class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNN, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
self.i2o = nn.Linear(input_size + hidden_size, output_size)
def forward(self, input, hidden):
# 将input和之前的网络中的隐藏层参数合并。
combined = torch.cat((input, hidden), 1)
hidden = self.i2h(combined) # 计算隐藏层参数
output = self.i2o(combined) # 计算网络输出的结果
return output, hidden
def init_hidden(self):
# 初始化隐藏层参数hidden
return torch.zeros(1, self.hidden_size)rnn实现预测字符文章来源地址https://www.toymoban.com/news/detail-519772.html
import torch
from torch import nn
import numpy as np
text = ['hey how are you','good i am fine','have a nice d文章来源：https://www.toymoban.com/news/detail-519772.html
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