‘’'class Dir_VAE(nn.Module):
def init(self):
super(Dir_VAE, self).init()
self.encoder = nn.Sequential(
nn.Conv2d(nc, ndf, 4,4, 0,bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf, ndf * 2, 4,4, 0,bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 2, ndf * 4, 4, 4, 0,bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 4, 512, 4, 2,0,bias=False),
nn.LeakyReLU(0.2, inplace=True),
)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(512, ngf * 4, 4, 2, 0, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 4, 0, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 2, ngf * 2, 4, 4, 0, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 2, nc, 4, 4, 0, bias=False),
nn.Sigmoid()
)
self.fc1 = nn.Linear(512, 256)
self.fc21 = nn.Linear(256, 10)
self.fc22 = nn.Linear(256, 10)
self.fc3 = nn.Linear(10, 256)
self.fc4 = nn.Linear(256, 512)
self.lrelu = nn.LeakyReLU()
self.relu = nn.ReLU()
# Dir prior
self.prior_mean, self.prior_var = map(nn.Parameter, prior(10, 0.3)) # 0.3 is a hyper param of Dirichlet distribution
self.prior_logvar = nn.Parameter(self.prior_var.log())
self.prior_mean.requires_grad = False
self.prior_var.requires_grad = False
self.prior_logvar.requires_grad = False
def encode(self, x):
conv = self.encoder(x);
print('Size', conv.shape)
h1 = self.fc1(conv.view(-1, 512))
return self.fc21(h1), self.fc22(h1)
def decode(self, gauss_z):
dir_z = F.softmax(gauss_z,dim=1)
h3 = self.relu(self.fc3(dir_z))
deconv_input = self.fc4(h3)
print('Deconv ', deconv_input.shape)
deconv_input = deconv_input.view(-1,512,1,1)
return self.decoder(deconv_input)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5*logvar)
eps = torch.randn_like(std)
return mu + eps*std
def forward(self, x):
mu, logvar = self.encode(x)
gauss_z = self.reparameterize(mu, logvar)
# gause_z is a variable that follows a multivariate normal distribution
# Inputting gause_z into softmax func yields a random variable that follows a Dirichlet distribution (Softmax func are used in decoder)
dir_z = F.softmax(gauss_z,dim=1) # This variable follows a Dirichlet distribution
return self.decode(gauss_z), mu, logvar, gauss_z, dir_z
# Reconstruction + KL divergence losses summed over all elements and batch
def loss_function(self, recon_x, x, mu, logvar, K):
beta =0.9
print('Recon ',recon_x.shape)
print('Data ' ,x.shape)
BCE = F.binary_cross_entropy(recon_x.view(-1, 65536), x.view(-1, 65536), reduction='sum')
KLD = -0.5 * torch.sum(1+logvar - mu**2 - torch.exp(logvar), axis=1)
return BCE + beta*KLD```
RuntimeError Traceback (most recent call last)
<ipython-input-8-ef9ef8984921> in <module>
267 # 学習(Train)
268 for epoch in range(50):
--> 269 train(epoch)
270 test(epoch)
271 with torch.no_grad():
<ipython-input-8-ef9ef8984921> in train(epoch)
226 optimizer.zero_grad()
227 recon_batch, mu, logvar, gauss_z, dir_z = model(data)
--> 228 loss = model.loss_function(recon_batch, data, mu, logvar, 10)
229 loss = loss.mean()
230 loss.backward()
<ipython-input-8-ef9ef8984921> in loss_function(self, recon_x, x, mu, logvar, K)
198 print('Recon ',recon_x.shape)
199 print('Data ' ,x.shape)
--> 200 BCE = F.binary_cross_entropy(recon_x.view(-1, 65536), x.view(-1, 65536), reduction='sum')
201 # ディリクレ事前分布と変分事後分布とのKLを計算
202 # Calculating KL with Dirichlet prior and variational posterior distributions
C:\Conda5\lib\site-packages\torch\nn\functional.py in binary_cross_entropy(input, target, weight, size_average, reduce, reduction)
2760 weight = weight.expand(new_size)
2761
-> 2762 return torch._C._nn.binary_cross_entropy(input, target, weight, reduction_enum)
2763
2764
RuntimeError: all elements of input should be between 0 and 1```'''