我在pytorch中运行LSTM，但据我所知，它只取序列长度= 1。当我将序列长度整形为4或其他数字时，就会得到输入和目标长度不匹配的错误。如果我同时对输入和目标进行整形，那么模型会抱怨它不接受多目标标签。 我的训练数据集有66512行和16839列，目标中有3个类别/类。我想使用批处理大小为200和序列长度为4，即在一个序列中使用4行数据。 请建议如何调整我的模型和/或数据，以便能够运行模型的各种序列长度(例如，4)。

batch_size=200

import torch

from torch.utils.data import TensorDataset

from torch.utils.data import DataLoader

train_target = torch.tensor(train_data[['Label1','Label2','Label3']].values.astype(np.float32))

train_target = np.argmax(train_target, axis=1)

train = torch.tensor(train_data.drop(['Label1','Label2','Label3'], axis = 1).values.astype(np.float32))

train_tensor = TensorDataset(train.unsqueeze(1), train_target)

train_loader = DataLoader(dataset = train_tensor, batch_size = batch_size, shuffle = True)

print(train.shape)

print(train_target.shape)

torch.Size([66512, 16839])

torch.Size([66512])

import torch.nn as nn

class LSTMModel(nn.Module):

def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):

super(LSTMModel, self).__init__()

# Hidden dimensions

self.hidden_dim = hidden_dim

# Number of hidden layers

self.layer_dim = layer_dim

# Building LSTM

self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)

# Readout layer

self.fc = nn.Linear(hidden_dim, output_dim)

def forward(self, x):

# Initialize hidden state with zeros

h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_().to(device)

# Initialize cell state

c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_().to(device)

out, (hn, cn) = self.lstm(x, (h0,c0))

# Index hidden state of last time step

out = self.fc(out[:, -1, :])

return out

input_dim = 16839

hidden_dim = 100

output_dim = 3

layer_dim = 1

batch_size = batch_size

num_epochs = 1

model = LSTMModel(input_dim, hidden_dim, layer_dim, output_dim)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model.to(device)

criterion = nn.CrossEntropyLoss()

learning_rate = 0.1

optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

print(len(list(model.parameters())))

for i in range(len(list(model.parameters()))):

print(list(model.parameters())[i].size())

6

torch.Size([400, 16839])

torch.Size([400, 100])

torch.Size([400])

torch.Size([400])

torch.Size([3, 100])

torch.Size([3])

for epoch in range(num_epochs):

for i, (train, train_target) in enumerate(train_loader):

# Load data as a torch tensor with gradient accumulation abilities

train = train.requires_grad_().to(device)

train_target = train_target.to(device)

# Clear gradients w.r.t. parameters

optimizer.zero_grad()

# Forward pass to get output/logits

outputs = model(train)

# Calculate Loss: softmax --> cross entropy loss

loss = criterion(outputs, train_target)

# Getting gradients w.r.t. parameters

loss.backward()

# Updating parameters

optimizer.step()

print('Epoch: {}. Loss: {}. Accuracy: {}'.format(epoch, np.around(loss.item(), 4), np.around(accuracy,4)))

问题来源StackOverflow 地址：/questions/59381695/lstm-in-pytorch-how-to-add-change-sequence-length-dimension