def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
auto_switch=True,
retry_fit=True,
absorb=True,
train_after_switch=True,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
validation_batch_size=None,
validation_freq=1,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
revert_after_fit=False):
"""
Custom fit function for the context model
auto_switch: Enable/disable autonomous context switching
train_after_switch:
retry_fit: Locate the next fitting context by re-performing fit.
absorb: Reset the switch sequence counter upon successful training.
This is mainly used to maintain switch sequencing for temporally-extended tasks
revert_after_fit This is a debug parameter to revert weights after performing a fit. This is used
to calculate the context deltas without incorrectly learning while auto switching
is disabled
"""
training._keras_api_gauge.get_cell('fit').set(True)
# Legacy graph support is contained in `training_v1.Model`.
version_utils.disallow_legacy_graph('Model', 'fit')
self._assert_compile_was_called()
self._check_call_args('fit')
if validation_split:
# Create the validation data using the training data. Only supported for
# `Tensor` and `NumPy` input.
(x, y, sample_weight), validation_data = (
data_adapter.train_validation_split(
(x, y, sample_weight),
validation_split=validation_split,
shuffle=False))
with self.distribute_strategy.scope(
), training_utils.RespectCompiledTrainableState(self):
# Creates a `tf.data.Dataset` and handles batch and epoch iteration.
data_handler = WindowedDataHandler(
x=x,
y=y,
sample_weight=sample_weight,
batch_size=batch_size,
steps_per_epoch=steps_per_epoch,
initial_epoch=initial_epoch,
epochs=epochs,
shuffle=shuffle,
class_weight=class_weight,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
model=self)
# Container that configures and calls `tf.keras.Callback`s.
if not isinstance(callbacks, callbacks_module.CallbackList):
callbacks = callbacks_module.CallbackList(
callbacks,
add_history=True,
add_progbar=bool(verbose & Verbosity.Progress),
model=self,
verbose=verbose,
epochs=epochs,
steps=data_handler.inferred_steps)
self.stop_training = False
train_function = self.make_train_function()
callbacks.on_train_begin()
self.initialize_fit()
# Handle fault-tolerance for multi-worker.
# TODO(omalleyt): Fix the ordering issues that mean this has to
# happen after `callbacks.on_train_begin`.
data_handler._initial_epoch = (
self._maybe_load_initial_epoch_from_ckpt(initial_epoch))
for epoch, window_iterator in data_handler.enumerate_epochs():
self.reset_metrics()
callbacks.on_epoch_begin(epoch)
dataset = tf.data.Dataset.zip(next(window_iterator))
switched_during_epoch = False # Indicate if the model has attempted at least one switch during this epoch
switched = True # Indicate if the model switched on the most recent fit iteration
weights = backend.batch_get_value(self.trainable_variables)
# Perform a 'fit call'. Assuming retry_fit, this call is re-attempted after each switch until a context fits
while switched and (retry_fit or not switched_during_epoch):
self.initialize_epoch(epoch)
iterator = iter(dataset)
# Perform a fit call
with data_handler.catch_stop_iteration():
for step in data_handler.steps():
with traceme.TraceMe('TraceContext',
graph_type='train',
epoch_num=epoch,
step_num=step,
batch_size=batch_size):
callbacks.on_train_batch_begin(step)
tmp_logs = train_function(iterator)
# Catch OutOfRangeError for Datasets of unknown size.
# This blocks until the batch has finished executing.
# TODO(b/150292341): Allow multiple async steps here.
if not data_handler.inferred_steps:
context.async_wait()
logs = tmp_logs # No error, now safe to assign to logs.
callbacks.on_train_batch_end(step, logs)
switched = not self.update_and_switch(
epoch, auto_switch, absorb, retry_fit, verbose)
switched_during_epoch |= switched
# If a switch occurred, we need to restore the weights
if switched or (switched_during_epoch
and not train_after_switch
) or revert_after_fit:
backend.batch_set_value(
zip(self.trainable_variables, weights))
self.reset_metrics()
epoch_logs = copy.copy(logs)
# Run validation.
if validation_data and self._should_eval(
epoch, validation_freq):
val_x, val_y, val_sample_weight = (
data_adapter.unpack_x_y_sample_weight(validation_data))
val_logs = self.evaluate(
x=val_x,
y=val_y,
sample_weight=val_sample_weight,
batch_size=validation_batch_size or batch_size,
steps=validation_steps,
callbacks=callbacks,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
return_dict=True)
val_logs = {
'val_' + name: val
for name, val in val_logs.items()
}
epoch_logs.update(val_logs)
callbacks.on_epoch_end(epoch, epoch_logs)
if self.stop_training:
break
callbacks.on_train_end()
return self.history
def fit(
self,
x: Optional[
Union[np.ndarray, tf.Tensor, tf.data.Dataset, tf.keras.utils.Sequence]
] = None,
y: Optional[
Union[np.ndarray, tf.Tensor, tf.data.Dataset, tf.keras.utils.Sequence]
] = None,
batch_size: Optional[int] = None,
epochs: int = 1,
verbose: int = 1,
callbacks: Optional[List[Callback]] = None,
validation_split: float = 0.0,
validation_data: Optional[Any] = None,
shuffle: bool = True,
class_weight: Optional[Dict[int, float]] = None,
sample_weight: Optional[np.ndarray] = None,
initial_epoch: int = 0,
steps_per_epoch: Optional[int] = None,
validation_steps: Optional[int] = None,
validation_batch_size: Optional[int] = None,
validation_freq: int = 1,
max_queue_size: int = 10,
workers: int = 1,
use_multiprocessing: bool = False,
):
"""Trains the model for a fixed number of epochs (iterations on a dataset).
Args:
x: Input data.
y: Target data.
batch_size: Number of samples per gradient update.
epochs: Number of epochs to train the model.
verbose: Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per
epoch.
callbacks: List of `keras.callbacks.Callback` instances.
validation_split: Fraction of the training data to be used as validation
data.
validation_data: Data on which to evaluate the loss and any model metrics
at the end of each epoch.
shuffle: whether to shuffle the training data before each epoch
class_weight: Optional dictionary mapping class indices (integers)
to a weight (float) value, used for weighting the loss
function (during training only).
sample_weight: Optional Numpy array of weights for
the training samples, used for weighting the loss function
(during training only).
initial_epoch: Epoch at which to start training
steps_per_epoch: Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch.
validation_steps: Total number of steps (batches of
samples) to draw before stopping when performing
validation at the end of every epoch.
validation_batch_size: Number of samples per validation batch.
validation_freq: specifies how many training epochs to run before a
new validation run is performed
max_queue_size: Maximum size for the generator queue.
workers: Maximum number of processes to spin up
when using process-based threading.
use_multiprocessing: If `True`, use process-based threading.
Returns:
A `History` object. Its `History.history` attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
Raises:
RuntimeError: 1. If the model was never compiled or,
2. If `model.fit` is wrapped in `tf.function`.
ValueError: In case of mismatch between the provided input data
and what the model expects.
"""
training._keras_api_gauge.get_cell("fit").set(True)
# Legacy graph support is contained in `training_v1.Model`.
version_utils.disallow_legacy_graph("Model", "fit")
self._assert_compile_was_called()
self._check_call_args("fit")
training._disallow_inside_tf_function("fit")
if validation_split:
# Create the validation data using the training data. Only supported for
# `Tensor` and `NumPy` input.
(
(x, y, sample_weight),
validation_data,
) = data_adapter.train_validation_split(
(x, y, sample_weight), validation_split=validation_split
)
if validation_data:
val_x, val_y, val_sample_weight = data_adapter.unpack_x_y_sample_weight(
validation_data
)
with self.distribute_strategy.scope(), training_utils.RespectCompiledTrainableState(
self
):
# Creates a `tf.data.Dataset` and handles batch and epoch iteration.
# Use our own custom data handler to handle increasing batch size
data_handler = CustomDataHandler(
x=x,
y=y,
sample_weight=sample_weight,
batch_size=batch_size,
steps_per_epoch=steps_per_epoch,
initial_epoch=initial_epoch,
epochs=epochs,
shuffle=shuffle,
class_weight=class_weight,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
model=self,
steps_per_execution=self._steps_per_execution,
)
# Container that configures and calls `tf.keras.Callback`s.
if not isinstance(callbacks, training.callbacks_module.CallbackList):
callbacks = training.callbacks_module.CallbackList(
callbacks,
add_history=True,
add_progbar=verbose != 0,
model=self,
verbose=verbose,
epochs=epochs,
steps=data_handler.inferred_steps,
)
self.stop_training = False
train_function = self.make_train_function()
self._train_counter.assign(0)
callbacks.on_train_begin()
training_logs = None
# Handle fault-tolerance for multi-worker.
data_handler._initial_epoch = self._maybe_load_initial_epoch_from_ckpt( # pylint: disable=protected-access
initial_epoch
)
for epoch, iterator in data_handler.enumerate_epochs():
self.reset_metrics()
callbacks.on_epoch_begin(epoch)
with data_handler.catch_stop_iteration():
for step in data_handler.steps():
with training.trace.Trace(
"TraceContext",
graph_type="train",
epoch_num=epoch,
step_num=step,
batch_size=batch_size,
):
callbacks.on_train_batch_begin(step)
tmp_logs = train_function(iterator)
if data_handler.should_sync:
context.async_wait()
logs = tmp_logs # No error, now safe to assign to logs.
end_step = step + data_handler.step_increment
callbacks.on_train_batch_end(end_step, logs)
epoch_logs = copy.copy(logs)
# Run validation.
if validation_data and self._should_eval(epoch, validation_freq):
# Create data_handler for evaluation and cache it.
if getattr(self, "_eval_data_handler", None) is None:
self._eval_data_handler = CustomDataHandler(
x=val_x,
y=val_y,
sample_weight=val_sample_weight,
batch_size=validation_batch_size or batch_size,
steps_per_epoch=validation_steps,
initial_epoch=0,
epochs=1,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
model=self,
steps_per_execution=self._steps_per_execution,
)
val_logs = self.evaluate(
x=val_x,
y=val_y,
sample_weight=val_sample_weight,
batch_size=validation_batch_size or batch_size,
steps=validation_steps,
callbacks=callbacks,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
return_dict=True,
)
val_logs = {"val_" + name: val for name, val in val_logs.items()}
epoch_logs.update(val_logs)
callbacks.on_epoch_end(epoch, epoch_logs)
training_logs = epoch_logs
if self.stop_training:
break
# If eval data_hanlder exists, delete it after all epochs are done.
if getattr(self, "_eval_data_handler", None) is not None:
del self._eval_data_handler
callbacks.on_train_end(logs=training_logs)
return self.history
def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
validation_batch_size=None,
validation_freq=1,
max_queue_size=10,
workers=1,
use_multiprocessing=False):
""" From tf.keras.Model. """
training._keras_api_gauge.get_cell('fit').set(True)
# Legacy graph support is contained in `training_v1.Model`.
version_utils.disallow_legacy_graph('Model', 'fit')
self._assert_compile_was_called()
self._check_call_args('fit')
training._disallow_inside_tf_function('fit')
if validation_split:
# Create the validation data using the training data. Only supported for
# `Tensor` and `NumPy` input.
(x, y, sample_weight), validation_data = (
data_adapter.train_validation_split(
(x, y, sample_weight), validation_split=validation_split))
if validation_data:
val_x, val_y, val_sample_weight = (
data_adapter.unpack_x_y_sample_weight(validation_data))
with self.distribute_strategy.scope(), \
training_utils.RespectCompiledTrainableState(self):
# Creates a `tf.data.Dataset` and handles batch and epoch iteration.
data_handler = data_adapter.DataHandler(
x=x,
y=y,
sample_weight=sample_weight,
batch_size=batch_size,
steps_per_epoch=steps_per_epoch,
initial_epoch=initial_epoch,
epochs=epochs,
shuffle=shuffle,
class_weight=class_weight,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
model=self,
steps_per_execution=self._steps_per_execution)
# Container that configures and calls `tf.keras.Callback`s.
if not isinstance(callbacks, callbacks_module.CallbackList):
callbacks = callbacks_module.CallbackList(
callbacks,
add_history=True,
add_progbar=verbose != 0,
model=self,
verbose=verbose,
epochs=epochs,
steps=data_handler.inferred_steps)
self.stop_training = False
train_function = self.make_train_function()
self._train_counter.assign(0)
callbacks.on_train_begin()
training_logs = None
# Handle fault-tolerance for multi-worker.
# TODO(omalleyt): Fix the ordering issues that mean this has to
# happen after `callbacks.on_train_begin`.
data_handler._initial_epoch = ( # pylint: disable=protected-access
self._maybe_load_initial_epoch_from_ckpt(initial_epoch))
for epoch, iterator in data_handler.enumerate_epochs():
self.reset_metrics()
callbacks.on_epoch_begin(epoch)
with data_handler.catch_stop_iteration():
if self._update_cycle > 1:
self._grad_accumulator.reset()
for step in data_handler.steps():
with trace.Trace(
'TraceContext',
graph_type='train',
epoch_num=epoch,
step_num=step,
batch_size=batch_size):
callbacks.on_train_batch_begin(step)
if self._update_cycle > 1:
for _ in range(self._update_cycle - 1):
self.accumulate_function(iterator)
tmp_logs = train_function(iterator)
if data_handler.should_sync:
context.async_wait()
logs = tmp_logs # No error, now safe to assign to logs.
end_step = step + data_handler.step_increment
callbacks.on_train_batch_end(end_step, logs)
epoch_logs = copy.copy(logs)
# Run validation.
if validation_data and self._should_eval(epoch, validation_freq):
# Create data_handler for evaluation and cache it.
if getattr(self, '_eval_data_handler', None) is None:
self._eval_data_handler = data_adapter.DataHandler(
x=val_x,
y=val_y,
sample_weight=val_sample_weight,
batch_size=validation_batch_size or batch_size,
steps_per_epoch=validation_steps,
initial_epoch=0,
epochs=1,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
model=self,
steps_per_execution=self._steps_per_execution)
val_logs = self.evaluate(
x=val_x,
y=val_y,
sample_weight=val_sample_weight,
batch_size=validation_batch_size or batch_size,
steps=validation_steps,
callbacks=callbacks,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
return_dict=True)
val_logs = {'val_' + name: val for name, val in val_logs.items()}
epoch_logs.update(val_logs)
callbacks.on_epoch_end(epoch, epoch_logs)
training_logs = epoch_logs
if self.stop_training:
break
# If eval data_hanlder exists, delete it after all epochs are done.
if getattr(self, '_eval_data_handler', None) is not None:
del self._eval_data_handler
callbacks.on_train_end(logs=training_logs)
return self.history
def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
dynamic_switch=True,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
validation_batch_size=None,
validation_freq=1,
max_queue_size=10,
workers=1,
use_multiprocessing=False):
training._keras_api_gauge.get_cell('fit').set(True)
# Legacy graph support is contained in `training_v1.Model`.
version_utils.disallow_legacy_graph('Model', 'fit')
self._assert_compile_was_called()
self._check_call_args('fit')
if validation_split:
# Create the validation data using the training data. Only supported for
# `Tensor` and `NumPy` input.
(x, y, sample_weight), validation_data = (
data_adapter.train_validation_split(
(x, y, sample_weight),
validation_split=validation_split,
shuffle=False))
with self.distribute_strategy.scope(
), training_utils.RespectCompiledTrainableState(self):
# Creates a `tf.data.Dataset` and handles batch and epoch iteration.
data_handler = WindowedDataHandler(
x=x,
y=y,
sample_weight=sample_weight,
batch_size=batch_size,
steps_per_epoch=steps_per_epoch,
initial_epoch=initial_epoch,
epochs=epochs,
shuffle=shuffle,
class_weight=class_weight,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
model=self)
# Container that configures and calls `tf.keras.Callback`s.
if not isinstance(callbacks, callbacks_module.CallbackList):
callbacks = callbacks_module.CallbackList(
callbacks,
add_history=True,
add_progbar=bool(verbose & Verbosity.Progress),
model=self,
verbose=verbose,
epochs=epochs,
steps=data_handler.inferred_steps)
self.stop_training = False
train_function = self.make_train_function()
callbacks.on_train_begin()
# Handle fault-tolerance for multi-worker.
# TODO(omalleyt): Fix the ordering issues that mean this has to
# happen after `callbacks.on_train_begin`.
data_handler._initial_epoch = (
self._maybe_load_initial_epoch_from_ckpt(initial_epoch))
for epoch, window_iterator in data_handler.enumerate_epochs():
self.reset_metrics()
callbacks.on_epoch_begin(epoch)
dataset = tf.data.Dataset.zip(next(window_iterator))
switched = True
weights = backend.batch_get_value(self.trainable_variables)
while switched:
self.initialize_epoch(epoch)
iterator = iter(dataset)
with data_handler.catch_stop_iteration():
for step in data_handler.steps():
with traceme.TraceMe('TraceContext',
graph_type='train',
epoch_num=epoch,
step_num=step,
batch_size=batch_size):
callbacks.on_train_batch_begin(step)
tmp_logs = train_function(iterator)
# Catch OutOfRangeError for Datasets of unknown size.
# This blocks until the batch has finished executing.
# TODO(b/150292341): Allow multiple async steps here.
if not data_handler.inferred_steps:
context.async_wait()
logs = tmp_logs # No error, now safe to assign to logs.
callbacks.on_train_batch_end(step, logs)
switched = not self.update_and_switch(
epoch, dynamic_switch, verbose)
# If a switch occurred, we need to restore the weights
if switched:
backend.batch_set_value(
zip(self.trainable_variables, weights))
self.reset_metrics()
epoch_logs = copy.copy(logs)
if self.accumulate_gradients:
self.optimizer.apply_gradients(
zip(self.accumulated_gradients,
self.trainable_variables))
# Run validation.
if validation_data and self._should_eval(
epoch, validation_freq):
val_x, val_y, val_sample_weight = (
data_adapter.unpack_x_y_sample_weight(validation_data))
val_logs = self.evaluate(
x=val_x,
y=val_y,
sample_weight=val_sample_weight,
batch_size=validation_batch_size or batch_size,
steps=validation_steps,
callbacks=callbacks,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
return_dict=True)
val_logs = {
'val_' + name: val
for name, val in val_logs.items()
}
epoch_logs.update(val_logs)
callbacks.on_epoch_end(epoch, epoch_logs)
if self.stop_training:
break
callbacks.on_train_end()
return self.history