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DCRNN
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removed logging of every horizon

This commit is contained in:
Chintan Shah 2019-10-08 02:10:59 -04:00
parent 765142de00
commit 02fb2430f0
1 changed files with 1 additions and 18 deletions
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19
model/pytorch/dcrnn_supervisor.py
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@ -109,20 +109,10 @@ class DCRNNSupervisor:
val_iterator = self._data['{}_loader'.format(dataset)].get_iterator() val_iterator = self._data['{}_loader'.format(dataset)].get_iterator()
losses = [] losses = []
per_timestep_loss = torch.zeros(12) # hardcoded batch size, horizon for _, (x, y) in enumerate(val_iterator):
num_batches = 0
for batch_i, (x, y) in enumerate(val_iterator):
x, y = self._prepare_data(x, y) x, y = self._prepare_data(x, y)
output = self.dcrnn_model(x) output = self.dcrnn_model(x)
# (horizon, batch_size, num_sensor * output_dim)
for t in range(y.size(0)):
per_timestep_loss[t] += self._compute_loss(y[t], output[t])
num_batches += 1
loss = self._compute_loss(y, output) loss = self._compute_loss(y, output)
losses.append(loss.item()) losses.append(loss.item())
@ -130,11 +120,6 @@ class DCRNNSupervisor:
self._writer.add_scalar('{} loss'.format(dataset), mean_loss, batches_seen) self._writer.add_scalar('{} loss'.format(dataset), mean_loss, batches_seen)
per_timestep_loss /= num_batches
for i, val in enumerate(per_timestep_loss):
self._logger.info("Dataset:{}, Timestep: {}, MAE:{:.4f}".format(dataset, i, val.item()))
return mean_loss return mean_loss
def _train(self, base_lr, def _train(self, base_lr,
@ -148,8 +133,6 @@ class DCRNNSupervisor:
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=steps, lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=steps,
gamma=lr_decay_ratio) gamma=lr_decay_ratio)
self.dcrnn_model = self.dcrnn_model.train()
self._logger.info('Start training ...') self._logger.info('Start training ...')
# this will fail if model is loaded with a changed batch_size # this will fail if model is loaded with a changed batch_size