Differences between torch.optim.AdamW and mindspore.nn.AdamWeightDecay
torch.optim.AdamW
class torch.optim.AdamW(
params,
lr=0.001,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0.01,
amsgrad=False
)
For more information, see torch.optim.AdamW.
mindspore.nn.AdamWeightDecay
class mindspore.nn.AdamWeightDecay(
params,
learning_rate=1e-3,
beta1=0.9,
beta2=0.999,
eps=1e-6,
weight_decay=0.0
)
For more information, see mindspore.nn.AdamWeightDecay.
Differences
Optimizers in PyTorch and MindSpore implement different algorithms. Please refer to the official website for more details on the formulas.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter 1 |
params |
params |
Consistent function |
Parameter 2 |
lr |
learning_rate |
Same function, different parameter names and default values |
|
Parameter 3 |
betas |
beta1, beta2 |
Same function, different parameter names |
|
Parameter 4 |
eps |
eps |
Same function, different default values |
|
Parameter 5 |
weight_decay |
weight_decay |
Consistent function |
|
Parameter 6 |
amsgrad |
- |
PyTorch |
Code Example
# MindSpore
import mindspore
from mindspore import nn
net = nn.Dense(2, 3)
optimizer = nn.AdamWeightDecay(net.trainable_params())
criterion = nn.MAELoss(reduction="mean")
def forward_fn(data, label):
logits = net(data)
loss = criterion(logits, label)
return loss, logits
grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
def train_step(data, label):
(loss, _), grads = grad_fn(data, label)
optimizer(grads)
return loss
# PyTorch
import torch
model = torch.nn.Linear(2, 3)
criterion = torch.nn.L1Loss(reduction='mean')
optimizer = torch.optim.AdamW(model.parameters())
def train_step(data, label):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, label)
loss.backward()
optimizer.step()