Pytorch搭建循环神经网络RNN（简单实战）

Pytorch搭建循环神经网络RNN（简单实战）

去年写了篇《循环神经网络》,里面主要介绍了循环神经网络的结构与Tensorflow实现。而本篇博客主要介绍基于Pytorch搭建RNN。

通过Sin预测Cos

import torch
import torch.nn as nn
import numpy as np
from matplotlib import pyplot as plt
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首先，我们定义一些超参数

TIME_STEP = 10  # rnn 时序步长数
INPUT_SIZE = 1  # rnn 的输入维度
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
H_SIZE = 64  # of rnn 隐藏单元个数
EPOCHS = 100  # 总共训练次数
h_state = None  # 隐藏层状态
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使用Numpy生成Sin和Cos函数

steps = np.linspace(0, np.pi*2, 256, dtype=np.float32)
x_np = np.sin(steps)
y_np = np.cos(steps)
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可视化数据

plt.figure(1)
plt.suptitle('Sin and Cos', fontsize='18')
plt.plot(steps, y_np, 'r-', label='target (cos)')
plt.plot(steps, x_np, 'b-', label='input (sin)')
plt.legend(loc='best')
plt.show()
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定义网络结构

class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()
        self.rnn = nn.RNN(
            input_size=INPUT_SIZE,
            hidden_size=H_SIZE,
            num_layers=1,
            batch_first=True,
        )
        self.out = nn.Linear(H_SIZE, 1)

    def forward(self, x, h_state):
        r_out, h_state = self.rnn(x, h_state)
        outs = []  # 保存所有的预测值
        for time_step in range(r_out.size(1)):  # 计算每一步长的预测值
            outs.append(self.out(r_out[:, time_step, :]))
        return torch.stack(outs, dim=1), h_state
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rnn = RNN().to(DEVICE)
optimizer = torch.optim.Adam(rnn.parameters())  # Adam优化，几乎不用调参
criterion = nn.MSELoss()  # 因为最终的结果是一个数值，所以损失函数用均方误差

rnn.train()
plt.figure(2)
for step in range(EPOCHS):
    start, end = step * np.pi, (step+1)*np.pi  # 一个时间周期
    steps = np.linspace(start, end, 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])  # shape (batch, time_step, input_size)
    y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])
    x = x.to(DEVICE)
    prediction, h_state = rnn(x, h_state) # rnn output
    # 这一步非常重要
    h_state = h_state.data  # 重置隐藏层的状态, 切断和前一次迭代的链接
    loss = criterion(prediction.cpu(), y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if (step+1) % 20 == 0:  # 每训练20个批次可视化一下效果，并打印一下loss
        print("EPOCHS: {},Loss:{:4f}".format(step, loss))
        plt.plot(steps, y_np.flatten(), 'r-')
        plt.plot(steps, prediction.cpu().data.numpy().flatten(), 'b-')
        plt.draw()
        plt.pause(0.01)
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运行结果如下：

EPOCHS: 19,Loss:0.052745

EPOCHS: 39,Loss:0.016266

EPOCHS: 59,Loss:0.005471

EPOCHS: 79,Loss:0.001329

EPOCHS: 99,Loss:0.002216