pytorch中如何在lstm中输入可变长的序列
上面两篇文章写得很好,把LSTM中训练变长序列所需的三个函数讲解的很清晰,但是这两篇文章没有给出完整的训练代码,并且没有写关于带label的情况,为此,本文给出一个完整的带label的训练代码:
import torch from torch import nn import torch.nn.utils.rnn as rnn_utils from torch.utils.data import DataLoader import torch.utils.data as data_ class MyData(data_.Dataset): def __init__(self, data, label): self.data = data self.label = label def __len__(self): return len(self.data) def __getitem__(self, idx): tuple_ = (self.data[idx], self.label[idx]) return tuple_ def collate_fn(data_tuple): # data_tuple是一个列表,列表中包含batchsize个元组,每个元组中包含数据和标签 data_tuple.sort(key=lambda x: len(x[0]), reverse=True) data = [sq[0] for sq in data_tuple] label = [sq[1] for sq in data_tuple] data_length = [len(sq) for sq in data] data = rnn_utils.pad_sequence(data, batch_first=True, padding_value=0.0) # 用零补充,使长度对齐 label = rnn_utils.pad_sequence(label, batch_first=True, padding_value=0.0) # 这行代码只是为了把列表变为tensor return data.unsqueeze(-1), label, data_length if __name__ == '__main__': EPOCH = 2 batchsize = 3 hiddensize = 4 num_layers = 2 learning_rate = 0.001 # 训练数据 train_x = [torch.FloatTensor([1, 1, 1, 1, 1, 1, 1]), torch.FloatTensor([2, 2, 2, 2, 2, 2]), torch.FloatTensor([3, 3, 3, 3, 3]), torch.FloatTensor([4, 4, 4, 4]), torch.FloatTensor([5, 5, 5]), torch.FloatTensor([6, 6]), torch.FloatTensor([7])] # 标签 train_y = [torch.rand(7, hiddensize), torch.rand(6, hiddensize), torch.rand(5, hiddensize), torch.rand(4, hiddensize), torch.rand(3, hiddensize), torch.rand(2, hiddensize), torch.rand(1, hiddensize)] data_ = MyData(train_x, train_y) data_loader = DataLoader(data_, batch_size=batchsize, shuffle=True, collate_fn=collate_fn) net = nn.LSTM(input_size=1, hidden_size=hiddensize, num_layers=num_layers, batch_first=True) criteria = nn.MSELoss() optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate) # 训练方法一 for epoch in range(EPOCH): for batch_id, (batch_x, batch_y, batch_x_len) in enumerate(data_loader): batch_x_pack = rnn_utils.pack_padded_sequence(batch_x, batch_x_len, batch_first=True) out, _ = net(batch_x_pack) # out.data's shape (所有序列总长度, hiddensize) out_pad, out_len = rnn_utils.pad_packed_sequence(out, batch_first=True) loss = criteria(out_pad, batch_y) optimizer.zero_grad() loss.backward() optimizer.step() print('epoch:{:2d}, batch_id:{:2d}, loss:{:6.4f}'.format(epoch, batch_id, loss)) # 训练方法二 for epoch in range(EPOCH): for batch_id, (batch_x, batch_y, batch_x_len) in enumerate(data_loader): batch_x_pack = rnn_utils.pack_padded_sequence(batch_x, batch_x_len, batch_first=True) batch_y_pack = rnn_utils.pack_padded_sequence(batch_y, batch_x_len, batch_first=True) out, _ = net(batch_x_pack) # out.data's shape (所有序列总长度, hiddensize) loss = criteria(out.data, batch_y_pack.data) optimizer.zero_grad() loss.backward() optimizer.step() print('epoch:{:2d}, batch_id:{:2d}, loss:{:6.4f}'.format(epoch, batch_id, loss)) print('Training done!')
运行结果:
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