Module keras.utils.dataset_creator
Input dataset creator for model.fit
.
Expand source code
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=g-classes-have-attributes
"""Input dataset creator for `model.fit`."""
import tensorflow.compat.v2 as tf
from tensorflow.python.util.tf_export import keras_export
@keras_export('keras.utils.experimental.DatasetCreator', v1=[])
class DatasetCreator(object):
"""Object that returns a `tf.data.Dataset` upon invoking.
`tf.keras.utils.experimental.DatasetCreator` is designated as a supported type
for `x`, or the input, in `tf.keras.Model.fit`. Pass an instance of this class
to `fit` when using a callable (with a `input_context` argument) that returns
a `tf.data.Dataset`.
```python
model = tf.keras.Sequential([tf.keras.layers.Dense(10)])
model.compile(tf.keras.optimizers.SGD(), loss="mse")
def dataset_fn(input_context):
global_batch_size = 64
batch_size = input_context.get_per_replica_batch_size(global_batch_size)
dataset = tf.data.Dataset.from_tensors(([1.], [1.])).repeat()
dataset = dataset.shard(
input_context.num_input_pipelines, input_context.input_pipeline_id)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(2)
return dataset
input_options = tf.distribute.InputOptions(
experimental_fetch_to_device=True,
experimental_per_replica_buffer_size=2)
model.fit(tf.keras.utils.experimental.DatasetCreator(
dataset_fn, input_options=input_options), epochs=10, steps_per_epoch=10)
```
`Model.fit` usage with `DatasetCreator` is intended to work across all
`tf.distribute.Strategy`s, as long as `Strategy.scope` is used at model
creation:
```python
strategy = tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver)
with strategy.scope():
model = tf.keras.Sequential([tf.keras.layers.Dense(10)])
model.compile(tf.keras.optimizers.SGD(), loss="mse")
...
```
Note: When using `DatasetCreator`, `steps_per_epoch` argument in `Model.fit`
must be provided as the cardinality of such input cannot be inferred.
Args:
dataset_fn: A callable that takes a single argument of type
`tf.distribute.InputContext`, which is used for batch size calculation and
cross-worker input pipeline sharding (if neither is needed, the
`InputContext` parameter can be ignored in the `dataset_fn`), and returns
a `tf.data.Dataset`.
input_options: Optional `tf.distribute.InputOptions`, used for specific
options when used with distribution, for example, whether to prefetch
dataset elements to accelerator device memory or host device memory, and
prefetch buffer size in the replica device memory. No effect if not used
with distributed training. See `tf.distribute.InputOptions` for more
information.
"""
def __init__(self, dataset_fn, input_options=None):
if not callable(dataset_fn):
raise TypeError('`dataset_fn` for `DatasetCreator` must be a `callable`.')
if input_options and (not isinstance(input_options,
tf.distribute.InputOptions)):
raise TypeError('`input_options` for `DatasetCreator` must be a '
'`tf.distribute.InputOptions`.')
self.dataset_fn = dataset_fn
self.input_options = input_options
def __call__(self, *args, **kwargs):
# When a `DatasetCreator` is invoked, it forwards args/kwargs straight to
# the callable.
dataset = self.dataset_fn(*args, **kwargs)
if not isinstance(dataset, tf.data.Dataset):
raise TypeError('The `callable` provided to `DatasetCreator` must return '
'a Dataset.')
return dataset
Classes
class DatasetCreator (dataset_fn, input_options=None)
-
Object that returns a
tf.data.Dataset
upon invoking.tf.keras.utils.experimental.DatasetCreator
is designated as a supported type forx
, or the input, intf.keras.Model.fit
. Pass an instance of this class tofit
when using a callable (with ainput_context
argument) that returns atf.data.Dataset
.model = tf.keras.Sequential([tf.keras.layers.Dense(10)]) model.compile(tf.keras.optimizers.SGD(), loss="mse") def dataset_fn(input_context): global_batch_size = 64 batch_size = input_context.get_per_replica_batch_size(global_batch_size) dataset = tf.data.Dataset.from_tensors(([1.], [1.])).repeat() dataset = dataset.shard( input_context.num_input_pipelines, input_context.input_pipeline_id) dataset = dataset.batch(batch_size) dataset = dataset.prefetch(2) return dataset input_options = tf.distribute.InputOptions( experimental_fetch_to_device=True, experimental_per_replica_buffer_size=2) model.fit(tf.keras.utils.experimental.DatasetCreator( dataset_fn, input_options=input_options), epochs=10, steps_per_epoch=10)
Model.fit
usage withDatasetCreator
is intended to work across alltf.distribute.Strategy
s, as long asStrategy.scope
is used at model creation:strategy = tf.distribute.experimental.ParameterServerStrategy( cluster_resolver) with strategy.scope(): model = tf.keras.Sequential([tf.keras.layers.Dense(10)]) model.compile(tf.keras.optimizers.SGD(), loss="mse") ...
Note: When using
DatasetCreator
,steps_per_epoch
argument inModel.fit
must be provided as the cardinality of such input cannot be inferred.Args
dataset_fn
- A callable that takes a single argument of type
tf.distribute.InputContext
, which is used for batch size calculation and cross-worker input pipeline sharding (if neither is needed, theInputContext
parameter can be ignored in thedataset_fn
), and returns atf.data.Dataset
. input_options
- Optional
tf.distribute.InputOptions
, used for specific options when used with distribution, for example, whether to prefetch dataset elements to accelerator device memory or host device memory, and prefetch buffer size in the replica device memory. No effect if not used with distributed training. Seetf.distribute.InputOptions
for more information.
Expand source code
class DatasetCreator(object): """Object that returns a `tf.data.Dataset` upon invoking. `tf.keras.utils.experimental.DatasetCreator` is designated as a supported type for `x`, or the input, in `tf.keras.Model.fit`. Pass an instance of this class to `fit` when using a callable (with a `input_context` argument) that returns a `tf.data.Dataset`. ```python model = tf.keras.Sequential([tf.keras.layers.Dense(10)]) model.compile(tf.keras.optimizers.SGD(), loss="mse") def dataset_fn(input_context): global_batch_size = 64 batch_size = input_context.get_per_replica_batch_size(global_batch_size) dataset = tf.data.Dataset.from_tensors(([1.], [1.])).repeat() dataset = dataset.shard( input_context.num_input_pipelines, input_context.input_pipeline_id) dataset = dataset.batch(batch_size) dataset = dataset.prefetch(2) return dataset input_options = tf.distribute.InputOptions( experimental_fetch_to_device=True, experimental_per_replica_buffer_size=2) model.fit(tf.keras.utils.experimental.DatasetCreator( dataset_fn, input_options=input_options), epochs=10, steps_per_epoch=10) ``` `Model.fit` usage with `DatasetCreator` is intended to work across all `tf.distribute.Strategy`s, as long as `Strategy.scope` is used at model creation: ```python strategy = tf.distribute.experimental.ParameterServerStrategy( cluster_resolver) with strategy.scope(): model = tf.keras.Sequential([tf.keras.layers.Dense(10)]) model.compile(tf.keras.optimizers.SGD(), loss="mse") ... ``` Note: When using `DatasetCreator`, `steps_per_epoch` argument in `Model.fit` must be provided as the cardinality of such input cannot be inferred. Args: dataset_fn: A callable that takes a single argument of type `tf.distribute.InputContext`, which is used for batch size calculation and cross-worker input pipeline sharding (if neither is needed, the `InputContext` parameter can be ignored in the `dataset_fn`), and returns a `tf.data.Dataset`. input_options: Optional `tf.distribute.InputOptions`, used for specific options when used with distribution, for example, whether to prefetch dataset elements to accelerator device memory or host device memory, and prefetch buffer size in the replica device memory. No effect if not used with distributed training. See `tf.distribute.InputOptions` for more information. """ def __init__(self, dataset_fn, input_options=None): if not callable(dataset_fn): raise TypeError('`dataset_fn` for `DatasetCreator` must be a `callable`.') if input_options and (not isinstance(input_options, tf.distribute.InputOptions)): raise TypeError('`input_options` for `DatasetCreator` must be a ' '`tf.distribute.InputOptions`.') self.dataset_fn = dataset_fn self.input_options = input_options def __call__(self, *args, **kwargs): # When a `DatasetCreator` is invoked, it forwards args/kwargs straight to # the callable. dataset = self.dataset_fn(*args, **kwargs) if not isinstance(dataset, tf.data.Dataset): raise TypeError('The `callable` provided to `DatasetCreator` must return ' 'a Dataset.') return dataset