Comparing the function difference with torch.optim.lr_scheduler.ExponentialLR

torch.optim.lr_scheduler.ExponentialLR

torch.optim.lr_scheduler.ExponentialLR(
    optimizer,
    gamma,
    last_epoch=-1,
    verbose=False
)

For more information, see torch.optim.lr_scheduler.ExponentialLR.

mindspore.nn.exponential_decay_lr

mindspore.nn.exponential_decay_lr(
      learning_rate,
      decay_rate,
      total_step,
      step_per_epoch,
      decay_epoch,
      is_stair=False
)

For more information, see mindspore.nn.exponential_decay_lr.

mindspore.nn.ExponentialDecayLR

mindspore.nn.ExponentialDecayLR(
  learning_rate,
  decay_rate,
  decay_steps,
  is_stair=False
)

For more information, see mindspore.nn.ExponentialDecayLR.

Differences

PyTorch (torch.optim.lr_scheduler.ExponentialLR): The calculating method is \(lr * gamma^{epoch}\) . When used, the optimizer is used as input and the learning rate is updated by calling the step method. When verbose is True, the relevant information is printed for each update.

MindSpore (mindspore.nn.exponential_decay_lr): The calculating method is \(lr * decay\_rate^{p}\) . exponential_decay_lr pre-generates the learning rate list and passes the list into the optimizer.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameter	Parameter 1	optimizer		Optimizer for PyTorch applications. MindSpore does not have this Parameter
	Parameter 2	gamma	decay_rate	Parameter of decay learning rate, same function, different Parameter name
	Parameter 3	last_epoch		MindSpore does not have this Parameter
	Parameter 4	verbose		PyTorch verbose prints information about each update when it is True. MindSpore does not have this Parameter.
	Parameter 5		learning_rate	MindSpore sets the initial value of the learning rate.
	Parameter 6		total_step	Total number of steps in MindSpore
	Parameter 7		step_per_epoch	The number of steps per epoch in MindSpore
	Parameter 8		decay_steps	The number of decay steps performed by MindSpore
	Parameter 9		is_stair	When MindSpore is_stair is True, the learning rate decays once every decay_steps.

MindSpore (mindspore.nn.ExponentialDecayLR): The calculating method is \(lr * decay\_rate^{p}\) . ExponentialDecayLR is passed in the optimizer for training in the way of the computational graph.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameter	Parameter 1	optimizer		Optimizer for PyTorch applications. MindSpore does not have this Parameter
	Parameter 2	gamma	decay_rate	Parameter of decay learning rate, same function, different Parameter name
	Parameter 3	last_epoch		MindSpore does not have this Parameter.
	Parameter 4	verbose		PyTorch verbose prints information about each update when it is True. MindSpore does not have this Parameter.
	Parameter 5		learning_rate	MindSpore sets the initial value of the learning rate.
	Parameter 6		decay_steps	The number of decay steps performed by MindSpore
	Parameter 7		is_stair	When MindSpore is_stair is True, the learning rate decays once every decay_steps.

Code Example

# In MindSpore：
import mindspore as ms
from mindspore import nn

# In MindSpore：exponential_decay_lr
learning_rate = 0.1
decay_rate = 0.9
total_step = 6
step_per_epoch = 2
decay_epoch = 1
output = nn.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
print(output)
# [0.1, 0.1, 0.09000000000000001, 0.09000000000000001, 0.08100000000000002, 0.08100000000000002]

# In MindSpore：ExponentialDecayLR
learning_rate = 0.1
decay_rate = 0.9
decay_steps = 4
global_step = ms.Tensor(2, ms.int32)
exponential_decay_lr = nn.ExponentialDecayLR(learning_rate, decay_rate, decay_steps)
result = exponential_decay_lr(global_step)
print(result)
# 0.09486833

# In torch:
import torch
import numpy as np
from torch import optim

model = torch.nn.Sequential(torch.nn.Linear(20, 1))
optimizer = optim.SGD(model.parameters(), 0.1)
exponential_decay_lr = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
myloss = torch.nn.MSELoss()
dataset = [(torch.tensor(np.random.rand(1, 20).astype(np.float32)), torch.tensor([1.]))]

for epoch in range(5):
    for input, target in dataset:
        optimizer.zero_grad()
        output = model(input)
        loss = myloss(output.view(-1), target)
        loss.backward()
        optimizer.step()
    exponential_decay_lr.step()
    print(exponential_decay_lr.get_last_lr())
#  out
# [0.09000000000000001]
# [0.08100000000000002]
# [0.07290000000000002]
# [0.06561000000000002]
# [0.05904900000000002]