Parameter-specific learning rate in PyTorch
How can I set a learning rate for each specific parameter (weights and biases) in a network?

On PyTorch's docs I found this:

optim.SGD([{'params': model.base.parameters()}, 
           {'params': model.classifier.parameters(), 'lr': 1e-3}], 
           lr=1e-2, momentum=0.9)


where model.classifier.parameters(), which defines a group of parameters obtains a specific learning rate of 1e-3. 

But how can I translate this into parameter level?

You can set parameter-specific learning rate by using the parameter names to set the learning rates e.g.

For a given network taken from PyTorch forum:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.layer1 = nn.Linear(1, 1)
        self.layer1.weight.data.fill_(1)
        self.layer1.bias.data.fill_(1)
        self.layer2 = nn.Linear(1, 1)
        self.layer2.weight.data.fill_(1)
        self.layer2.bias.data.fill_(1)

    def forward(self, x):
        x = self.layer1(x)
        return self.layer2(x)

net = Net()
for name, param in net.named_parameters():
    print(name)


The parameters are:

layer1.weight
layer1.bias
layer2.weight
layer2.bias


Then, you can use the parameter names to set their specific learning rates as follows:

optimizer = optim.Adam([
            {'params': net.layer1.weight},
            {'params': net.layer1.bias, 'lr': 0.01},
            {'params': net.layer2.weight, 'lr': 0.001}
        ], lr=0.1, weight_decay=0.0001)

out = net(torch.Tensor([[1]]))
out.backward()
optimizer.step()
print("weight", net.layer1.weight.data.numpy(), "grad", net.layer1.weight.grad.data.numpy())
print("bias", net.layer1.bias.data.numpy(), "grad", net.layer1.bias.grad.data.numpy())
print("weight", net.layer2.weight.data.numpy(), "grad", net.layer2.weight.grad.data.numpy())
print("bias", net.layer2.bias.data.numpy(), "grad", net.layer2.bias.grad.data.numpy())


Output:

weight [[0.9]] grad [[1.0001]]
bias [0.99] grad [1.0001]
weight [[0.999]] grad [[2.0001]]
bias [1.] grad [1.]

