3、pytorch实现最基础的MLP网络

%matplotlib inline
import numpy as np
import torch
from torch import nn
import matplotlib.pyplot as plt

d = 1
n = 200
X = torch.rand(n,d)  #200*1, batch * feature_dim
#y = 3*torch.sin(X) + 5* torch.cos(X**2)
y = 4 * torch.sin(np.pi * X) * torch.cos(6*np.pi*X**2)

#注意这里hid_dim 设置是超参数(如果太小，效果就不好)，使用tanh还是relu效果也不同，优化器自选
hid_dim_1 = 128
hid_dim_2 = 32
d_out = 1

model = nn.Sequential(nn.Linear(d,hid_dim_1),
                     nn.Tanh(),
                     nn.Linear(hid_dim_1, hid_dim_2),
                     nn.Tanh(),
                     nn.Linear(hid_dim_2, d_out)
                     )
loss_func = nn.MSELoss()
optim = torch.optim.SGD(model.parameters(), 0.05)

epochs = 6000
print("epoch\t loss\t")
for i in range(epochs):
    y_hat = model(X)
    loss = loss_func(y_hat, y)
    optim.zero_grad()
    loss.backward()
    optim.step()
    if((i+1)%100 == 0):
        print("{}\t {:.5f}".format(i+1,loss.item()))

#这个地方容易出错，测试时不要用原来的x，因为原来的x不是从小到达排序，导致x在连线时会混乱，所以要用np.linspace重新来构造
test_x  = torch.tensor(np.linspace(0,1,50), dtype = torch.float32).reshape(-1,1)
final_y = model(test_x)
plt.scatter(X,y)
plt.plot(test_x.detach(),final_y.detach(),"r")  #不使用detach会报错
print("over")

epoch	 loss	
100	 3.84844
200	 3.83552
300	 3.78960
400	 3.64596
500	 3.43755
600	 3.17153
700	 2.59001
800	 2.21228
900	 1.87939
1000	 1.55716
1100	 1.41315
1200	 1.26750
1300	 1.05869
1400	 0.91269
1500	 0.81320
1600	 0.74047
1700	 0.67874
1800	 0.61939
1900	 0.56204
2000	 0.51335
2100	 0.47797
2200	 0.45317
2300	 0.43151
2400	 0.40505
2500	 0.37628
2600	 0.34879
2700	 0.32457
2800	 0.30431
2900	 0.28866
3000	 0.30260
3100	 0.26200
3200	 0.30286
3300	 0.25229
3400	 0.21422
3500	 0.22737
3600	 0.22905
3700	 0.19909
3800	 0.24601
3900	 0.17733
4000	 0.22905
4100	 0.15704
4200	 0.21570
4300	 0.14141
4400	 0.14657
4500	 0.14609
4600	 0.11998
4700	 0.12598
4800	 0.10871
4900	 0.08616
5000	 0.18319
5100	 0.08111
5200	 0.08213
5300	 0.11087
5400	 0.06879
5500	 0.07235
5600	 0.11281
5700	 0.06817
5800	 0.08423
5900	 0.06886
6000	 0.06301