agent.load_state_dict(checkpoint['agent'])
start_epoch = checkpoint['epoch'] + 1
print('loaded agent from', args.load)
if args.parallel:
agent = nn.DataParallel(agent)
seg_model = nn.DataParallel(seg_model)
seg_model.eval().cuda()
agent.cuda()
optimizer = optim.Adam(agent.parameters(),
lr=args.lr,
weight_decay=args.wd)
lr_scheduler = utils.LrScheduler(optimizer, args.lr, args.lr_decay_ratio,
args.epoch_step)
for epoch in range(start_epoch, start_epoch + args.max_epochs + 1):
lr_scheduler.adjust_learning_rate(epoch)
if args.cl_step < num_blocks:
args.cl_step = 1 + 1 * (epoch // 10)
else:
args.cl_step = num_blocks
print('training the last %d blocks ...' % args.cl_step)
train(epoch)
with torch.no_grad():
if epoch != 0 and epoch % 10 == 0:
test(epoch)
vocab_size = args.max_vocab_size
model = BiLSTM(vocab_size=vocab_size,
embed_dim=args.embed_dim,
hidden_dim=args.hidden_dim,
num_tags=len(tag2idx),
embed_matrix=embed_mat)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True) # Set "from_logits=True" may be more numerically stable
#%% Degine optimizer
total_steps = len(list(train_batches)) * args.epochs
warm_steps = int(total_steps * args.warm_frac)
lr_scheduler = utils.LrScheduler(args.lr, warm_steps, total_steps)
wd_scheduler = utils.WdScheduler(1e-2, warm_steps, total_steps)
## Adam optimizer
# optimizer = tf.keras.optimizers.Adam(learning_rate = lr_scheduler)
## AdamW optimizer
optimizer = tfa.optimizers.AdamW(learning_rate=lr_scheduler,
weight_decay=lambda: None)
optimizer.weight_decay = lambda: wd_scheduler(optimizer.iterations)
#%%
@tf.function(
experimental_relax_shapes=True
) # Passing tensors with different shapes - need to relax shapes to avoid unnecessary retracing
