Pytorch使用MNIST數(shù)據(jù)集實現(xiàn)CGAN和生成指定的數(shù)字方式
CGAN的全拼是Conditional Generative Adversarial Networks,條件生成對抗網(wǎng)絡(luò),在初始GAN的基礎(chǔ)上增加了圖片的相應(yīng)信息。
這里用傳統(tǒng)的卷積方式實現(xiàn)CGAN。
import torch from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision import transforms from torch import optim import torch.nn as nn import matplotlib.pyplot as plt import numpy as np from torch.autograd import Variable import pickle import copy import matplotlib.gridspec as gridspec import os def save_model(model, filename): #保存為CPU中可以打開的模型 state = model.state_dict() x=state.copy() for key in x: x[key] = x[key].clone().cpu() torch.save(x, filename) def showimg(images,count): images=images.to('cpu') images=images.detach().numpy() images=images[[6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96]] images=255*(0.5*images+0.5) images = images.astype(np.uint8) grid_length=int(np.ceil(np.sqrt(images.shape[0]))) plt.figure(figsize=(4,4)) width = images.shape[2] gs = gridspec.GridSpec(grid_length,grid_length,wspace=0,hspace=0) for i, img in enumerate(images): ax = plt.subplot(gs[i]) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_aspect('equal') plt.imshow(img.reshape(width,width),cmap = plt.cm.gray) plt.axis('off') plt.tight_layout() # plt.tight_layout() plt.savefig(r'./CGAN/images/%d.png'% count, bbox_inches='tight') def loadMNIST(batch_size): #MNIST圖片的大小是28*28 trans_img=transforms.Compose([transforms.ToTensor()]) trainset=MNIST('./data',train=True,transform=trans_img,download=True) testset=MNIST('./data',train=False,transform=trans_img,download=True) # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") trainloader=DataLoader(trainset,batch_size=batch_size,shuffle=True,num_workers=10) testloader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=10) return trainset,testset,trainloader,testloader class discriminator(nn.Module): def __init__(self): super(discriminator,self).__init__() self.dis=nn.Sequential( nn.Conv2d(1,32,5,stride=1,padding=2), nn.LeakyReLU(0.2,True), nn.MaxPool2d((2,2)), nn.Conv2d(32,64,5,stride=1,padding=2), nn.LeakyReLU(0.2,True), nn.MaxPool2d((2,2)) ) self.fc=nn.Sequential( nn.Linear(7 * 7 * 64, 1024), nn.LeakyReLU(0.2, True), nn.Linear(1024, 10), nn.Sigmoid() ) def forward(self, x): x=self.dis(x) x=x.view(x.size(0),-1) x=self.fc(x) return x class generator(nn.Module): def __init__(self,input_size,num_feature): super(generator,self).__init__() self.fc=nn.Linear(input_size,num_feature) #1*56*56 self.br=nn.Sequential( nn.BatchNorm2d(1), nn.ReLU(True) ) self.gen=nn.Sequential( nn.Conv2d(1,50,3,stride=1,padding=1), nn.BatchNorm2d(50), nn.ReLU(True), nn.Conv2d(50,25,3,stride=1,padding=1), nn.BatchNorm2d(25), nn.ReLU(True), nn.Conv2d(25,1,2,stride=2), nn.Tanh() ) def forward(self, x): x=self.fc(x) x=x.view(x.size(0),1,56,56) x=self.br(x) x=self.gen(x) return x if __name__=="__main__": criterion=nn.BCELoss() num_img=100 z_dimension=110 D=discriminator() G=generator(z_dimension,3136) #1*56*56 trainset, testset, trainloader, testloader = loadMNIST(num_img) # data D=D.cuda() G=G.cuda() d_optimizer=optim.Adam(D.parameters(),lr=0.0003) g_optimizer=optim.Adam(G.parameters(),lr=0.0003) ''' 交替訓(xùn)練的方式訓(xùn)練網(wǎng)絡(luò) 先訓(xùn)練判別器網(wǎng)絡(luò)D再訓(xùn)練生成器網(wǎng)絡(luò)G 不同網(wǎng)絡(luò)的訓(xùn)練次數(shù)是超參數(shù) 也可以兩個網(wǎng)絡(luò)訓(xùn)練相同的次數(shù), 這樣就可以不用分別訓(xùn)練兩個網(wǎng)絡(luò) ''' count=0 #鑒別器D的訓(xùn)練,固定G的參數(shù) epoch = 119 gepoch = 1 for i in range(epoch): for (img, label) in trainloader: labels_onehot = np.zeros((num_img,10)) labels_onehot[np.arange(num_img),label.numpy()]=1 # img=img.view(num_img,-1) # img=np.concatenate((img.numpy(),labels_onehot)) # img=torch.from_numpy(img) img=Variable(img).cuda() real_label=Variable(torch.from_numpy(labels_onehot).float()).cuda()#真實label為1 fake_label=Variable(torch.zeros(num_img,10)).cuda()#假的label為0 #compute loss of real_img real_out=D(img) #真實圖片送入判別器D輸出0~1 d_loss_real=criterion(real_out,real_label)#得到loss real_scores=real_out#真實圖片放入判別器輸出越接近1越好 #compute loss of fake_img z=Variable(torch.randn(num_img,z_dimension)).cuda()#隨機生成向量 fake_img=G(z)#將向量放入生成網(wǎng)絡(luò)G生成一張圖片 fake_out=D(fake_img)#判別器判斷假的圖片 d_loss_fake=criterion(fake_out,fake_label)#假的圖片的loss fake_scores=fake_out#假的圖片放入判別器輸出越接近0越好 #D bp and optimize d_loss=d_loss_real+d_loss_fake d_optimizer.zero_grad() #判別器D的梯度歸零 d_loss.backward() #反向傳播 d_optimizer.step() #更新判別器D參數(shù) #生成器G的訓(xùn)練compute loss of fake_img for j in range(gepoch): z =torch.randn(num_img, 100) # 隨機生成向量 z=np.concatenate((z.numpy(),labels_onehot),axis=1) z=Variable(torch.from_numpy(z).float()).cuda() fake_img = G(z) # 將向量放入生成網(wǎng)絡(luò)G生成一張圖片 output = D(fake_img) # 經(jīng)過判別器得到結(jié)果 g_loss = criterion(output, real_label)#得到假的圖片與真實標簽的loss #bp and optimize g_optimizer.zero_grad() #生成器G的梯度歸零 g_loss.backward() #反向傳播 g_optimizer.step()#更新生成器G參數(shù) temp=real_label if (i%10==0) and (i!=0): print(i) torch.save(G.state_dict(),r'./CGAN/Generator_cuda_%d.pkl'%i) torch.save(D.state_dict(), r'./CGAN/Discriminator_cuda_%d.pkl' % i) save_model(G, r'./CGAN/Generator_cpu_%d.pkl'%i) #保存為CPU中可以打開的模型 save_model(D, r'./CGAN/Discriminator_cpu_%d.pkl'%i) #保存為CPU中可以打開的模型 print('Epoch [{}/{}], d_loss: {:.6f}, g_loss: {:.6f} ' 'D real: {:.6f}, D fake: {:.6f}'.format( i, epoch, d_loss.data[0], g_loss.data[0], real_scores.data.mean(), fake_scores.data.mean())) temp=temp.to('cpu') _,x=torch.max(temp,1) x=x.numpy() print(x[[6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96]]) showimg(fake_img,count) plt.show() count += 1
和基礎(chǔ)GAN Pytorch使用MNIST數(shù)據(jù)集實現(xiàn)基礎(chǔ)GAN 里面的卷積版網(wǎng)絡(luò)比較起來,這里修改的主要是這幾個地方:
生成網(wǎng)絡(luò)的輸入值增加了真實圖片的類標簽,生成網(wǎng)絡(luò)的初始向量z_dimension之前用的是100維,由于MNIST有10類,Onehot以后一張圖片的類標簽是10維,所以將類標簽放在后面z_dimension=100+10=110維;
訓(xùn)練生成器的時候,由于生成網(wǎng)絡(luò)的輸入向量z_dimension=110維,而且是100維隨機向量和10維真實圖片標簽拼接,需要做相應(yīng)的拼接操作;
z =torch.randn(num_img, 100) # 隨機生成向量 z=np.concatenate((z.numpy(),labels_onehot),axis=1) z=Variable(torch.from_numpy(z).float()).cuda()
由于計算Loss和生成網(wǎng)絡(luò)的輸入向量都需要用到真實圖片的類標簽,需要重新生成real_label,對label進行onehot。其中real_label就是真實圖片的標簽,當num_img=100時,real_label的維度是(100,10);
labels_onehot = np.zeros((num_img,10)) labels_onehot[np.arange(num_img),label.numpy()]=1 img=Variable(img).cuda() real_label=Variable(torch.from_numpy(labels_onehot).float()).cuda()#真實label為1 fake_label=Variable(torch.zeros(num_img,10)).cuda()#假的label為0
real_label的維度是(100,10),計算Loss的時候也要有對應(yīng)的維度,判別網(wǎng)絡(luò)的輸出也不再是標量,而是要修改為10維;
nn.Linear(1024, 10)
在輸出圖片的同時輸出期望的類標簽。
temp=temp.to('cpu') _,x=torch.max(temp,1)#返回值有兩個,第一個是按列的最大值,第二個是相應(yīng)最大值的列標號 x=x.numpy() print(x[[6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96]])
epoch等于0、25、50、75、100時訓(xùn)練的結(jié)果:
可以看到訓(xùn)練到后面圖像反而變模糊可能是訓(xùn)練過擬合
用模型生成指定的數(shù)字:
在訓(xùn)練的過程中保存了訓(xùn)練好的模型,根據(jù)輸出圖片的清晰度,用清晰度較高的模型,使用隨機向量和10維類標簽來指定生成的數(shù)字。
import torch import torch.nn as nn import pickle import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec num_img=9 class discriminator(nn.Module): def __init__(self): super(discriminator, self).__init__() self.dis = nn.Sequential( nn.Conv2d(1, 32, 5, stride=1, padding=2), nn.LeakyReLU(0.2, True), nn.MaxPool2d((2, 2)), nn.Conv2d(32, 64, 5, stride=1, padding=2), nn.LeakyReLU(0.2, True), nn.MaxPool2d((2, 2)) ) self.fc = nn.Sequential( nn.Linear(7 * 7 * 64, 1024), nn.LeakyReLU(0.2, True), nn.Linear(1024, 10), nn.Sigmoid() ) def forward(self, x): x = self.dis(x) x = x.view(x.size(0), -1) x = self.fc(x) return x class generator(nn.Module): def __init__(self, input_size, num_feature): super(generator, self).__init__() self.fc = nn.Linear(input_size, num_feature) # 1*56*56 self.br = nn.Sequential( nn.BatchNorm2d(1), nn.ReLU(True) ) self.gen = nn.Sequential( nn.Conv2d(1, 50, 3, stride=1, padding=1), nn.BatchNorm2d(50), nn.ReLU(True), nn.Conv2d(50, 25, 3, stride=1, padding=1), nn.BatchNorm2d(25), nn.ReLU(True), nn.Conv2d(25, 1, 2, stride=2), nn.Tanh() ) def forward(self, x): x = self.fc(x) x = x.view(x.size(0), 1, 56, 56) x = self.br(x) x = self.gen(x) return x def show(images): images = images.detach().numpy() images = 255 * (0.5 * images + 0.5) images = images.astype(np.uint8) plt.figure(figsize=(4, 4)) width = images.shape[2] gs = gridspec.GridSpec(1, num_img, wspace=0, hspace=0) for i, img in enumerate(images): ax = plt.subplot(gs[i]) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_aspect('equal') plt.imshow(img.reshape(width, width), cmap=plt.cm.gray) plt.axis('off') plt.tight_layout() plt.tight_layout() # plt.savefig(r'drive/深度學(xué)習(xí)/DCGAN/images/%d.png' % count, bbox_inches='tight') return width def show_all(images_all): x=images_all[0] for i in range(1,len(images_all),1): x=np.concatenate((x,images_all[i]),0) print(x.shape) x = 255 * (0.5 * x + 0.5) x = x.astype(np.uint8) plt.figure(figsize=(9, 10)) width = x.shape[2] gs = gridspec.GridSpec(10, num_img, wspace=0, hspace=0) for i, img in enumerate(x): ax = plt.subplot(gs[i]) ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_aspect('equal') plt.imshow(img.reshape(width, width), cmap=plt.cm.gray) plt.axis('off') plt.tight_layout() # 導(dǎo)入相應(yīng)的模型 z_dimension = 110 D = discriminator() G = generator(z_dimension, 3136) # 1*56*56 D.load_state_dict(torch.load(r'./CGAN/Discriminator.pkl')) G.load_state_dict(torch.load(r'./CGAN/Generator.pkl')) # 依次生成0到9 lis=[] for i in range(10): z = torch.randn((num_img, 100)) # 隨機生成向量 x=np.zeros((num_img,10)) x[:,i]=1 z = np.concatenate((z.numpy(), x),1) z = torch.from_numpy(z).float() fake_img = G(z) # 將向量放入生成網(wǎng)絡(luò)G生成一張圖片 lis.append(fake_img.detach().numpy()) output = D(fake_img) # 經(jīng)過判別器得到結(jié)果 show(fake_img) plt.savefig('./CGAN/generator/%d.png' % i, bbox_inches='tight') show_all(lis) plt.savefig('./CGAN/generator/all.png', bbox_inches='tight') plt.show()
生成的結(jié)果是:
以上這篇Pytorch使用MNIST數(shù)據(jù)集實現(xiàn)CGAN和生成指定的數(shù)字方式就是小編分享給大家的全部內(nèi)容了,希望能給大家一個參考,也希望大家多多支持腳本之家。
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