def log_outputs(outputs, batch, summarizer, global_step, prefix):
preimage = masked_symbolic_state_index(batch['preimage'],
batch['preimage_mask'])
preimage_preds = outputs['preimage_preds'].argmax(-1)
preimage_preds.masked_fill_(batch['preimage_loss_mask'] == 0, 2)
preimage.masked_fill_(batch['preimage_loss_mask'] == 0, 2)
preimage_acc, preimage_mask_acc = masked_binary_accuracy(
tu.to_onehot(preimage_preds, 3), preimage)
focus = masked_symbolic_state_index(batch['goal'], batch['focus_mask'])
focus_preds = outputs['focus_preds'].argmax(-1)
focus_acc, focus_mask_acc = masked_binary_accuracy(
tu.to_onehot(focus_preds, 3), focus)
reachable_acc = classification_accuracy(outputs['reachable_preds'],
batch['reachable'])
summarizer.add_scalar(prefix + 'acc/focus',
focus_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/focus_mask',
focus_mask_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/preimage',
preimage_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/preimage_mask',
preimage_mask_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/reachable',
reachable_acc,
global_step=global_step)
def log_outputs(outputs, batch, summarizer, global_step, prefix):
preimage = masked_symbolic_state_index(batch['preimage'],
batch['preimage_mask'])
preimage_preds = outputs['preimage_preds'].argmax(-1)
preimage_preds.masked_fill_(batch['preimage_loss_mask'] == 0, 2)
preimage.masked_fill_(batch['preimage_loss_mask'] == 0, 2)
preimage_acc, preimage_mask_acc = masked_binary_accuracy(
tu.to_onehot(preimage_preds, 3), preimage)
reachable_acc = classification_accuracy(outputs['reachable_preds'],
batch['reachable'])
satisfied_acc = classification_accuracy(
outputs['satisfied_preds'], batch['satisfied'][:, -1].long())
dependency_acc = classification_accuracy(
outputs['dependency_preds'], batch['dependency'][:, -1].long())
summarizer.add_scalar(prefix + 'acc/preimage',
preimage_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/preimage_mask',
preimage_mask_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/reachable',
reachable_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/satisfied',
satisfied_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/dependency',
dependency_acc,
global_step=global_step)
def log_outputs(outputs, batch, summarizer, global_step, prefix):
subgoal = masked_symbolic_state_index(batch['subgoal'],
batch['subgoal_mask'])
subgoal_acc, subgoal_mask_acc = masked_binary_accuracy(
outputs['subgoal_preds'], subgoal)
summarizer.add_scalar(prefix + 'acc/subgoal',
subgoal_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/subgoal_mask',
subgoal_mask_acc,
global_step=global_step)
def log_outputs(outputs, batch, summarizer, global_step, prefix):
subgoal = masked_symbolic_state_index(batch['subgoal'],
batch['subgoal_mask'])
subgoal_preds = outputs['subgoal_preds'].argmax(-1)
subgoal_acc, subgoal_mask_acc = masked_binary_accuracy(
tu.to_onehot(subgoal_preds, 3), subgoal)
satisfied_acc = classification_accuracy(
outputs['satisfied_preds'], batch['satisfied'][:, -1].long())
dependency_acc = classification_accuracy(
outputs['dependency_preds'], batch['dependency'][:, -1].long())
summarizer.add_scalar(prefix + 'acc/subgoal',
subgoal_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/subgoal_mask',
subgoal_mask_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/satisfied',
satisfied_acc,
global_step=global_step)
summarizer.add_scalar(prefix + 'acc/dependency',
dependency_acc,
global_step=global_step)