Pytorch - TORCH.NN.INIT 参数初始化方法

Pytorch - TORCH.NN.INIT 参数初始化方法

路径：

https://pytorch.org/docs/master/nn.init.html#nn-init-doc

初始化函数：torch.nn.init

# -*- coding: utf-8 -*- """ Created on 2019 @author: fancp """ import torch import torch.nn as nn w = torch.empty(3,5) #1.均匀分布 - u(a,b) #torch.nn.init.uniform_(tensor, a=0.0, b=1.0) print(nn.init.uniform_(w)) # ============================================================================= # tensor([[0.9160, 0.1832, 0.5278, 0.5480, 0.6754], # [0.9509, 0.8325, 0.9149, 0.8192, 0.9950], # [0.4847, 0.4148, 0.8161, 0.0948, 0.3787]]) # ============================================================================= #2.正态分布 - N(mean, std) #torch.nn.init.normal_(tensor, mean=0.0, std=1.0) print(nn.init.normal_(w)) # ============================================================================= # tensor([[ 0.4388, 0.3083, -0.6803, -1.1476, -0.6084], # [ 0.5148, -0.2876, -1.2222, 0.6990, -0.1595], # [-2.0834, -1.6288, 0.5057, -0.5754, 0.3052]]) # ============================================================================= #3.常数 - 固定值 val #torch.nn.init.constant_(tensor, val) print(nn.init.constant_(w, 0.3)) # ============================================================================= # tensor([[0.3000, 0.3000, 0.3000, 0.3000, 0.3000], # [0.3000, 0.3000, 0.3000, 0.3000, 0.3000], # [0.3000, 0.3000, 0.3000, 0.3000, 0.3000]]) # ============================================================================= #4.全1分布 #torch.nn.init.ones_(tensor) print(nn.init.ones_(w)) # ============================================================================= # tensor([[1., 1., 1., 1., 1.], # [1., 1., 1., 1., 1.], # [1., 1., 1., 1., 1.]]) # ============================================================================= #5.全0分布 #torch.nn.init.zeros_(tensor) print(nn.init.zeros_(w)) # ============================================================================= # tensor([[0., 0., 0., 0., 0.], # [0., 0., 0., 0., 0.], # [0., 0., 0., 0., 0.]]) # ============================================================================= #6.对角线为 1，其它为 0 #torch.nn.init.eye_(tensor) print(nn.init.eye_(w)) # ============================================================================= # tensor([[1., 0., 0., 0., 0.], # [0., 1., 0., 0., 0.], # [0., 0., 1., 0., 0.]]) # ============================================================================= #7.xavier_uniform 初始化 #torch.nn.init.xavier_uniform_(tensor, gain=1.0) #From - Understanding the difficulty of training deep feedforward neural networks - Bengio 2010 print(nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))) # ============================================================================= # tensor([[-0.1270, 0.3963, 0.9531, -0.2949, 0.8294], # [-0.9759, -0.6335, 0.9299, -1.0988, -0.1496], # [-0.7224, 0.2181, -1.1219, 0.8629, -0.8825]]) # ============================================================================= #8.xavier_normal 初始化 #torch.nn.init.xavier_normal_(tensor, gain=1.0) print(nn.init.xavier_normal_(w)) # ============================================================================= # tensor([[ 1.0463, 0.1275, -0.3752, 0.1858, 1.1008], # [-0.5560, 0.2837, 0.1000, -0.5835, 0.7886], # [-0.2417, 0.1763, -0.7495, 0.4677, -0.1185]]) # ============================================================================= #9.kaiming_uniform 初始化 #torch.nn.init.kaiming_uniform_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu') #From - Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - HeKaiming 2015 print(nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu')) # ============================================================================= # tensor([[-0.7712, 0.9344, 0.8304, 0.2367, 0.0478], # [-0.6139, -0.3916, -0.0835, 0.5975, 0.1717], # [ 0.3197, -0.9825, -0.5380, -1.0033, -0.3701]]) # ============================================================================= #10.kaiming_normal 初始化 #torch.nn.init.kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu') print(nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')) # ============================================================================= # tensor([[-0.0210, 0.5532, -0.8647, 0.9813, 0.0466], # [ 0.7713, -1.0418, 0.7264, 0.5547, 0.7403], # [-0.8471, -1.7371, 1.3333, 0.0395, 1.0787]]) # ============================================================================= #11.正交矩阵 - (semi)orthogonal matrix #torch.nn.init.orthogonal_(tensor, gain=1) #From - Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe 2013 print(nn.init.orthogonal_(w)) # ============================================================================= # tensor([[-0.0346, -0.7607, -0.0428, 0.4771, 0.4366], # [-0.0412, -0.0836, 0.9847, 0.0703, -0.1293], # [-0.6639, 0.4551, 0.0731, 0.1674, 0.5646]]) # ============================================================================= #12.稀疏矩阵 - sparse matrix #torch.nn.init.sparse_(tensor, sparsity, std=0.01) #From - Deep learning via Hessian-free optimization - Martens 2010 print(nn.init.sparse_(w, sparsity=0.1)) # ============================================================================= # tensor([[ 0.0000, 0.0000, -0.0077, 0.0000, -0.0046], # [ 0.0152, 0.0030, 0.0000, -0.0029, 0.0005], # [ 0.0199, 0.0132, -0.0088, 0.0060, 0.0000]]) # =============================================================================