一. 参数
parser = argparse.ArgumentParser()
parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs of training')
parser.add_argument('--batch_size', type=int, default=64, help='size of the batches')
parser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate')
parser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient')
parser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient')
parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation')
parser.add_argument('--latent_dim', type=int, default=100, help='dimensionality of the latent space')
parser.add_argument('--n_classes', type=int, default=10, help='number of classes for dataset')
parser.add_argument('--img_size', type=int, default=32, help='size of each image dimension')
parser.add_argument('--channels', type=int, default=1, help='number of image channels')
parser.add_argument('--sample_interval', type=int, default=400, help='interval between image sampling')
opt = parser.parse_args()
print(opt)
二. 生成器
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.label_emb = nn.Embedding(opt.n_classes, opt.n_classes)
def block(in_feat, out_feat, normalize=True):
layers = [ nn.Linear(in_feat, out_feat)]
if normalize:
layers.append(nn.BatchNorm1d(out_feat, 0.8))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
*block(opt.latent_dim+opt.n_classes, 128, normalize=False),
*block(128, 256),
*block(256, 512),
*block(512, 1024),
nn.Linear(1024, int(np.prod(img_shape))),
nn.Tanh()
)
def forward(self, noise, labels):
# Concatenate label embedding and image to produce input
gen_input = torch.cat((self.label_emb(labels), noise), -1)
img = self.model(gen_input)
img = img.view(img.size(0), *img_shape)
return img分析:
一些函数：
1. nn.embeding( )
>>> import torch
>>> import torch.nn as nn
>>> from torch.autograd import Variable
>>> label=nn.Embedding(10,10)
>>> label(Variable(torch.LongTensor([4])))
tensor([[ 1.1337, -0.1294, 0.5304, -0.2917, -0.6218, -0.2924, -0.8620,
1.1923, -1.1058, -0.1389]])2. np.prod( )
连乘函数
3.torch.cat( )
>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.4090, 1.2366, -1.1015],
[ 1.3993, -2.5494, 0.1595]])
>>> y = torch.randn(2, 3)
>>> y
tensor([[-1.0984, 0.8635, -0.6777],
[-1.0466, -0.9148, 0.7278]])
>>> torch.cat((x, y), 0)
tensor([[ 0.4090, 1.2366, -1.1015],
[ 1.3993, -2.5494, 0.1595],
[-1.0984, 0.8635, -0.6777],
[-1.0466, -0.9148, 0.7278]])
>>> torch.cat((x, y), 1)
tensor([[ 0.4090, 1.2366, -1.1015, -1.0984, 0.8635, -0.6777],
[ 1.3993, -2.5494, 0.1595, -1.0466, -0.9148, 0.7278]])
>>> torch.cat((x, y), -1)
tensor([[ 0.4090, 1.2366, -1.1015, -1.0984, 0.8635, -0.6777],
[ 1.3993, -2.5494, 0.1595, -1.0466, -0.9148, 0.7278]])torch.cat( ( ) ,0 ) 是竖着连接
torch.cat( ( ) ,1 or-1 ) 是横着连接
三. 辨别器
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.label_embedding = nn.Embedding(opt.n_classes, opt.n_classes)
self.model = nn.Sequential(
nn.Linear(opt.n_classes + int(np.prod(img_shape)), 512),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(512, 512),
nn.Dropout(0.4),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(512, 512),
nn.Dropout(0.4),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(512, 1)
)
def forward(self, img, labels):
# Concatenate label embedding and image to produce input
d_in = torch.cat((img.view(img.size(0), -1), self.label_embedding(labels)), -1)
validity = self.model(d_in)
return validity
分析：
源代码网址：https://github.com/eriklindernoren/PyTorch-GAN/tree/master/implementations/cgan