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，它只需要序列长度= 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。如果我同时重塑输入和目标，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。我想使用批大小 200 和序列长度 4，即在一个序列中使用 4 行数据。

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

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

这就是最终有效的方法 - 将输入数据重塑为 4 个序列，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。现在看起来很容易，但当时非常棘手。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])

Answer 2

您已经设置了input_dim = 16839，因此您的模型需要形状(batch_size, seq_len, 16839)的输入。

您从中绘制批次的train_tensor的形状为(66512, 1, 16839).所以你的批次是形状(batch_size, 1, 16839).这是有效的，因为它满足上述要求。

但是，如果您尝试重塑相同的训练张量以使其seq_len= 4，则input_dim维将不再是 16839，因此与模型的预期不匹配，这就是您得到维度不匹配错误的原因。