比较与torch.optim.Adadelta的功能差异

torch.optim.Adadelta

class torch.optim.Adadelta(
    params,
    lr=1.0,
    rho=0.9,
    eps=1e-06,
    weight_decay=0
)

更多内容详见torch.optim.Adadelta。

mindspore.ops.ApplyAdadelta

class mindspore.ops.ApplyAdadelta(*args, **kwargs)(
    var,
    accum,
    accum_update,
    lr,
    rho,
    epsilon,
    grad
)

更多内容详见mindspore.ops.ApplyAdadelta。

使用方式

PyTorch：需要将期望更新的参数放入1个迭代类型参数params后传入，且设置了step方法执行单步优化返回损失值。

MindSpore：需要分别传入期望更新的参数var，accum，accum_update，grad。

代码示例

# The following implements Adadelta with MindSpore.
import numpy as np
import torch
import mindspore.nn as nn
from mindspore import Tensor, Parameter
import mindspore.ops as ops
from mindspore import dtype as mstype

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.apply_adadelta = ops.ApplyAdadelta()
        self.var = Parameter(Tensor(np.random.rand(1, 1).astype(np.float32)), name="var")
        self.accum = Parameter(Tensor(np.random.rand(1, 1).astype(np.float32)), name="accum")
        self.accum_update = Parameter(Tensor(np.random.rand(1, 1).astype(np.float32)), name="accum_update")
    def construct(self, lr, rho, epsilon, grad):
        return self.apply_adadelta(self.var, self.accum, self.accum_update, lr, rho, epsilon, grad)

np.random.seed(0)
net = Net()
lr = Tensor(0.001, mstype.float32)
rho = Tensor(0.0, mstype.float32)
epsilon = Tensor(1e-6, mstype.float32)
grad = Tensor(np.random.rand(1, 1).astype(np.float32))
var, accum, accum_update = net(lr, rho, epsilon, grad)
print(var)
print(accum)
print(accum_update)
# Out:
# [[0.5480]]
# [[0.2969]]
# [[0.6028]]

# The following implements Adadelta with torch.
input_x = torch.tensor(np.random.rand(1, 20).astype(np.float32))
input_y = torch.tensor([1.])
net = torch.nn.Sequential(torch.nn.Linear(input_x.shape[-1], 1))
loss = torch.nn.MSELoss()
optimizer = torch.optim.Adadelta(net.parameters())
l = loss(net(input_x).view(-1), input_y) / 2
optimizer.zero_grad()
l.backward()
optimizer.step()
print(loss(net(input_x).view(-1), input_y).item() / 2)
# Out:
# 0.5616