def _get_input_fn(x,
y,
input_fn,
feed_fn,
batch_size,
shuffle=False,
epochs=1):
"""Make inputs into input and feed functions."""
if input_fn is None:
if x is None:
raise ValueError('Either x or input_fn must be provided.')
if contrib_framework.is_tensor(x) or (y is not None and
contrib_framework.is_tensor(y)):
raise ValueError(
'Inputs cannot be tensors. Please provide input_fn.')
df = data_feeder.setup_train_data_feeder(x,
y,
n_classes=None,
batch_size=batch_size,
shuffle=shuffle,
epochs=epochs)
return df.input_builder, df.get_feed_dict_fn()
if (x is not None) or (y is not None):
raise ValueError('Can not provide both input_fn and x or y.')
if batch_size is not None:
raise ValueError('Can not provide both input_fn and batch_size.')
return input_fn, feed_fn
def fit(self, x, y=None, monitors=None, logdir=None, steps=None):
"""Trains a k-means clustering on x.
Note: See TensorFlowEstimator for logic for continuous training and graph
construction across multiple calls to fit.
Args:
x: training input matrix of shape [n_samples, n_features].
y: labels. Should be None.
monitors: 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.
steps: number of training steps. If not None, overrides the value passed
in constructor.
Returns:
Returns self.
"""
assert y is None
if logdir is not None:
self._model_dir = logdir
self._data_feeder = data_feeder.setup_train_data_feeder(x, None, self._num_clusters, self.batch_size)
self._train_model(
input_fn=self._data_feeder.input_builder,
feed_fn=self._data_feeder.get_feed_dict_fn(),
steps=steps or self.steps,
monitors=monitors,
init_feed_fn=self._data_feeder.get_feed_dict_fn(),
)
return self
def _get_input_fn(x, y, input_fn, feed_fn, batch_size, shuffle=False, epochs=1):
"""Make inputs into input and feed functions."""
if input_fn is None:
if x is None:
raise ValueError('Either x or input_fn must be provided.')
if contrib_framework.is_tensor(x) or (y is not None and
contrib_framework.is_tensor(y)):
raise ValueError('Inputs cannot be tensors. Please provide input_fn.')
if feed_fn is not None:
raise ValueError('Can not provide both feed_fn and x or y.')
df = data_feeder.setup_train_data_feeder(x, y, n_classes=None,
batch_size=batch_size,
shuffle=shuffle,
epochs=epochs)
return df.input_builder, df.get_feed_dict_fn()
if (x is not None) or (y is not None):
raise ValueError('Can not provide both input_fn and x or y.')
if batch_size is not None:
raise ValueError('Can not provide both input_fn and batch_size.')
return input_fn, feed_fn
def fit(self, x, y=None, monitors=None, logdir=None, steps=None):
"""Trains a k-means clustering on x.
Note: See TensorFlowEstimator for logic for continuous training and graph
construction across multiple calls to fit.
Args:
x: training input matrix of shape [n_samples, n_features].
y: labels. Should be None.
monitors: 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.
steps: number of training steps. If not None, overrides the value passed
in constructor.
Returns:
Returns self.
"""
assert y is None
if logdir is not None:
self._model_dir = logdir
self._data_feeder = data_feeder.setup_train_data_feeder(
x, None, self._num_clusters, self.batch_size)
self._train_model(input_fn=self._data_feeder.input_builder,
feed_fn=self._data_feeder.get_feed_dict_fn(),
steps=steps or self.steps,
monitors=monitors,
init_feed_fn=self._data_feeder.get_feed_dict_fn())
return self
def predict(self,
x=None,
input_fn=None,
batch_size=None,
outputs=None,
axis=1):
"""Predict class or regression for `x`."""
if x is not None:
predict_data_feeder = setup_train_data_feeder(x,
None,
n_classes=None,
batch_size=batch_size
or self.batch_size,
shuffle=False,
epochs=1)
result = super(DeprecatedMixin, self)._infer_model(
input_fn=predict_data_feeder.input_builder,
feed_fn=predict_data_feeder.get_feed_dict_fn(),
outputs=outputs)
else:
result = super(DeprecatedMixin,
self)._infer_model(input_fn=input_fn,
outputs=outputs)
if self.__deprecated_n_classes > 1 and axis is not None:
return np.argmax(result, axis)
return result
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, steps=None, monitors=None, logdir=None):
"""Neural network model from provided `model_fn` and training data.
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).
steps: int, number of steps to train.
If None or 0, train for `self.steps`.
monitors: List of `BaseMonitor` objects 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.
"""
if logdir is not None:
self._model_dir = logdir
self._data_feeder = setup_train_data_feeder(
x, y, n_classes=self.n_classes, batch_size=self.batch_size)
self._train_model(input_fn=self._data_feeder.input_builder,
feed_fn=self._data_feeder.get_feed_dict_fn(),
steps=steps or self.steps,
monitors=monitors)
return self
def fit(self, x, y, steps=None, monitors=None, logdir=None):
"""Neural network model from provided `model_fn` and training data.
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).
steps: int, number of steps to train.
If None or 0, train for `self.steps`.
monitors: List of `BaseMonitor` objects 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.
"""
if logdir is not None:
self._model_dir = logdir
self._data_feeder = setup_train_data_feeder(
x, y, n_classes=self.n_classes, batch_size=self.batch_size)
self._train_model(input_fn=self._data_feeder.input_builder,
feed_fn=self._data_feeder.get_feed_dict_fn(),
steps=steps or self.steps,
monitors=monitors)
return self
def _get_predict_input_fn(x, y, batch_size):
df = data_feeder.setup_train_data_feeder(x,
y,
n_classes=None,
batch_size=batch_size,
shuffle=False,
epochs=1)
return df.input_builder, df.get_feed_dict_fn()
def evaluate(self, x=None, y=None, input_fn=None, steps=None):
"""See base class."""
feed_fn = None
if x is not None:
eval_data_feeder = setup_train_data_feeder(
x, y, n_classes=self.n_classes, batch_size=self.batch_size, epochs=1
)
input_fn, feed_fn = (eval_data_feeder.input_builder, eval_data_feeder.get_feed_dict_fn())
return self._evaluate_model(input_fn=input_fn, feed_fn=feed_fn, steps=steps or self.steps)
def evaluate(self, x=None, y=None, input_fn=None, steps=None):
"""See base class."""
feed_fn = None
if x is not None:
eval_data_feeder = setup_train_data_feeder(
x, y, n_classes=self.n_classes, batch_size=self.batch_size, epochs=1)
input_fn, feed_fn = (eval_data_feeder.input_builder,
eval_data_feeder.get_feed_dict_fn())
return self._evaluate_model(
input_fn=input_fn, feed_fn=feed_fn, steps=steps or self.steps)
def _get_predict_input_fn(x, batch_size):
# TODO(ipoloshukin): Remove this when refactor of data_feeder is done
if hasattr(x, 'create_graph'):
def input_fn():
return x.create_graph()
return input_fn, None
df = data_feeder.setup_train_data_feeder(x, None,
n_classes=None,
batch_size=batch_size)
return df.input_builder, df.get_feed_dict_fn()
def _get_predict_input_fn(x, batch_size):
# TODO(ipoloshukin): Remove this when refactor of data_feeder is done
if hasattr(x, 'create_graph'):
def input_fn():
return x.create_graph()
return input_fn, None
df = data_feeder.setup_train_data_feeder(x, None,
n_classes=None,
batch_size=batch_size, epochs=1)
return df.input_builder, df.get_feed_dict_fn()
def predict(self, x=None, input_fn=None, batch_size=None, outputs=None,
axis=1):
if x is not None:
predict_data_feeder = setup_train_data_feeder(
x, None, n_classes=None,
batch_size=batch_size or self.batch_size,
shuffle=False, epochs=1)
result = super(DeprecatedMixin, self)._infer_model(
input_fn=predict_data_feeder.input_builder,
feed_fn=predict_data_feeder.get_feed_dict_fn(),
outputs=outputs)
else:
result = super(DeprecatedMixin, self)._infer_model(
input_fn=input_fn, outputs=outputs)
if self.__deprecated_n_classes > 1 and axis is not None:
return np.argmax(result, axis)
return result
def _predict(self, x, axis=-1, batch_size=None):
if self._graph is None:
raise NotFittedError()
# Use the batch size for fitting if the user did not specify one.
if batch_size is None:
batch_size = self.batch_size
predict_data_feeder = setup_train_data_feeder(
x, None, n_classes=None, batch_size=batch_size, shuffle=False, epochs=1
)
preds = self._infer_model(
input_fn=predict_data_feeder.input_builder, feed_fn=predict_data_feeder.get_feed_dict_fn()
)
if self.n_classes > 1 and axis != -1:
preds = preds["predictions"].argmax(axis=axis)
else:
preds = preds["predictions"]
return preds
def _predict(self, x, axis=-1, batch_size=None):
if self._graph is None:
raise NotFittedError()
# Use the batch size for fitting if the user did not specify one.
if batch_size is None:
batch_size = self.batch_size
predict_data_feeder = setup_train_data_feeder(
x, None, n_classes=None,
batch_size=batch_size,
shuffle=False, epochs=1)
preds = self._infer_model(
input_fn=predict_data_feeder.input_builder,
feed_fn=predict_data_feeder.get_feed_dict_fn())
if self.n_classes > 1 and axis != -1:
preds = preds['predictions'].argmax(axis=axis)
else:
preds = preds['predictions']
return preds
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
# Attach monitor to this estimator.
self._monitor.set_estimator(self)
# 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 _get_input_fn(x, y, batch_size):
df = data_feeder.setup_train_data_feeder(x,
y,
n_classes=None,
batch_size=batch_size)
return df.input_builder, df.get_feed_dict_fn()
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 _get_predict_input_fn(x, y, batch_size):
df = data_feeder.setup_train_data_feeder(
x, y, n_classes=None, batch_size=batch_size,
shuffle=False, epochs=1)
return df.input_builder, df.get_feed_dict_fn()
def _get_input_fn(x, y, batch_size):
df = data_feeder.setup_train_data_feeder(
x, y, n_classes=None, batch_size=batch_size)
return df.input_builder, df.get_feed_dict_fn()
