model = Key()
else:
raise Exception('Incorrect model name')
if args.cuda:
model.cuda()
# Loss function
if args.model == 'popularity':
loss = torch.nn.MSELoss()
else:
loss = torch.nn.CrossEntropyLoss()
# Optimizer
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# Saved losses for plotting
losses = []
val_losses = []
accs = []
val_accs = []
r_sqs = []
val_r_sqs = []
def train(epoch):
# adaptive learning rate
adjust_learning_rate(epoch, optimizer, adjust_every=10, rate=0.9)
mean_training_loss = 0.0
