tb1.py

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-1-4fd28661ea7d> in <module>()
     40 
     41 for c in range(10):
---> 42      decoder_dist(input_[:,c].contiguous(), hidden) #RuntimeError: Expected hidden size (1, 4, 64), got (1, 32, 64)
     43 

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    222         for hook in self._forward_pre_hooks.values():
    223             hook(self, input)
--> 224         result = self.forward(*input, **kwargs)
    225         for hook in self._forward_hooks.values():
    226             hook_result = hook(self, input, result)

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py in forward(self, *inputs, **kwargs)
     58             return self.module(*inputs[0], **kwargs[0])
     59         replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
---> 60         outputs = self.parallel_apply(replicas, inputs, kwargs)
     61         return self.gather(outputs, self.output_device)
     62 

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py in parallel_apply(self, replicas, inputs, kwargs)
     68 
     69     def parallel_apply(self, replicas, inputs, kwargs):
---> 70         return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
     71 
     72     def gather(self, outputs, output_device):

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py in parallel_apply(modules, inputs, kwargs_tup, devices)
     65         output = results[i]
     66         if isinstance(output, Exception):
---> 67             raise output
     68         outputs.append(output)
     69     return outputs

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py in _worker(i, module, input, kwargs, results, lock, device)
     40         try:
     41             with torch.cuda.device(device):
---> 42                 output = module(*input, **kwargs)
     43             with lock:
     44                 results[i] = output

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    222         for hook in self._forward_pre_hooks.values():
    223             hook(self, input)
--> 224         result = self.forward(*input, **kwargs)
    225         for hook in self._forward_hooks.values():
    226             hook_result = hook(self, input, result)

<ipython-input-1-4fd28661ea7d> in forward(self, input, hidden)
     17         batch_size = input.size(0)
     18         encoded = self.encoder(input)
---> 19         output, hidden = self.rnn(encoded.view(1, batch_size, -1), hidden)
     20         output = self.decoder(output.view(batch_size, -1))
     21         return output, hidden

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    222         for hook in self._forward_pre_hooks.values():
    223             hook(self, input)
--> 224         result = self.forward(*input, **kwargs)
    225         for hook in self._forward_hooks.values():
    226             hook_result = hook(self, input, result)

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
    160             flat_weight=flat_weight
    161         )
--> 162         output, hidden = func(input, self.all_weights, hx)
    163         if is_packed:
    164             output = PackedSequence(output, batch_sizes)

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/_functions/rnn.py in forward(input, *fargs, **fkwargs)
    349         else:
    350             func = AutogradRNN(*args, **kwargs)
--> 351         return func(input, *fargs, **fkwargs)
    352 
    353     return forward

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/autograd/function.py in _do_forward(self, *input)
    282         self._nested_input = input
    283         flat_input = tuple(_iter_variables(input))
--> 284         flat_output = super(NestedIOFunction, self)._do_forward(*flat_input)
    285         nested_output = self._nested_output
    286         nested_variables = _unflatten(flat_output, self._nested_output)

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/autograd/function.py in forward(self, *args)
    304     def forward(self, *args):
    305         nested_tensors = _map_variable_tensor(self._nested_input)
--> 306         result = self.forward_extended(*nested_tensors)
    307         del self._nested_input
    308         self._nested_output = result

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/nn/_functions/rnn.py in forward_extended(self, input, weight, hx)
    291             hy = tuple(h.new() for h in hx)
    292 
--> 293         cudnn.rnn.forward(self, input, hx, weight, output, hy)
    294 
    295         self.save_for_backward(input, hx, weight, output)

~/miniconda3/envs/torchenv/lib/python3.6/site-packages/torch/backends/cudnn/rnn.py in forward(fn, input, hx, weight, output, hy)
    264         if tuple(hx.size()) != hidden_size:
    265             raise RuntimeError('Expected hidden size {}, got {}'.format(
--> 266                 hidden_size, tuple(hx.size())))
    267         if cx is not None and tuple(cx.size()) != hidden_size:
    268             raise RuntimeError('Expected cell size {}, got {}'.format(

RuntimeError: Expected hidden size (1, 4, 64), got (1, 32, 64)