[GitHub] peschn opened a new issue #7382: GRU not working with layout 'NTC'

[gi...@git.apache.org]

peschn opened a new issue #7382: GRU not working with layout 'NTC'
URL: https://github.com/apache/incubator-mxnet/issues/7382
 
 
   For bugs or installation issues, please provide the following information.
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   ## Environment info
   Operating System: Ubuntu 16.04.2 LTS
   
   Package used (Python/R/Scala/Julia): Python
   
   MXNet version: mxnet-cu80==0.10.1b20170803
   
   If you are using python package, please provide
   
   Python version and distribution: python 2.7.13, used with anaconda 4.2.23
   
   R `sessionInfo()`:
   
   ## Error Message:
   Please paste the full error message, including stack trace.
   
   ???
   Traceback (most recent call last):
     File "simply/mx/test_rnn.py", line 14, in <module>
       output, hn = layer(input, h0)
     File "/home/huxley/dev/miniconda2/envs/mxnet/lib/python2.7/site-packages/mxnet/gluon/block.py", line 251, in __call__
       return self.forward(*args)
     File "/home/huxley/dev/miniconda2/envs/mxnet/lib/python2.7/site-packages/mxnet/gluon/rnn/rnn_layer.py", line 162, in forward
       str(info['shape']), str(state.shape)))
   ValueError: Invalid recurrent state shape. Expecting (2, 2L, 100), got (2L, 8L, 100L).
   ???
   
   ## Minimum reproducible example
   if you are using your own code, please provide a short script that reproduces the error.
   
   ## Steps to reproduce
   or if you are running standard examples, please provide the commands you have run that lead to the error.
   
   ???python
   import mxnet as mx
   
   batch_size = 8
   timesteps = 7
   nhidden = 100
   layers = 2
   nin = 10
   
   layer = mx.gluon.rnn.GRU(nhidden, layers, layout='NTC')
   layer.initialize()
   
   input = mx.nd.random_uniform(shape=(batch_size, timesteps, nin))
   h0 = mx.nd.random_uniform(shape=(layers, batch_size, nhidden))
   output, hn = layer(input, h0)
   ???
   ## What have you tried to solve it?
   
   The only thing that solves it is changing the layout (and the data accordingly) to 'TNC'.
   
 
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