Pytorch 中的 LSTM：如何添加/更改序列长度维度？

Question

我在 pytorch 中运行 LSTM，但据我了解，它只采用序列长度 = 1。当我将序列长度重塑为 4 或其他数字时，我会收到输入和目标长度不匹配的错误。如果我重塑输入和目标，则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。我想使用批量大小 200 和序列长度 4，即在序列中使用 4 行数据。

请告知如何调整我的模型和/或数据，以便能够运行各种序列长度（例如 4）的模型。

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)))

Answer 1

这就是最终起作用的方法 - 将输入数据重塑为 4 个序列，并且每个序列有一个目标值，为此我根据问题逻辑选择了目标序列中的最后一个值。现在看来很容易，但当时却非常棘手。发布的其余代码是相同的。

train_target = torch.tensor(train_data[['Label1','Label2','Label3']].iloc[3::4].values.astype(np.float32))
train_target = np.argmax(train_target, axis=1)
train = torch.tensor(train_data.drop(['Label1','Label2','Label3'], axis = 1).values.reshape(-1, 4, 16839).astype(np.float32)) 
train_tensor = TensorDataset(train, train_target) 
train_loader = DataLoader(dataset = train_tensor, batch_size = batch_size, shuffle = True)

print(train.shape)
print(train_target.shape)

torch.Size([16628, 4, 16839])
torch.Size([16628])