我正在进行情感分析，我想把输出分成4类。对于损失，我使用交叉熵。
问题是PyTorch交叉熵需要输入**（batch_size，output），这是我遇到的麻烦。
我正在处理批大小为12**，序列大小为32

import torch.nn as nn

class RNN(nn.Module):
    def __init__(self, hidden_dim = 256, input_size = 32 , num_layers = 1, num_classes=4, vocab_size = len(vocab_to_int)+1, embedding_dim=100):
        super().__init__()
        self.input_size = input_size
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.num_classes = num_classes
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers)
        self.fc1 = nn.Linear(hidden_dim, 50)
        self.fc2 = nn.Linear(50, 4)

    
    def forward(self, x, hidden):
        x = self.embedding(x)
        x = x.view(32, 12, 100)
        x, hidden = self.lstm(x, hidden)
        x = x.contiguous().view(-1, 256)
        x = self.fc1(x) # output shape ([384, 50])
        x = self.fc2(x) # output shape [384, 4]
        return x, hidden
    
    def init_hidden(self, batch_size=12):
        weight = next(self.parameters()).data
        hidden = (weight.new(self.num_layers, 12, self.hidden_dim).zero_().cuda(), weight.new(self.num_layers, 12, self.hidden_dim).zero_().cuda())
        return hidden