Pytorch 学习（8）：Recurrent layers (循环层)实现之GRUCell

Pytorch 学习（8）：Recurrent layers (循环层)实现之GRUCell

GRU是Gated Recurrent Unit，GRU是LSTM的一个变化形式。先看一个GRUCell的小例子

rnn = nn.GRUCell(10, 20)
input = Variable(torch.randn(6, 3, 10))
hx = Variable(torch.randn(3, 20))
output = []
for i in range(6):
     hx = rnn(input[i], hx)
     output.append(hx)

 在rnn = nn.GRUCell(10, 20)代码中，GRUCell进行初始化，GRUCell的__init__方法调用父类class Module(object)的__init__方法:

class Module(object):
    r"""Base class for all neural network modules.

    Your models should also subclass this class.

    Modules can also contain other Modules, allowing to nest them in
    a tree structure. You can assign the submodules as regular attributes::

        import torch.nn as nn
        import torch.nn.functional as F

        class Model(nn.Module):
            def __init__(self):
                super(Model, self).__init__()