def train_reorder_dream():
dr_model.train() # turn on training mode for dropout
dr_hidden = dr_model.init_hidden(dr_config.batch_size)
total_loss = 0
start_time = time()
num_batchs = ceil(len(train_ub) / dr_config.batch_size)
for i, x in enumerate(batchify(train_ub, dr_config.batch_size, is_reordered=True)):
baskets, lens, ids, r_baskets, h_baskets = x
dr_hidden = repackage_hidden(dr_hidden) # repackage hidden state for RNN
dr_model.zero_grad() # optim.zero_grad()
dynamic_user, _ = dr_model(baskets, lens, dr_hidden)
loss = reorder_bpr_loss(r_baskets, h_baskets, dynamic_user, dr_model.encode.weight, dr_config)
try:
loss.backward()
except RuntimeError: # for debugging
print('caching')
tmp = {'baskets': baskets, 'ids': ids, 'r_baskets': r_baskets, 'h_baskets': h_baskets,
'dynamic_user': dynamic_user, 'item_embedding': dr_model.encode.weight}
print(baskets)
print(ids)
print(r_baskets)
print(h_baskets)
print(dr_model.encode.weight)
print(dynamic_user.data)
with open('tmp.pkl', 'wb') as f:
pickle.dump(tmp, f, pickle.HIGHEST_PROTOCOL)
break
# Clip to avoid gradient exploding
torch.nn.utils.clip_grad_norm(dr_model.parameters(), dr_config.clip)
# Parameter updating
# manual SGD
# for p in dr_model.parameters(): # Update parameters by -lr*grad
# p.data.add_(- dr_config.learning_rate, p.grad.data)
# adam
grad_norm = get_grad_norm(dr_model)
previous_params = deepcopy(list(dr_model.parameters()))
optim.step()
total_loss += loss.data
params = deepcopy(list(dr_model.parameters()))
delta = get_weight_update(previous_params, params)
weight_update_ratio = get_ratio_update(delta, params)
# Logging
if i % dr_config.log_interval == 0 and i > 0:
elapsed = (time() - start_time) * 1000 / dr_config.log_interval
cur_loss = total_loss[0] / dr_config.log_interval / dr_config.batch_size # turn tensor into float
total_loss = 0
start_time = time()
print(
'[Training]| Epochs {:3d} | Batch {:5d} / {:5d} | ms/batch {:02.2f} | Loss {:05.2f} |'.format(epoch, i,
num_batchs,
elapsed,
cur_loss))
def train_dream():
dr_model.train() # turn on training mode for dropout
dr_hidden = dr_model.init_hidden(dr_config.batch_size)
total_loss = 0
start_time = time()
num_batchs = ceil(len(train_ub) / dr_config.batch_size)
for i, x in enumerate(batchify(train_ub, dr_config.batch_size)):
baskets, lens, _ = x
dr_hidden = repackage_hidden(
dr_hidden) # repackage hidden state for RNN
dr_model.zero_grad() # optim.zero_grad()
dynamic_user, _ = dr_model(baskets, lens, dr_hidden)
loss = bpr_loss(baskets, dynamic_user, dr_model.encode.weight,
dr_config)
loss.backward()
# Clip to avoid gradient exploding
torch.nn.utils.clip_grad_norm(dr_model.parameters(), dr_config.clip)
# Parameter updating
# manual SGD
# for p in dr_model.parameters(): # Update parameters by -lr*grad
# p.data.add_(- dr_config.learning_rate, p.grad.data)
# adam
grad_norm = get_grad_norm(dr_model)
previous_params = deepcopy(list(dr_model.parameters()))
optim.step()
total_loss += loss.data
params = deepcopy(list(dr_model.parameters()))
delta = get_weight_update(previous_params, params)
weight_update_ratio = get_ratio_update(delta, params)
# Logging
if i % dr_config.log_interval == 0 and i > 0:
elapsed = (time() - start_time) * 1000 / dr_config.log_interval
cur_loss = total_loss.item(
) / dr_config.log_interval / dr_config.batch_size # turn tensor into float
total_loss = 0
start_time = time()
print(
'[Training]| Epochs {:3d} | Batch {:5d} / {:5d} | ms/batch {:02.2f} | Loss {:05.2f} |'
.format(epoch, i, num_batchs, elapsed, cur_loss))
writer.add_scalar('model/train_loss', cur_loss,
epoch * num_batchs + i)
writer.add_scalar('model/grad_norm', grad_norm,
epoch * num_batchs + i)
writer.add_scalar('model/weight_update_ratio', weight_update_ratio,
epoch * num_batchs + i)