print('\n> Starting 10-fold CV.')
for k, (train_loader, test_loader) in enumerate(dataset.split_cross_val(10)):
# initialize model and optimizer every fold
model = BaseModel(lstm_units,
dataset.max_sentence_length, args.num_sentiments,
initial_word_embedding.clone(), args.use_gaze)
optimizer = SGD(model.parameters(),
lr=INITIAL_LR,
momentum=0.95,
nesterov=True)
# optimizer_scheduler = lr_scheduler.StepLR(
# optimizer, step_size=halve_lr_every_passes, gamma=0.5)
if USE_CUDA:
model = model.cuda()
for e in range(NUM_EPOCHS):
train_loss, train_results = iterate(train_loader)
# save the training metrics of last epoch
for metric, value in train_results.items():
train_metrics[metric].append(value)
test_loss, test_results = iterate(test_loader, train=False)
for metric, value in test_results.items():
test_metrics[metric].append(value)
print('\nFold', k, end='')
print_metrics(train_results, 'TRAIN')
print_metrics(test_results, 'TEST')
def build_model(args, pretrained_embedding, v_dim, a_dim, l_dim):
from model import BaseModel
model = BaseModel(args, pretrained_embedding, v_dim, a_dim, l_dim)
return model.cuda()
