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[英]LSTM: How to effectively comprehend and use return_sequences=False?
[英]How can I do return_sequences for a stacked LSTM model with PyTorch?
我有一个 Tensorflow/Keras 模型:
self.model.add(Bidirectional(LSTM(lstm1_size, input_shape=(
seq_length, feature_dim), return_sequences=True)))
self.model.add(BatchNormalization())
self.model.add(Dropout(0.2))
self.model.add(Bidirectional(
LSTM(lstm2_size, return_sequences=True)))
self.model.add(BatchNormalization())
self.model.add(Dropout(0.2))
# BOTTLENECK HERE
self.model.add(Bidirectional(
LSTM(lstm3_size, return_sequences=True)))
self.model.add(BatchNormalization())
self.model.add(Dropout(0.2))
self.model.add(Bidirectional(
LSTM(lstm4_size, return_sequences=True)))
self.model.add(BatchNormalization())
self.model.add(Dropout(0.2))
self.model.add(Bidirectional(
LSTM(lstm5_size, return_sequences=True)))
self.model.add(BatchNormalization())
self.model.add(Dropout(0.2))
self.model.add(Dense(feature_dim, activation='linear'))
如何使用return_sequences
创建堆叠的 PyTorch 模型? 我对return_sequences
理解是它返回 LSTM 每一层的“输出”,然后将其送入下一层。
我将如何使用 PyToch 实现这一点?
PyTorch 总是返回序列。
https://pytorch.org/docs/stable/nn.html#lstm
例子:
import torch as t
batch_size = 2
time_steps = 10
features = 2
data = t.empty(batch_size, time_steps, features).normal_()
lstm = t.nn.LSTM(input_size=2, hidden_size=3, bidirectional=True, batch_first=True)
output, (h_n, c_n) = lstm(data)
[output.shape, h_n.shape, c_n.shape]
[torch.Size([2, 10, 6]), torch.Size([2, 2, 3]), torch.Size([2, 2, 3])]
class Net(t.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.lstm_1 = t.nn.LSTM(input_size=2, hidden_size=3, bidirectional=True, batch_first=True)
self.lstm_2 = t.nn.LSTM(input_size=2*3, hidden_size=4, bidirectional=True, batch_first=True)
def forward(self, input):
output, (h_n, c_n) = self.lstm_1(input)
output, (h_n, c_n) = self.lstm_2(output)
return output
net = Net()
net(data).shape
torch.Size([2, 10, 8])
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