深度学习--RNN实战与存在问题
时间序列预测
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from matplotlib import pyplot as plt
#数量
num_time_steps = 50
#输入的维度
input_size = 1
#隐藏层大小
hidden_size = 16
#输出的维度
output_size = 1
#学习率
lr=0.01
class Net(nn.Module):
def __init__(self, ):
super(Net, self).__init__()
#直接使用RNN类来构建
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):
out, hidden_prev = self.rnn(x, hidden_prev)
# [b, seq, h]
out = out.view(-1, hidden_size)
out = self.linear(out)
out = out.unsqueeze(dim=0)
return out, hidden_prev
model = Net()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr)
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 = 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()
loss = criterion(output, y)
model.zero_grad()
loss.backward()
# for p in model.parameters():
# print(p.grad.norm())
# torch.nn.utils.clip_grad_norm_(p, 10)
optimizer.step()
if iter % 100 == 0:
print("Iteration: {} loss {}".format(iter, loss.item()))
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)
predictions = []
input = x[:, 0, :]
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()
梯度弥散和梯度爆炸
梯度弥散:通俗来讲就是梯度消失,导数为0
梯度爆炸:在反向传播的过程中,出现了一个很大的导数,在参数更新时,脱离了合理区域。
标签:实战,RNN,--,steps,time,hidden,prev,data,size From: https://www.cnblogs.com/ssl-study/p/17353645.html