def compute_validation_metrics(self) -> workload.Response:
(
validation_data,
validation_steps,
) = self._validation_input_manager.get_validation_input_and_num_batches(
)
metrics_values = self.model.evaluate(validation_data,
steps=validation_steps,
verbose=0)
# If the model was compiled with metrics=None, metrics_value will be a single value.
if not isinstance(metrics_values, (tuple, list)):
metrics_values = (metrics_values, )
if self.hvd_config.use:
for index, metric_value in enumerate(metrics_values):
metrics_values[index] = np.array(hvd.allreduce(metric_value))
num_inputs = self._validation_input_manager.stop_validation_input_and_get_num_inputs(
)
if not self.is_chief:
return workload.Skipped()
metrics = make_logs(self.model, {},
metrics_values,
ModeKeys.TEST,
prefix="val_")
check.gt(len(metrics), 0)
return {"num_inputs": num_inputs, "validation_metrics": metrics}
def _launch_evaluate(self) -> Any:
validation_data = self.validation_data
steps = None
# Support the deprecated SequenceAdapter API.
if isinstance(validation_data, keras.SequenceAdapter):
# Ignore these settings and use the same settings as for the fit call.
validation_data = validation_data.sequence
if isinstance(validation_data, tf.keras.utils.Sequence):
# Calculate the length of our validation shard.
steps = len(validation_data)
if self.context.distributed.get_size() > 1:
size = self.context.distributed.get_size()
rank = self.context.distributed.get_rank()
steps = steps // size + (1 if steps % size > rank else 0)
# Handle args from fit(): shuffle, workers, use_multiprocessing, and max_queue_size.
enqueuer = keras._build_enqueuer(
sequence=validation_data,
workers=self.context._fit_workers,
use_multiprocessing=self.context._fit_use_multiprocessing,
max_queue_size=self.context._fit_max_queue_size,
shard_rank=self.context.distributed.get_rank(),
num_shards=self.context.distributed.get_size(),
repeat=False,
shuffle=False,
shuffle_seed=0,
prior_batches_trained=0,
)
enqueuer.start()
self.enqueuers.append(enqueuer)
validation_data = enqueuer.data()
# Starting in TF 2.2 users may define custom test_step() that do
# not use the model metrics.
use_model_metrics = version.parse(tf.__version__) < version.parse("2.2.0")
evaluate_kwargs = {} if use_model_metrics else {"return_dict": True}
metrics_values = self.model.evaluate(
validation_data,
callbacks=self.callback_list,
steps=steps,
verbose=0,
workers=0,
**evaluate_kwargs,
)
logging.debug(f"Worker finished model.evaluate() with metrics: {metrics_values}.")
# If the model was compiled with metrics=None, metrics_value will be a single value.
if not isinstance(metrics_values, (tuple, list, dict)):
metrics_values = (metrics_values,)
if use_model_metrics:
metrics = make_logs(self.model, {}, metrics_values, ModeKeys.TEST, prefix="val_")
else:
check.is_instance(metrics_values, dict)
metrics = {f"val_{k}": v for k, v in metrics_values.items()}
return metrics
def _launch_evaluate(self) -> Any:
(
validation_data,
validation_steps,
) = self._validation_input_manager.get_validation_input_and_num_batches(
)
# Starting in TF 2.2 users may define custom test_step() that do
# not use the model metrics.
use_model_metrics = version.parse(
tf.__version__) < version.parse("2.2.0")
evaluate_kwargs = {} if use_model_metrics else {"return_dict": True}
metrics_values = self.model.evaluate(
validation_data,
steps=validation_steps,
verbose=0,
callbacks=self.callback_list,
**evaluate_kwargs,
)
logging.debug(
f"Worker finished model.evaluate() with metrics: {metrics_values}."
)
# If the model was compiled with metrics=None, metrics_value will be a single value.
if not isinstance(metrics_values, (tuple, list, dict)):
metrics_values = (metrics_values, )
if use_model_metrics:
metrics = make_logs(self.model, {},
metrics_values,
ModeKeys.TEST,
prefix="val_")
else:
check.is_instance(metrics_values, dict)
metrics = {f"val_{k}": v for k, v in metrics_values.items()}
_ = self._validation_input_manager.stop_validation_input_and_get_num_inputs(
)
return metrics
def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
model._make_predict_function()
f = model.predict_function
# Callbacks batch end.
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', batch_index, batch_logs)
progbar.on_batch_end(batch_index, batch_logs)
def experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
steps = training_utils.infer_steps_for_dataset(dataset, steps,
steps_name='steps')
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
out_labels = model.metrics_names
def _test_step_fn(inputs):
"""A fn that returns output of single test step."""
inputs, targets = inputs
(distribution_strategy_context.get_replica_context().merge_call(
_build_model, args=(model, mode, inputs, targets)))
(_, outputs, updates, _) = (
_per_device_execution_function(
distributed_training_utils.get_distributed_model(model, mode),
mode))
with ops.control_dependencies([updates]):
return outputs
test_input_data = iterator.get_next()
per_replica_outputs = current_strategy.experimental_run_v2(
_test_step_fn, args=(test_input_data,))
output_tensors = {}
for label, output in zip(out_labels, per_replica_outputs):
if label == 'loss':
reduce_op = ds_reduce_util.ReduceOp.SUM
else:
# We reduce all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
reduce_op = ds_reduce_util.ReduceOp.MEAN
output_tensors[label] = current_strategy.reduce(reduce_op, output)
test_op = control_flow_ops.group(list(output_tensors.values()))
if verbose >= 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
outs = [0.] * len(model.metrics_names)
if steps is not None:
target_steps = steps
else:
raise ValueError('Number of steps could not be infered from the data, '
'please pass the steps argument.')
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.batch_get_value([test_op, output_tensors])
except errors.OutOfRangeError:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
target_steps = current_step
break
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose == 1:
progbar.update(current_step + 1)
current_step += 1
if verbose >= 1:
# Progress bar finishes at the end.
progbar.update(target_steps)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (target_steps)
if len(outs) == 1:
return outs[0]
return outs
def run_one_epoch(model,
iterator,
execution_function,
dataset_size=None,
batch_size=None,
strategy=None,
steps_per_epoch=None,
num_samples=None,
mode=ModeKeys.TRAIN,
training_context=None,
total_epochs=None):
"""Run the execution function with the data from iterator.
Given the dataset iterator and execution function, get the data from iterator
and call it with the execution function to get the result (metric/loss).
It will run for steps_per_epoch or until to the iterator is fully consumed.
Args:
model: The keras model to run.
iterator: the dataset iterator to fetch the data.
execution_function: a tf.function that can be called with data.
dataset_size: the size of iterator, None when unknown.
batch_size: The size of the current batch.
strategy: the distribution strategy instance from the model.
steps_per_epoch: the number of steps to run for the epoch.
num_samples: the number of samples for the whole epoch if known. This can be
used to calculate the final partial batch, and scale the loss.
mode: the mode for the current epoch.
training_context: the context that contains callbacks and progress bar.
total_epochs: the total number of epochs that will be run.
Used when throw error when the iterator unexpectedly
reaches its end.
Returns:
The loss and metric value from the model.
"""
# Only use the sample to count if there is a partial batch at the end.
use_steps = num_samples is None
if mode == ModeKeys.PREDICT:
aggregator = training_utils.OutputsAggregator(
use_steps=use_steps,
steps=steps_per_epoch,
num_samples=num_samples,
batch_size=batch_size)
else:
aggregator = training_utils.MetricsAggregator(
use_steps=use_steps, steps=steps_per_epoch, num_samples=num_samples)
callbacks = training_context.callbacks
progbar = training_context.progbar
if callbacks.model.stop_training:
return
target_steps = steps_per_epoch or np.inf
step = 0
while step < target_steps:
if use_steps:
current_batch_size = 1
elif step < target_steps - 1:
current_batch_size = batch_size
else:
current_batch_size = num_samples - step * batch_size
with training_context.on_batch(
step=step, mode=mode, size=current_batch_size) as batch_logs:
try:
batch_outs = execution_function(iterator)
except (StopIteration, errors.OutOfRangeError):
# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
# Are there any other C++ errors tf function should recapture?
# The only acceptable case here is that the input has a unknown
# length, and configured to fully consume it.
if (dataset_size is None
and steps_per_epoch is None
and step > 0):
# The input passed by the user ran out of batches.
# Now we know the cardinality of the input(dataset or generator).
steps_per_epoch = step
aggregator.steps = steps_per_epoch
if mode == ModeKeys.TRAIN:
progbar.params['steps'] = steps_per_epoch
progbar.progbar.target = steps_per_epoch
else:
callbacks.model.stop_training = True
logging.warning(
'Your input ran out of data; interrupting training. '
'Make sure that your dataset or generator can generate at '
'least `steps_per_epoch * epochs` batches (in this case, '
'{} batches). You may need to use the repeat() function '
'when building your dataset.'.format(
total_epochs * steps_per_epoch))
# In either case, break out the loop for training batch.
# Also note the training_context that data inputs are exhausted, so all
# the post batch hooks can be skipped.
batch_logs['data_exhausted'] = True
break
if mode != ModeKeys.PREDICT:
data_batch_size = batch_outs['batch_size']
batch_outs = (batch_outs['total_loss'] + batch_outs['output_losses']
+ batch_outs['metrics'])
if current_batch_size != data_batch_size:
batch_logs['size'] = data_batch_size
current_batch_size = data_batch_size
else:
batch_outs = _aggregate_predict_results(strategy, batch_outs, model)
if step == 0:
aggregator.create(batch_outs)
if use_steps:
aggregator.aggregate(batch_outs)
else:
aggregator.aggregate(
batch_outs,
batch_start=step * batch_size,
batch_end=step * batch_size + current_batch_size)
cbks.make_logs(model, batch_logs, batch_outs, mode)
step += 1
if callbacks.model.stop_training:
break
# End of an epoch.
aggregator.finalize()
return aggregator.results
def fit_generator(model,
generator,
steps_per_epoch=None,
epochs=1,
verbose=1,
callbacks=None,
validation_data=None,
validation_steps=None,
class_weight=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
shuffle=True,
initial_epoch=0):
"""See docstring for `Model.fit_generator`."""
epoch = initial_epoch
do_validation = bool(validation_data)
if not context.executing_eagerly():
model._make_train_function()
if do_validation:
model._make_test_function()
is_sequence = isinstance(generator, Sequence)
if not is_sequence and use_multiprocessing and workers > 1:
logging.warning(
UserWarning('Using a generator with `use_multiprocessing=True`'
' and multiple workers may duplicate your data.'
' Please consider using the`keras.utils.Sequence'
' class.'))
if steps_per_epoch is None:
if is_sequence:
steps_per_epoch = len(generator)
else:
raise ValueError('`steps_per_epoch=None` is only valid for a'
' generator based on the `keras.utils.Sequence`'
' class. Please specify `steps_per_epoch` or use'
' the `keras.utils.Sequence` class.')
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)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
# 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)
progbar.on_batch_end(step, batch_logs)
step += 1
callbacks.on_train_begin()
# Construct epoch logs.
epoch_logs = {}
while epoch < epochs:
for m in model.stateful_metric_functions:
m.reset_states()
callbacks.on_epoch_begin(epoch)
steps_done = 0
batch_index = 0
while steps_done < steps_per_epoch:
generator_output = next(output_generator)
if not hasattr(generator_output, '__len__'):
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' +
str(generator_output))
if len(generator_output) == 2:
x, y = generator_output
sample_weight = None
elif len(generator_output) == 3:
x, y, sample_weight = generator_output
else:
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' +
str(generator_output))
# build batch logs
batch_logs = {}
if isinstance(x, list):
batch_size = x[0].shape[0]
elif isinstance(x, dict):
batch_size = list(x.values())[0].shape[0]
else:
batch_size = x.shape[0]
batch_logs['batch'] = batch_index
batch_logs['size'] = batch_size
callbacks.on_batch_begin(batch_index, batch_logs)
outs = model.train_on_batch(x,
y,
sample_weight=sample_weight,
class_weight=class_weight)
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(model.metrics_names, outs):
batch_logs[l] = o
callbacks.on_batch_end(batch_index, batch_logs)
batch_index += 1
steps_done += 1
# Epoch finished.
if steps_done >= steps_per_epoch and do_validation:
if val_gen:
val_outs = evaluate_generator(
model,
validation_data,
validation_steps,
workers=workers,
use_multiprocessing=use_multiprocessing,
max_queue_size=max_queue_size)
else:
# No need for try/except because
# data has already been validated.
val_outs = model.evaluate(
val_x,
val_y,
batch_size=batch_size,
sample_weight=val_sample_weights,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(model.metrics_names, val_outs):
epoch_logs['val_' + l] = o
if callbacks.model.stop_training:
break
callbacks.on_epoch_end(epoch, epoch_logs)
epoch += 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.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=0,
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)
progbar.on_epoch_end(epoch, epoch_logs)
# Recreate dataset iterator for the next epoch.
if reset_dataset_after_each_epoch and epoch < epochs - 1:
generator = dataset_ops.make_one_shot_iterator(original_dataset)
callbacks._call_end_hook(mode)
if enqueuer is not None:
enqueuer.stop()
if should_set_learning_phase:
backend.set_eager_learning_phase(old_learning_phase)
if mode == ModeKeys.TRAIN:
return model.history
return 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.
Arguments:
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.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,
(dataset_ops.DatasetV1, dataset_ops.DatasetV2))
# 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, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)):
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 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 K.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(model)):
actual_inputs = ins
else:
actual_inputs = ins()
batch_outs = f(actual_inputs)
except 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(
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 experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU tf.distribute.Strategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
dataset_fully_shaped = dist_utils.is_dataset_shape_fully_defined(dataset)
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(
dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(
padding_handler.padding_mask, padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.unbatch()
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = dist_utils.get_iterator(dataset, current_strategy)
scope = dist_utils.distributed_scope(strategy=current_strategy,
learning_phase=0)
scope.__enter__()
def _predict_step_fn(inputs):
"""A fn that returns output of single prediction step."""
(distribution_strategy_context.get_replica_context().merge_call(
_build_model, args=(model, mode, inputs)))
(_, outputs, updates, _) = _per_replica_execution_function(
dist_utils.get_distributed_model(model, mode), mode)
with ops.control_dependencies([updates]):
return [array_ops.identity(out) for out in outputs]
# TODO(hongjunchoi): When numpy array is passed as an input to `predict()`
# use numpy arrays directly to avoid cumulating unnecessary input pipeline
# ops.
predict_input_data = iterator.get_next()
per_replica_outputs = current_strategy.run(_predict_step_fn,
args=(predict_input_data, ))
output_tensors = dist_utils.flatten_per_replica_values(
current_strategy, per_replica_outputs)
if verbose >= 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
dist_utils._copy_weights_to_distributed_model(model, mode)
dist_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
num_model_outputs = len(model.output_names)
unconcatenated_outs = [[] for _ in range(num_model_outputs)]
if steps is not None:
target_steps = steps
else:
raise ValueError(
'Number of steps could not be inferred from the data, '
'please pass the steps argument.')
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
predict_ops = control_flow_ops.group(output_tensors)
_, batch_outs = K.batch_get_value([predict_ops, output_tensors])
except errors.OutOfRangeError:
warning_msg = (
'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps))
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
break
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i in range(num_model_outputs):
output_start_index = i * current_strategy.num_replicas_in_sync
output_end_index = (output_start_index +
current_strategy.num_replicas_in_sync)
single_model_output = batch_outs[
output_start_index:output_end_index]
unconcatenated_outs[i].extend(single_model_output)
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose == 1:
progbar.update(current_step + 1)
current_step += 1
if verbose >= 1:
# Progress bar finishes at the end.
progbar.update(current_step)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(out, axis=0) for out in unconcatenated_outs
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
def fit(self,
model,
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_freq=1,
**kwargs):
batch_size = model._validate_or_infer_batch_size(
batch_size, steps_per_epoch, x)
strategy = _get_distribution_strategy(model)
batch_size, steps_per_epoch = dist_utils.process_batch_and_step_size(
strategy, x, batch_size, steps_per_epoch, ModeKeys.TRAIN)
dist_utils.validate_callbacks(input_callbacks=callbacks,
optimizer=model.optimizer)
# Enter tf.distribute.Strategy scope.
with strategy.scope():
training_data_adapter, validation_adapter = _process_training_inputs(
model,
x,
y,
batch_size=batch_size,
sample_weights=sample_weight,
class_weights=class_weight,
validation_split=validation_split,
steps_per_epoch=steps_per_epoch,
shuffle=shuffle,
validation_data=validation_data,
validation_steps=validation_steps,
distribution_strategy=strategy)
total_samples = _get_total_number_of_samples(training_data_adapter)
use_sample = total_samples is not None
do_validation = (validation_adapter is not None)
if not steps_per_epoch:
steps_per_epoch = training_data_adapter.get_size()
# tf.print('{} on {} steps.'.format(ModeKeys.TRAIN, steps_per_epoch))
training_context = TrainingContext()
initial_epoch = model._maybe_load_initial_epoch_from_ckpt(
initial_epoch, ModeKeys.TRAIN)
training_dataset = training_data_adapter.get_dataset()
# Raise an error if steps_per_epoch isn't specified but the dataset
# is infinite.
# TODO(scottzhu): This check should probably happen in the adapter
training_utils.infer_steps_for_dataset(
training_dataset,
steps_per_epoch,
steps_name='steps_per_epoch',
epochs=0)
training_dataset = strategy.experimental_distribute_dataset(
training_dataset)
_update_sample_weight_mode(model, ModeKeys.TRAIN, training_dataset)
training_function = training_v2_utils._get_or_make_execution_function(
model, ModeKeys.TRAIN)
training_data_iter = None
# Only recreate iterator when the data has a fixed length, which will be
# fully consumed every epoch, or has a unknown length (dataset, generator)
# and will be fully consumed (steps_per_epoch is None)
recreate_training_iterator = (training_data_adapter.get_size()
is not None
or steps_per_epoch is None)
if do_validation:
if not validation_steps:
validation_steps = validation_adapter.get_size()
eval_function = training_v2_utils._get_or_make_execution_function(
model, ModeKeys.TEST)
eval_data_iter = None
validation_dataset = validation_adapter.get_dataset()
# Raise an error if validation_steps isn't specified but the validation
# dataset is infinite.
# TODO(scottzhu): This check should probably happen in the adapter
training_utils.infer_steps_for_dataset(
validation_dataset,
validation_steps,
steps_name='validation_steps',
epochs=0)
validation_dataset = strategy.experimental_distribute_dataset(
validation_dataset)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
samples=total_samples,
count_mode='samples' if use_sample else 'steps',
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=ModeKeys.TRAIN)
with training_context.on_start(model, callbacks, use_sample,
verbose, ModeKeys.TRAIN):
# TODO(scottzhu): Handle TPUStrategy training loop
for epoch in range(initial_epoch, epochs):
if training_context.callbacks.model.stop_training:
break
# Training
with training_context.on_epoch(
epoch, ModeKeys.TRAIN) as epoch_logs:
model.reset_metrics()
if training_data_iter is None or recreate_training_iterator:
if (training_data_iter is not None
and distribution_strategy_context.
has_strategy()):
# TODO(kaftan): remove this when MultiDeviceIterator is a
## compositetensor (unless this is more efficient)
training_data_iter._initializer # pylint: disable=pointless-statement
else:
training_data_iter = iter(training_dataset)
training_result = run_one_epoch(
model,
training_data_iter,
training_function,
dataset_size=training_data_adapter.get_size(),
batch_size=training_data_adapter.batch_size(),
strategy=strategy,
steps_per_epoch=steps_per_epoch,
num_samples=total_samples,
mode=ModeKeys.TRAIN,
training_context=training_context,
total_epochs=epochs)
cbks.make_logs(model, epoch_logs, training_result,
ModeKeys.TRAIN)
# Evaluation
if (do_validation
and training_utils.should_run_validation(
validation_freq, epoch)
and not callbacks.model.stop_training):
if (eval_data_iter is not None
and distribution_strategy_context.
has_strategy()):
# TODO(kaftan): remove this when MultiDeviceIterator is a
## compositetensor (unless this is more efficient)
eval_data_iter._initializer # pylint: disable=pointless-statement
else:
eval_data_iter = iter(validation_dataset)
val_total_samples = _get_total_number_of_samples(
validation_adapter)
eval_context = TrainingContext()
with eval_context.on_start(model,
callbacks,
use_sample,
verbose=0,
mode=ModeKeys.TEST):
with eval_context.on_epoch(
epoch, ModeKeys.TEST):
model.reset_metrics()
eval_result = run_one_epoch(
model,
eval_data_iter,
eval_function,
dataset_size=validation_adapter.
get_size(),
batch_size=validation_adapter.
batch_size(),
strategy=strategy,
steps_per_epoch=validation_steps,
num_samples=val_total_samples,
mode=ModeKeys.TEST,
training_context=eval_context,
total_epochs=1)
cbks.make_logs(model,
epoch_logs,
eval_result,
ModeKeys.TEST,
prefix='val_')
return model.history
def _model_iteration(self,
model,
mode,
x=None,
y=None,
batch_size=None,
verbose=1,
sample_weight=None,
steps=None,
callbacks=None,
**kwargs):
batch_size = model._validate_or_infer_batch_size(batch_size, steps, x)
strategy = _get_distribution_strategy(model)
if strategy:
batch_size, steps = dist_utils.process_batch_and_step_size(
strategy, x, batch_size, steps, mode)
dist_utils.validate_callbacks(input_callbacks=callbacks,
optimizer=model.optimizer)
# Enter tf.distribute.Strategy scope.
scope = dist_utils.distributed_scope(strategy=strategy,
learning_phase=0)
scope.__enter__()
adapter = _process_inputs(model,
x,
y,
batch_size=batch_size,
sample_weights=sample_weight,
steps=steps,
distribution_strategy=strategy)
if not steps:
steps = adapter.get_size()
# tf.print('{} on {} steps.'.format(ModeKeys.TRAIN, steps_per_epoch))
training_context = TrainingContext()
_update_sample_weight_mode(model, mode, adapter)
execution_function = _make_execution_function(model, mode)
data_iterator = _create_dataset_iterator(strategy,
adapter.get_dataset())
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
batch_size=batch_size,
epochs=1,
steps_per_epoch=steps,
samples=None,
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=mode)
with training_context.on_start(model, callbacks, verbose, mode):
# TODO(scottzhu): Handle TPUStrategy training loop
with training_context.on_epoch(0, mode) as epoch_logs:
model.reset_metrics()
result = run_one_epoch(model,
data_iterator,
execution_function,
dataset_size=adapter.get_size(),
strategy=strategy,
steps_per_epoch=steps,
mode=mode,
training_context=training_context,
current_epoch=1)
cbks.make_logs(model, epoch_logs, result, mode)
if strategy:
scope.__exit__(None, None, None)
if len(result) == 1:
result = result[0]
return result
def run_one_epoch(model,
iterator,
execution_function,
dataset_size=None,
strategy=None,
steps_per_epoch=None,
mode=ModeKeys.TRAIN,
training_context=None,
total_epochs=None):
"""Run the execution function with the data from iterator.
Given the dataset iterator and execution function, get the data from iterator
and call it with the execution function to get the result (metric/loss).
It will run for steps_per_epoch or until to the iterator is fully consumed.
Args:
model: The keras model to run.
iterator: the dataset iterator to fetch the data.
execution_function: a tf.function that can be called with data.
dataset_size: the size of iterator, None when unknown.
strategy: the distribution strategy instance from the model.
steps_per_epoch: the number of steps to run for the epoch.
mode: the mode for the current epoch.
training_context: the context that contains callbacks and progress bar.
total_epochs: the total number of epochs that will be run.
Used when throw error when the iterator unexpectedly
reaches its end.
Returns:
The loss and metric value from the model.
"""
if mode == ModeKeys.PREDICT:
aggregator = training_utils.OutputsAggregator(
use_steps=True, num_samples_or_steps=steps_per_epoch)
else:
aggregator = training_utils.MetricsAggregator(
use_steps=True, num_samples_or_steps=steps_per_epoch)
callbacks = training_context.callbacks
progbar = training_context.progbar
if callbacks.model.stop_training:
return
target_steps = steps_per_epoch or np.inf
step = 0
while step < target_steps:
# TODO(scottzhu): Maybe update the training context to take into account
# whether a batch of training happens. Then it could still use a
# context manager
batch_logs = {'batch': step, 'size': 1}
training_context.callbacks._call_batch_hook(
mode, 'begin', step, batch_logs)
training_context.progbar.on_batch_begin(step, batch_logs)
try:
batch_outs = execution_function(iterator)
except (StopIteration, errors.OutOfRangeError):
# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
# Are there any other C++ errors tf function should recapture?
# The only acceptable case here is that the input has a unknown
# length, and configured to fully consume it.
if (dataset_size is None
and steps_per_epoch is None
and step > 0):
# The input passed by the user ran out of batches.
# Now we know the cardinality of the input(dataset or generator).
steps_per_epoch = step
aggregator.num_samples_or_steps = steps_per_epoch
progbar.params['steps'] = steps_per_epoch
progbar.progbar.target = steps_per_epoch
else:
callbacks.model.stop_training = True
logging.warning(
'Your input ran out of data; interrupting training. '
'Make sure that your dataset or generator can generate at '
'least `steps_per_epoch * epochs` batches (in this case, '
'{} batches). You may need to use the repeat() function '
'when building your dataset.'.format(
total_epochs * steps_per_epoch))
# In either case, break out the loop for training batch.
break
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if strategy:
batch_outs = dist_utils._per_replica_aggregate_batch(
batch_outs, model, mode)
if step == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs)
cbks.make_logs(model, batch_logs, batch_outs, mode)
training_context.callbacks._call_batch_hook(
mode, 'end', step, batch_logs)
training_context.progbar.on_batch_end(step, batch_logs)
step += 1
if callbacks.model.stop_training:
break
# End of an epoch.
aggregator.finalize()
results = aggregator.results
return results
def _model_iteration(
self, model, mode, x=None, y=None, batch_size=None, verbose=1,
sample_weight=None, steps=None, callbacks=None, **kwargs):
batch_size = model._validate_or_infer_batch_size(
batch_size, steps, x)
strategy = _get_distribution_strategy(model)
batch_size, steps = dist_utils.process_batch_and_step_size(
strategy, x, batch_size, steps, mode)
dist_utils.validate_callbacks(input_callbacks=callbacks,
optimizer=model.optimizer)
# Enter tf.distribute.Strategy scope.
with dist_utils.distributed_scope(
strategy=strategy, learning_phase=0):
adapter = _process_inputs(
model,
x,
y,
batch_size=batch_size,
sample_weights=sample_weight,
steps=steps,
distribution_strategy=strategy)
if not steps:
steps = adapter.get_size()
# tf.print('{} on {} steps.'.format(ModeKeys.TRAIN, steps_per_epoch))
training_context = TrainingContext()
dataset = adapter.get_dataset()
# Raise an error if `steps` isn't specified but the dataset
# is infinite.
# TODO(scottzhu): This check should probably happen in the adapter
training_utils.infer_steps_for_dataset(
dataset, steps, steps_name='steps', epochs=0)
dataset = strategy.experimental_distribute_dataset(dataset)
_update_sample_weight_mode(model, mode, dataset)
execution_function = training_v2_utils._get_or_make_execution_function(
model, mode)
data_iterator = iter(dataset)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
batch_size=batch_size,
epochs=1,
steps_per_epoch=steps,
samples=None,
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=mode)
with training_context.on_start(model, callbacks, verbose, mode):
# TODO(scottzhu): Handle TPUStrategy training loop
with training_context.on_epoch(0, mode) as epoch_logs:
model.reset_metrics()
result = run_one_epoch(
model,
data_iterator,
execution_function,
dataset_size=adapter.get_size(),
strategy=strategy,
steps_per_epoch=steps,
mode=mode,
training_context=training_context,
total_epochs=1)
cbks.make_logs(model, epoch_logs, result, mode)
if len(result) == 1:
result = result[0]
return result
def model_iteration(model,
data,
steps_per_epoch=None,
epochs=1,
verbose=1,
callbacks=None,
validation_data=None,
validation_steps=None,
class_weight=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
shuffle=False,
initial_epoch=0,
mode='train',
batch_size=None,
**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: Verbosity mode, 0, 1 or 2.
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.
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 'train'/'test'/'predict'.
batch_size: Integer batch size or None if unknown. Will only be used if
`data` is in NumPy/Tensor format.
**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']
# 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
should_set_learning_phase = context.executing_eagerly(
) and model.run_eagerly
is_sequence = isinstance(generator, data_utils.Sequence)
_validate_arguments(is_sequence, 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.
output_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,
verbose=0, # Handle ProgBar as part of Callbacks once hooks are ready.
mode=mode)
# TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
progbar = training_utils.get_progbar(model, count_mode)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
if mode == 'predict':
aggregator = training_utils.OutputsAggregator(True, steps_per_epoch)
else:
aggregator = training_utils.MetricsAggregator(True, steps_per_epoch)
if should_set_learning_phase:
old_learning_phase = backend.learning_phase()
backend.set_learning_phase(1 if mode == 'train' else 0)
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
progbar.on_train_begin()
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
# Setup work for each epoch.
model.reset_metrics()
epoch_logs = {}
callbacks.on_epoch_begin(epoch, epoch_logs, mode=mode)
progbar.on_epoch_begin(epoch, epoch_logs)
for step in range(steps_per_epoch):
batch_data = _get_next_batch(output_generator, mode)
if batch_data is None:
callbacks.model.stop_training = True
break
# `batch_size` used for validation data if validation
# data is NumPy/EagerTensors.
batch_size = int(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)
progbar.on_batch_begin(step, batch_logs)
batch_outs = batch_function(*batch_data)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
# 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)
progbar.on_batch_end(step, 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 epoch during training.
if do_validation 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=0,
mode='test')
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 == 'train':
# Epochs only apply to `fit`.
callbacks.on_epoch_end(epoch, epoch_logs, mode=mode)
progbar.on_epoch_end(epoch, epoch_logs)
callbacks._call_end_hook(mode)
if enqueuer is not None:
enqueuer.stop()
if should_set_learning_phase:
backend.set_learning_phase(old_learning_phase)
if mode == 'train':
return model.history
return results
def experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
steps = training_utils.infer_steps_for_dataset(dataset, steps,
steps_name='steps')
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
out_labels = model.metrics_names
step_fn = _make_step_fn(model, ModeKeys.TEST, current_strategy, out_labels)
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name in model.metrics_names[1:]:
tensor = model._all_stateful_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
# TODO(priyag): Use steps_per_run when we use new metrics as they will
# allow handling metric computation at each step using variables.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
test_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
outs = [0.] * len(model.metrics_names)
if steps is not None:
target_steps = steps
else:
target_steps = np.inf
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.batch_get_value([test_op, output_tensors])
except errors.OutOfRangeError:
if steps is not None:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
else:
warning_msg = 'Number of steps ran: {} steps'.format(current_step)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
target_steps = current_step
break
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose >= 1:
progbar.update(current_step + 1)
current_step += 1
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (target_steps)
if len(outs) == 1:
return outs[0]
return outs
def model_iteration(model,
data,
steps_per_epoch=None,
epochs=1,
verbose=1,
callbacks=None,
validation_data=None,
validation_steps=None,
validation_freq=1,
train_class_weight=None,
val_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):
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,
(dataset_ops.DatasetV2, dataset_ops.DatasetV1))
if is_dataset:
original_dataset = data
if steps_per_epoch is None:
reset_dataset_after_each_epoch = True
steps_per_epoch = training_utils.infer_steps_for_dataset(
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)
# print(train_class_weight, 'before make execution')
######################################################################
batch_function = _make_execution_function(
model,
mode,
train_class_weight=train_class_weight,
val_class_weight=val_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,
verbose=verbose,
count_mode=count_mode,
mode=mode)
if mode == ModeKeys.PREDICT:
aggregator = training_utils.OutputsAggregator(True,
steps=steps_per_epoch)
else:
aggregator = training_utils.MetricsAggregator(True,
steps=steps_per_epoch)
should_set_learning_phase = context.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)
print(initial_epoch, mode)
# TODO: mode is a bug?
# https://github.com/tensorflow/tensorflow/blob/r2.2/tensorflow/python/keras/engine/training.py
initial_epoch = model._maybe_load_initial_epoch_from_ckpt(initial_epoch)
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(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.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,
val_class_weight=val_class_weight, ######## HACK
workers=workers,
use_multiprocessing=use_multiprocessing,
max_queue_size=max_queue_size,
callbacks=callbacks,
verbose=0,
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 = dataset_ops.make_one_shot_iterator(original_dataset)
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
def fit(self,
x: tf.data.Dataset = None,
batch_size: int = None,
epochs=1,
callbacks: List[Callback] = None,
validation_data: tf.data.Dataset = None,
initial_epoch=0,
steps_per_epoch: int = None,
validation_steps: int = None,
validation_freq=1,
verbose=1,
**kwargs):
do_validation = (validation_data is not None) and (validation_steps
is not None)
model_checkpoint = get_callback(callbacks, ModelCheckpoint)
tensorboard: Optional[TensorBoard] = get_callback(
callbacks, TensorBoard)
callbacks: CallbackList = configure_callbacks(
callbacks,
model=self,
do_validation=do_validation,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
samples=steps_per_epoch,
verbose=verbose,
mode=ModeKeys.TRAIN)
if model_checkpoint is not None:
model_checkpoint.save_weights_only = True
if (tensorboard is not None) and (tensorboard.update_freq != "epoch"):
tensorboard._samples_seen = initial_epoch * steps_per_epoch
tensorboard._total_batches_seen = initial_epoch * steps_per_epoch
train_aggregator = MetricsAggregator(use_steps=True,
num_samples=steps_per_epoch)
val_aggregator = MetricsAggregator(use_steps=True,
num_samples=validation_steps)
callbacks.on_train_begin()
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
epoch_logs = {}
callbacks.on_epoch_begin(epoch, epoch_logs)
# region Training
for step, batch in zip(range(steps_per_epoch), x):
batch_logs = {'batch': step, 'size': 1}
callbacks.on_batch_begin(step, batch_logs)
batch_outputs = self.train_step(batch)
if not (isinstance(batch_outputs, tuple)
or isinstance(batch_outputs, list)):
batch_outputs = [batch_outputs]
batch_outputs = [output.numpy() for output in batch_outputs]
if step == 0:
train_aggregator.create(batch_outputs)
train_aggregator.aggregate(batch_outputs)
batch_logs = make_logs(self, batch_logs, batch_outputs,
ModeKeys.TRAIN)
callbacks.on_batch_end(step, batch_logs)
if callbacks.model.stop_training:
break
train_aggregator.finalize()
epoch_logs = make_logs(self, epoch_logs, train_aggregator.results,
ModeKeys.TRAIN)
# endregion
# region Validation
if do_validation and (epoch % validation_freq) == 0:
for val_step, batch in zip(range(validation_steps),
validation_data):
val_results = self.compute_loss(batch)
if not (isinstance(val_results, tuple)
or isinstance(val_results, list)):
val_results = [val_results]
val_results = [output.numpy() for output in val_results]
if val_step == 0:
val_aggregator.create(val_results)
val_aggregator.aggregate(val_results)
val_aggregator.finalize()
epoch_logs = make_logs(self,
epoch_logs,
val_aggregator.results,
ModeKeys.TRAIN,
prefix="val_")
# endregion
callbacks.on_epoch_end(epoch, epoch_logs)
callbacks.on_train_end()
return self.history
def _launch_evaluate(self) -> Any:
validation_data = self.validation_data
steps = None
if isinstance(validation_data, tf.keras.utils.Sequence):
# Calculate the length of our validation shard.
steps = len(validation_data)
if self.context.distributed.get_size() > 1:
size = self.context.distributed.get_size()
rank = self.context.distributed.get_rank()
steps = steps // size + (1 if steps % size > rank else 0)
# Handle args from fit(): shuffle, workers, use_multiprocessing, and max_queue_size.
enqueuer = keras._build_enqueuer(
sequence=validation_data,
workers=self.context._fit_workers,
use_multiprocessing=self.context._fit_use_multiprocessing,
max_queue_size=self.context._fit_max_queue_size,
shard_rank=self.context.distributed.get_rank(),
num_shards=self.context.distributed.get_size(),
repeat=False,
shuffle=False,
shuffle_seed=0,
prior_batches_trained=0,
)
enqueuer.start()
self.enqueuers.append(enqueuer)
validation_data = enqueuer.data()
if isinstance(validation_data, tf.data.Dataset):
# Handle validation_steps, which in Keras only applies to tf.data.Datasets.
steps = self.context._fit_validation_steps
# Starting in TF 2.2 users may define custom test_step() that do
# not use the model metrics.
use_model_metrics = not (
version.parse(tf.__version__) >= version.parse("2.2.0")
and is_tf2_enabled() and tf.executing_eagerly())
evaluate_kwargs = {} if use_model_metrics else {"return_dict": True}
if self.env.test_mode:
steps = 1
metrics_values = self.model.evaluate(
validation_data,
callbacks=self.callback_list,
steps=steps,
verbose=0,
workers=0,
**evaluate_kwargs,
)
logging.debug(
f"Worker finished model.evaluate() with metrics: {metrics_values}."
)
# Clean up the enqueuer if we started one.
if isinstance(self.validation_data, tf.keras.utils.Sequence):
enqueuer.stop()
self.enqueuers.remove(enqueuer)
# A special side-effect of converting the keras sequence to a generator and passing
# steps explicitly is that keras will exit our generator after N steps and the
# Sequence.on_epoch_end() that normally runs after the last yield won't run at all
# because the fit loop will call next() exactly `steps` times. So we try to match the
# exact keras behavior by manually calling on_epoch_end() here.
self.validation_data.on_epoch_end()
# If the model was compiled with metrics=None, metrics_value will be a single value.
if not isinstance(metrics_values, (tuple, list, dict)):
metrics_values = (metrics_values, )
if use_model_metrics:
metrics = make_logs(self.model, {},
metrics_values,
ModeKeys.TEST,
prefix="val_")
else:
check.is_instance(metrics_values, dict)
metrics = {f"val_{k}": v for k, v in metrics_values.items()}
return metrics
def fit(self,
model,
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_freq=1,
**kwargs):
batch_size = model._validate_or_infer_batch_size(
batch_size, steps_per_epoch, x)
strategy = _get_distribution_strategy(model)
if strategy:
batch_size, steps_per_epoch = dist_utils.process_batch_and_step_size(
strategy, x, batch_size, steps_per_epoch, ModeKeys.TRAIN)
dist_utils.validate_callbacks(input_callbacks=callbacks,
optimizer=model.optimizer)
# Enter tf.distribute.Strategy scope.
scope = dist_utils.distributed_scope(strategy=strategy,
learning_phase=1)
scope.__enter__()
training_data_adapter, validation_adapter = _process_training_inputs(
model,
x,
y,
batch_size=batch_size,
sample_weights=sample_weight,
class_weights=class_weight,
validation_split=validation_split,
steps_per_epoch=steps_per_epoch,
shuffle=shuffle,
validation_data=validation_data,
validation_steps=validation_steps,
distribution_strategy=strategy)
do_validation = (validation_adapter is not None)
if not steps_per_epoch:
steps_per_epoch = training_data_adapter.get_size()
# tf.print('{} on {} steps.'.format(ModeKeys.TRAIN, steps_per_epoch))
training_context = TrainingContext()
initial_epoch = model._maybe_load_initial_epoch_from_ckpt(
initial_epoch, ModeKeys.TRAIN)
_update_sample_weight_mode(model, ModeKeys.TRAIN,
training_data_adapter)
training_function = _make_execution_function(model, ModeKeys.TRAIN)
training_data_iter = None
# Only recreate iterator when the data has a fixed length, which will be
# fully consumed every epoch, or has a unknown length (dataset, generator)
# and will be fully consumed (steps_per_epoch is None)
recreate_training_iterator = (training_data_adapter.get_size()
is not None or steps_per_epoch is None)
if do_validation:
if not validation_steps:
validation_steps = validation_adapter.get_size()
eval_function = _make_execution_function(model, ModeKeys.TEST)
eval_data_iter = None
recreate_eval_iterator = (validation_adapter.get_size() is not None
or validation_steps is None)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
samples=None,
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=ModeKeys.TRAIN)
with training_context.on_start(model, callbacks, verbose,
ModeKeys.TRAIN):
# TODO(scottzhu): Handle TPUStrategy training loop
for epoch in range(initial_epoch, epochs):
if training_context.callbacks.model.stop_training:
break
# Training
with training_context.on_epoch(epoch,
ModeKeys.TRAIN) as epoch_logs:
model.reset_metrics()
if training_data_iter is None or recreate_training_iterator:
training_data_iter = _create_dataset_iterator(
strategy, training_data_adapter.get_dataset())
training_result = run_one_epoch(
model,
training_data_iter,
training_function,
dataset_size=training_data_adapter.get_size(),
strategy=strategy,
steps_per_epoch=steps_per_epoch,
mode=ModeKeys.TRAIN,
training_context=training_context,
current_epoch=epoch)
cbks.make_logs(model, epoch_logs, training_result,
ModeKeys.TRAIN)
# Evaluation
if (do_validation and training_utils.should_run_validation(
validation_freq, epoch)
and not callbacks.model.stop_training):
if eval_data_iter is None or recreate_eval_iterator:
eval_data_iter = _create_dataset_iterator(
strategy, validation_adapter.get_dataset())
eval_context = TrainingContext()
with eval_context.on_start(model,
callbacks,
verbose=0,
mode=ModeKeys.TEST):
with eval_context.on_epoch(epoch, ModeKeys.TEST):
model.reset_metrics()
eval_result = run_one_epoch(
model,
eval_data_iter,
eval_function,
dataset_size=validation_adapter.get_size(),
strategy=strategy,
steps_per_epoch=validation_steps,
mode=ModeKeys.TEST,
training_context=eval_context,
current_epoch=epochs)
cbks.make_logs(model,
epoch_logs,
eval_result,
ModeKeys.TRAIN,
prefix='val_')
if strategy:
scope.__exit__(None, None, None)
return model.history
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.
Arguments:
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: Verbosity mode, 0, 1 or 2
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.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,
(dataset_ops.DatasetV1, dataset_ops.DatasetV2))
# 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.infer_steps_for_dataset(
inputs, steps_per_epoch, epochs=epochs, steps_name=steps_name)
input_iterator = _get_iterator(inputs, model._distribution_strategy)
if mode == ModeKeys.TRAIN:
_print_train_info(inputs, val_inputs, steps_per_epoch, verbose)
# Enter DistributionStrategy scope.
if model._distribution_strategy:
scope = model._distribution_strategy.scope()
scope.__enter__()
# Get step function and loop type.
f = _make_execution_function(model, mode)
use_steps = is_dataset or steps_per_epoch is not None
do_validation = val_inputs is not None
# Convert Eager Tensors to NumPy arrays to support batching/shuffling.
inputs, targets, sample_weights = training_utils. \
convert_eager_tensors_to_numpy((inputs, targets, sample_weights))
# 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)
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
# 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, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)):
val_iterator = _get_iterator(val_inputs, model._distribution_strategy)
val_inputs = _prepare_feed_values(model, val_iterator, val_targets,
val_sample_weights, ModeKeys.TEST)
# 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,
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=mode)
# TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
progbar = training_utils.get_progbar(model, count_mode)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
# 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 K.is_sparse(feed_tensor):
indices_for_conversion_to_dense.append(i)
# Select aggregation method.
if mode == ModeKeys.PREDICT:
aggregator = training_utils.OutputsAggregator(use_steps,
num_samples_or_steps)
else:
aggregator = training_utils.MetricsAggregator(use_steps,
num_samples_or_steps)
if model._compile_distribution and not validation_in_fit:
distributed_training_utils._copy_weights_to_distributed_model(
model, model._distributed_model)
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
progbar.on_train_begin()
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
# Setup work for each epoch
epoch_logs = {}
model.reset_metrics()
if mode == ModeKeys.TRAIN:
callbacks.on_epoch_begin(epoch, epoch_logs)
progbar.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)
progbar.on_batch_begin(step, batch_logs)
# Get outputs.
try:
# `ins` can be callable in DistributionStrategy + eager case.
actual_inputs = ins() if callable(ins) else ins
batch_outs = f(actual_inputs)
except errors.OutOfRangeError:
if not is_dataset:
# We ran out of batches while the user passed an iterator (legacy).
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))
callbacks.model.stop_training = True
else:
# The dataset passed by the user ran out of batches.
# Now we know the cardinality of the dataset.
if step > 0:
steps_per_epoch = step
aggregator.num_samples_or_steps = steps_per_epoch
progbar.params['steps'] = steps_per_epoch
progbar.progbar.target = steps_per_epoch
break
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if model._distribution_strategy:
batch_outs = distributed_training_utils._per_device_aggregate_batch(
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)
progbar.on_batch_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.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.
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)
progbar.on_batch_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)
progbar.on_batch_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.should_run_validation(
validation_freq, epoch) and not callbacks.model.stop_training):
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)
progbar.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)
callbacks._call_end_hook(mode)
if model._distribution_strategy:
if model._compile_distribution and not validation_in_fit:
# TODO(priyag, psv): Copy back metrics to the original model as well?
distributed_training_utils._copy_weights_to_original_model(
model, model._distributed_model, mode)
scope.__exit__(None, None, None)
if mode == ModeKeys.TRAIN:
return model.history
return results
def run_one_epoch(model,
iterator,
execution_function,
dataset_size=None,
strategy=None,
steps_per_epoch=None,
mode=ModeKeys.TRAIN,
training_context=None,
current_epoch=1):
"""Run the execution function with the data from iterator.
Given the dataset iterator and execution function, get the data from iterator
and call it with the execution function to get the result (metric/loss).
It will run for steps_per_epoch or until to the iterator is fully consumed.
Args:
model: The keras model to run.
iterator: the dataset iterator to fetch the data.
execution_function: a tf.function that can be called with data.
dataset_size: the size of iterator, None when unknown.
strategy: the distribution strategy instance from the model.
steps_per_epoch: the number of steps to run for the epoch.
mode: the mode for the current epoch.
training_context: the context that contains callbacks and progress bar.
current_epoch: the epoch number. Used when throw error when the
the iterator is unexpected reach its end.
Returns:
The loss and metric value from the model.
"""
if mode == ModeKeys.PREDICT:
aggregator = training_utils.OutputsAggregator(
use_steps=True, num_samples_or_steps=steps_per_epoch)
else:
aggregator = training_utils.MetricsAggregator(
use_steps=True, num_samples_or_steps=steps_per_epoch)
callbacks = training_context.callbacks
progbar = training_context.progbar
if callbacks.model.stop_training:
return
target_steps = steps_per_epoch or np.inf
step = 0
while step < target_steps:
with training_context.on_batch(step, mode=mode) as batch_logs:
try:
batch_ins = create_batch_inputs(iterator, mode, model,
strategy)
batch_outs = execution_function(batch_ins)
except StopIteration:
# The only acceptable case here is that the input has a unknown
# length, and configured to fully consume it.
if (dataset_size is None and steps_per_epoch is None
and step > 0):
# The input passed by the user ran out of batches.
# Now we know the cardinality of the input(dataset or generator).
steps_per_epoch = step
aggregator.num_samples_or_steps = steps_per_epoch
progbar.params['steps'] = steps_per_epoch
progbar.progbar.target = steps_per_epoch
else:
callbacks.model.stop_training = True
logging.warning(
'Your input ran out of data; interrupting training. '
'Make sure that your dataset or generator can generate at '
'least {} batches. You may need to use the repeat() function '
'when building your dataset.'.format(current_epoch *
steps_per_epoch))
# In either case, break out the loop for training batch.
break
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if strategy:
batch_outs = dist_utils._per_replica_aggregate_batch(
batch_outs, model, mode)
if step == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs)
cbks.make_logs(model, batch_logs, batch_outs, mode)
step += 1
if callbacks.model.stop_training:
break
# End of an epoch.
aggregator.finalize()
results = aggregator.results
return 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,
mode='train',
validation_in_fit=False,
**kwargs):
"""Loop function for arrays of data with modes 'train'/'test'/'predict'.
Arguments:
model: Keras Model instance.
inputs: Either a list of arrays or a dictionary.
targets: List of target 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: Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_inputs: List of input arrays.
val_targets: List 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`.
mode: One of 'train'/'test'/'predict'.
validation_in_fit: if true, then this method is invoked from within
training iteration (for validation). In this case, do not copy weights
when using a tf.distribute.Strategy.
**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['steps']
_validate_arguments(steps_per_epoch, validation_steps, kwargs)
if mode == 'train':
_print_train_info(inputs, val_inputs, steps_per_epoch, verbose)
# Enter DistributionStrategy scope.
if model._distribution_strategy:
scope = model._distribution_strategy.scope()
scope.__enter__()
# Get step function and loop type.
f = _make_execution_function(model, mode)
use_steps = steps_per_epoch is not None
do_validation = val_inputs is not None
# Prepare input data.
ins = _prepare_feed_values(model, inputs, targets, sample_weights, mode)
num_samples_or_steps = _get_num_samples_or_steps(ins, batch_size,
steps_per_epoch)
# 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,
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=mode)
# TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
progbar = training_utils.get_progbar(model, count_mode)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
# 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 K.is_sparse(feed_tensor):
indices_for_conversion_to_dense.append(i)
# Select aggregation method.
if mode == 'predict':
aggregator = training_utils.OutputsAggregator(use_steps,
num_samples_or_steps)
else:
aggregator = training_utils.MetricsAggregator(use_steps,
num_samples_or_steps)
if model._distribution_strategy and not validation_in_fit:
training_distributed._copy_weights_to_distributed_model(
model, model._grouped_model)
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
progbar.on_train_begin()
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
# Setup work for each epoch
epoch_logs = {}
model.reset_metrics()
callbacks.on_epoch_begin(epoch, epoch_logs, mode=mode)
progbar.on_epoch_begin(epoch, epoch_logs)
if use_steps:
# Step-wise loop.
for step in range(steps_per_epoch):
batch_logs = {'batch': step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
progbar.on_batch_begin(step, batch_logs)
# Get outputs.
try:
# `ins` can be callable in DistributionStrategy + eager case.
actual_inputs = ins() if callable(ins) else ins
batch_outs = f(actual_inputs)
except errors.OutOfRangeError:
logging.warning(
'Your dataset iterator ran out of data; '
'interrupting training. Make sure that your dataset '
'can generate at least `steps_per_epoch * epochs` '
'batches (in this case, %d batches). You may need to'
'use the repeat() function when building your '
'dataset.' % steps_per_epoch * epochs)
break
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if model._distribution_strategy:
batch_outs = training_distributed._per_device_aggregate_batch(
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)
progbar.on_batch_end(step, batch_logs)
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.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.
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)
progbar.on_batch_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)
progbar.on_batch_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 epoch during training.
if do_validation and not callbacks.model.stop_training:
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='test',
validation_in_fit=True)
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 == 'train':
# Epochs only apply to `fit`.
callbacks.on_epoch_end(epoch, epoch_logs, mode=mode)
progbar.on_epoch_end(epoch, epoch_logs)
callbacks._call_end_hook(mode)
if model._distribution_strategy:
# TODO(priyag, psv): Copy back metrics to the original model as well?
if not validation_in_fit:
training_distributed._copy_weights_to_original_model(
model, model._grouped_model, mode)
scope.__exit__(None, None, None)
if mode == 'train':
return model.history
return results
def experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU tf.distribute.Strategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = dist_utils.get_iterator(dataset, current_strategy)
scope = dist_utils.distributed_scope(strategy=current_strategy,
learning_phase=0)
scope.__enter__()
out_labels = model.metrics_names
def _test_step_fn(inputs):
"""A fn that returns output of single test step."""
if isinstance(inputs, (tuple, list)) and len(inputs) == 2:
inputs, targets = inputs
else:
targets = None
(distribution_strategy_context.get_replica_context().merge_call(
_build_model, args=(model, mode, inputs, targets)))
(_, outputs, updates, _) = _per_replica_execution_function(
dist_utils.get_distributed_model(model, mode), mode)
with ops.control_dependencies([updates]):
return [array_ops.identity(out) for out in outputs]
test_input_data = iterator.get_next()
per_replica_outputs = current_strategy.run(_test_step_fn,
args=(test_input_data, ))
output_tensors = {}
for label, output in zip(out_labels, per_replica_outputs):
if label == 'loss':
reduce_op = ds_reduce_util.ReduceOp.SUM
else:
# We reduce all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
reduce_op = ds_reduce_util.ReduceOp.MEAN
output_tensors[label] = current_strategy.reduce(reduce_op,
output,
axis=None)
test_op = control_flow_ops.group(list(output_tensors.values()))
if verbose >= 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
dist_utils._copy_weights_to_distributed_model(model, mode)
dist_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
outs = [0.] * len(model.metrics_names)
if steps is not None:
target_steps = steps
else:
raise ValueError(
'Number of steps could not be inferred from the data, '
'please pass the steps argument.')
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.batch_get_value([test_op, output_tensors])
except errors.OutOfRangeError:
warning_msg = (
'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps))
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
target_steps = current_step
break
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose == 1:
progbar.update(current_step + 1)
current_step += 1
if verbose >= 1:
# Progress bar finishes at the end.
progbar.update(target_steps)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (target_steps)
if len(outs) == 1:
return outs[0]
return outs
def experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset, current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_eval_function(model):
model._make_eval_function()
return (model._eval_function.inputs, model._eval_function.outputs,
model._eval_function.updates_op,
model._eval_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_eval_function."""
inputs, targets = inputs
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model, current_strategy, mode=mode, inputs=inputs, targets=targets)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, mode, inputs, targets)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.extended.call_for_each_replica(
_per_device_eval_function,
args=(distributed_training_utils.get_distributed_model(
model, ModeKeys.TEST),))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
combined_fn = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
for label, output in zip(model.metrics_names, combined_fn.outputs):
if label == 'loss':
reduce_op = ds_reduce_util.ReduceOp.SUM
else:
# We reduce all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
reduce_op = ds_reduce_util.ReduceOp.MEAN
ctx.set_last_step_output(label, output, reduce_op)
return combined_fn.updates_op
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name in model.metrics_names[1:]:
tensor = model._all_stateful_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
# TODO(priyag): Use steps_per_run when we use new metrics as they will
# allow handling metric computation at each step using variables.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
test_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
assert steps is not None
outs = [0.] * len(model.metrics_names)
for step in range(steps):
batch_logs = {'batch': step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
_, batch_outs = K.get_session().run([test_op, output_tensors])
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', step, batch_logs)
if verbose >= 1:
progbar.update(step + 1)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (steps)
if len(outs) == 1:
return outs[0]
return outs
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,
(dataset_ops.DatasetV2, dataset_ops.DatasetV1))
if is_dataset:
original_dataset = data
if steps_per_epoch is None:
reset_dataset_after_each_epoch = True
steps_per_epoch = training_utils.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.OutputsAggregator(True,
steps=steps_per_epoch)
else:
aggregator = training_utils.MetricsAggregator(True,
steps=steps_per_epoch)
should_set_learning_phase = context.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(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.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 = dataset_ops.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
def fit(
self, model, 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_freq=1,
max_queue_size=10, workers=1, use_multiprocessing=False, **kwargs):
batch_size = model._validate_or_infer_batch_size(
batch_size, steps_per_epoch, x)
strategy = _get_distribution_strategy(model)
batch_size, steps_per_epoch = dist_utils.process_batch_and_step_size(
strategy,
x,
batch_size,
steps_per_epoch,
ModeKeys.TRAIN,
validation_split=validation_split)
dist_utils.validate_callbacks(input_callbacks=callbacks,
optimizer=model.optimizer)
# Enter tf.distribute.Strategy scope.
with strategy.scope():
training_data_adapter, validation_adapter = _process_training_inputs(
model,
x,
y,
batch_size=batch_size,
epochs=epochs,
sample_weights=sample_weight,
class_weights=class_weight,
validation_split=validation_split,
steps_per_epoch=steps_per_epoch,
shuffle=shuffle,
validation_data=validation_data,
validation_steps=validation_steps,
distribution_strategy=strategy,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing)
total_samples = _get_total_number_of_samples(training_data_adapter)
use_sample = total_samples is not None
do_validation = (validation_adapter is not None)
recreate_training_iterator = (
training_data_adapter.should_recreate_iterator(steps_per_epoch))
if not steps_per_epoch:
# TODO(b/139762795): Add step inference for when steps is None to
# prevent end of sequence warning message.
steps_per_epoch = training_data_adapter.get_size()
# tf.print('{} on {} steps.'.format(ModeKeys.TRAIN, steps_per_epoch))
training_context = TrainingContext()
training_dataset = training_data_adapter.get_dataset()
# Raise an error if steps_per_epoch isn't specified but the dataset
# is infinite.
# TODO(scottzhu): This check should probably happen in the adapter
inferred_steps = training_utils.infer_steps_for_dataset(
model,
training_dataset,
steps_per_epoch,
steps_name='steps_per_epoch',
epochs=0)
steps_per_epoch = (
inferred_steps if steps_per_epoch is None else steps_per_epoch)
training_dataset = strategy.experimental_distribute_dataset(
training_dataset)
training_function = training_v2_utils._get_or_make_execution_function(
model, ModeKeys.TRAIN)
training_data_iter = None
if do_validation:
validation_dataset = validation_adapter.get_dataset()
if not validation_steps:
# Raise an error if validation_steps isn't specified but the
# validation dataset is infinite.
validation_steps = (
validation_adapter.get_size() or
training_utils.infer_steps_for_dataset(
model,
validation_dataset,
validation_steps,
steps_name='validation_steps'))
eval_function = training_v2_utils._get_or_make_execution_function(
model, ModeKeys.TEST)
eval_data_iter = None
validation_dataset = strategy.experimental_distribute_dataset(
validation_dataset)
val_total_samples = _get_total_number_of_samples(validation_adapter)
else:
val_total_samples = None
if verbose and (total_samples or steps_per_epoch):
_print_train_info(total_samples, steps_per_epoch, val_total_samples,
validation_steps)
training_callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
samples=total_samples or steps_per_epoch,
count_mode='samples' if use_sample else 'steps',
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=ModeKeys.TRAIN)
with training_context.on_start(model, training_callbacks, use_sample,
verbose, ModeKeys.TRAIN):
initial_epoch = model._maybe_load_initial_epoch_from_ckpt(
initial_epoch, ModeKeys.TRAIN)
for epoch in range(initial_epoch, epochs):
if training_context.callbacks.model.stop_training:
break
# Training
with training_context.on_epoch(epoch, ModeKeys.TRAIN) as epoch_logs:
model.reset_metrics()
if training_data_iter is None or recreate_training_iterator:
if (training_data_iter is not None and
distribution_strategy_context.has_strategy()):
# TODO(kaftan): remove this when MultiDeviceIterator is a
## compositetensor (unless this is more efficient)
training_data_iter._initializer # pylint: disable=pointless-statement
else:
training_data_iter = iter(training_dataset)
training_result = run_one_epoch(
model,
training_data_iter,
training_function,
dataset_size=training_data_adapter.get_size(),
batch_size=training_data_adapter.batch_size(),
strategy=strategy,
steps_per_epoch=steps_per_epoch,
num_samples=total_samples,
mode=ModeKeys.TRAIN,
training_context=training_context,
total_epochs=epochs)
cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
# In the case of steps_per_epoch = None, the final cardinality will
# be determined when the inputs are fully consumed (eg dataset or
# generator). Update the steps_per_epoch to the new value.
if (steps_per_epoch is None
and training_context.progbar.progbar.target is not None):
steps_per_epoch = training_context.progbar.progbar.target
# Evaluation
if (do_validation and
training_utils.should_run_validation(validation_freq, epoch) and
not training_callbacks.model.stop_training):
if (eval_data_iter is not None and
distribution_strategy_context.has_strategy()):
# TODO(kaftan): remove this when MultiDeviceIterator is a
## compositetensor (unless this is more efficient)
eval_data_iter._initializer # pylint: disable=pointless-statement
else:
eval_data_iter = iter(validation_dataset)
validation_callbacks = cbks.configure_callbacks(
training_callbacks,
model,
batch_size=batch_size,
epochs=1,
steps_per_epoch=validation_steps,
samples=val_total_samples or validation_steps,
count_mode='samples' if use_sample else 'steps',
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=ModeKeys.TEST)
eval_context = TrainingContext()
with eval_context.on_start(
model,
validation_callbacks,
use_sample,
verbose=0,
mode=ModeKeys.TEST):
with eval_context.on_epoch(epoch, ModeKeys.TEST):
model.reset_metrics()
eval_result = run_one_epoch(
model,
eval_data_iter,
eval_function,
dataset_size=validation_adapter.get_size(),
batch_size=validation_adapter.batch_size(),
strategy=strategy,
steps_per_epoch=validation_steps,
num_samples=val_total_samples,
mode=ModeKeys.TEST,
training_context=eval_context,
total_epochs=1)
cbks.make_logs(model, epoch_logs, eval_result, ModeKeys.TEST,
prefix='val_')
return model.history
def ndarray_fit(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):
"""
Fitting procedure for a model given numpy iterables
"""
# TODO: build support for TF datasets
do_validation = val_inputs is not None
is_dataset = isinstance(inputs,
(dataset_ops.DatasetV1, dataset_ops.DatasetV2))
if "steps" in kwargs:
steps_per_epoch = kwargs.pop("steps")
elif kwargs:
raise ValueError("Unkown argument {}".format(kwargs))
if not is_dataset:
num_samples_or_steps = np.ceil(inputs.shape[0] / batch_size)
else:
num_samples_or_steps = steps_per_epoch
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,
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=mode)
count_mode = "samples"
progbar = training_utils.get_progbar(model, count_mode)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
progbar.on_train_begin()
if targets is None:
targets = []
else:
if not isinstance(targets, list):
targets = [targets]
aggregator = training_utils.MetricsAggregator(
True,
num_samples=None if steps_per_epoch else num_samples_or_steps,
steps=steps_per_epoch)
for e in range(initial_epoch, epochs):
epoch_logs = {}
progbar.on_epoch_begin(e, epoch_logs)
model.reset_metrics()
if mode == ModeKeys.TRAIN:
callbacks.on_epoch_begin(e, epoch_logs)
bm = BatchManager(inputs.shape[0], batch_size, shuffle=shuffle)
posterior_weight = tf.constant(inputs.shape[0], model.dtype)
for batch_index, batch in enumerate(bm):
batch_logs = {"batch": batch_index, "size": 1}
progbar.on_batch_begin(batch_index, batch_logs)
callbacks._call_batch_hook(mode, "begin", batch_index, batch_logs)
# TODO: rewrite to not do boolean check each loop
if targets:
ybatch = targets[0][batch]
xbatch = inputs[batch]
batch_outs = model.compute_grads(xbatch, ybatch, posterior_weight)
if mode == ModeKeys.TRAIN:
model.optimizer.apply_gradients(
zip(model.grads, model.trainable_variables))
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs)
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
progbar.on_batch_end(batch_index, batch_logs)
callbacks._call_batch_hook(mode, "end", batch_index, batch_logs)
aggregator.finalize()
results = aggregator.results
epoch_logs = cbks.make_logs(model, epoch_logs, results, mode)
progbar.on_epoch_end(e, epoch_logs)
if mode == ModeKeys.TRAIN:
# Epochs only apply to `fit`.
callbacks.on_epoch_end(e, epoch_logs)
if len(results) == 1:
results = results[0]
callbacks._call_end_hook(mode)
print("")
if mode == ModeKeys.TRAIN:
return model.history
return results
def experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
steps = training_utils.infer_steps_for_dataset(dataset, steps,
steps_name='steps')
dataset_fully_shaped = (distributed_training_utils.
is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(padding_handler.padding_mask,
padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset, current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _predict_step_fn(inputs):
"""A fn that returns output of single prediction step."""
(distribution_strategy_context.get_replica_context().merge_call(
_build_model, args=(model, mode, inputs)))
(_, outputs, updates, _) = (
_per_device_execution_function(
distributed_training_utils.get_distributed_model(model, mode),
mode))
with ops.control_dependencies([updates]):
return outputs
# TODO(hongjunchoi): When numpy array is passed as an input to `predict()`
# use numpy arrays directly to avoid cumulating unnecessary input pipeline
# ops.
predict_input_data = iterator.get_next()
per_replica_outputs = current_strategy.experimental_run_v2(
_predict_step_fn, args=(predict_input_data,))
output_tensors = distributed_training_utils.flatten_perdevice_values(
current_strategy, per_replica_outputs)
if verbose >= 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
num_model_outputs = len(model.output_names)
unconcatenated_outs = [[] for _ in range(num_model_outputs)]
if steps is not None:
target_steps = steps
else:
raise ValueError('Number of steps could not be infered from the data, '
'please pass the steps argument.')
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
predict_ops = control_flow_ops.group(output_tensors)
_, batch_outs = K.batch_get_value([predict_ops, output_tensors])
except errors.OutOfRangeError:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
break
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i in range(num_model_outputs):
output_start_index = i * current_strategy.num_replicas_in_sync
output_end_index = (
output_start_index + current_strategy.num_replicas_in_sync)
single_model_output = batch_outs[output_start_index:output_end_index]
unconcatenated_outs[i].extend(single_model_output)
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose == 1:
progbar.update(current_step + 1)
current_step += 1
if verbose >= 1:
# Progress bar finishes at the end.
progbar.update(current_step)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
def experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
dataset_fully_shaped = (distributed_training_utils.
is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(padding_handler.padding_mask,
padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset, current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs,
model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_predict_function."""
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model, current_strategy, mode, inputs=inputs)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, mode, inputs)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.extended.call_for_each_replica(
_per_device_predict_function,
args=(distributed_training_utils.get_distributed_model(
model, ModeKeys.PREDICT),))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
combined_fn = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
assert steps is not None
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = [[] for _ in model.outputs]
for step in range(steps):
batch_logs = {'batch': step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
_, batch_outs = K.get_session().run([predict_op, output_tensors])
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', step, batch_logs)
if verbose >= 1:
progbar.update(step + 1)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
def experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
steps = training_utils.infer_steps_for_dataset(dataset,
steps,
steps_name='steps')
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_eval_function(model):
model._make_eval_function()
return (model._eval_function.inputs, model._eval_function.outputs,
model._eval_function.updates_op,
model._eval_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_eval_function."""
inputs, targets = inputs
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model,
current_strategy,
mode=mode,
inputs=inputs,
targets=targets)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, mode, inputs, targets)
(grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args
) = current_strategy.extended.call_for_each_replica(
_per_device_eval_function,
args=(distributed_training_utils.get_distributed_model(
model, ModeKeys.TEST), ))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
combined_fn = K.function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
for label, output in zip(model.metrics_names, combined_fn.outputs):
if label == 'loss':
reduce_op = ds_reduce_util.ReduceOp.SUM
else:
# We reduce all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
reduce_op = ds_reduce_util.ReduceOp.MEAN
ctx.set_last_step_output(label, output, reduce_op)
return combined_fn.updates_op
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name in model.metrics_names[1:]:
tensor = model._all_stateful_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
# TODO(priyag): Use steps_per_run when we use new metrics as they will
# allow handling metric computation at each step using variables.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn,
iterator,
iterations=1,
initial_loop_values=initial_loop_values)
test_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(
model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
outs = [0.] * len(model.metrics_names)
if steps is not None:
target_steps = steps
else:
target_steps = np.inf
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.get_session().run([test_op, output_tensors])
except errors.OutOfRangeError:
if steps is not None:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
else:
warning_msg = 'Number of steps ran: {} steps'.format(
current_step)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
target_steps = current_step
break
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose >= 1:
progbar.update(current_step + 1)
current_step += 1
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (target_steps)
if len(outs) == 1:
return outs[0]
return outs
def _model_iteration(
self, model, mode, x=None, y=None, batch_size=None, verbose=1,
sample_weight=None, steps=None, callbacks=None, max_queue_size=10,
workers=1, use_multiprocessing=False, **kwargs):
batch_size = model._validate_or_infer_batch_size(
batch_size, steps, x)
strategy = _get_distribution_strategy(model)
batch_size, steps = dist_utils.process_batch_and_step_size(
strategy, x, batch_size, steps, mode)
dist_utils.validate_callbacks(input_callbacks=callbacks,
optimizer=model.optimizer)
# Enter tf.distribute.Strategy scope.
with strategy.scope():
adapter = _process_inputs(
model,
mode,
x,
y,
batch_size=batch_size,
sample_weights=sample_weight,
steps=steps,
distribution_strategy=strategy,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing)
total_samples = _get_total_number_of_samples(adapter)
use_sample = total_samples is not None
dataset = adapter.get_dataset()
if not steps:
# Raise an error if `steps` isn't specified but the dataset
# is infinite.
steps = adapter.get_size() or training_utils.infer_steps_for_dataset(
model, dataset, steps, steps_name='steps')
# tf.print('{} on {} steps.'.format(ModeKeys.TRAIN, steps_per_epoch))
training_context = TrainingContext()
dataset = strategy.experimental_distribute_dataset(dataset)
execution_function = training_v2_utils._get_or_make_execution_function(
model, mode)
data_iterator = iter(dataset)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
batch_size=batch_size,
epochs=1,
steps_per_epoch=steps,
samples=use_sample,
count_mode='samples' if use_sample else 'steps',
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=mode)
with training_context.on_start(
model, callbacks, use_sample, verbose, mode):
with training_context.on_epoch(0, mode) as epoch_logs:
model.reset_metrics()
result = run_one_epoch(
model,
data_iterator,
execution_function,
dataset_size=adapter.get_size(),
batch_size=adapter.batch_size(),
strategy=strategy,
steps_per_epoch=steps,
num_samples=total_samples,
mode=mode,
training_context=training_context,
total_epochs=1)
cbks.make_logs(model, epoch_logs, result, mode)
if len(result) == 1:
result = result[0]
return result
def experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
steps = training_utils.infer_steps_for_dataset(dataset,
steps,
steps_name='steps')
dataset_fully_shaped = (
distributed_training_utils.is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(
dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(
padding_handler.padding_mask, padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs, model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_predict_function."""
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model, current_strategy, mode, inputs=inputs)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, mode, inputs)
(grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args
) = current_strategy.extended.call_for_each_replica(
_per_device_predict_function,
args=(distributed_training_utils.get_distributed_model(
model, ModeKeys.PREDICT), ))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
combined_fn = K.function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn,
iterator,
iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(
model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = [[] for _ in model.outputs]
if steps is not None:
target_steps = steps
else:
target_steps = np.inf
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.get_session().run([predict_op, output_tensors])
except errors.OutOfRangeError:
if steps is not None:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
else:
warning_msg = 'Number of steps ran: {} steps'.format(
current_step)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
break
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose >= 1:
progbar.update(current_step + 1)
current_step += 1
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
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: Verbosity mode, 0, 1 or 2.
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.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, (dataset_ops.DatasetV2, dataset_ops.DatasetV1))
if is_dataset:
original_dataset = data
if steps_per_epoch is None:
reset_dataset_after_each_epoch = True
steps_per_epoch = training_utils.infer_steps_for_dataset(
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,
verbose=0, # Handle ProgBar as part of Callbacks once hooks are ready.
mode=mode)
# TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
progbar = training_utils.get_progbar(model, count_mode)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
if mode == ModeKeys.PREDICT:
aggregator = training_utils.OutputsAggregator(True, steps_per_epoch)
else:
aggregator = training_utils.MetricsAggregator(True, steps_per_epoch)
should_set_learning_phase = context.executing_eagerly() and model.run_eagerly
if should_set_learning_phase:
old_learning_phase = backend.learning_phase()
backend.set_eager_learning_phase(1 if mode == ModeKeys.TRAIN else 0)
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
progbar.on_train_begin()
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)
progbar.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, mode)
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.num_samples_or_steps = steps_per_epoch
if mode == ModeKeys.TRAIN:
progbar.params['steps'] = steps_per_epoch
progbar.progbar.target = 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(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)
progbar.on_batch_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)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
# 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)
progbar.on_batch_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.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=0,
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)
progbar.on_epoch_end(epoch, epoch_logs)
# Recreate dataset iterator for the next epoch.
if reset_dataset_after_each_epoch and epoch < epochs - 1:
generator = dataset_ops.make_one_shot_iterator(original_dataset)
callbacks._call_end_hook(mode)
if enqueuer is not None:
enqueuer.stop()
if should_set_learning_phase:
backend.set_eager_learning_phase(old_learning_phase)
if mode == ModeKeys.TRAIN:
return model.history
return 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.
Arguments:
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: Verbosity mode, 0, 1 or 2
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.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,
(dataset_ops.DatasetV1, dataset_ops.DatasetV2))
# 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.infer_steps_for_dataset(
inputs, steps_per_epoch, epochs=epochs, steps_name=steps_name)
input_iterator = _get_iterator(inputs, model._distribution_strategy)
if mode == ModeKeys.TRAIN:
_print_train_info(inputs, val_inputs, steps_per_epoch, verbose)
# Enter DistributionStrategy scope.
if model._distribution_strategy:
scope = distributed_training_utils.distributed_scope(
strategy=model._distribution_strategy,
learning_phase=(1 if mode == ModeKeys.TRAIN else 0))
scope.__enter__()
# Get step function and loop type.
f = _make_execution_function(model, mode)
use_steps = is_dataset or steps_per_epoch is not None
do_validation = val_inputs is not None
# Convert Eager Tensors to NumPy arrays to support batching/shuffling.
inputs, targets, sample_weights = training_utils. \
convert_eager_tensors_to_numpy((inputs, targets, sample_weights))
# 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)
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
# 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, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)):
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.infer_steps_for_dataset(
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)
# 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,
verbose=0, # Handle ProgBarLogger separately in this loop.
mode=mode)
# TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
progbar = training_utils.get_progbar(model, count_mode)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
# 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 K.is_sparse(feed_tensor):
indices_for_conversion_to_dense.append(i)
# Select aggregation method.
if mode == ModeKeys.PREDICT:
aggregator = training_utils.OutputsAggregator(use_steps,
num_samples_or_steps)
else:
aggregator = training_utils.MetricsAggregator(use_steps,
num_samples_or_steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
progbar.on_train_begin()
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
# Setup work for each epoch
epoch_logs = {}
model.reset_metrics()
if mode == ModeKeys.TRAIN:
callbacks.on_epoch_begin(epoch, epoch_logs)
progbar.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)
progbar.on_batch_begin(step, batch_logs)
# Get outputs.
try:
# `ins` can be callable in DistributionStrategy + eager case.
actual_inputs = ins() if callable(ins) else ins
batch_outs = f(actual_inputs)
except 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.num_samples_or_steps = steps_per_epoch
if mode == ModeKeys.TRAIN:
progbar.params['steps'] = steps_per_epoch
progbar.progbar.target = 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._per_device_aggregate_batch(
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)
progbar.on_batch_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.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.
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)
progbar.on_batch_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)
progbar.on_batch_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.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._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)
progbar.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)
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._copy_weights_to_original_model(model, mode)
scope.__exit__(None, None, None)
if mode == ModeKeys.TRAIN:
return model.history
return results
