def test_concat_single_batch(self):
aggregator = training_utils_v1.OutputsAggregator(use_steps=True)
data = _TEST_DATA.copy()
aggregator.create(data)
assert len(aggregator.results) == 1
assert isinstance(aggregator.results[0],
training_utils_v1.ConcatAggregator)
aggregator.aggregate(data)
aggregator.finalize()
assert aggregator.results is data # No copy.
def test_slice_single_batch(self):
aggregator = training_utils_v1.OutputsAggregator(use_steps=False,
num_samples=6)
data = _TEST_DATA.copy()
aggregator.create(data)
assert len(aggregator.results) == 1
assert isinstance(aggregator.results[0],
training_utils_v1.SliceAggregator)
aggregator.aggregate(data, 0, 6)
aggregator.finalize()
assert aggregator.results is data # No copy.
def _run_with_steps(self):
aggregator = training_utils_v1.OutputsAggregator(use_steps=True)
for i, batch in enumerate(np.array_split(_TEST_DATA, 4)):
if i == 0:
aggregator.create(batch)
aggregator.aggregate(batch)
assert len(aggregator.results) == 1
assert isinstance(aggregator.results[0],
training_utils_v1.ConcatAggregator)
aggregator.finalize()
return aggregator.results
def test_nested_aggregation(self):
aggregator = training_utils_v1.OutputsAggregator(use_steps=False,
num_samples=6)
batches = np.array_split(_TEST_DATA, 4)
batch_start = 0
for i, batch in enumerate(zip(batches, batches)):
if i == 0:
aggregator.create(batch)
batch_end = batch_start + batch[0].shape[0]
aggregator.aggregate(batch, batch_start, batch_end)
batch_start = batch_end
assert len(aggregator.results) == 2
aggregator.finalize()
self.assertAllEqual(aggregator.results, (_TEST_DATA, _TEST_DATA))
def _run_without_steps(self):
aggregator = training_utils_v1.OutputsAggregator(use_steps=False,
num_samples=6)
batch_start = 0
for i, batch in enumerate(np.array_split(_TEST_DATA, 4)):
if i == 0:
aggregator.create(batch)
batch_end = batch_start + batch.shape[0]
aggregator.aggregate(batch, batch_start, batch_end)
batch_start = batch_end
assert len(aggregator.results) == 1
assert isinstance(aggregator.results[0],
training_utils_v1.SliceAggregator)
aggregator.finalize()
return aggregator.results
def model_iteration(model,
inputs,
targets=None,
sample_weights=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
shuffle=True,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
validation_freq=1,
mode=ModeKeys.TRAIN,
validation_in_fit=False,
prepared_feed_values_from_dataset=False,
steps_name="steps",
**kwargs):
"""Loop function for arrays of data with modes TRAIN/TEST/PREDICT.
Args:
model: Keras Model instance.
inputs: Either a list or dictionary of arrays, or a dataset instance.
targets: List/dictionary of input arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: Integer batch size or None if unknown.
epochs: Number of times to iterate over the data
verbose: 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.
Note that the progress bar is not particularly useful when
logged to a file, so verbose=2 is recommended when not running
interactively (eg, in a production environment).
callbacks: List of callbacks to be called during training
val_inputs: Either a list or dictionary of arrays, or a dataset
instance.
val_targets: List/dictionary of target arrays.
val_sample_weights: Optional list of sample weight arrays.
shuffle: Whether to shuffle the data at the beginning of each epoch
concatenation of list the display names of the outputs of `f` and the
list of display names of the outputs of `f_val`.
initial_epoch: Epoch at which to start training (useful for resuming a
previous training run)
steps_per_epoch: Total number of steps (batches of samples) before
declaring one epoch finished and starting the next epoch. Ignored with
the default value of `None`.
validation_steps: Number of steps to run validation for (only if doing
validation from data tensors). Ignored with the default value of
`None`.
validation_freq: Only relevant if validation data is provided. Integer
or `collections.abc.Container` instance (e.g. list, tuple, etc.). If
an integer, specifies how many training epochs to run before a new
validation run is performed, e.g. `validation_freq=2` runs validation
every 2 epochs. If a Container, specifies the epochs on which to run
validation, e.g. `validation_freq=[1, 2, 10]` runs validation at the
end of the 1st, 2nd, and 10th epochs.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
validation_in_fit: if true, then this method is invoked from within
training iteration (for validation). In the case where `val_inputs` is
a dataset, this flag indicates that its iterator and feed values are
already created so should properly reuse resources.
prepared_feed_values_from_dataset: if True, `inputs` is a list of feed
tensors returned from `_prepare_feed_values` call on the validation
dataset, so do not call it again on `inputs`. Should only be used for
inline validation (i.e., only if `validation_in_fit` is also True).
steps_name: The string name of the steps argument, either `steps`,
`validation_steps`, or `steps_per_epoch`. Only used for error message
formatting.
**kwargs: Additional arguments for backwards compatibility.
Returns:
- In TRAIN mode: `History` object.
- In TEST mode: Evaluation metrics.
- In PREDICT mode: Outputs of the Model called on inputs.
Raises:
ValueError: in case of invalid arguments.
"""
# Backwards compatibility.
if "steps" in kwargs:
steps_per_epoch = kwargs.pop("steps")
if kwargs:
raise TypeError("Unknown arguments: %s" % (kwargs, ))
# In case we were passed a dataset, we extract symbolic tensors from it.
reset_dataset_after_each_epoch = False
input_iterator = None
is_dataset = isinstance(inputs,
(tf.compat.v1.data.Dataset, tf.data.Dataset))
# TODO(fchollet): consider moving `steps_per_epoch` inference to
# _standardize_user_data and set reset_dataset_after_each_epoch as an
# attribute on the dataset instance.
if is_dataset:
if steps_per_epoch is None:
reset_dataset_after_each_epoch = True
steps_per_epoch = training_utils_v1.infer_steps_for_dataset(
model,
inputs,
steps_per_epoch,
epochs=epochs,
steps_name=steps_name,
)
input_iterator = _get_iterator(inputs, model._distribution_strategy)
# Enter tf.distribute.Strategy scope.
if model._distribution_strategy:
scope = distributed_training_utils_v1.distributed_scope(
strategy=model._distribution_strategy,
learning_phase=(1 if mode == ModeKeys.TRAIN else 0),
)
scope.__enter__()
use_steps = is_dataset or steps_per_epoch is not None
do_validation = val_inputs is not None
# Prepare input data.
inputs = input_iterator or inputs
if validation_in_fit and prepared_feed_values_from_dataset:
# When invoking validation in training loop, avoid creating iterator and
# list of feed values for the same validation dataset multiple times
# (which essentially would call `iterator.get_next()` that slows down
# execution and leads to OOM errors eventually.
ins = inputs
else:
ins = _prepare_feed_values(model, inputs, targets, sample_weights,
mode)
# `ins` is a function when a distribute strategy is used in Eager mode.
# In that case `is_dataset` is True. The code branches that have
# requirements about the type of `ins` do not trigger in the distributed
# case.
if not is_dataset:
num_samples_or_steps = _get_num_samples_or_steps(
ins, batch_size, steps_per_epoch)
else:
num_samples_or_steps = steps_per_epoch
# Update sample_weight_mode of the model if sample_weights is specified by
# the user. We need to call this function after we have a handle on the
# inputs (both numpy arrays and datasets) in order to determine if the user
# has specified sample_weights.
_update_sample_weight_mode(model, mode, ins)
# Get step function and loop type. As part of building the execution
# function we recompile the metrics based on the updated
# sample_weight_mode value.
f = _make_execution_function(model, mode)
# Prepare validation data. Hold references to the iterator and the input
# list to properly reinitialize and reuse in multiple validation passes.
val_iterator = None
if isinstance(val_inputs, (tf.compat.v1.data.Dataset, tf.data.Dataset)):
if validation_steps is None:
# Because we pass an iterator feed instead of a Dataset to the eval
# model_iteration() call, it will not trigger the dataset-input path
# that determines the number of steps required. To avoid this issue,
# set validation_steps here if validation_steps is None.
validation_steps = training_utils_v1.infer_steps_for_dataset(
model,
val_inputs,
validation_steps,
epochs=epochs,
steps_name="validation_steps",
)
val_iterator = _get_iterator(val_inputs, model._distribution_strategy)
val_inputs = _prepare_feed_values(model, val_iterator, val_targets,
val_sample_weights, ModeKeys.TEST)
# Get num steps for printing.
val_samples_or_steps = validation_steps
else:
# Get num samples for printing.
val_samples_or_steps = (val_inputs
and tf.nest.flatten(val_inputs)[0].shape[0]
or None)
if mode == ModeKeys.TRAIN and verbose:
_print_train_info(num_samples_or_steps, val_samples_or_steps,
is_dataset)
# Configure callbacks.
count_mode = "steps" if use_steps else "samples"
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
samples=num_samples_or_steps,
count_mode=count_mode,
verbose=verbose,
mode=mode,
)
# Find beforehand arrays that need sparse-to-dense conversion.
if issparse is not None and not use_steps:
indices_for_conversion_to_dense = []
feed = _get_model_feed(model, mode)
for i, (input_data, feed_tensor) in enumerate(zip(ins, feed)):
if issparse(input_data) and not backend.is_sparse(feed_tensor):
indices_for_conversion_to_dense.append(i)
# Select aggregation method.
if mode == ModeKeys.PREDICT:
aggregator = training_utils_v1.OutputsAggregator(
use_steps,
num_samples=None if steps_per_epoch else num_samples_or_steps,
steps=steps_per_epoch,
)
else:
aggregator = training_utils_v1.MetricsAggregator(
use_steps,
num_samples=None if steps_per_epoch else num_samples_or_steps,
steps=steps_per_epoch,
)
if model._compile_distribution:
distributed_training_utils_v1._copy_weights_to_distributed_model(
model, mode)
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
initial_epoch = model._maybe_load_initial_epoch_from_ckpt(
initial_epoch, mode)
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
# Setup work for each epoch
epoch_logs = {}
if mode != ModeKeys.PREDICT:
# Collecting and resetting metrics has non-zero cost and will
# needlessly slow down model.predict.
model.reset_metrics()
if mode == ModeKeys.TRAIN:
callbacks.on_epoch_begin(epoch, epoch_logs)
if use_steps:
# Step-wise loop.
if steps_per_epoch is None:
# Loop over dataset until `OutOfRangeError` is raised.
target_steps = np.inf
else:
# Loop over dataset for the specified number of steps.
target_steps = steps_per_epoch
step = 0
while step < target_steps:
batch_logs = {"batch": step, "size": 1}
callbacks._call_batch_hook(mode, "begin", step, batch_logs)
# Get outputs.
try:
# `ins` can be callable in tf.distribute.Strategy + eager
# case.
if not callable(ins) or (
model._distribution_strategy
and not distributed_training_utils_v1.
is_distributing_by_cloning( # noqa: E501
model)):
actual_inputs = ins
else:
actual_inputs = ins()
batch_outs = f(actual_inputs)
except tf.errors.OutOfRangeError:
if is_dataset:
# The dataset passed by the user ran out of batches.
# Now we know the cardinality of the dataset. If
# steps_per_epoch was specified, then running out of
# data is unexpected, so we stop training and inform the
# user.
if steps_per_epoch:
callbacks.model.stop_training = True
logging.warning(
"Your dataset ran out of data; interrupting "
"training. Make sure that your dataset can "
"generate at least `%s * epochs` batches (in "
"this case, %d batches). You may need to use "
"the repeat() function when building your "
"dataset." %
(steps_name, steps_per_epoch * epochs))
elif step > 0:
steps_per_epoch = step
aggregator.steps = steps_per_epoch
else:
# We ran out of batches while the user passed an
# iterator (legacy).
callbacks.model.stop_training = True
logging.warning(
"Your dataset iterator ran out of data; "
"interrupting training. Make sure that your "
"iterator can generate at least `%s * epochs` "
"batches (in this case, %d batches). You may need "
"to use the repeat() function when building your "
"dataset." %
(steps_name, steps_per_epoch * epochs))
break
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if model._distribution_strategy:
batch_outs = distributed_training_utils_v1._per_replica_aggregate_batch( # noqa: E501
model._distribution_strategy, batch_outs, model, mode)
# Aggregate results.
if step == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs)
# Callbacks batch end.
batch_logs = cbks.make_logs(model, batch_logs, batch_outs,
mode)
callbacks._call_batch_hook(mode, "end", step, batch_logs)
step += 1
if callbacks.model.stop_training:
break
else:
# Sample-wise loop.
index_array = np.arange(num_samples_or_steps)
if shuffle == "batch":
index_array = training_utils_v1.batch_shuffle(
index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = make_batches(num_samples_or_steps, batch_size)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
# Slice into a batch.
if len(batches) == 1:
# If we only have one batch, do not slice. This takes care
# of composite tensors in non-Dataset modes; we currently
# don't support slicing them.
# TODO(b/133517906): Add slicing support.
ins_batch = ins
else:
try:
if ins and isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1],
batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
except TypeError:
raise TypeError("TypeError while preparing batch. "
"If using HDF5 input data, "
'pass shuffle="batch".')
# Sparse to dense conversion.
if issparse is not None:
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
# Callbacks batch_begin.
batch_logs = {"batch": batch_index, "size": len(batch_ids)}
callbacks._call_batch_hook(mode, "begin", batch_index,
batch_logs)
# Get outputs.
batch_outs = f(ins_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
# Aggregate results.
if batch_index == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs, batch_start, batch_end)
# Callbacks batch end.
batch_logs = cbks.make_logs(model, batch_logs, batch_outs,
mode)
callbacks._call_batch_hook(mode, "end", batch_index,
batch_logs)
if callbacks.model.stop_training:
break
aggregator.finalize()
results = aggregator.results
epoch_logs = cbks.make_logs(model, epoch_logs, results, mode)
if len(results) == 1:
results = results[0]
# Run the test loop every `validation_freq` epochs during training.
if (do_validation and training_utils_v1.should_run_validation(
validation_freq, epoch) and not callbacks.model.stop_training):
if model._compile_distribution:
# Since we create a new clone from the original model we need to
# copy the weights back to the original model before we can run
# validation.
distributed_training_utils_v1._copy_weights_to_original_model(
model, ModeKeys.TRAIN)
val_results = model_iteration(
model,
val_inputs,
targets=val_targets,
sample_weights=val_sample_weights,
batch_size=batch_size,
steps_per_epoch=validation_steps,
callbacks=callbacks,
verbose=0,
mode=ModeKeys.TEST,
validation_in_fit=True,
prepared_feed_values_from_dataset=(val_iterator is not None),
steps_name="validation_steps",
)
if not isinstance(val_results, list):
val_results = [val_results]
epoch_logs = cbks.make_logs(model,
epoch_logs,
val_results,
mode,
prefix="val_")
if val_iterator and epoch < epochs - 1:
_reinitialize_iterator(val_iterator,
model._distribution_strategy)
if mode == ModeKeys.TRAIN:
# Epochs only apply to `fit`.
callbacks.on_epoch_end(epoch, epoch_logs)
# Reinitialize dataset iterator for the next epoch.
if reset_dataset_after_each_epoch and epoch < epochs - 1:
_reinitialize_iterator(input_iterator,
model._distribution_strategy)
model._successful_loop_finish = True
callbacks._call_end_hook(mode)
if model._distribution_strategy:
if model._compile_distribution:
# TODO(priyag, psv): Copy back metrics to the original model as
# well?
distributed_training_utils_v1._copy_weights_to_original_model(
model, mode)
scope.__exit__(None, None, None)
if mode == ModeKeys.TRAIN:
return model.history
return results
def model_iteration(model,
data,
steps_per_epoch=None,
epochs=1,
verbose=1,
callbacks=None,
validation_data=None,
validation_steps=None,
validation_freq=1,
class_weight=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
shuffle=False,
initial_epoch=0,
mode=ModeKeys.TRAIN,
batch_size=None,
steps_name='steps',
**kwargs):
"""Loop function for arrays of data with modes TRAIN/TEST/PREDICT.
Arguments:
model: Keras Model instance.
data: Either a tuple of NumPy/Tensor inputs (i.e. `(x,)` or `(x, y)` or
`(x, y, sample_weights)`) or a generator or
`keras.utils.data_utils.Sequence` object or Eager Iterator or Dataset.
steps_per_epoch: Total number of steps (batches of samples) before
declaring one epoch finished and starting the next epoch. Ignored with
the default value of `None`.
epochs: Number of times to iterate over the data.
verbose: 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.
Note that the progress bar is not particularly useful when
logged to a file, so verbose=2 is recommended when not running
interactively (eg, in a production environment).
callbacks: List of callbacks to be called during training.
validation_data: Either a tuple of NumPy/Tensor inputs (i.e. `(x,)` or
`(x, y)` or `(x, y, sample_weights)`) or a generator or
`keras.utils.data_utils.Sequence` object or Eager Iterator or Dataset.
validation_steps: Total number of steps (batches of samples) before
declaring validation finished.
validation_freq: Only relevant if validation data is provided. Integer or
`collections.abc.Container` instance (e.g. list, tuple, etc.). If an
integer, specifies how many training epochs to run before a new
validation run is performed, e.g. `validation_freq=2` runs
validation every 2 epochs. If a Container, specifies the epochs on
which to run validation, e.g. `validation_freq=[1, 2, 10]` runs
validation at the end of the 1st, 2nd, and 10th epochs.
class_weight: Dictionary mapping class indices to a weight for the class.
max_queue_size: Integer. Maximum size for the generator queue. If
unspecified, `max_queue_size` will default to 10.
workers: Integer. Maximum number of processes to spin up when using
process-based threading. If unspecified, `workers` will default to 1. If
0, will execute the generator on the main thread.
use_multiprocessing: Boolean. If `True`, use process-based threading. If
unspecified, `use_multiprocessing` will default to `False`. Note that
because this implementation relies on multiprocessing, you should not
pass non-picklable arguments to the generator as they can't be passed
easily to children processes.
shuffle: Boolean. Whether to shuffle the order of the batches at the
beginning of each epoch. Only used with instances of `Sequence`
(`keras.utils.Sequence`). Has no effect when `steps_per_epoch` is not
`None`.
initial_epoch: Epoch at which to start training (useful for resuming a
previous training run).
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
batch_size: Integer batch size or None if unknown. Will only be used if
`data` is in NumPy/Tensor format.
steps_name: The string name of the steps argument, either `steps`,
`validation_steps`, or `steps_per_epoch`. Only used for error message
formatting.
**kwargs: Additional arguments for backwards compatibility. `steps` is
accepted as an alias for `steps_per_epoch`.
Returns:
- In TRAIN mode: `History` object.
- In TEST mode: Evaluation metrics.
- In PREDICT mode: Outputs of the Model called on inputs.
Raises:
ValueError: in case of invalid arguments.
"""
if 'steps' in kwargs:
steps_per_epoch = kwargs['steps']
# Determine the number of steps per epoch and whether we should reset the
# dataset at the end of each epoch.
reset_dataset_after_each_epoch = False
original_dataset = None
is_dataset = isinstance(data, (tf.data.Dataset, tf.compat.v1.data.Dataset))
if is_dataset:
original_dataset = data
if steps_per_epoch is None:
reset_dataset_after_each_epoch = True
steps_per_epoch = training_utils_v1.infer_steps_for_dataset(
model,
data,
steps_per_epoch,
epochs=epochs,
steps_name=steps_name)
# Convert to a format that supports `next(generator)`.
generator, steps_per_epoch = convert_to_generator_like(
data,
steps_per_epoch=steps_per_epoch,
batch_size=batch_size,
epochs=epochs - initial_epoch,
shuffle=shuffle)
do_validation = validation_data is not None
is_sequence = isinstance(generator, data_utils.Sequence)
_validate_arguments(is_sequence, is_dataset, use_multiprocessing, workers,
steps_per_epoch, validation_data, validation_steps,
mode, kwargs)
batch_function = _make_execution_function(model,
mode,
class_weight=class_weight)
# Create the queue for the generator.
enqueuer = None
if not is_dataset:
generator, enqueuer = _make_enqueued_generator(
generator,
workers=workers,
use_multiprocessing=use_multiprocessing,
max_queue_size=max_queue_size,
shuffle=shuffle)
num_samples_or_steps, use_steps = _get_num_samples_or_steps(
data, steps_per_epoch)
count_mode = 'steps' if use_steps else 'samples'
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=do_validation,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
batch_size=batch_size,
samples=num_samples_or_steps,
count_mode=count_mode,
verbose=verbose,
mode=mode)
if mode == ModeKeys.PREDICT:
aggregator = training_utils_v1.OutputsAggregator(True,
steps=steps_per_epoch)
else:
aggregator = training_utils_v1.MetricsAggregator(True,
steps=steps_per_epoch)
should_set_learning_phase = tf.executing_eagerly() and model.run_eagerly
if should_set_learning_phase:
learning_phase_scope = backend.eager_learning_phase_scope(
1 if mode == ModeKeys.TRAIN else 0)
learning_phase_scope.__enter__()
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
initial_epoch = model._maybe_load_initial_epoch_from_ckpt(
initial_epoch, mode)
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
# Setup work for each epoch.
model.reset_metrics()
epoch_logs = {}
if mode == ModeKeys.TRAIN:
callbacks.on_epoch_begin(epoch, epoch_logs)
if steps_per_epoch is None:
# Loop over dataset until `OutOfRangeError` is raised.
target_steps = np.inf
else:
# Loop over dataset for the specified number of steps.
target_steps = steps_per_epoch
step = 0
while step < target_steps:
batch_data = _get_next_batch(generator)
if batch_data is None:
if is_dataset:
# The dataset passed by the user ran out of batches.
# Now we know the cardinality of the dataset.
# If steps_per_epoch was specified, then running out of data is
# unexpected, so we stop training and inform the user.
if steps_per_epoch:
callbacks.model.stop_training = True
logging.warning(
'Your dataset ran out of data; interrupting training. '
'Make sure that your dataset can generate at least '
'`%s * epochs` batches (in this case, %d batches). '
'You may need to use the repeat() function when '
'building your dataset.' %
(steps_name, steps_per_epoch * epochs))
elif step > 0:
steps_per_epoch = step
aggregator.steps = steps_per_epoch
else:
# We ran out of batches while the user passed an iterator (legacy).
callbacks.model.stop_training = True
logging.warning(
'Your dataset iterator ran out of data; '
'interrupting training. Make sure that your iterator '
'can generate at least `%s * epochs` '
'batches (in this case, %d batches). You may need to'
'use the repeat() function when building your '
'dataset.' % (steps_name, steps_per_epoch * epochs))
break
# `batch_size` used for validation data if validation
# data is NumPy/EagerTensors.
batch_size = int(tf.nest.flatten(batch_data)[0].shape[0])
# Callbacks batch begin.
batch_logs = {'batch': step, 'size': batch_size}
callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
is_deferred = not model._is_compiled
batch_outs = batch_function(*batch_data)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
aggregator.create(batch_outs)
if is_deferred:
# Set callbacks params. We do this here when model is compiled only
# in the first iteration of this loop (deferred build scenario).
cbks.set_callback_parameters(
callbacks,
model,
do_validation=do_validation,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
samples=num_samples_or_steps,
verbose=verbose,
mode=mode)
# Aggregate results.
aggregator.aggregate(batch_outs)
# Callbacks batch end.
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', step, batch_logs)
step += 1
if callbacks.model.stop_training:
break
aggregator.finalize()
results = aggregator.results
epoch_logs = cbks.make_logs(model, epoch_logs, results, mode)
if len(results) == 1:
results = results[0]
# Run the test loop every epoch during training.
if (do_validation and training_utils_v1.should_run_validation(
validation_freq, epoch) and not callbacks.model.stop_training):
val_results = model_iteration(
model,
validation_data,
steps_per_epoch=validation_steps,
batch_size=batch_size,
class_weight=class_weight,
workers=workers,
use_multiprocessing=use_multiprocessing,
max_queue_size=max_queue_size,
callbacks=callbacks,
verbose=verbose,
mode=ModeKeys.TEST,
steps_name='validation_steps')
if not isinstance(val_results, list):
val_results = [val_results]
epoch_logs = cbks.make_logs(model,
epoch_logs,
val_results,
mode,
prefix='val_')
if mode == ModeKeys.TRAIN:
# Epochs only apply to `fit`.
callbacks.on_epoch_end(epoch, epoch_logs)
# Recreate dataset iterator for the next epoch.
if reset_dataset_after_each_epoch and epoch < epochs - 1:
generator = tf.compat.v1.data.make_one_shot_iterator(
original_dataset)
model._successful_loop_finish = True
callbacks._call_end_hook(mode)
if enqueuer is not None:
enqueuer.stop()
if should_set_learning_phase:
learning_phase_scope.__exit__(None, None, None)
if mode == ModeKeys.TRAIN:
return model.history
return results
