```
import torch
import torch.nn as nn
N,D_in,H,D_out=64,1000,100,10
model=torch.nn.Sequential(
torch.nn.Linear(D_in,H,bias=True),
torch.nn.ReLU(),
torch.nn.Linear(H,D_out,bias=True)
)
#torch.nn.init.normal_(model[0].weight)
#torch.nn.init.normal_(model[2].weight)
loss_fn=torch.nn.MSELoss(reduction='sum')
x=torch.randn(N,D_in)
y=torch.randn(N,D_out)
learning_rate=1e-3
for i in range(1000000):
y_pred=model(x)
loss=loss_fn(y,y_pred)
if(i%2000==0):
print(i,loss.item())
loss.backward()
#with torch.no_grad():
for param in model.parameters():
param=param-param.grad*learning_rate
```

output:

0 654.9241333007812

2000 654.9241333007812

4000 654.9241333007812

6000 654.9241333007812

Loss is not reducingâ€¦