def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
if isinstance(inputs, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)):
inputs = distributed_training_utils.get_iterator(
inputs, model._distribution_strategy)
def get_distributed_inputs():
return distributed_training_utils._prepare_feed_values(
model, inputs, targets, sample_weights, mode)
# In the eager case, we want to call the input method per step, so return
# a lambda from here that can be called. Note that this is applicable only
# in Distribution Strategy case as it follows the same code path for both
# eager and graph modes.
# TODO(priyag,omalleyt): Either we should move the training DS with
# IteratorV2 to use training_generator code path, or figure out how to
# set a symbolic Iterator out of a Dataset when in eager mode.
if context.executing_eagerly():
return get_distributed_inputs
else:
return get_distributed_inputs()
if isinstance(
inputs,
(dataset_ops.DatasetV1, dataset_ops.DatasetV2, iterator_ops.Iterator)):
inputs, targets, sample_weights = model._standardize_user_data(
inputs, extract_tensors_from_dataset=True)
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
ins = inputs + targets + sample_weights
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True] # Add learning phase value.
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
if isinstance(inputs, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)):
inputs = distributed_training_utils.get_iterator(
inputs, model._distribution_strategy)
def get_distributed_inputs():
return distributed_training_utils._prepare_feed_values(
model, inputs, targets, sample_weights, mode)
# In the eager case, we want to call the input method per step, so return
# a lambda from here that can be called. Note that this is applicable only
# in Distribution Strategy case as it follows the same code path for both
# eager and graph modes.
# TODO(priyag,omalleyt): Either we should move the training DS with
# IteratorV2 to use training_generator code path, or figure out how to
# set a symbolic Iterator out of a Dataset when in eager mode.
if context.executing_eagerly():
return get_distributed_inputs
else:
return get_distributed_inputs()
if isinstance(inputs, (dataset_ops.DatasetV1, dataset_ops.DatasetV2,
iterator_ops.Iterator)):
inputs, targets, sample_weights = model._standardize_user_data(
inputs,
extract_tensors_from_dataset=True)
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
ins = inputs + targets + sample_weights
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True] # Add learning phase value.
return ins
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 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_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 _get_iterator(inputs, distribution_strategy=None):
if distribution_strategy:
return distributed_training_utils.get_iterator(inputs,
distribution_strategy)
return training_utils.get_iterator(inputs)
def _get_iterator(inputs, distribution_strategy=None):
if distribution_strategy:
return distributed_training_utils.get_iterator(
inputs, distribution_strategy)
return training_utils.get_iterator(inputs)
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
