深度學習與神經網絡 之 時間序列預測實戰（RNN）

預測一個正弦曲線的下一段的波形

 

例如輸入[0,49]的值，要求預測[1,50]的值

 

我們這是數字數據，就不需要embedding了，所以word_vec也就是1

batch也就是1，沒有多個

word_num即sequence設置爲50，就是1次喂50個點的數據

所以，輸入數據的shape是[1,50,1]，這裏採用的是第②種表達方式

start是開始的點

 

 

 

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


num_time_steps = 50
input_size = 1
hidden_size = 16
output_size = 1
lr=0.01



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=1,
            batch_first=True,
        )
        for p in self.rnn.parameters():
          nn.init.normal_(p, mean=0.0, std=0.001)

        self.linear = nn.Linear(hidden_size, output_size)

    def forward(self, x, hidden_prev):  #hidden_prev是h0

       out, hidden_prev = self.rnn(x, hidden_prev)
       # [b, seq, h]
       out = out.view(-1, hidden_size)  #把out打平
       out = self.linear(out)
       out = out.unsqueeze(dim=0)  #插入一個新的維度，因爲後面要和y做MSE比較
       return out, hidden_prev




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

hidden_prev = torch.zeros(1, 1, hidden_size)  #h0

for iter in range(6000):
    start = np.random.randint(3, size=1)[0]
    time_steps = np.linspace(start, start + 10, num_time_steps)
    data = np.sin(time_steps)
    data = data.reshape(num_time_steps, 1)
    x = torch.tensor(data[:-1]).float().view(1, num_time_steps - 1, 1)
    y = torch.tensor(data[1:]).float().view(1, num_time_steps - 1, 1)

    output, hidden_prev = model(x, hidden_prev)
    hidden_prev = hidden_prev.detach()

    #將x和h0送進去得到output，output又和y進行MSE誤差更新
    loss = criterion(output, y)
    model.zero_grad()
    loss.backward()
    optimizer.step()

    if iter % 100 == 0:
        print("Iteration: {} loss {}".format(iter, loss.item()))

start = np.random.randint(3, size=1)[0]  #start在0-3之間隨機地初始化，只會取0，1，2
time_steps = np.linspace(start, start + 10, num_time_steps)#將[start,start+10]的波形作爲一個輸入，在其中採樣50個點
data = np.sin(time_steps)
data = data.reshape(num_time_steps, 1) #reshape成50行1列
x = torch.tensor(data[:-1]).float().view(1, num_time_steps - 1, 1)
y = torch.tensor(data[1:]).float().view(1, num_time_steps - 1, 1)
#首先x和y都是sin函數的函數值，y是相當於x的x軸整體向右平移了一個得到的



predictions = []
input = x[:, 0, :]
#由input得到predict點，在作爲下一個的input,......
for _ in range(x.shape[1]):
  input = input.view(1, 1, 1)
  (pred, hidden_prev) = model(input, hidden_prev)
  input = pred
  predictions.append(pred.detach().numpy().ravel()[0])

x = x.data.numpy().ravel()
y = y.data.numpy()
plt.scatter(time_steps[:-1], x.ravel(), s=90)
plt.plot(time_steps[:-1], x.ravel())

plt.scatter(time_steps[1:], predictions)
plt.show()