import numpy.random import randint
import numpy as np
import torch
from torch import nn, optim
from matplotlib import pyplot as plt
start = randint(3) # [0, 3)
time_steps = np.linspace(start, start + 10, num_time_steps) # 返回num_time_steps个点
data = np.sin(time_steps) # [50]
data = data.reshape(num_time_steps, -1) # [50, 1]
x = torch.tensor(data[:-1]).float().view(1, num_time_steps - 1, 1) # 0~48
y = torch.tensor(data[1:]).float().view(1, num_time_steps - 1, 1) # 1~49

start 表示的含义从几何上来说就是图上红色左边框的对应的横坐标的值，因为我们要确定一个起点，从这个起点开始向后取50个点，如果每次这个起点都是相同的，就会被这个网络记住

x 是50个数据点中的前49个，我们利用这49个点，每个点都向后预测一个单位的数据，得到$\hat y$，然后将$\hat y$与$y$进行对比

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,
)
self.linear = nn.Linear(hidden_size, output_size)
def forward(self, x, h0):
out, h0 = self.rnn(x, h0)
# [b, 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, h0

model = Net()
criterion = nn.MSELoss()
h0 = torch.zeros(1, 1, hidden_size) # [b, 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 = 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, h0 = model(x, h0)
h0 = h0.detach()
loss = criterion(output, y)
loss.backward()
optimizer.step()
if iter % 100 == 0:
print("Iteration: {} loss {}".format(iter, loss.item()))

predictions = []
input = x[:, 0, :]
for _ in range(x.shape[1]):
input = input.view(1, 1, 1)
(pred, h0) = model(input, h0)
input = pred
predictions.append(pred.detach().numpy().ravel()[0])

from numpy.random import randint
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,
)
self.linear = nn.Linear(hidden_size, output_size)
def forward(self, x, h0):
out, h0 = self.rnn(x, h0)
# [b, seq, h]
out = out.view(-1, hidden_size)
out = self.linear(out)
out = out.unsqueeze(dim=0)
return out, h0
model = Net()
criterion = nn.MSELoss()
h0 = torch.zeros(1, 1, hidden_size)
for iter in range(6000):
start = randint(3)
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, h0 = model(x, h0)
h0 = h0.detach()
loss = criterion(output, y)
loss.backward()
optimizer.step()
if iter % 100 == 0:
print("Iteration: {} loss {}".format(iter, loss.item()))
start = randint(3)
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)
predictions = []
input = x[:, 0, :]
for _ in range(x.shape[1]):
input = input.view(1, 1, 1)
(pred, h0) = model(input, h0)
input = pred
predictions.append(pred.detach().numpy().ravel()[0])
x = x.data.numpy().ravel() # flatten操作
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()