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 dist_utils.distributed_scope(strategy=strategy, learning_phase=1):
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)
training_dataset = training_data_adapter.get_dataset()
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
recreate_eval_iterator = (validation_adapter.get_size()
is not None
or validation_steps is None)
validation_dataset = validation_adapter.get_dataset()
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=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:
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(),
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:
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)
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_')
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)
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()
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,
current_epoch=1)
cbks.make_logs(model, epoch_logs, result, mode)
if len(result) == 1:
result = result[0]
return result
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 = 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."""
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(
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,
axis=None)
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 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
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_replica_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_per_replica_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 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.infer_steps_for_dataset(
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.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
# 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)
# `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.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)
# Get num steps for printing.
val_samples_or_steps = validation_steps
else:
# Get num samples for printing.
val_samples_or_steps = val_inputs and 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,
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=None if steps_per_epoch else num_samples_or_steps,
steps=steps_per_epoch)
else:
aggregator = training_utils.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._copy_weights_to_distributed_model(
model, mode)
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
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 tf.distribute.Strategy + eager case.
if not callable(ins) or (
model._distribution_strategy
and not distributed_training_utils.
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
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_replica_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.
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)
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
def experimental_tpu_fit_loop(model,
dataset,
epochs=100,
verbose=1,
callbacks=None,
initial_epoch=0,
steps_per_epoch=None,
val_dataset=None,
validation_steps=None,
validation_freq=1):
"""Fit loop for training with TPU tf.distribute.Strategy.
Arguments:
model: Keras Model instance.
dataset: Dataset that returns inputs and targets
epochs: Number of times to iterate over the data
verbose: Integer, Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
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`.
val_dataset: Dataset for validation data.
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.
Returns:
Returns `None`.
Raises:
ValueError: in case of invalid arguments.
"""
mode = ModeKeys.TRAIN
# TODO(fchollet): add support for `steps_per_epoch=None` in TPU loops.
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
steps_per_epoch = training_utils.infer_steps_for_dataset(
dataset, steps_per_epoch, epochs, steps_name='steps_per_epoch')
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=1)
scope.__enter__()
out_labels = model.metrics_names or []
step_fn = _make_train_step_fn(model, ModeKeys.TRAIN, 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_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
if steps_per_epoch is not None:
iteration_value = min(steps_per_epoch,
current_strategy.extended.steps_per_run)
else:
raise ValueError('Number of steps could not be infered from the data, '
'please pass the steps_per_epoch argument.')
steps_per_run = K.variable(value=iteration_value,
dtype='int32',
name='steps_per_run')
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn,
iterator,
iterations=steps_per_run,
initial_loop_values=initial_loop_values)
train_op = ctx.run_op
output_tensors = ctx.last_step_outputs
do_validation = bool(validation_steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(
model, mode)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=do_validation,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
verbose=verbose,
count_mode='steps',
mode=mode)
# Calculate the steps each time on the device.
steps_to_run = (
[current_strategy.extended.steps_per_run] *
(steps_per_epoch // current_strategy.extended.steps_per_run))
if steps_per_epoch % current_strategy.extended.steps_per_run:
steps_to_run.append(steps_per_epoch %
current_strategy.extended.steps_per_run)
target_steps = len(steps_to_run)
callbacks._call_begin_hook(mode)
initial_epoch = model._maybe_load_initial_epoch_from_ckpt(
initial_epoch, mode)
for epoch in range(initial_epoch, epochs):
distributed_training_utils._reset_metrics(model)
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
step_index = 0
prev_step_count = None
current_step = 0
while current_step < target_steps:
step_count = steps_to_run[current_step]
batch_logs = {
'batch': step_index,
'size': 1,
'num_steps': step_count
}
callbacks._call_batch_hook(mode, 'begin', step_index, batch_logs)
if prev_step_count is None or step_count != prev_step_count:
steps_per_run.load(step_count, K.get_session())
prev_step_count = step_count
try:
_, outputs = K.batch_get_value([train_op, output_tensors])
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).' % steps_per_epoch *
epochs)
break
batch_logs.update(outputs)
callbacks._call_batch_hook(mode, 'end', step_index, batch_logs)
step_index = step_index + step_count
current_step += 1
if callbacks.model.stop_training:
break
if (do_validation and training_utils.should_run_validation(
validation_freq, epoch)):
logging.info('Running validation at fit epoch: %s', epoch)
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_outs = experimental_tpu_test_loop( # pylint: disable=undefined-variable
model,
val_dataset,
steps=validation_steps,
verbose=verbose,
callbacks=callbacks)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for label, val_out in zip(out_labels, val_outs):
epoch_logs['val_' + label] = val_out
callbacks.on_epoch_end(epoch, epoch_logs)
if callbacks.model.stop_training:
break
callbacks._call_end_hook(mode)
if model._compile_distribution:
# Copy the weights back from the replicated model to the original model.
distributed_training_utils._copy_weights_to_original_model(
model, ModeKeys.TRAIN)
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 = 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,
val_inputs=val_inputs,
val_targets=val_targets,
val_sample_weights=val_sample_weights,
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):
# Reset stateful metrics
for m in model.stateful_metric_functions:
m.reset_states()
# Update callbacks
callbacks.on_epoch_begin(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:
outs = f(ins)
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
callbacks.on_batch_end(step_index, batch_logs)
if callbacks.model.stop_training:
break
if do_validation:
val_outs = test_loop(
model,
val_inputs,
val_targets,
sample_weights=val_sample_weights,
steps=validation_steps,
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
else:
# Sample-wise fit loop.
if shuffle == 'batch':
index_array = training_utils.batch_shuffle(index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = make_batches(num_train_samples, batch_size)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
try:
if 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".')
batch_logs = {}
batch_logs['batch'] = batch_index
batch_logs['size'] = len(batch_ids)
callbacks.on_batch_begin(batch_index, batch_logs)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
outs = f(ins_batch)
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)
if callbacks.model.stop_training:
break
if batch_index == len(batches) - 1: # Last batch.
if do_validation:
val_outs = test_loop(
model,
val_inputs,
val_targets,
sample_weights=val_sample_weights,
batch_size=batch_size,
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
callbacks.on_epoch_end(epoch, epoch_logs)
if callbacks.model.stop_training:
break
callbacks.on_train_end()
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 tf.distribute.Strategy 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__()
model._update_sample_weight_modes(sample_weights=sample_weights)
# 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)
# `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
# 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()
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 = {}
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 tf.distribute.Strategy + 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_replica_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
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_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 experimental_tpu_fit_loop(model,
dataset,
epochs=100,
verbose=1,
callbacks=None,
initial_epoch=0,
steps_per_epoch=None,
val_dataset=None,
validation_steps=None,
validation_freq=1):
"""Fit loop for training with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset that returns inputs and targets
epochs: Number of times to iterate over the data
verbose: Integer, Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
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`.
val_dataset: Dataset for validation data.
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.
Returns:
Returns `None`.
Raises:
ValueError: in case of invalid arguments.
"""
mode = ModeKeys.TRAIN
# TODO(fchollet): add support for `steps_per_epoch=None` in TPU loops.
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset, current_strategy)
steps_per_epoch = training_utils.infer_steps_for_dataset(
dataset, steps_per_epoch, epochs, steps_name='steps_per_epoch')
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=1)
scope.__enter__()
out_labels = model.metrics_names or []
step_fn = _make_train_step_fn(model, ModeKeys.TRAIN, 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_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
if steps_per_epoch is not None:
iteration_value = min(steps_per_epoch,
current_strategy.extended.steps_per_run)
else:
raise ValueError('Number of steps could not be infered from the data, '
'please pass the steps_per_epoch argument.')
steps_per_run = K.variable(
value=iteration_value,
dtype='int32',
name='steps_per_run')
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn, iterator, iterations=steps_per_run,
initial_loop_values=initial_loop_values)
train_op = ctx.run_op
output_tensors = ctx.last_step_outputs
do_validation = bool(validation_steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
verbose=verbose,
count_mode='steps',
mode=mode)
# Calculate the steps each time on the device.
steps_to_run = ([current_strategy.extended.steps_per_run] *
(steps_per_epoch //
current_strategy.extended.steps_per_run))
if steps_per_epoch % current_strategy.extended.steps_per_run:
steps_to_run.append(
steps_per_epoch % current_strategy.extended.steps_per_run)
target_steps = len(steps_to_run)
callbacks._call_begin_hook(mode)
for epoch in range(initial_epoch, epochs):
distributed_training_utils._reset_metrics(model)
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
step_index = 0
prev_step_count = None
current_step = 0
while current_step < target_steps:
step_count = steps_to_run[current_step]
batch_logs = {'batch': step_index, 'size': 1, 'num_steps': step_count}
callbacks._call_batch_hook(mode, 'begin', step_index, batch_logs)
if prev_step_count is None or step_count != prev_step_count:
steps_per_run.load(step_count, K.get_session())
prev_step_count = step_count
try:
_, outputs = K.batch_get_value([train_op, output_tensors])
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).' %
steps_per_epoch * epochs)
break
batch_logs.update(outputs)
callbacks._call_batch_hook(mode, 'end', step_index, batch_logs)
step_index = step_index + step_count
current_step += 1
if callbacks.model.stop_training:
break
if (do_validation and
training_utils.should_run_validation(validation_freq, epoch)):
logging.info('Running validation at fit epoch: %s', epoch)
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_outs = experimental_tpu_test_loop( # pylint: disable=undefined-variable
model,
val_dataset,
steps=validation_steps,
verbose=verbose,
callbacks=callbacks)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for label, val_out in zip(out_labels, val_outs):
epoch_logs['val_' + label] = val_out
callbacks.on_epoch_end(epoch, epoch_logs)
if callbacks.model.stop_training:
break
callbacks._call_end_hook(mode)
if model._compile_distribution:
# Copy the weights back from the replicated model to the original model.
distributed_training_utils._copy_weights_to_original_model(
model, ModeKeys.TRAIN)
scope.__exit__(None, None, None)
return model.history