def train_model(starting_epoch,
num_epochs,
model,
optimizer,
criterion,
trainloader,
testloader,
device,
weight_file,
loss_weights=None):
"""
Function to train and validate the generator for all epochs.
:return: None
"""
min_loss = 0.0
start_time = time.time()
for epoch in range(starting_epoch, num_epochs):
print('Training...')
training_loop(epoch, num_epochs, model, optimizer, criterion, device,
trainloader, loss_weights)
print('Validation...')
loss = validation_loop(epoch, num_epochs, model, criterion, device,
testloader, loss_weights)
sms.send('Epoch {} Loss: {}'.format(epoch + 1, loss), "6304876751",
"att")
if epoch == 0 or loss < min_loss:
min_loss = loss
torch.save(model.state_dict(), weight_file)
sms.send('Weights saved', "6304876751", "att")
end_time = time.time()
print('Time: {}'.format(end_time - start_time))
def train_model():
"""
Function to train and validate the generator for all epochs.
:return: None
"""
min_loss = 0.0
start_time = time.time()
for epoch in range(NUM_EPOCHS):
print('Training...')
training_loop(epoch)
print('Validation...')
loss = testing_loop(epoch)
sms.send('Epoch {} Loss: {}'.format(epoch + 1, loss), "6304876751", "att")
if epoch == 0 or loss < min_loss:
min_loss = loss
torch.save(model.state_dict(), weight_file)
sms.send('Weights saved', "6304876751", "att")
end_time = time.time()
print('Time: {}'.format(end_time - start_time))