Pytorch - TORCH.NN.INIT 參數(shù)初始化的操作
路徑:
https://pytorch.org/docs/master/nn.init.html#nn-init-doc
初始化函數(shù):torch.nn.init
# -*- coding: utf-8 -*- """ Created on 2019 @author: fancp """ import torch import torch.nn as nn w = torch.empty(3,5) #1.均勻分布 - 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.正態(tài)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.常數(shù) - 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.對(duì)角線為 1,其它為 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.正交矩陣 - (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.稀疏矩陣 - 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]]) # =============================================================================
補(bǔ)充:【pytorch參數(shù)初始化】 pytorch默認(rèn)參數(shù)初始化以及自定義參數(shù)初始化
本文用兩個(gè)問(wèn)題來(lái)引入
1.pytorch自定義網(wǎng)絡(luò)結(jié)構(gòu)不進(jìn)行參數(shù)初始化會(huì)怎樣,參數(shù)值是隨機(jī)的嗎?
2.如何自定義參數(shù)初始化?
先回答第一個(gè)問(wèn)題
在pytorch中,有自己默認(rèn)初始化參數(shù)方式,所以在你定義好網(wǎng)絡(luò)結(jié)構(gòu)以后,不進(jìn)行參數(shù)初始化也是可以的。
1.Conv2d繼承自_ConvNd,在_ConvNd中,可以看到默認(rèn)參數(shù)就是進(jìn)行初始化的,如下圖所示
2.torch.nn.BatchNorm2d也一樣有默認(rèn)初始化的方式
3.torch.nn.Linear也如此
現(xiàn)在來(lái)回答第二個(gè)問(wèn)題。
pytorch中對(duì)神經(jīng)網(wǎng)絡(luò)模型中的參數(shù)進(jìn)行初始化方法如下:
from torch.nn import init #define the initial function to init the layer's parameters for the network def weigth_init(m): if isinstance(m, nn.Conv2d): init.xavier_uniform_(m.weight.data) init.constant_(m.bias.data,0.1) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0,0.01) m.bias.data.zero_()
首先定義了一個(gè)初始化函數(shù),接著進(jìn)行調(diào)用就ok了,不過(guò)要先把網(wǎng)絡(luò)模型實(shí)例化:
#Define Network model = Net(args.input_channel,args.output_channel) model.apply(weigth_init)
此上就完成了對(duì)模型中訓(xùn)練參數(shù)的初始化。
在知乎上也有看到一個(gè)類似的版本,也相應(yīng)的貼上來(lái)作為參考了:
def initNetParams(net): '''Init net parameters.''' for m in net.modules(): if isinstance(m, nn.Conv2d): init.xavier_uniform(m.weight) if m.bias: init.constant(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant(m.weight, 1) init.constant(m.bias, 0) elif isinstance(m, nn.Linear): init.normal(m.weight, std=1e-3) if m.bias: init.constant(m.bias, 0) initNetParams(net)
再說(shuō)一下關(guān)于模型的保存及加載
1.保存有兩種方式,第一種是保存模型的整個(gè)結(jié)構(gòu)信息和參數(shù),第二種是只保存模型的參數(shù)
#保存整個(gè)網(wǎng)絡(luò)模型及參數(shù) torch.save(net, 'net.pkl') #僅保存模型參數(shù) torch.save(net.state_dict(), 'net_params.pkl')
2.加載對(duì)應(yīng)保存的兩種網(wǎng)絡(luò)
# 保存和加載整個(gè)模型 torch.save(model_object, 'model.pth') model = torch.load('model.pth') # 僅保存和加載模型參數(shù) torch.save(model_object.state_dict(), 'params.pth') model_object.load_state_dict(torch.load('params.pth'))
以上為個(gè)人經(jīng)驗(yàn),希望能給大家一個(gè)參考,也希望大家多多支持腳本之家。如有錯(cuò)誤或未考慮完全的地方,望不吝賜教。
相關(guān)文章
python 使用 requests 模塊發(fā)送http請(qǐng)求 的方法
本文分步驟給大家介紹了python 使用 requests 模塊發(fā)送http請(qǐng)求 的方法,非常不錯(cuò),具有一定的參考借鑒價(jià)值,需要的朋友可以參考下2018-12-12Python利用networkx畫(huà)圖繪制Les?Misérables人物關(guān)系
這篇文章主要為大家介紹了Python利用networkx畫(huà)圖處理繪制Les?Misérables悲慘世界里的人物關(guān)系圖,有需要的朋友可以借鑒參考下,希望能夠有所幫助,祝大家多多進(jìn)步,早日升職加薪2022-05-05如何搜索查找并解決Django相關(guān)的問(wèn)題
每個(gè)程序員都會(huì)在開(kāi)發(fā)過(guò)程中遇到這樣或那樣的問(wèn)題, 有時(shí)光靠一個(gè)人是無(wú)法解決所有問(wèn)題的, 所以我們應(yīng)該找到適當(dāng)?shù)牡胤教釂?wèn).2014-06-06Python如何使用print()函數(shù)輸出格式化字符串
Python中內(nèi)置的%操作符和format函數(shù),都可以用于格式化字符串,下面這篇文章主要給大家介紹了關(guān)于Python如何使用print()函數(shù)輸出格式化字符串的相關(guān)資料,需要的朋友可以參考下2021-08-08Python轉(zhuǎn)json時(shí)出現(xiàn)中文亂碼的問(wèn)題及解決
這篇文章主要介紹了Python轉(zhuǎn)json時(shí)出現(xiàn)中文亂碼的問(wèn)題及解決方案,具有很好的參考價(jià)值,希望對(duì)大家有所幫助。如有錯(cuò)誤或未考慮完全的地方,望不吝賜教2023-02-02OpenCV實(shí)現(xiàn)對(duì)象跟蹤的方法
OpenCV 是一個(gè)很好的處理圖像和視頻的工具,本文主要介紹了OpenCV 進(jìn)行對(duì)象跟蹤,文中通過(guò)示例代碼介紹的非常詳細(xì),具有一定的參考價(jià)值,感興趣的小伙伴們可以參考一下2021-10-10PyCharm GUI界面開(kāi)發(fā)和exe文件生成的實(shí)現(xiàn)
這篇文章主要介紹了PyCharm GUI界面開(kāi)發(fā)和exe文件生成,文中通過(guò)示例代碼介紹的非常詳細(xì),對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值,需要的朋友們下面隨著小編來(lái)一起學(xué)習(xí)學(xué)習(xí)吧2020-03-03