def _evaluate_model(self,
input_fn,
steps,
feed_fn=None,
metrics=None,
name=''):
if self._config.execution_mode not in ('all', 'evaluate',
'eval_evalset'):
return
checkpoint_path = self._model_dir
eval_dir = os.path.join(self._model_dir,
'eval' if not name else 'eval_' + name)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, targets = input_fn()
self._check_inputs(features, targets)
eval_dict = self._get_eval_ops(features, targets, metrics)
update_op, eval_dict = self._extract_metric_update_ops(eval_dict)
eval_results, _ = evaluate(graph=g,
output_dir=eval_dir,
checkpoint_path=checkpoint_path,
eval_dict=eval_dict,
update_op=update_op,
global_step_tensor=global_step,
supervisor_master=self._config.master,
feed_fn=feed_fn,
max_steps=steps)
return eval_results
def _evaluate_model(self,
input_fn,
steps,
feed_fn=None,
metrics=None,
name=''):
if self._config.execution_mode not in ('all', 'evaluate', 'eval_evalset'):
return
checkpoint_path = self._model_dir
eval_dir = os.path.join(self._model_dir, 'eval' if not name else
'eval_' + name)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, targets = input_fn()
self._check_inputs(features, targets)
eval_dict = self._get_eval_ops(features, targets, metrics)
update_op, eval_dict = self._extract_metric_update_ops(eval_dict)
eval_results, _ = evaluate(graph=g,
output_dir=eval_dir,
checkpoint_path=checkpoint_path,
eval_dict=eval_dict,
update_op=update_op,
global_step_tensor=global_step,
supervisor_master=self._config.master,
feed_fn=feed_fn,
max_steps=steps)
return eval_results
def _evaluate_model(self,
input_fn,
steps,
feed_fn=None,
metrics=None,
name=''):
# TODO(wicke): Remove this once Model and associated code are gone.
if (hasattr(self._config, 'execution_mode') and
self._config.execution_mode not in ('all', 'evaluate', 'eval_evalset')):
return None, None
# Check that model has been trained.
checkpoint_path = self._model_dir
latest_path = saver.latest_checkpoint(checkpoint_path)
if not latest_path:
raise NotFittedError("Couldn't find trained model at %s."
% checkpoint_path)
# Setup output directory.
eval_dir = os.path.join(self._model_dir, 'eval' if not name else
'eval_' + name)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, labels = input_fn()
self._check_inputs(features, labels)
# The default return type of _get_eval_ops is ModelFnOps. But there are
# some subclasses of tf.contrib.learn.Estimator which override this
# method and use the legacy signature, namely _get_eval_ops returns an
# `eval_dict` dictionary of Tensors. The following else-statement code
# covers these cases, but will soon be deleted after the subclasses are
# updated.
# TODO(b/32664904): Update subclasses and delete the else-statement.
eval_ops = self._get_eval_ops(features, labels, metrics)
if isinstance(eval_ops, ModelFnOps): # Default signature
eval_dict = eval_ops.eval_metric_ops
else: # Legacy signature
eval_dict = eval_ops
update_op, eval_dict = self._extract_metric_update_ops(eval_dict)
eval_results, current_global_step = graph_actions.evaluate(
graph=g,
output_dir=eval_dir,
checkpoint_path=checkpoint_path,
eval_dict=eval_dict,
update_op=update_op,
global_step_tensor=global_step,
supervisor_master=self._config.evaluation_master,
feed_fn=feed_fn,
max_steps=steps)
return eval_results, current_global_step
def _evaluate_model(self,
input_fn,
steps,
feed_fn=None,
metrics=None,
name=''):
# TODO(wicke): Remove this once Model and associated code are gone.
if (hasattr(self._config, 'execution_mode') and
self._config.execution_mode not in ('all', 'evaluate', 'eval_evalset')):
return None, None
# Check that model has been trained.
checkpoint_path = self._model_dir
latest_path = saver.latest_checkpoint(checkpoint_path)
if not latest_path:
raise NotFittedError("Couldn't find trained model at %s."
% checkpoint_path)
# Setup output directory.
eval_dir = os.path.join(self._model_dir, 'eval' if not name else
'eval_' + name)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, labels = input_fn()
self._check_inputs(features, labels)
# The default return type of _get_eval_ops is ModelFnOps. But there are
# some subclasses of tf.contrib.learn.Estimator which override this
# method and use the legacy signature, namely _get_eval_ops returns an
# `eval_dict` dictionary of Tensors. The following else-statement code
# covers these cases, but will soon be deleted after the subclasses are
# updated.
# TODO(b/32664904): Update subclasses and delete the else-statement.
eval_ops = self._get_eval_ops(features, labels, metrics)
if isinstance(eval_ops, model_fn_lib.ModelFnOps): # Default signature
eval_dict = eval_ops.eval_metric_ops
else: # Legacy signature
eval_dict = eval_ops
update_op, eval_dict = self._extract_metric_update_ops(eval_dict)
eval_results, current_global_step = graph_actions.evaluate(
graph=g,
output_dir=eval_dir,
checkpoint_path=checkpoint_path,
eval_dict=eval_dict,
update_op=update_op,
global_step_tensor=global_step,
supervisor_master=self._config.evaluation_master,
feed_fn=feed_fn,
max_steps=steps)
return eval_results, current_global_step
def _evaluate_model(self,
input_fn,
steps,
feed_fn=None,
metrics=None,
name=''):
# TODO(wicke): Remove this once Model and associated code are gone.
if (hasattr(self._config, 'execution_mode')
and self._config.execution_mode
not in ('all', 'evaluate', 'eval_evalset')):
return None, None
# Check that model has been trained.
checkpoint_path = self._model_dir
latest_path = saver.latest_checkpoint(checkpoint_path)
if not latest_path:
raise NotFittedError("Couldn't find trained model at %s." %
checkpoint_path)
# Setup output directory.
eval_dir = os.path.join(self._model_dir,
'eval' if not name else 'eval_' + name)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, targets = input_fn()
self._check_inputs(features, targets)
eval_dict = self._get_eval_ops(features, targets, metrics)
update_op, eval_dict = self._extract_metric_update_ops(eval_dict)
eval_results, current_global_step = graph_actions.evaluate(
graph=g,
output_dir=eval_dir,
checkpoint_path=checkpoint_path,
eval_dict=eval_dict,
update_op=update_op,
global_step_tensor=global_step,
supervisor_master=self._config.master,
feed_fn=feed_fn,
max_steps=steps)
return eval_results, current_global_step
def _evaluate_model(self,
input_fn,
steps,
feed_fn=None,
metrics=None,
name=''):
# TODO(wicke): Remove this once Model and associated code are gone.
if (hasattr(self._config, 'execution_mode') and
self._config.execution_mode not in ('all', 'evaluate', 'eval_evalset')):
return None, None
# Check that model has been trained.
checkpoint_path = self._model_dir
latest_path = saver.latest_checkpoint(checkpoint_path)
if not latest_path:
raise NotFittedError("Couldn't find trained model at %s."
% checkpoint_path)
# Setup output directory.
eval_dir = os.path.join(self._model_dir, 'eval' if not name else
'eval_' + name)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, targets = input_fn()
self._check_inputs(features, targets)
eval_dict = self._get_eval_ops(features, targets, metrics)
update_op, eval_dict = self._extract_metric_update_ops(eval_dict)
eval_results, current_global_step = graph_actions.evaluate(
graph=g,
output_dir=eval_dir,
checkpoint_path=checkpoint_path,
eval_dict=eval_dict,
update_op=update_op,
global_step_tensor=global_step,
supervisor_master=self._config.evaluation_master,
feed_fn=feed_fn,
max_steps=steps)
return eval_results, current_global_step
def _evaluate_model(self, input_fn, steps, feed_fn=None, metrics=None):
if self._config.execution_mode not in ('all', 'evaluate',
'eval_evalset'):
return
checkpoint_path = saver.latest_checkpoint(self._model_dir)
eval_dir = os.path.join(self._model_dir, 'eval')
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, targets = input_fn()
self._check_inputs(features, targets)
eval_dict = self._get_eval_ops(
features, targets, metrics if metrics is not None else
self._get_default_metric_functions())
eval_results, _ = evaluate(graph=g,
output_dir=eval_dir,
checkpoint_path=checkpoint_path,
eval_dict=eval_dict,
global_step_tensor=global_step,
supervisor_master=self._config.master,
feed_fn=feed_fn,
max_steps=steps)
return eval_results
def _evaluate_model(self, input_fn, steps, feed_fn=None, metrics=None):
if self._config.execution_mode not in ('all', 'evaluate', 'eval_evalset'):
return
checkpoint_path = saver.latest_checkpoint(self._model_dir)
eval_dir = os.path.join(self._model_dir, 'eval')
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, targets = input_fn()
self._check_inputs(features, targets)
eval_dict = self._get_eval_ops(features, targets,
metrics if metrics is not None else
self._get_default_metric_functions())
eval_results, _ = evaluate(
graph=g,
output_dir=eval_dir,
checkpoint_path=checkpoint_path,
eval_dict=eval_dict,
global_step_tensor=global_step,
supervisor_master=self._config.master,
feed_fn=feed_fn,
max_steps=steps)
return eval_results
