pytorch tensor 初始化_Pytorch - nn.init 参数初始化方法

Pytorch 的参数初始化 -

给定非线性函数的推荐增益值(gain value)：nonlinearity 非线性函数gain 增益

Linear / Identity1

Conv{1,2,3}D1

Sigmoid1

Tanh$\frac{5}{3}$

ReLU$\sqrt{2}$

Leaky Relu$\sqrt{\frac{2}{1 + \text{negative_slope}^2}}$Gain is a proportional value that shows the relationship between the magnitude of the input to the magnitude of the output signal at steady state. Many systems contain a method by which the gain can be altered, providing more or less "power" to the system.

-- From wiki.

1. torch.nn.init.calculate_gaintorch.nn.init.calculate_gain(nonlinearity, param=None)nonlinearlity - 非线性函数名

param - 非线性函数的可选参数

如：import torch.nn as nn

gain = nn.init.calculate_gain('leaky_relu')

>>> 1.414...

2. torch.nn.init 初始化函数import torch

import torch.nn as nn

w = torch.empty(2, 3)

# 1. 均匀分布 - u(a,b)

# torch.nn.init.uniform_(tensor, a=0, b=1)

nn.init.uniform_(w)

# tensor([[ 0.0578, 0.3402, 0.5034],

# [ 0.7865, 0.7280, 0.6269]])

# 2. 正态分布 - N(mean, std)

# torch.nn.init.normal_(tensor, mean=0, std=1)

nn.init.normal_(w)

# tensor([[ 0.3326, 0.0171, -0.6745],

# [ 0.1669, 0.1747, 0.0472]])

# 3. 常数 - 固定值 val

# torch.nn.init.constant_(tensor, val)

nn.init.constant_(w, 0.3)

# tensor([[ 0.3000, 0.3000, 0.3000],

# [ 0.3000, 0.3000, 0.3000]])

# 4. 对角线为 1，其它为 0

# torch.nn.init.eye_(tensor)

nn.init.eye_(w)

# tensor([[ 1., 0., 0.],

# [ 0., 1., 0.]])

# 5. Dirac delta 函数初始化，仅适用于 {3, 4, 5}-维的 torch.Tensor

# torch.nn.init.dirac_(tensor)

w1 = torch.empty(3, 16, 5, 5)

nn.init.dirac_(w1)

# 6. xavier_uniform 初始化

# torch.nn.init.xavier_uniform_(tensor, gain=1)

# From - Understanding the difficulty of training deep feedforward neural networks - Bengio 2010

nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))

# tensor([[ 1.3374, 0.7932, -0.0891],

# [-1.3363, -0.0206, -0.9346]])

# 7. xavier_normal 初始化

# torch.nn.init.xavier_normal_(tensor, gain=1)

nn.init.xavier_normal_(w)

# tensor([[-0.1777, 0.6740, 0.1139],

# [ 0.3018, -0.2443, 0.6824]])

# 8. kaiming_uniform 初始化

# From - Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - HeKaiming 2015

# torch.nn.init.kaiming_uniform_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')

nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu')

# tensor([[ 0.6426, -0.9582, -1.1783],

# [-0.0515, -0.4975, 1.3237]])

# 9. kaiming_normal 初始化

# torch.nn.init.kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')

nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')

# tensor([[ 0.2530, -0.4382, 1.5995],

# [ 0.0544, 1.6392, -2.0752]])

# 10. 正交矩阵 - (semi)orthogonal matrix

# From - Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe 2013

# torch.nn.init.orthogonal_(tensor, gain=1)

nn.init.orthogonal_(w)

# tensor([[ 0.5786, -0.5642, -0.5890],

# [-0.7517, -0.0886, -0.6536]])

# 11. 稀疏矩阵 - sparse matrix

# 非零元素采用正态分布 N(0, 0.01) 初始化.

# From - Deep learning via Hessian-free optimization - Martens 2010

# torch.nn.init.sparse_(tensor, sparsity, std=0.01)

nn.init.sparse_(w, sparsity=0.1)

# tensor(1.00000e-03 *

# [[-0.3382, 1.9501, -1.7761],

# [ 0.0000, 0.0000, 0.0000]])