},no_inplace}.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.

Hi,
@zhenxuan00 @taufikxu
Thanks for your codes! When I ran the code, it seems no path library in python. (in checkpoints.py) How can I solve it? Thank you!