网站没快照,公司两个网站如何都备案,深圳信用网,玉林专业网站建设前言 本篇主要是结合手写数字例子,结合PyTorch 介绍一下Gan 实战
第一轮训练效果 第20轮训练效果,已经可以生成数字了 68 轮 目录#xff1a; 谷歌云服务器#xff08;Google Colab#xff09; 整体训练流程 Python 代码 一 谷歌云服务器#xff08;Google Colab…前言 本篇主要是结合手写数字例子,结合PyTorch 介绍一下Gan 实战
第一轮训练效果 第20轮训练效果,已经可以生成数字了 68 轮 目录 谷歌云服务器Google Colab 整体训练流程 Python 代码 一 谷歌云服务器Google Colab 个人用的一直是联想小新笔记本,虽然非常稳定方便。但是现在跑深度学习,性能确实有点跟不上. 1.1 打开谷歌云服务器Google Colab https://colab.research.google.com/ 1. 2 新建笔记 1 1.4 选择T4GPU 1.5 点击运行按钮
可以看到当前硬件的情况 二 整体训练流程 三 PyTorch 例子
# -*- coding: utf-8 -*-Created on Fri Mar 1 13:27:49 2024author: chengxf2import torch.optim as optim #优化器
import numpy as np
import matplotlib.pyplot as plt
import torchvision
from torchvision import transforms
import torch
import torch.nn as nn#第一步加载手写数字集
def loadData():#同时归一化数据集(-1,1)style transforms.Compose([transforms.ToTensor(), #0-1 归一化0-1 channel,height,widthtransforms.Normalize(mean0.5, std0.5) #变成了-1,1 ])trainData torchvision.datasets.MNIST(data,trainTrue,transformstyle,downloadTrue)dataloader torch.utils.data.DataLoader(trainData,batch_size 16,shuffleTrue)imgs,_ next(iter(dataloader))#torch.Size([64, 1, 28, 28])print(\n imgs shape ,imgs.shape)return dataloaderclass Generator(nn.Module):定义生成器输入z 随机噪声[batch, input_size]输出x 图片 [batch, height, width, channel]def __init__(self,input_size):super(Generator,self).__init__()self.net nn.Sequential(nn.Linear(in_features input_size , out_features 256),nn.ReLU(),nn.Linear(in_features 256 , out_features 512),nn.ReLU(),nn.Linear(in_features 512 , out_features 28*28),nn.Tanh())def forward(self, z):# z 随机输入[batch, dim]x self.net(z)#[batch, height, width, channel]#print(x.shape)x x.view(-1,28,28,1)return xclass Discriminator(nn.Module):定义鉴别器输入x 图片 [batch, height, width, channel]输出y: 二分类图片的概率 BCELoss 计算交叉熵损失def __init__(self):super(Discriminator,self).__init__()#开始的维度和终止的维度默认值分别是1和-1self.flatten nn.Flatten()self.net nn.Sequential(nn.Linear(in_features 28*28 , out_features 512),nn.LeakyReLU(), #负值的时候保留梯度信息nn.Linear(in_features 512 , out_features 256),nn.LeakyReLU(),nn.Linear(in_features 256 , out_features 1),nn.Sigmoid())def forward(self, x):x self.flatten(x)#print(x.shape)out self.net(x)return outdef gen_img_plot(model, epoch, test_input):out model(test_input).detach().cpu()out out.numpy()imgs np.squeeze(out)fig plt.figure(figsize(4,4))for i in range(out.shape[0]):plt.subplot(4,4,i1)img (imgs[i]1)/2.0#[-1,1]plt.imshow(img)plt.axis(off)plt.show()def train():#1 初始化参数device cuda if torch.cuda.is_available() else cpu#2 加载训练数据dataloader loadData()test_input torch.randn(16,100,devicedevice)#3 超参数maxIter 20 #最大训练次数input_size 100batchNum 16input_size 100#4 初始化模型gen Generator(100).to(device)dis Discriminator().to(device)#5 优化器,损失函数d_optim torch.optim.Adam(dis.parameters(), lr1e-4)g_optim torch.optim.Adam(gen.parameters(),lr1e-4)loss_fn torch.nn.BCELoss()#6 loss 变化列表D_loss []G_loss []for epoch in range(0,maxIter):d_epoch_loss 0.0g_epoch_loss 0.0#count len(dataloader)for step ,(realImgs, _) in enumerate(dataloader):realImgs realImgs.to(device)random_noise torch.randn(batchNum, input_size).to(device)#先训练判别器d_optim.zero_grad()real_output dis(realImgs)d_real_loss loss_fn(real_output, torch.ones_like(real_output))d_real_loss.backward()#不要训练生成器所以要生成器detachfake_img gen(random_noise)fake_output dis(fake_img.detach())d_fake_loss loss_fn(fake_output, torch.zeros_like(fake_output))d_fake_loss.backward()d_loss d_real_lossd_fake_lossd_optim.step()#优化生成器g_optim.zero_grad()fake_output dis(fake_img.detach())g_loss loss_fn(fake_output, torch.ones_like(fake_output))g_loss.backward()g_optim.step()with torch.no_grad():d_epoch_loss d_lossg_epoch_loss g_losscount 16 with torch.no_grad():d_epoch_loss/countg_epoch_loss/countD_loss.append(d_epoch_loss)G_loss.append(g_epoch_loss)gen_img_plot(gen, epoch, test_input)print(Epoch: ,epoch)print(-----finised-----)if __name__ __main__:train() 参考
10.完整课程简介_哔哩哔哩_bilibili
理论【PyTorch][chapter 19][李宏毅深度学习]【无监督学习][ GAN]【理论】-CSDN博客
