def _define_train_step(self, optimizer=None, var_list=None):
""" TODO: should be modified for tframe.optimizer
self._train_step will be plugged only here
"""
if not self._loss.activated:
raise AssertionError('!! loss has not been activated yet')
with tf.name_scope('Optimizer'):
if optimizer is None: console.show_status(
'Optimizer defined in trainer hub initialized.', '++')
optimizer = hub.get_optimizer(optimizer)
self._optimizer = optimizer
self.set_train_step(var_list)
def build(self, **kwargs):
# Smooth out flags before important actions
hub.smooth_out_conflicts()
# Initialize pruner if necessary
if any([
hub.prune_on, hub.weights_mask_on, hub.etch_on,
hub.force_to_use_pruner
]):
# import here to prevent circular import (temporarily)
from tframe.operators.prune.pruner import Pruner
tfr.context.pruner = Pruner(self)
# If optimizer if not provided here, try hub.get_optimizer()
# this requires that th.optimizer and th.learning_rate have been provided
if 'optimizer' not in kwargs: kwargs['optimizer'] = hub.get_optimizer()
# Call successor's _build method
self._build(**kwargs)
# Initialize monitor
self._init_monitor()
# Set built flag
self._built = True
# Show build info
console.show_status('Model built successfully:')
self.agent.take_notes('Model built successfully')
self.agent.take_notes('Structure:', date_time=False)
# Description may be a model structure
description = self.description
if not isinstance(description, (tuple, list)):
description = [description]
for line in description:
assert isinstance(line, str)
console.supplement(line)
self.agent.take_notes(line, date_time=False)
# Add metric slot to update group
batch_metric = kwargs.get('batch_metric', [])
if batch_metric:
if not isinstance(batch_metric, (tuple, list)):
batch_metric = [batch_metric]
for metric_str in batch_metric:
assert isinstance(metric_str, str)
metric_slot = self.metrics_manager.get_slot_by_name(metric_str)
self._update_group.add(metric_slot)
# Register eval_metric if provided
eval_metric = kwargs.get('eval_metric', None)
if eval_metric is not None:
assert isinstance(eval_metric, str)
self.metrics_manager.register_eval_slot(eval_metric)
def _define_train_step(self, optimizer=None, var_list=None):
""" TODO: should be modified for tframe.optimizer
self._train_step will be plugged only here
"""
if not self._loss.activated:
raise AssertionError('!! loss has not been activated yet')
with tf.name_scope('Optimizer'):
if optimizer is None:
optimizer = hub.get_optimizer()
console.show_status(
'Optimizer defined in trainer hub initialized.', '++')
# TODO: BETA
if hub.use_rtrl:
raise AssertionError('use_rtrl option has been deprecated')
from tframe.optimizers.rtrl_opt import RealTimeOptimizer
optimizer = RealTimeOptimizer(self, optimizer)
self._optimizer = optimizer
self.set_train_step(var_list)