def train(self, data_iterator):
"""Train a keras model on a worker and send asynchronous updates
to parameter server
"""
feature_iterator, label_iterator = tee(data_iterator, 2)
x_train = np.asarray([x for x, y in feature_iterator])
y_train = np.asarray([y for x, y in label_iterator])
if x_train.size == 0:
return
optimizer = get_optimizer(self.master_optimizer)
self.model = model_from_yaml(self.yaml, self.custom_objects)
self.model.compile(optimizer=get_optimizer(self.master_optimizer),
loss=self.master_loss, metrics=self.master_metrics)
self.model.set_weights(self.parameters.value)
epochs = self.train_config['epochs']
batch_size = self.train_config.get('batch_size')
nb_train_sample = x_train.shape[0]
nb_batch = int(np.ceil(nb_train_sample / float(batch_size)))
index_array = np.arange(nb_train_sample)
batches = [
(i * batch_size, min(nb_train_sample, (i + 1) * batch_size))
for i in range(0, nb_batch)
]
if self.frequency == 'epoch':
for epoch in range(epochs):
weights_before_training = self.client.get_parameters()
self.model.set_weights(weights_before_training)
self.train_config['epochs'] = 1
if x_train.shape[0] > batch_size:
self.model.fit(x_train, y_train, **self.train_config)
print(f"Fot model on data with size {nb_train_sample}")
self.train_config['epochs'] = epochs
weights_after_training = self.model.get_weights()
deltas = subtract_params(
weights_before_training, weights_after_training)
self.client.update_parameters(deltas)
elif self.frequency == 'batch':
for epoch in range(epochs):
if x_train.shape[0] > batch_size:
for (batch_start, batch_end) in batches:
weights_before_training = self.client.get_parameters()
self.model.set_weights(weights_before_training)
batch_ids = index_array[batch_start:batch_end]
x = slice_arrays(x_train, batch_ids)
y = slice_arrays(y_train, batch_ids)
self.model.train_on_batch(x, y)
weights_after_training = self.model.get_weights()
deltas = subtract_params(
weights_before_training, weights_after_training)
self.client.update_parameters(deltas)
else:
raise ValueError(
'frequency parameter can be `epoch` or `batch, got {}'.format(self.frequency))
yield []
def slice_arrays(arrays, indices, contiguous=True):
"""Slices batches out of provided arrays (workaround for eager tensors).
Unfortunately eager tensors don't have the same slicing behavior as
Numpy arrays (they follow the same slicing behavior as symbolic TF tensors),
hence we cannot use `generic_utils.slice_arrays` directly
and we have to implement this workaround based on `concat`. This has a
performance cost.
Args:
arrays: Single array or list of arrays.
indices: List of indices in the array that should be included in the output
batch.
contiguous: Boolean flag indicating whether the indices are contiguous.
Returns:
Slice of data (either single array or list of arrays).
"""
converted_to_list = False
if not isinstance(arrays, list):
converted_to_list = True
arrays = [arrays]
if any(tf.is_tensor(x) for x in arrays):
if not contiguous:
entries = [[x[i:i + 1] for i in indices] for x in arrays]
slices = [tf.concat(x, axis=0) for x in entries]
else:
slices = [x[indices[0]:indices[-1] + 1] for x in arrays]
else:
slices = generic_utils.slice_arrays(arrays, indices)
if converted_to_list:
slices = slices[0]
return slices
def _eval_loop(f, ins, batch_size=32):
'''Abstract method to loop over some data in batches.
# Arguments
f: Keras function returning a list of tensors.
ins: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
# 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 scalar outputs.
'''
num_sample = ins[0].shape[0]
outs = []
batches = make_batches(num_sample, batch_size)
index_array = np.arange(num_sample)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(ins[-1], float):
# 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)
batch_outs = f(ins_batch)
if isinstance(batch_outs, list):
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(np.zeros((num_sample, )))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_ids] = batch_out
else:
if batch_index == 0:
outs.append(np.zeros((num_sample, )))
outs[i][batch_ids] = batch_out
if len(outs) == 1:
return outs[0]
return outs
def __getitem__(self, index):
assert 0 <= index < self.length
if index == 0:
self.epoch += 1
if self.shuffle == "batch":
self.index_array = batch_shuffle(self.index_array,
self.batch_size)
elif self.shuffle:
np.random.shuffle(self.index_array)
batches = make_batches(self.num_samples, self.batch_size)
self.batch_enumerator = enumerate(batches)
batch_index, (batch_start, batch_end) = next(self.batch_enumerator)
batch_ids = self.index_array[batch_start:batch_end]
try:
x_batch = slice_arrays(self.x, batch_ids)[0]
y_batch = slice_arrays(self.y, batch_ids)[0]
sample_weights_batch = slice_arrays(self.sample_weights,
batch_ids)[0]
except TypeError:
raise TypeError('TypeError while preparing batch. '
'If using HDF5 input data, '
'pass shuffle="batch".')
if self.incremental != False and self.incremental[0] <= self.epoch:
x_batch_adv = self.attack.perturb(
x_batch, y_batch,
min((self.epoch - self.incremental[0]) /
(self.incremental[1] - self.incremental[0]), 1))
elif self.incremental == False or self.incremental[1] <= self.epoch:
x_batch_adv = self.attack.perturb(x_batch, y_batch, 1)
else:
x_batch_adv = x_batch
return x_batch_adv, y_batch, sample_weights_batch
def model_iteration(model,
inputs,
targets=None,
sample_weights=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
shuffle=True,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
validation_freq=1,
mode=ModeKeys.TRAIN,
validation_in_fit=False,
prepared_feed_values_from_dataset=False,
steps_name="steps",
**kwargs):
"""Loop function for arrays of data with modes TRAIN/TEST/PREDICT.
Args:
model: Keras Model instance.
inputs: Either a list or dictionary of arrays, or a dataset instance.
targets: List/dictionary of input arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: Integer batch size or None if unknown.
epochs: Number of times to iterate over the data
verbose: 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.
Note that the progress bar is not particularly useful when
logged to a file, so verbose=2 is recommended when not running
interactively (eg, in a production environment).
callbacks: List of callbacks to be called during training
val_inputs: Either a list or dictionary of arrays, or a dataset
instance.
val_targets: List/dictionary of target arrays.
val_sample_weights: Optional list of sample weight arrays.
shuffle: Whether to shuffle the data at the beginning of each epoch
concatenation of list the display names of the outputs of `f` and the
list of display names of the outputs of `f_val`.
initial_epoch: Epoch at which to start training (useful for resuming a
previous training run)
steps_per_epoch: Total number of steps (batches of samples) before
declaring one epoch finished and starting the next epoch. Ignored with
the default value of `None`.
validation_steps: Number of steps to run validation for (only if doing
validation from data tensors). Ignored with the default value of
`None`.
validation_freq: Only relevant if validation data is provided. Integer
or `collections.abc.Container` instance (e.g. list, tuple, etc.). If
an integer, specifies how many training epochs to run before a new
validation run is performed, e.g. `validation_freq=2` runs validation
every 2 epochs. If a Container, specifies the epochs on which to run
validation, e.g. `validation_freq=[1, 2, 10]` runs validation at the
end of the 1st, 2nd, and 10th epochs.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
validation_in_fit: if true, then this method is invoked from within
training iteration (for validation). In the case where `val_inputs` is
a dataset, this flag indicates that its iterator and feed values are
already created so should properly reuse resources.
prepared_feed_values_from_dataset: if True, `inputs` is a list of feed
tensors returned from `_prepare_feed_values` call on the validation
dataset, so do not call it again on `inputs`. Should only be used for
inline validation (i.e., only if `validation_in_fit` is also True).
steps_name: The string name of the steps argument, either `steps`,
`validation_steps`, or `steps_per_epoch`. Only used for error message
formatting.
**kwargs: Additional arguments for backwards compatibility.
Returns:
- In TRAIN mode: `History` object.
- In TEST mode: Evaluation metrics.
- In PREDICT mode: Outputs of the Model called on inputs.
Raises:
ValueError: in case of invalid arguments.
"""
# Backwards compatibility.
if "steps" in kwargs:
steps_per_epoch = kwargs.pop("steps")
if kwargs:
raise TypeError("Unknown arguments: %s" % (kwargs, ))
# In case we were passed a dataset, we extract symbolic tensors from it.
reset_dataset_after_each_epoch = False
input_iterator = None
is_dataset = isinstance(inputs,
(tf.compat.v1.data.Dataset, tf.data.Dataset))
# TODO(fchollet): consider moving `steps_per_epoch` inference to
# _standardize_user_data and set reset_dataset_after_each_epoch as an
# attribute on the dataset instance.
if is_dataset:
if steps_per_epoch is None:
reset_dataset_after_each_epoch = True
steps_per_epoch = training_utils_v1.infer_steps_for_dataset(
model,
inputs,
steps_per_epoch,
epochs=epochs,
steps_name=steps_name,
)
input_iterator = _get_iterator(inputs, model._distribution_strategy)
# Enter tf.distribute.Strategy scope.
if model._distribution_strategy:
scope = distributed_training_utils_v1.distributed_scope(
strategy=model._distribution_strategy,
learning_phase=(1 if mode == ModeKeys.TRAIN else 0),
)
scope.__enter__()
use_steps = is_dataset or steps_per_epoch is not None
do_validation = val_inputs is not None
# Prepare input data.
inputs = input_iterator or inputs
if validation_in_fit and prepared_feed_values_from_dataset:
# When invoking validation in training loop, avoid creating iterator and
# list of feed values for the same validation dataset multiple times
# (which essentially would call `iterator.get_next()` that slows down
# execution and leads to OOM errors eventually.
ins = inputs
else:
ins = _prepare_feed_values(model, inputs, targets, sample_weights,
mode)
# `ins` is a function when a distribute strategy is used in Eager mode.
# In that case `is_dataset` is True. The code branches that have
# requirements about the type of `ins` do not trigger in the distributed
# case.
if not is_dataset:
num_samples_or_steps = _get_num_samples_or_steps(
ins, batch_size, steps_per_epoch)
else:
num_samples_or_steps = steps_per_epoch
# Update sample_weight_mode of the model if sample_weights is specified by
# the user. We need to call this function after we have a handle on the
# inputs (both numpy arrays and datasets) in order to determine if the user
# has specified sample_weights.
_update_sample_weight_mode(model, mode, ins)
# Get step function and loop type. As part of building the execution
# function we recompile the metrics based on the updated
# sample_weight_mode value.
f = _make_execution_function(model, mode)
# Prepare validation data. Hold references to the iterator and the input
# list to properly reinitialize and reuse in multiple validation passes.
val_iterator = None
if isinstance(val_inputs, (tf.compat.v1.data.Dataset, tf.data.Dataset)):
if validation_steps is None:
# Because we pass an iterator feed instead of a Dataset to the eval
# model_iteration() call, it will not trigger the dataset-input path
# that determines the number of steps required. To avoid this issue,
# set validation_steps here if validation_steps is None.
validation_steps = training_utils_v1.infer_steps_for_dataset(
model,
val_inputs,
validation_steps,
epochs=epochs,
steps_name="validation_steps",
)
val_iterator = _get_iterator(val_inputs, model._distribution_strategy)
val_inputs = _prepare_feed_values(model, val_iterator, val_targets,
val_sample_weights, ModeKeys.TEST)
# Get num steps for printing.
val_samples_or_steps = validation_steps
else:
# Get num samples for printing.
val_samples_or_steps = (val_inputs
and tf.nest.flatten(val_inputs)[0].shape[0]
or None)
if mode == ModeKeys.TRAIN and verbose:
_print_train_info(num_samples_or_steps, val_samples_or_steps,
is_dataset)
# Configure callbacks.
count_mode = "steps" if use_steps else "samples"
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
samples=num_samples_or_steps,
count_mode=count_mode,
verbose=verbose,
mode=mode,
)
# Find beforehand arrays that need sparse-to-dense conversion.
if issparse is not None and not use_steps:
indices_for_conversion_to_dense = []
feed = _get_model_feed(model, mode)
for i, (input_data, feed_tensor) in enumerate(zip(ins, feed)):
if issparse(input_data) and not backend.is_sparse(feed_tensor):
indices_for_conversion_to_dense.append(i)
# Select aggregation method.
if mode == ModeKeys.PREDICT:
aggregator = training_utils_v1.OutputsAggregator(
use_steps,
num_samples=None if steps_per_epoch else num_samples_or_steps,
steps=steps_per_epoch,
)
else:
aggregator = training_utils_v1.MetricsAggregator(
use_steps,
num_samples=None if steps_per_epoch else num_samples_or_steps,
steps=steps_per_epoch,
)
if model._compile_distribution:
distributed_training_utils_v1._copy_weights_to_distributed_model(
model, mode)
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
initial_epoch = model._maybe_load_initial_epoch_from_ckpt(
initial_epoch, mode)
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
# Setup work for each epoch
epoch_logs = {}
if mode != ModeKeys.PREDICT:
# Collecting and resetting metrics has non-zero cost and will
# needlessly slow down model.predict.
model.reset_metrics()
if mode == ModeKeys.TRAIN:
callbacks.on_epoch_begin(epoch, epoch_logs)
if use_steps:
# Step-wise loop.
if steps_per_epoch is None:
# Loop over dataset until `OutOfRangeError` is raised.
target_steps = np.inf
else:
# Loop over dataset for the specified number of steps.
target_steps = steps_per_epoch
step = 0
while step < target_steps:
batch_logs = {"batch": step, "size": 1}
callbacks._call_batch_hook(mode, "begin", step, batch_logs)
# Get outputs.
try:
# `ins` can be callable in tf.distribute.Strategy + eager
# case.
if not callable(ins) or (
model._distribution_strategy
and not distributed_training_utils_v1.
is_distributing_by_cloning( # noqa: E501
model)):
actual_inputs = ins
else:
actual_inputs = ins()
batch_outs = f(actual_inputs)
except tf.errors.OutOfRangeError:
if is_dataset:
# The dataset passed by the user ran out of batches.
# Now we know the cardinality of the dataset. If
# steps_per_epoch was specified, then running out of
# data is unexpected, so we stop training and inform the
# user.
if steps_per_epoch:
callbacks.model.stop_training = True
logging.warning(
"Your dataset ran out of data; interrupting "
"training. Make sure that your dataset can "
"generate at least `%s * epochs` batches (in "
"this case, %d batches). You may need to use "
"the repeat() function when building your "
"dataset." %
(steps_name, steps_per_epoch * epochs))
elif step > 0:
steps_per_epoch = step
aggregator.steps = steps_per_epoch
else:
# We ran out of batches while the user passed an
# iterator (legacy).
callbacks.model.stop_training = True
logging.warning(
"Your dataset iterator ran out of data; "
"interrupting training. Make sure that your "
"iterator can generate at least `%s * epochs` "
"batches (in this case, %d batches). You may need "
"to use the repeat() function when building your "
"dataset." %
(steps_name, steps_per_epoch * epochs))
break
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if model._distribution_strategy:
batch_outs = distributed_training_utils_v1._per_replica_aggregate_batch( # noqa: E501
model._distribution_strategy, batch_outs, model, mode)
# Aggregate results.
if step == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs)
# Callbacks batch end.
batch_logs = cbks.make_logs(model, batch_logs, batch_outs,
mode)
callbacks._call_batch_hook(mode, "end", step, batch_logs)
step += 1
if callbacks.model.stop_training:
break
else:
# Sample-wise loop.
index_array = np.arange(num_samples_or_steps)
if shuffle == "batch":
index_array = training_utils_v1.batch_shuffle(
index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = make_batches(num_samples_or_steps, batch_size)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
# Slice into a batch.
if len(batches) == 1:
# If we only have one batch, do not slice. This takes care
# of composite tensors in non-Dataset modes; we currently
# don't support slicing them.
# TODO(b/133517906): Add slicing support.
ins_batch = ins
else:
try:
if ins and isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1],
batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
except TypeError:
raise TypeError("TypeError while preparing batch. "
"If using HDF5 input data, "
'pass shuffle="batch".')
# Sparse to dense conversion.
if issparse is not None:
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
# Callbacks batch_begin.
batch_logs = {"batch": batch_index, "size": len(batch_ids)}
callbacks._call_batch_hook(mode, "begin", batch_index,
batch_logs)
# Get outputs.
batch_outs = f(ins_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
# Aggregate results.
if batch_index == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs, batch_start, batch_end)
# Callbacks batch end.
batch_logs = cbks.make_logs(model, batch_logs, batch_outs,
mode)
callbacks._call_batch_hook(mode, "end", batch_index,
batch_logs)
if callbacks.model.stop_training:
break
aggregator.finalize()
results = aggregator.results
epoch_logs = cbks.make_logs(model, epoch_logs, results, mode)
if len(results) == 1:
results = results[0]
# Run the test loop every `validation_freq` epochs during training.
if (do_validation and training_utils_v1.should_run_validation(
validation_freq, epoch) and not callbacks.model.stop_training):
if model._compile_distribution:
# Since we create a new clone from the original model we need to
# copy the weights back to the original model before we can run
# validation.
distributed_training_utils_v1._copy_weights_to_original_model(
model, ModeKeys.TRAIN)
val_results = model_iteration(
model,
val_inputs,
targets=val_targets,
sample_weights=val_sample_weights,
batch_size=batch_size,
steps_per_epoch=validation_steps,
callbacks=callbacks,
verbose=0,
mode=ModeKeys.TEST,
validation_in_fit=True,
prepared_feed_values_from_dataset=(val_iterator is not None),
steps_name="validation_steps",
)
if not isinstance(val_results, list):
val_results = [val_results]
epoch_logs = cbks.make_logs(model,
epoch_logs,
val_results,
mode,
prefix="val_")
if val_iterator and epoch < epochs - 1:
_reinitialize_iterator(val_iterator,
model._distribution_strategy)
if mode == ModeKeys.TRAIN:
# Epochs only apply to `fit`.
callbacks.on_epoch_end(epoch, epoch_logs)
# Reinitialize dataset iterator for the next epoch.
if reset_dataset_after_each_epoch and epoch < epochs - 1:
_reinitialize_iterator(input_iterator,
model._distribution_strategy)
model._successful_loop_finish = True
callbacks._call_end_hook(mode)
if model._distribution_strategy:
if model._compile_distribution:
# TODO(priyag, psv): Copy back metrics to the original model as
# well?
distributed_training_utils_v1._copy_weights_to_original_model(
model, mode)
scope.__exit__(None, None, None)
if mode == ModeKeys.TRAIN:
return model.history
return results
def predict_loop(model, f, ins, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
# Arguments
model: Keras model instance.
f: Keras function returning a list of tensors.
ins: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `predict_loop` finished.
Ignored with the default value of `None`.
# Returns
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
num_samples = check_num_samples(ins,
batch_size=batch_size,
steps=steps,
steps_name='steps')
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
indices_for_conversion_to_dense = []
is_sparse = False
for i in range(len(model._feed_inputs)):
if issparse(ins[i]) and not K.is_sparse(model._feed_inputs[i]):
indices_for_conversion_to_dense.append(i)
elif issparse(ins[i]) and K.is_sparse(model._feed_inputs[i]):
is_sparse = True
if steps is not None:
# Step-based predictions.
# Since we do not know how many samples
# we will see, we cannot pre-allocate
# the returned Numpy arrays.
# Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = []
for step in range(steps):
batch_outs = f(ins)
batch_outs = to_list(batch_outs)
if step == 0:
for batch_out in batch_outs:
unconcatenated_outs.append([])
for i, batch_out in enumerate(batch_outs):
unconcatenated_outs[i].append(batch_out)
if verbose == 1:
progbar.update(step + 1)
if is_sparse:
if len(unconcatenated_outs) == 1:
return vstack(unconcatenated_outs[0], 'csr')
return [
vstack(unconcatenated_outs[i], 'csr')
for i in range(len(unconcatenated_outs))
]
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
else:
# Sample-based predictions.
outs = []
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if ins and isinstance(ins[-1], float):
# 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)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
batch_outs = to_list(batch_outs)
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
shape = (num_samples, ) + batch_out.shape[1:]
if is_sparse:
outs.append(lil_matrix(shape, dtype=batch_out.dtype))
else:
outs.append(np.zeros(shape, dtype=batch_out.dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if is_sparse:
return unpack_singleton(list(map(lambda oo: oo.tocsr(), outs)))
return unpack_singleton(outs)
def testslice_arrays():
input_a = np.random.random((10, 3))
slice_arrays(None)
slice_arrays(input_a, 0)
slice_arrays(input_a, 0, 1)
slice_arrays(input_a, stop=2)
input_a = [None, [1, 1], None, [1, 1]]
slice_arrays(input_a, 0)
slice_arrays(input_a, 0, 1)
slice_arrays(input_a, stop=2)
input_a = [None]
slice_arrays(input_a, 0)
slice_arrays(input_a, 0, 1)
slice_arrays(input_a, stop=2)
input_a = None
slice_arrays(input_a, 0)
slice_arrays(input_a, 0, 1)
slice_arrays(input_a, stop=2)
def testslice_arrays():
input_a = np.random.random((10, 3))
slice_arrays(None)
slice_arrays(input_a, 0)
slice_arrays(input_a, 0, 1)
slice_arrays(input_a, stop=2)
input_a = [None, [1, 1], None, [1, 1]]
slice_arrays(input_a, 0)
slice_arrays(input_a, 0, 1)
slice_arrays(input_a, stop=2)
input_a = [None]
slice_arrays(input_a, 0)
slice_arrays(input_a, 0, 1)
slice_arrays(input_a, stop=2)
input_a = None
slice_arrays(input_a, 0)
slice_arrays(input_a, 0, 1)
slice_arrays(input_a, stop=2)
