PyTorch实现基于RNN的正弦波形下一段波形的预测

一、实现过程

RNN具体模型请参考这里。
基于RNN的正弦波形下一段波形的预测代码如下：

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

num_time_steps = 50
input_size = 1
hidden_size = 16
num_layers = 1
output_size = 1

# 设计模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.rnn = nn.RNN(
            input_size=input_size,
            hidden_size=hidden_size,
            num_layers=num_layers,
            batch_first=True
        )
        self.linear = nn.Linear(hidden_size,output_size)
    def forward(self,x,hidden_prev):
        out,hidden_prev = self.rnn(x,hidden_prev)
        # [1,seq,h] => [seq,h]
        out = out.view(-1,hidden_size)
        out = self.linear(out)  # [seq,h] => [seq,1]
        out = out.unsqueeze(dim=0)  # => [1,seq,1]
        return out,hidden_prev

#
model = Net()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(),lr=0.001)

# 训练
hidden_prev = torch.zeros(1,1,hidden_size)
for iter in range(6000):
    start = np.random.randint(3, size=1)[0]
    time_steps = np.linspace(start, start + 10, num_time_steps)
    data