Pytorch使用MNIST数据集实现CGAN和生成指定的数字方式

Pytorch使用使用MNIST数据集实现数据集实现CGAN和生成指定的数字方式和生成指定的数字方式

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CGAN的全拼是Conditional Generative Adversarial Networks，条件生成对抗网络，在初始GAN的基础上增加了图片的相应信

息。

这里用传统的卷积方式实现CGAN。

import torch

from torch.utils.data import DataLoader

from torch.autograd import Variable

import pickle

import copy

import matplotlib.gridspec as gridspec

import os

def save_model(model, filename): #保存为CPU中可以打开的模型

state = model.state_dict()

x=state.copy()

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])

# plt.tight_layout()

plt.savefig(r'./CGAN/images/%d.png'% count, bbox_inches='tight')

def loadMNIST(batch_size): #MNIST图片的大小是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)

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),