AdamW is very similar to Adam. It only differs in the way how the weight decay is implemented. The way how it's implemented in Adam came from the good old vanilla SGD optimizers which isn't mathematically correct. AdamW fixes this implementation mistake.
import torch# N is batch size; D_in is input dimension;# H is hidden dimension; D_out is output dimension.N, D_in, H, D_out = 64, 1000, 100, 10# Create random Tensors to hold inputs and outputs.x = torch.randn(N, D_in)y = torch.randn(N, D_out)# Use the nn package to define our model and loss function.model = torch.nn.Sequential(torch.nn.Linear(D_in, H),torch.nn.ReLU(),torch.nn.Linear(H, D_out),)loss_fn = torch.nn.MSELoss(reduction='sum')# Use the optim package to define an Optimizer that will update the weights of# the model for us. Here we will use AdamW; the optim package contains many other# optimization algorithms. The first argument to the Adam constructor tells the# optimizer which Tensors it should update.learning_rate = 1e-4optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=0.01, amsgrad=False)for t in range(500):# Forward pass: compute predicted y by passing x to the model.y_pred = model(x)# Compute and print loss.loss = loss_fn(y_pred, y)print(t, loss.item())# Before the backward pass, use the optimizer object to zero all of the# gradients for the Tensors it will update (which are the learnable weights# of the model)optimizer.zero_grad()# Backward pass: compute gradient of the loss with respect to model parametersloss.backward()# Calling the step function on an Optimizer makes an update to its parametersoptimizer.step()
# TensorFlow Addons is a repository of contributions that conform to well- established API patterns# But implement new functionality not available in core TensorFlow.!pip install tensorflow-addons# importing the libraryimport tensorflow as tfimport tensorflow_addons as tfaopt = tfa.optimizers.AdamW(learning_rate=0.1,weight_decay=0.01, amsgrad=False)var1 = tf.Variable(10.0)loss = lambda: (var1 ** 2)/2.0 # d(loss)/d(var1) == var1step_count = opt.minimize(loss, [var1]).numpy()var1.numpy()