How to implement separate learning rate or optimiz

问题:

In my structure of NN， I wanna use different learning rate or optimizer , e.g. AdaGrad, in each layer. How to implement it? Wait for your help. Thks.

回答1:

After you setup optimizer to the model, each parameter of link in the model has update_rule attribute (e.g. AdaGradRule in this case), which defines how to update this parameter.

And each update_rule has hyperparam attribute separately, so you can overwrite these hyperparam for each parameter in the link.

Below is a sample code,

class MLP(chainer.Chain):

    def __init__(self, n_units, n_out):
        super(MLP, self).__init__()
        with self.init_scope():
            # input size of each layer will be inferred when omitted
            self.l1 = L.Linear(n_units)  # n_in -> n_units
            self.l2 = L.Linear(n_units)  # n_units -> n_units
            self.l3 = L.Linear(n_out)  # n_units -> n_out

    def __call__(self, x):
        h1 = F.relu(self.l1(x))
        h2 = F.relu(self.l2(h1))
        return self.l3(h2)

model = MLP(args.unit, 10)
classifier_model = L.Classifier(model)
if args.gpu >= 0:
    chainer.cuda.get_device_from_id(args.gpu).use()  # Make a specified GPU current
    classifier_model.to_gpu()  # Copy the model to the GPU

# Setup an optimizer
optimizer = chainer.optimizers.AdaGrad()
optimizer.setup(classifier_model)

# --- After `optimizer.setup()`, you can modify `hyperparam` of each parameter ---

# 1. Change `update_rule` for specific parameter
#    `l1` is `Linear` link, which has parameter `W` and `b`
classifier_model.predictor.l1.W.update_rule.hyperparam.lr = 0.01

# 2. Change `update_rule` for all parameters (W & b) of one link
for param in classifier_model.predictor.l2.params():
    param.update_rule.hyperparam.lr = 0.01

# --- You can setup trainer module to train the model in the following...
...