how could I run this on Python 3

[selenewang]

RuntimeError Traceback (most recent call last)
in ()
----> 1 loss_full= train_early_stopping(64, X_train, y_train, X_test, y_test, word_attn, sent_attn, word_optmizer, sent_optimizer, criterion, 5000, 1000, 50)

in train_early_stopping(mini_batch_size, X_train, y_train, X_test, y_test, word_attn_model, sent_attn_model, word_attn_optimiser, sent_attn_optimiser, loss_criterion, num_epoch, print_val_loss_every, print_loss_every)
13 try:
14 tokens, labels = next(g)
---> 15 loss = train_data(tokens, labels, word_attn_model, sent_attn_model, word_attn_optimiser, sent_attn_optimiser, loss_criterion)
16 acc = test_accuracy_mini_batch(tokens, labels, word_attn_model, sent_attn_model)
17 accuracy_full.append(acc)

in train_data(mini_batch, targets, word_attn_model, sent_attn_model, word_optimizer, sent_optimizer, criterion)
12 else:
13 s = torch.cat((s,_s),0)
---> 14 y_pred, state_sent, _ = sent_attn_model(s, state_sent)
15 loss = criterion(y_pred, targets)
16 loss.backward()

C:\ProgramData\Anaconda3\lib\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)

in forward(self, word_attention_vectors, state_sent)
36 print(state_sent.size())
37
---> 38 output_sent, state_sent = self.sent_gru(word_attention_vectors, state_sent)
39 sent_squish = batch_matmul_bias(output_sent, self.weight_W_sent,self.bias_sent, nonlinearity='tanh')
40 sent_attn = batch_matmul(sent_squish, self.weight_proj_sent)

C:\ProgramData\Anaconda3\lib\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)

C:\ProgramData\Anaconda3\lib\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)

C:\ProgramData\Anaconda3\lib\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

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn_functions\rnn.py in forward(input, weight, hidden)
242 input = input.transpose(0, 1)
243
--> 244 nexth, output = func(input, hidden, weight)
245
246 if batch_first and batch_sizes is None:

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn_functions\rnn.py in forward(input, hidden, weight)
82 l = i * num_directions + j
83
---> 84 hy, output = inner(input, hidden[l], weight[l])
85 next_hidden.append(hy)
86 all_output.append(output)

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn_functions\rnn.py in forward(input, hidden, weight)
111 steps = range(input.size(0) - 1, -1, -1) if reverse else range(input.size(0))
112 for i in steps:
--> 113 hidden = inner(input[i], hidden, *weight)
114 # hack to handle LSTM
115 output.append(hidden[0] if isinstance(hidden, tuple) else hidden)

C:\ProgramData\Anaconda3\lib\site-packages\torch\nn_functions\rnn.py in GRUCell(input, hidden, w_ih, w_hh, b_ih, b_hh)
54 gi = F.linear(input, w_ih, b_ih)
55 gh = F.linear(hidden, w_hh, b_hh)
---> 56 i_r, i_i, i_n = gi.chunk(3, 1)
57 h_r, h_i, h_n = gh.chunk(3, 1)
58

C:\ProgramData\Anaconda3\lib\site-packages\torch\autograd\variable.py in chunk(self, num_chunks, dim)
745
746 def chunk(self, num_chunks, dim=0):
--> 747 return Chunk.apply(self, num_chunks, dim)
748
749 def squeeze(self, dim=None):

C:\ProgramData\Anaconda3\lib\site-packages\torch\autograd_functions\tensor.py in forward(ctx, i, num_chunks, dim)
540 def forward(ctx, i, num_chunks, dim=0):
541 ctx.dim = dim
--> 542 result = i.chunk(num_chunks, dim)
543 ctx.mark_shared_storage(*((i, chunk) for chunk in result))
544 return result

C:\ProgramData\Anaconda3\lib\site-packages\torch\tensor.py in chunk(self, n_chunks, dim)
172 See :func:torch.chunk.
173 """
--> 174 return torch.chunk(self, n_chunks, dim)
175
176 def matmul(self, other):

C:\ProgramData\Anaconda3\lib\site-packages\torch\functional.py in chunk(tensor, chunks, dim)
42 if dim < 0:
43 dim += tensor.dim()
---> 44 split_size = (tensor.size(dim) + chunks - 1) // chunks
45 return split(tensor, split_size, dim)
46

RuntimeError: invalid argument 2: dimension 1 out of range of 1D tensor at d:\projects\pytorch\torch\lib\th\generic/THTensor.c:24

---

Got this runtime Error when I try to run it on Python3. Could anyone help me with this?

Thanks a lot!

[AotY]

I have the same problem, in Python 3.

[Sandeep42]

I am sorry but I will no longer be maintaining this project, especially when it comes to Python 3 support. Please have a look at the other implementation of the same attention model.
https://github.com/EdGENetworks/anuvada

[AotY]

Thanks.