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GitiHubi / deepAI

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

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'nn.init.xavier_uniform is deprecated' and 'size mismatch, m1: [128 x 384], m2: [618 x 512]'

FrankPSch opened this issue · comments

commented

Hi there,

a very readable and instructive contribution, thanks for sharing! In which environment does this run sucessfully?

I am trying to run 'GTC_2018_Lab' and 'GTC_2018_Lab-solutions' with the version below, both result in the following two errors.

My environment is Win10 with Anaconda. To solve it, I tried with USE_CUDA=True / False and Python 3.6 / 3.7 and Cudatoolkit 10 / Cudatoolkit 9. Then I downgraded from pytorch 1.0.1 using conda install pytorch=0.4.1 cuda90 -c pytorch. I did not find earlier Pytorch versions via Conda or compiled for Windows on the Pytorch Website.

Also, I changed the shape of the first and last layer to match for the second error but without a change.

Thank you!

First Error ( I deleted some lines in the middle):

[LOG 20190329-05:48:27] decoder architecture:

decoder(
  (decoder_L1): Linear(in_features=3, out_features=4, bias=True)
  (decoder_R1): LeakyReLU(negative_slope=0.4, inplace)
  (decoder_L2): Linear(in_features=4, out_features=8, bias=True)
  (decoder_R2): LeakyReLU(negative_slope=0.4, inplace)
  (decoder_L3): Linear(in_features=8, out_features=16, bias=True)
  (decoder_R3): LeakyReLU(negative_slope=0.4, inplace)
  (decoder_L4): Linear(in_features=16, out_features=32, bias=True)
  (decoder_R4): LeakyReLU(negative_slope=0.4, inplace)
  (decoder_L5): Linear(in_features=32, out_features=64, bias=True)
  (decoder_R5): LeakyReLU(negative_slope=0.4, inplace)
  (decoder_L6): Linear(in_features=64, out_features=128, bias=True)
  (decoder_R6): LeakyReLU(negative_slope=0.4, inplace)
  (decoder_L7): Linear(in_features=128, out_features=256, bias=True)
  (decoder_R7): LeakyReLU(negative_slope=0.4, inplace)
  (decoder_L8): Linear(in_features=256, out_features=512, bias=True)
  (decoder_R8): LeakyReLU(negative_slope=0.4, inplace)
  (decoder_L9): Linear(in_features=512, out_features=618, bias=True)
  (decoder_R9): LeakyReLU(negative_slope=0.4, inplace)
  (dropout): Dropout(p=0.0, inplace)
)

C:\Users\frank\.conda\envs\py36_cuda9\lib\site-packages\ipykernel_launcher.py:10: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.
  # Remove the CWD from sys.path while we load stuff.
C:\Users\frank\.conda\envs\py36_cuda9\lib\site-packages\ipykernel_launcher.py:15: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.
  from ipykernel import kernelapp as app
C:\Users\frank\.conda\envs\py36_cuda9\lib\site-packages\ipykernel_launcher.py:20: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.
C:\Users\frank\.conda\envs\py36_cuda9\lib\site-packages\ipykernel_launcher.py:25: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.

...

C:\Users\frank\.conda\envs\py36torch\lib\site-packages\ipykernel_launcher.py:46: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.
C:\Users\frank\.conda\envs\py36torch\lib\site-packages\ipykernel_launcher.py:51: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.

Second error:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-32-7d9371378df5> in <module>
     37 
     38         # run forward pass
---> 39         z_representation = encoder_train(mini_batch_torch) # encode mini-batch data
     40         mini_batch_reconstruction = decoder_train(z_representation) # decode mini-batch data
     41 

~\.conda\envs\py36_cuda9\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    487             result = self._slow_forward(*input, **kwargs)
    488         else:
--> 489             result = self.forward(*input, **kwargs)
    490         for hook in self._forward_hooks.values():
    491             hook_result = hook(self, input, result)

<ipython-input-23-0afc78290eab> in forward(self, x)
     57 
     58         # define forward pass through the network
---> 59         x = self.encoder_R1(self.dropout(self.encoder_L1(x)))
     60         x = self.encoder_R2(self.dropout(self.encoder_L2(x)))
     61         x = self.encoder_R3(self.dropout(self.encoder_L3(x)))

~\.conda\envs\py36_cuda9\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    487             result = self._slow_forward(*input, **kwargs)
    488         else:
--> 489             result = self.forward(*input, **kwargs)
    490         for hook in self._forward_hooks.values():
    491             hook_result = hook(self, input, result)

~\.conda\envs\py36_cuda9\lib\site-packages\torch\nn\modules\linear.py in forward(self, input)
     65     @weak_script_method
     66     def forward(self, input):
---> 67         return F.linear(input, self.weight, self.bias)
     68 
     69     def extra_repr(self):

~\.conda\envs\py36_cuda9\lib\site-packages\torch\nn\functional.py in linear(input, weight, bias)
   1350     if input.dim() == 2 and bias is not None:
   1351         # fused op is marginally faster
-> 1352         ret = torch.addmm(torch.jit._unwrap_optional(bias), input, weight.t())
   1353     else:
   1354         output = input.matmul(weight.t())

RuntimeError: size mismatch, m1: [128 x 384], m2: [618 x 512] at c:\a\w\1\s\tmp_conda_3.6_104352\conda\conda-bld\pytorch_1550400396997\work\aten\src\thc\generic/THCTensorMathBlas.cu:266
commented

The contributor of this repository has solved my error messages. Here are the causes and the resolution:

(1) The Pytorch program library has evolved since the creation of the Lab and some of the function names are depricated. This resulted in the warning regarding the Xavier initialization of the network parameters ("UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_"). To solve this, the lab was migrated to the current Pytorch version: 1.0.1.post2 (Python version: 3.7.0).

(2) Together with NVIDIA, the Lab was designed in such a way that the tasks of Chapter 3 could be successfully solved first (i.e. the additional one-hot coding of the two booking attributes "WAERS" and "BUKRS") before the network training could be started. If these two attributes are not encoded accordingly, the dimensionality of the input vectors corresponds to only 384 dimensions and not the 618 dimensions required by the network architecture. To solve this, the dimensonality of the input vector was defined dynamically within the network architecture definition as a function of the dimensionality of the data set.

Thanks a lot to the authors for this nice example!