RuntimeError: dimension specified as 0 but tensor has no dimensions

[DonaldChung-HK]

when I am running train the model

for _ in range(n_iterations):

### Transfer learning 
RL.transfer_learning(transfer_data, n_epochs=n_transfer)
_, prediction = estimate_and_update(n_to_generate)
prediction_log.append(prediction)
if len(np.where(prediction >= threshold)[0])/len(prediction) > 0.15:
    threshold = min(threshold + 0.05, 0.8)

### Policy gtadient with experience replay 
for _ in range(n_policy_replay):
    rewards.append(RL.policy_gradient_replay(gen_data, replay, threshold=threshold, n_batch=10))
    print(rewards[-1])

_, prediction = estimate_and_update(n_to_generate)
prediction_log.append(prediction)
if len(np.where(prediction >= threshold)[0])/len(prediction) > 0.15:
    threshold = min(threshold + 0.05, 0.8)

### Policy graient without experinece replay 
for _ in range(n_policy):
    rewards.append(RL.policy_gradient(gen_data, threshold=threshold, n_batch=10))
    print(rewards[-1]) 

_, prediction = estimate_and_update(n_to_generate)
prediction_log.append(prediction)
if len(np.where(prediction >= threshold)[0])/len(prediction) > 0.15:
    threshold = min(threshold + 0.05, 0.8)

I experience this error

---

RuntimeError Traceback (most recent call last)
in ()
10
11 ### Transfer learning
---> 12 RL.transfer_learning(transfer_data, n_epochs=n_transfer)
13 _, prediction = estimate_and_update(n_to_generate)
14 prediction_log.append(prediction)

~/ReLeaSE/reinforcement.py in transfer_learning(self, data, n_epochs, augment)
131
132 def transfer_learning(self, data, n_epochs, augment=False):
--> 133 _ = self.generator.fit(data, n_epochs, augment=augment)

~/ReLeaSE/stackRNN.py in fit(self, data, n_epochs, all_losses, print_every, plot_every, augment)
330 for epoch in range(1, n_epochs + 1):
331 inp, target = data.random_training_set(smiles_augmentation)
--> 332 loss = self.train_step(inp, target)
333 loss_avg += loss
334

~/ReLeaSE/stackRNN.py in train_step(self, inp, target)
285 for c in range(len(inp)):
286 output, hidden, stack = self(inp[c], hidden, stack)
--> 287 loss += self.criterion(output, target[c])
288
289 loss.backward()

~/anaconda3/envs/ReLeaSE/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
489 result = self._slow_forward(*input, **kwargs)
490 else:
--> 491 result = self.forward(*input, **kwargs)
492 for hook in self._forward_hooks.values():
493 hook_result = hook(self, input, result)

~/anaconda3/envs/ReLeaSE/lib/python3.6/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
757 _assert_no_grad(target)
758 return F.cross_entropy(input, target, self.weight, self.size_average,
--> 759 self.ignore_index, self.reduce)
760
761

~/anaconda3/envs/ReLeaSE/lib/python3.6/site-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce)
1440 >>> loss.backward()
1441 """
-> 1442 return nll_loss(log_softmax(input, 1), target, weight, size_average, ignore_index, reduce)
1443
1444

~/anaconda3/envs/ReLeaSE/lib/python3.6/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce)
1326 raise ValueError('Expected 2 or more dimensions (got {})'.format(dim))
1327
-> 1328 if input.size(0) != target.size(0):
1329 raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'
1330 .format(input.size(0), target.size(0)))

RuntimeError: dimension specified as 0 but tensor has no dimensions

[Mariewelt]

Hi @Donald954732

Looks like you are using pytorch 0.4. The error you got was due to updates in pytorch between versions 0.3 and 0.4. I updated the code, now everything should work just fine. Let me know if you have any issues!