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()