Differences with torch.optim.RMSProp
torch.optim.RMSProp
class torch.optim.RMSProp(
params,
lr=0.01,
alpha=0.99,
eps=1e-08,
weight_decay=0,
momentum=0,
centered=False
)
For more information, see torch.optim.RMSProp.
mindspore.nn.RMSProp
class mindspore.nn.RMSProp(
params,
learning_rate=0.1,
decay=0.9,
momentum=0.0,
epsilon=1e-10,
use_locking=False,
centered=False,
loss_scale=1.0,
weight_decay=0.0
)
For more information, see mindspore.nn.RMSProp.
Differences
PyTorch and MindSpore implement different algorithms for this optimizer. Please refer to the formula on the official website for details.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter 1 |
params |
params |
Consistent function |
Parameter 2 |
lr |
learning_rate |
Consistent function, different parameter names and default values |
|
Parameter 3 |
alpha |
decay |
Consistent function, different parameter names and default values |
|
Parameter 4 |
eps |
epsilon |
Consistent function, different parameter names and default values |
|
Parameter 5 |
weight_decay |
weight_decay |
Consistent function |
|
Parameter 6 |
momentum |
momentum |
Consistent function |
|
Parameter 7 |
centered |
centered |
Consistent function |
|
Parameter 8 |
- |
use_locking |
MindSpore |
|
Parameter 9 |
- |
loss_scale |
MindSpore |
Code Example
# MindSpore
import mindspore
from mindspore import nn
net = nn.Dense(2, 3)
optimizer = nn.RMSProp(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.RMSProp(model.parameters())
def train_step(data, label):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, label)
loss.backward()
optimizer.step()