比较与torch.nn.RNNCell的差异

torch.nn.RNNCell

class torch.nn.RNNCell(
    input_size,
    hidden_size,
    bias=True,
    nonlinearity='tanh')(input, hidden) -> Tensor

更多内容详见torch.nn.RNNCell。

mindspore.nn.RNNCell

class mindspore.nn.RNNCell(
    input_size: int,
    hidden_size: int,
    has_bias: bool=True,
    nonlinearity: str = 'tanh')(x, hx) -> Tensor

更多内容详见mindspore.nn.RNNCell。

差异对比

PyTorch：循环神经网络单元。

MindSpore：MindSpore此API实现功能与PyTorch基本一致。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	input_size	input_size	-
	参数2	hidden_size	hidden_size	-
	参数3	bias	has_bias	功能一致，参数名不同
	参数4	nonlinearity	nonlinearity	-
输入	输入1	input	x	功能一致，参数名不同
	输入2	hidden	hx	功能一致，参数名不同

代码示例1

# PyTorch
import torch
from torch import tensor
import numpy as np

rnncell = torch.nn.RNNCell(2, 3, nonlinearity="relu", bias=False)
input = torch.tensor(np.array([[3.0, 4.0]]).astype(np.float32))
hidden = torch.tensor(np.array([[1.0, 2.0, 3]]).astype(np.float32))
output = rnncell(input, hidden)
print(output)
# tensor([[0.5022, 0.0000, 1.4989]], grad_fn=<ReluBackward0>)

# MindSpore
import mindspore.nn as nn
from mindspore import Tensor
import numpy as np

rnncell = nn.RNNCell(2, 3, nonlinearity="relu", has_bias=False)
x = Tensor(np.array([[3.0, 4.0]]).astype(np.float32))
hx = Tensor(np.array([[1.0, 2.0, 3]]).astype(np.float32))
output = rnncell(x, hx)
print(output)
# [[2.4998584 0.        1.9334991]]