def Train():
# Setting some hyperparameters
batchSize = 64 # We set the size of the batch.
imageSize = 64 # We set the size of the generated images (64x64).
# Creating the transformations
transform = transforms.Compose([transforms.Resize(imageSize), transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5,
0.5)), ]) # We create a list of transformations (scaling, tensor conversion, normalization) to apply to the input images.
dataset = dset.CIFAR10(root='./Data', download=True,
transform=transform) # We download the training set in the ./Data folder and we apply the previous transformations on each image.
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batchSize, shuffle=True,
num_workers=0) # We use dataLoader to get the images of the training set batch by batch.
# Defining the weights_init function that takes as input a neural network m and that will initialize all its weights.
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
# Creating the generator
netG = Generator.G()
netG.apply(weights_init)
# Creating the discriminator
netD = Discriminator.D()
netD.apply(weights_init)
# Training the DCGANs
criterion = nn.BCELoss()
optimizerD = optim.Adam(netD.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=0.0002, betas=(0.5, 0.999))
#Total Training will take an input from the user and set the total number of epochs to complete.
Total_Training = 25
#opening or creating the Neural Network Loss log. If file exists, overwrites the file for a new session.
file = open("Neural Network Loss.txt", "w")
for epoch in range(Total_Training):
for i, data in enumerate(dataloader, 0):
#initiating a timer to determine estimated completion time
start = time.time()
# 1st Step: Updating the weights of the neural network of the discriminator
netD.zero_grad()
# Training the discriminator with a real image of the dataset
real, _ = data
input = Variable(real)
target = Variable(torch.ones(input.size()[0]))
output = netD(input)
errD_real = criterion(output, target)
# Training the discriminator with a fake image generated by the generator
noise = Variable(torch.randn(input.size()[0], 100, 1, 1))
fake = netG(noise)
target = Variable(torch.zeros(input.size()[0]))
output = netD(fake.detach())
errD_fake = criterion(output, target)
# Backpropagating the total error
errD = errD_real + errD_fake
errD.backward()
optimizerD.step()
# 2nd Step: Updating the weights of the neural network of the generator
netG.zero_grad()
target = Variable(torch.ones(input.size()[0]))
output = netD(fake)
errG = criterion(output, target)
errG.backward()
optimizerG.step()
# 3rd Step: Printing the losses and saving the real images and the generated images of the minibatch every 100 steps
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f' % (epoch, Total_Training, i, len(dataloader), errD.item(), errG.item()))
if i % 100 == 0:
if not os.path.exists('./results'):
os.makedirs('./results')
vutils.save_image(real, '%s/real_samples.png' % "./results", normalize=True)
fake = netG(noise)
vutils.save_image(fake.data, '%s/fake_samples_epoch_%03d.png' % ("./results", epoch), normalize=True)
#ending the timer to create our estimated completion times
end = time.time()
#calculating the epoch trainging time
epoch_time = len(dataloader) * (end-start)
epoch_time = str(datetime.timedelta(seconds=epoch_time))
#printing the estimated time of completion for one epoch
print("Estimated Time too Epoch Completion: {:0>8}".format(str(datetime.timedelta(seconds=epoch_time))))
#estimating the completion time for all epochs entered by the user
Total_Training_Time = epoch_time * Total_Training
Total_Training_Time = str(datetime.timedelta(seconds=Total_Training_Time))
#printing the estimation to the screen for total training
print("Estimated Time too Training Completion: {:0>8}".format(str(datetime.timedelta(seconds=Total_Training_Time))))
#Appending the Neural Network Loss Log
file = open("Neural Network Loss.txt", "a+")
file.write('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f' % (epoch, Total_Training, i, len(dataloader), errD.item(), errG.item()))
file.write("\nEstimated Time too Epoch Completion: {:0>8}".format(str(datetime.timedelta(seconds=epoch_time))))
file.write("\nEstimated Time too Training Completion: {:0>8}\n\n".format(str(datetime.timedelta(seconds=Total_Training_Time))))
#Closing Neural Network Loss Log
file.close()
def __init__(self):
self.Discrimintor = Disc.D()
self.Generator = Gen.G()
