Differences with torch.optim.Adagrad

torch.optim.Adagrad

class torch.optim.Adagrad(
    params,
    lr=0.01,
    lr_decay=0,
    weight_decay=0,
    initial_accumulator_value=0,
    eps=1e-10
)

For more information, see torch.optim.Adagrad.

mindspore.nn.Adagrad

class mindspore.nn.Adagrad(
    params,
    accum=0.1,
    learning_rate=0.001,
    update_slots=True,
    loss_scale=1.0,
    weight_decay=0.0
)

For more information, see mindspore.nn.Adagrad.

Differences

PyTorch and MindSpore implement different algorithms for this optimizer. PyTorch decays the learning rate in each round of iteration and adds eps to the division calculation to maintain computational stability, while MindSpore does not have this process.

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	lr_decay	-	PyTorch’s lr_decay indicates the decay value of the learning rate, and MindSpore does not have this parameter
	Parameter 4	weight_decay	weight_decay	Consistent function
	Parameter 5	initial_accumulator_value	accum	Same function, different parameter names and default values
	Parameter 6	eps	-	PyTorch eps is used to add to the denominator of a division to increase computational stability, and MindSpore does not have this parameter
	Parameter 7	-	update_slots	MindSpore update_slots indicates whether to update the accumulator, and PyTorch does not have this parameter
	Parameter 8	-	loss_scale	MindSpore loss_scale is the gradient scaling factor, and PyTorch does not have this parameter

Code Example

# MindSpore
import mindspore
from mindspore import nn

net = nn.Dense(2, 3)
optimizer = nn.Adagrad(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.Adagrad(model.parameters())
def train_step(data, label):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, label)
    loss.backward()
    optimizer.step()