어설 션`THIndexTensor_ (size) (target, 0) == batch_size '실패했습니다.

Question

PyTorch로 파이썬 코드를 작성할 때 발생했습니다. CrossEntropyLoss를 사용하여 간단한 언어 모델을 구성하려고하지만이 오류가 발생했습니다. 실제로, 나는이 코드를 this에 따라 썼다. 여기 내 코드가있다. 여기

import numpy as np 
import torch 
from torch.autograd import Variable 
import torch.nn as nn 

data = '...' 
words = list(set(data)) 
word2ind = {word: i for i, word in enumerate(words)} 
ind2word = {i: word for i, word in enumerate(words)} 

class RNN(nn.Module): 
    def __init__(self, input_size, hidden_size, output_size): 
     super(RNN, self).__init__() 
     self.hidden_size = hidden_size 
     self.in2h = nn.Linear(input_size-1+hidden_size, hidden_size) 
     self.in2o = nn.Linear(input_size-1+hidden_size, output_size) 
     self.o2o = nn.Linear(hidden_size+output_size, output_size) 
     self.softmax = nn.LogSoftmax() 

    def forward(self, inputs, hidden): 
     input_combined = torch.cat((inputs.float(), hidden.float()), 1) 
     print(type(input_combined.data)) 
     hidden = self.in2h(input_combined) 
     output = self.in2o(input_combined) 
     output_combined = torch.cat((hidden, output), 1) 
     output = self.o2o(output_combined) 
     output = self.softmax(output) 
     print(output) 
     return output, hidden 

    def init_hidden(self): 
     return Variable(torch.from_numpy(np.zeros((1, self.hidden_size))).type(torch.LongTensor)) 

def form_onehot(sent): 
    one_hot = np.zeros((len(data), len(words)), dtype=np.int64) 
    for i, word in enumerate(sent): 
     one_hot[i, word2ind[word]] = 1 
    return torch.LongTensor(one_hot) 

def random_choice(vec): 
    return np.random.choice(range(len(words)), p=vec) 

def train(rnn, learning_rate, optimizer, criterion, input_tensor, target_tensor): 
    hidden = rnn.init_hidden() 
    optimizer.zero_grad() 
    for i in range(input_tensor.size(1)): 
     output, hidden = rnn(input_tensor[i, :].unsqueeze(0), hidden) 
     loss = criterion(output, target_tensor[i]) 
     loss.backward() 
     optimizer.step() 
    return output, loss.data[0]/input_tensor.size()[0] 

onehot_data = form_onehot(data) 
rnn = RNN(len(words), 10, len(words)) 
learning_rate = 0.1 
criterion = nn.CrossEntropyLoss() 
optimizer = torch.optim.SGD(rnn.parameters(), lr=learning_rate) 
input_tensor = Variable(onehot_data[:, :-1].type(torch.FloatTensor)) 
print(type(input_tensor.data)) 
target_tensor = Variable(onehot_data[:, 1:]) 
int_target_tensor = Variable(onehot_data[1:, :].type(torch.LongTensor)) 
output, loss = train(rnn, learning_rate, optimizer, criterion, input_tensor, int_target_tensor) 

오류입니다 :

--------------------------------------------------------------------------- 
RuntimeError        Traceback (most recent call last) 
<ipython-input-449-8abc91b616c7> in <module>() 
----> 1 output, loss = train(rnn, learning_rate, optimizer, criterion, input_tensor, int_target_tensor) 

<ipython-input-445-72363097fc21> in train(rnn, learning_rate, optimizer, criterion, input_tensor, target_tensor) 
    52   output, hidden = rnn(input_tensor[i, :].unsqueeze(0), hidden) 
    53   print(output.size(), target_tensor[i].size()) 
---> 54   loss = criterion(output, target_tensor[i]) 
    55   print('aaaaaaaaaaa') 
    56   loss.backward() 

D:\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs) 
    204 
    205  def __call__(self, *input, **kwargs): 
--> 206   result = self.forward(*input, **kwargs) 
    207   for hook in self._forward_hooks.values(): 
    208    hook_result = hook(self, input, result) 

D:\Anaconda3\lib\site-packages\torch\nn\modules\loss.py in forward(self, input, target) 
    319   _assert_no_grad(target) 
    320   return F.cross_entropy(input, target, 
--> 321        self.weight, self.size_average) 
    322 
    323 

D:\Anaconda3\lib\site-packages\torch\nn\functional.py in cross_entropy(input, target, weight, size_average) 
    535     for each minibatch. 
    536  """ 
--> 537  return nll_loss(log_softmax(input), target, weight, size_average) 
    538 
    539 

D:\Anaconda3\lib\site-packages\torch\nn\functional.py in nll_loss(input, target, weight, size_average) 
    503  else: 
    504   raise ValueError('Expected 2 or 4 dimensions (got {})'.format(dim)) 
--> 505  return f(input, target) 
    506 
    507 

D:\Anaconda3\lib\site-packages\torch\nn\_functions\thnn\auto.py in forward(self, input, target) 
    39   output = input.new(1) 
    40   getattr(self._backend, update_output.name)(self._backend.library_state, input, target, 
---> 41             output, *self.additional_args) 
    42   return output 
    43 

RuntimeError: Assertion `THIndexTensor_(size)(target, 0) == batch_size' failed. at d:\downloads\pytorch-master-1\torch\lib\thnn\generic/ClassNLLCriterion.c:50 

정말 지금 여기 붙어있다, 그래서 어떤 응답은 이해할 수있을 것이다.

Answer 1

발견. target_tensor의 모양 때문입니다. 변수 목록 (torch.LongTensor)이어야합니다.