def __init__(self, val_X, val_y, n_classes=0, print_steps=100,
early_stopping_rounds=None):
super(ValidationMonitor, self).__init__(print_steps=print_steps,
early_stopping_rounds=early_stopping_rounds)
self.val_feeder = setup_train_data_feeder(val_X, val_y, n_classes, -1)
self.print_val_loss_buffer = []
self.all_val_loss_buffer = []
def __init__(self, val_X, val_y, n_classes=0, print_steps=100,
early_stopping_rounds=None):
super(ValidationMonitor, self).__init__(print_steps=print_steps,
early_stopping_rounds=early_stopping_rounds)
self.val_feeder = setup_train_data_feeder(val_X, val_y, n_classes, -1)
self.print_val_loss_buffer = []
self.all_val_loss_buffer = []
def fit(self, X, y, monitor=None, logdir=None):
"""Builds a neural network model given provided `model_fn` and training
data X and y.
Note: called first time constructs the graph and initializers
variables. Consecutives times it will continue training the same model.
This logic follows partial_fit() interface in scikit-learn.
To restart learning, create new estimator.
Args:
X: matrix or tensor of shape [n_samples, n_features...]. Can be
iterator that returns arrays of features. The training input
samples for fitting the model.
y: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
iterator that returns array of targets. The training target values
(class labels in classification, real numbers in regression).
monitor: Monitor object to print training progress and invoke early stopping
logdir: the directory to save the log file that can be used for
optional visualization.
Returns:
Returns self.
"""
# Sets up data feeder.
self._data_feeder = setup_train_data_feeder(X, y, self.n_classes,
self.batch_size)
if monitor is None:
self._monitor = monitors.default_monitor(verbose=self.verbose)
else:
self._monitor = monitor
if not self.continue_training or not self._initialized:
# Sets up model and trainer.
self._setup_training()
self._initialized = True
else:
self._data_feeder.set_placeholders(self._inp, self._out)
# Sets up summary writer for later optional visualization.
# Due to not able to setup _summary_writer in __init__ as it's not a
# parameter of the model, here we need to check if such variable exists
# and if it's None or not (in case it was setup in a previous run).
# It is initialized only in the case where it wasn't before and log dir
# is provided.
if logdir:
if (not hasattr(self, "_summary_writer")
or (hasattr(self, "_summary_writer")
and self._summary_writer is None)):
self._setup_summary_writer(logdir)
else:
self._summary_writer = None
# Train model for given number of steps.
trainer.train(self._session,
self._train,
self._model_loss,
self._global_step,
self._data_feeder.get_feed_dict_fn(),
steps=self.steps,
monitor=self._monitor,
summary_writer=self._summary_writer,
summaries=self._summaries,
feed_params_fn=self._data_feeder.get_feed_params)
return self
def fit(self, X, y, monitor=None, logdir=None):
"""Builds a neural network model given provided `model_fn` and training
data X and y.
Note: called first time constructs the graph and initializers
variables. Consecutives times it will continue training the same model.
This logic follows partial_fit() interface in scikit-learn.
To restart learning, create new estimator.
Args:
X: matrix or tensor of shape [n_samples, n_features...]. Can be
iterator that returns arrays of features. The training input
samples for fitting the model.
y: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
iterator that returns array of targets. The training target values
(class labels in classification, real numbers in regression).
monitor: Monitor object to print training progress and invoke early stopping
logdir: the directory to save the log file that can be used for
optional visualization.
Returns:
Returns self.
"""
# Sets up data feeder.
self._data_feeder = setup_train_data_feeder(X, y,
self.n_classes,
self.batch_size)
if monitor is None:
self._monitor = monitors.default_monitor(verbose=self.verbose)
else:
self._monitor = monitor
if not self.continue_training or not self._initialized:
# Sets up model and trainer.
self._setup_training()
self._initialized = True
else:
self._data_feeder.set_placeholders(self._inp, self._out)
# Sets up summary writer for later optional visualization.
# Due to not able to setup _summary_writer in __init__ as it's not a
# parameter of the model, here we need to check if such variable exists
# and if it's None or not (in case it was setup in a previous run).
# It is initialized only in the case where it wasn't before and log dir
# is provided.
if logdir:
if (not hasattr(self, "_summary_writer") or
(hasattr(self, "_summary_writer") and self._summary_writer is None)):
self._setup_summary_writer(logdir)
else:
self._summary_writer = None
# Train model for given number of steps.
trainer.train(
self._session, self._train,
self._model_loss, self._global_step,
self._data_feeder.get_feed_dict_fn(),
steps=self.steps,
monitor=self._monitor,
summary_writer=self._summary_writer,
summaries=self._summaries,
feed_params_fn=self._data_feeder.get_feed_params)
return self
