Define Autoencoder
#dropout_p= 0.5
class Autoencoder(nn.Module):
def __init__(self):
    super(Autoencoder, self).__init__()
    self.encoder = nn.Sequential(
       # nn.Dropout2d(dropout_p= 0.5),
        nn.Conv2d(1, 16,kernel_size=3,stride =1, padding= 1,bias= False),  
        nn.ReLU(True),
        nn.MaxPool2d( 2, 2,return_indices=True),  
        nn.Conv2d(16,32,kernel_size=3,stride =1, padding=1),  
        nn.ReLU(True),
        nn.MaxPool2d(2, 2,return_indices=True),  
        nn.Conv2d(32,64,kernel_size=3,stride =1, padding= 1),  
        nn.ReLU(True),
        nn.MaxPool2d(2, 2,return_indices=True),  
        
    )
    self.decoder = nn.Sequential(
        nn.MaxUnpool2d(2, 2),
        nn.ConvTranspose2d(64, 32, kernel_size=3), 
        nn.ReLU(True),
        nn.MaxUnpool2d(2, 2),
        nn.ConvTranspose2d(32, 16, kernel_size=3),  
        nn.ReLU(True),
        nn.MaxUnpool2d(2, 2),
        nn.ConvTranspose2d(16, 8, kernel_size=3)
        #nn.Tanh()
    )

def forward(self, x):
    x = self.encoder(x)
    encoded_x = x
    x = self.decoder(x)
    #x=F.dropout(x,training=self.training)
    return x, encoded_x

num_epochs = 5
batch_size = 128
learning_rate = 1e-5
model= Autoencoder()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
for data in loader_train_set:
img, label = data
img = img.view(-1,1,320,240)
img = Variable(img)
# img = torch.from_numpy(numpy.array(img))
encoded_output = model.encoder(img)
loss = criterion(encoded_output, img)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(‘epoch [{}/{}], loss:{:.4f}’.format(epoch+1, num_epochs, loss.data()))