Pytorch RNN根据正弦sin模拟余弦cos曲线

循环神经网络（RNN）让神经网络有了记忆，能够更好的模拟序列化的数据。虽然RNN的原理很简单，但代码特别是参数上，需要花一些时间去理解。以下我们用Pytorch中的RNN类，实现用sin曲线预测cos曲线的模型。

代码示例

1、生成数据集

仅演示效果，和模型逻辑无关

import numpy as np
import matplotlib.pyplot as plt

steps = np.linspace(0, 2*np.pi, 100, dtype=np.float32)
x_np = np.sin(steps)
y_np = np.cos(steps)

plt.plot(steps, x_np, 'b-', label='input (sin)')
plt.plot(steps, y_np, 'r-', label='target (cos)')
plt.legend()
plt.show()

2、定义模型

import torch.nn as nn

class Net(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.rnn = nn.RNN(
            input_size=input_size,
            hidden_size=hidden_size,
            batch_first=True,
        )
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x, h_state):
        outs, h_state = self.rnn(x, h_state)
        # 用10个输出，拟合10个目标点
        return self.fc(outs), h_state

import numpy as np
import torch

input_size = 1
output_size = 1
time_step = 10
hidden_size = 32

net = Net(input_size, hidden_size, output_size)

4、模型训练

epoch = 100
loss_fn = nn.MSELoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.02)
plt.figure(figsize=(20, 4))
plt.ion()
h_state = None
for i in range(epoch):
    steps = np.linspace(i * np.pi, (i + 1) * np.pi, time_step, dtype=np.float32)
    x_np = np.sin(steps)
    y_np = np.cos(steps)
    x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])
    y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])
    y_pred, h_state = net(x, h_state)
    # 也可以用 h_state = h_state.detach()
    h_state = h_state.data
    loss = loss_fn(y_pred, y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    # 模型可视化
    plt.plot(steps, y_np, 'b-')
    plt.plot(steps, y_pred.data.numpy().flatten(), 'r-')
    plt.draw(); plt.pause(0.05)
plt.ioff()
plt.show()

这个例子虽然是回归场景，但预测的也是一个曲线，RNN中每次输出值都压入了预测值，不是常规的回归任务，不是很好理解。