优化器的作用_2
发布时间：2024-06-24 点击量：76
pytorch中优化器的作用是把参数修正代码单独分离出来供直接调用。
示例：
没有优化器的代码
def training_loop(n_epochs, learning_rate, params, t_u, t_c):
for epoch in range(1, n_epochs + 1):
if params.grad is not None:
params.grad.zero_()
t_p = model(t_u, *params)
loss = loss_fn(t_p, t_c)
loss.backward()
params = (params - learning_rate * params.grad).detach().requires_grad_()
if epoch % 500 == 0:
print('Epoch %d, Loss %f' % (epoch, float(loss)))
return params 使用优化器的代码
def training_loop(n_epochs, optimizer, params, t_u, t_c):
for epoch in range(1, n_epochs + 1):
t_p = model(t_u, *params)
loss = loss_fn(t_p, t_c)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % 500 == 0:
print('Epoch %d, Loss %f' % (epoch, float(loss)))
return params
# In[10]:
params = torch.tensor([1.0, 0.0], requires_grad=True)
learning_rate = 1e-2
optimizer = optim.SGD([params], lr=learning_rate)
training_loop(
n_epochs = 5000,
optimizer = optimizer,
params = params,
t_u = t_un,
t_c = t_c)
