Differences with torch.optim.Rprop
torch.optim.Rprop
class torch.optim.Rprop(
params,
lr=0.01,
etas=(0.5, 1.2),
step_sizes=(1e-06, 50)
)
For more information, see torch.optim.Rprop.
mindspore.nn.Rprop
class mindspore.nn.Rprop(
params,
learning_rate=0.1,
etas=(0.5, 1.2),
step_sizes=(1e-06, 50),
weight_decay=0.0,
)
For more information, see mindspore.nn.Rprop.
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 |
etas |
etas |
onsistent function, different parameter names |
|
Parameter 4 |
step_sizes |
step_sizes |
Consistent function |
|
Parameter 5 |
- |
weight_decay |
PyTorch does not have this parameter |
Code Example
# MindSpore.
import mindspore
from mindspore import nn
net = nn.Dense(2, 3)
optimizer = nn.Rprop(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.Rprop(model.parameters())
def train_step(data, label):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, label)
loss.backward()
optimizer.step()