463 lines
21 KiB
Python
463 lines
21 KiB
Python
![]() |
# Copyright 2020 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.
|
||
|
# ==============================================================================
|
||
|
"""Keras string lookup preprocessing layer."""
|
||
|
|
||
|
|
||
|
import numpy as np
|
||
|
import tensorflow.compat.v2 as tf
|
||
|
|
||
|
from keras.engine import base_preprocessing_layer
|
||
|
from keras.layers.preprocessing import index_lookup
|
||
|
|
||
|
# isort: off
|
||
|
from tensorflow.python.platform import tf_logging as logging
|
||
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.layers.IntegerLookup",
|
||
|
"keras.layers.experimental.preprocessing.IntegerLookup",
|
||
|
v1=[],
|
||
|
)
|
||
|
class IntegerLookup(index_lookup.IndexLookup):
|
||
|
"""A preprocessing layer which maps integer features to contiguous ranges.
|
||
|
|
||
|
This layer maps a set of arbitrary integer input tokens into indexed integer
|
||
|
output via a table-based vocabulary lookup. The layer's output indices will
|
||
|
be contiguously arranged up to the maximum vocab size, even if the input
|
||
|
tokens are non-continguous or unbounded. The layer supports multiple options
|
||
|
for encoding the output via `output_mode`, and has optional support for
|
||
|
out-of-vocabulary (OOV) tokens and masking.
|
||
|
|
||
|
The vocabulary for the layer must be either supplied on construction or
|
||
|
learned via `adapt()`. During `adapt()`, the layer will analyze a data set,
|
||
|
determine the frequency of individual integer tokens, and create a
|
||
|
vocabulary from them. If the vocabulary is capped in size, the most frequent
|
||
|
tokens will be used to create the vocabulary and all others will be treated
|
||
|
as OOV.
|
||
|
|
||
|
There are two possible output modes for the layer. When `output_mode` is
|
||
|
`"int"`, input integers are converted to their index in the vocabulary (an
|
||
|
integer). When `output_mode` is `"multi_hot"`, `"count"`, or `"tf_idf"`,
|
||
|
input integers are encoded into an array where each dimension corresponds to
|
||
|
an element in the vocabulary.
|
||
|
|
||
|
The vocabulary can optionally contain a mask token as well as an OOV token
|
||
|
(which can optionally occupy multiple indices in the vocabulary, as set
|
||
|
by `num_oov_indices`).
|
||
|
The position of these tokens in the vocabulary is fixed. When `output_mode`
|
||
|
is `"int"`, the vocabulary will begin with the mask token at index 0,
|
||
|
followed by OOV indices, followed by the rest of the vocabulary. When
|
||
|
`output_mode` is `"multi_hot"`, `"count"`, or `"tf_idf"` the vocabulary will
|
||
|
begin with OOV indices and instances of the mask token will be dropped.
|
||
|
|
||
|
For an overview and full list of preprocessing layers, see the preprocessing
|
||
|
[guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
|
||
|
|
||
|
Args:
|
||
|
max_tokens: Maximum size of the vocabulary for this layer. This should
|
||
|
only be specified when adapting the vocabulary or when setting
|
||
|
`pad_to_max_tokens=True`. If None, there is no cap on the size of the
|
||
|
vocabulary. Note that this size includes the OOV and mask tokens.
|
||
|
Defaults to None.
|
||
|
num_oov_indices: The number of out-of-vocabulary tokens to use. If this
|
||
|
value is more than 1, OOV inputs are modulated to determine their OOV
|
||
|
value. If this value is 0, OOV inputs will cause an error when calling
|
||
|
the layer. Defaults to 1.
|
||
|
mask_token: An integer token that represents masked inputs. When
|
||
|
`output_mode` is `"int"`, the token is included in vocabulary and mapped
|
||
|
to index 0. In other output modes, the token will not appear in the
|
||
|
vocabulary and instances of the mask token in the input will be dropped.
|
||
|
If set to None, no mask term will be added. Defaults to None.
|
||
|
oov_token: Only used when `invert` is True. The token to return for OOV
|
||
|
indices. Defaults to -1.
|
||
|
vocabulary: Optional. Either an array of integers or a string path to a
|
||
|
text file. If passing an array, can pass a tuple, list, 1D numpy array,
|
||
|
or 1D tensor containing the integer vocbulary terms. If passing a file
|
||
|
path, the file should contain one line per term in the vocabulary. If
|
||
|
this argument is set, there is no need to `adapt()` the layer.
|
||
|
vocabulary_dtype: The dtype of the vocabulary terms, for example
|
||
|
`"int64"` or `"int32"`. Defaults to `"int64"`.
|
||
|
idf_weights: Only valid when `output_mode` is `"tf_idf"`. A tuple, list,
|
||
|
1D numpy array, or 1D tensor or the same length as the vocabulary,
|
||
|
containing the floating point inverse document frequency weights, which
|
||
|
will be multiplied by per sample term counts for the final `tf_idf`
|
||
|
weight. If the `vocabulary` argument is set, and `output_mode` is
|
||
|
`"tf_idf"`, this argument must be supplied.
|
||
|
invert: Only valid when `output_mode` is `"int"`. If True, this layer will
|
||
|
map indices to vocabulary items instead of mapping vocabulary items to
|
||
|
indices. Default to False.
|
||
|
output_mode: Specification for the output of the layer. Defaults to
|
||
|
`"int"`. Values can be `"int"`, `"one_hot"`, `"multi_hot"`, `"count"`,
|
||
|
or `"tf_idf"` configuring the layer as follows:
|
||
|
- `"int"`: Return the vocabulary indices of the input tokens.
|
||
|
- `"one_hot"`: Encodes each individual element in the input into an
|
||
|
array the same size as the vocabulary, containing a 1 at the element
|
||
|
index. If the last dimension is size 1, will encode on that
|
||
|
dimension. If the last dimension is not size 1, will append a new
|
||
|
dimension for the encoded output.
|
||
|
- `"multi_hot"`: Encodes each sample in the input into a single array
|
||
|
the same size as the vocabulary, containing a 1 for each vocabulary
|
||
|
term present in the sample. Treats the last dimension as the sample
|
||
|
dimension, if input shape is (..., sample_length), output shape will
|
||
|
be (..., num_tokens).
|
||
|
- `"count"`: As `"multi_hot"`, but the int array contains a count of
|
||
|
the number of times the token at that index appeared in the sample.
|
||
|
- `"tf_idf"`: As `"multi_hot"`, but the TF-IDF algorithm is applied to
|
||
|
find the value in each token slot.
|
||
|
For `"int"` output, any shape of input and output is supported. For all
|
||
|
other output modes, currently only output up to rank 2 is supported.
|
||
|
pad_to_max_tokens: Only applicable when `output_mode` is `"multi_hot"`,
|
||
|
`"count"`, or `"tf_idf"`. If True, the output will have its feature axis
|
||
|
padded to `max_tokens` even if the number of unique tokens in the
|
||
|
vocabulary is less than max_tokens, resulting in a tensor of shape
|
||
|
[batch_size, max_tokens] regardless of vocabulary size. Defaults to
|
||
|
False.
|
||
|
sparse: Boolean. Only applicable when `output_mode` is `"multi_hot"`,
|
||
|
`"count"`, or `"tf_idf"`. If True, returns a `SparseTensor` instead of a
|
||
|
dense `Tensor`. Defaults to False.
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
**Creating a lookup layer with a known vocabulary**
|
||
|
|
||
|
This example creates a lookup layer with a pre-existing vocabulary.
|
||
|
|
||
|
>>> vocab = [12, 36, 1138, 42]
|
||
|
>>> data = tf.constant([[12, 1138, 42], [42, 1000, 36]]) # Note OOV tokens
|
||
|
>>> layer = tf.keras.layers.IntegerLookup(vocabulary=vocab)
|
||
|
>>> layer(data)
|
||
|
<tf.Tensor: shape=(2, 3), dtype=int64, numpy=
|
||
|
array([[1, 3, 4],
|
||
|
[4, 0, 2]])>
|
||
|
|
||
|
**Creating a lookup layer with an adapted vocabulary**
|
||
|
|
||
|
This example creates a lookup layer and generates the vocabulary by
|
||
|
analyzing the dataset.
|
||
|
|
||
|
>>> data = tf.constant([[12, 1138, 42], [42, 1000, 36]])
|
||
|
>>> layer = tf.keras.layers.IntegerLookup()
|
||
|
>>> layer.adapt(data)
|
||
|
>>> layer.get_vocabulary()
|
||
|
[-1, 42, 1138, 1000, 36, 12]
|
||
|
|
||
|
Note that the OOV token -1 have been added to the vocabulary. The remaining
|
||
|
tokens are sorted by frequency (42, which has 2 occurrences, is first) then
|
||
|
by inverse sort order.
|
||
|
|
||
|
>>> data = tf.constant([[12, 1138, 42], [42, 1000, 36]])
|
||
|
>>> layer = tf.keras.layers.IntegerLookup()
|
||
|
>>> layer.adapt(data)
|
||
|
>>> layer(data)
|
||
|
<tf.Tensor: shape=(2, 3), dtype=int64, numpy=
|
||
|
array([[5, 2, 1],
|
||
|
[1, 3, 4]])>
|
||
|
|
||
|
|
||
|
**Lookups with multiple OOV indices**
|
||
|
|
||
|
This example demonstrates how to use a lookup layer with multiple OOV
|
||
|
indices. When a layer is created with more than one OOV index, any OOV
|
||
|
tokens are hashed into the number of OOV buckets, distributing OOV tokens in
|
||
|
a deterministic fashion across the set.
|
||
|
|
||
|
>>> vocab = [12, 36, 1138, 42]
|
||
|
>>> data = tf.constant([[12, 1138, 42], [37, 1000, 36]])
|
||
|
>>> layer = tf.keras.layers.IntegerLookup(
|
||
|
... vocabulary=vocab, num_oov_indices=2)
|
||
|
>>> layer(data)
|
||
|
<tf.Tensor: shape=(2, 3), dtype=int64, numpy=
|
||
|
array([[2, 4, 5],
|
||
|
[1, 0, 3]])>
|
||
|
|
||
|
Note that the output for OOV token 37 is 1, while the output for OOV token
|
||
|
1000 is 0. The in-vocab terms have their output index increased by 1 from
|
||
|
earlier examples (12 maps to 2, etc) in order to make space for the extra
|
||
|
OOV token.
|
||
|
|
||
|
**One-hot output**
|
||
|
|
||
|
Configure the layer with `output_mode='one_hot'`. Note that the first
|
||
|
`num_oov_indices` dimensions in the ont_hot encoding represent OOV values.
|
||
|
|
||
|
>>> vocab = [12, 36, 1138, 42]
|
||
|
>>> data = tf.constant([12, 36, 1138, 42, 7]) # Note OOV tokens
|
||
|
>>> layer = tf.keras.layers.IntegerLookup(
|
||
|
... vocabulary=vocab, output_mode='one_hot')
|
||
|
>>> layer(data)
|
||
|
<tf.Tensor: shape=(5, 5), dtype=float32, numpy=
|
||
|
array([[0., 1., 0., 0., 0.],
|
||
|
[0., 0., 1., 0., 0.],
|
||
|
[0., 0., 0., 1., 0.],
|
||
|
[0., 0., 0., 0., 1.],
|
||
|
[1., 0., 0., 0., 0.]], dtype=float32)>
|
||
|
|
||
|
**Multi-hot output**
|
||
|
|
||
|
Configure the layer with `output_mode='multi_hot'`. Note that the first
|
||
|
`num_oov_indices` dimensions in the multi_hot encoding represent OOV tokens
|
||
|
|
||
|
>>> vocab = [12, 36, 1138, 42]
|
||
|
>>> data = tf.constant([[12, 1138, 42, 42],
|
||
|
... [42, 7, 36, 7]]) # Note OOV tokens
|
||
|
>>> layer = tf.keras.layers.IntegerLookup(
|
||
|
... vocabulary=vocab, output_mode='multi_hot')
|
||
|
>>> layer(data)
|
||
|
<tf.Tensor: shape=(2, 5), dtype=float32, numpy=
|
||
|
array([[0., 1., 0., 1., 1.],
|
||
|
[1., 0., 1., 0., 1.]], dtype=float32)>
|
||
|
|
||
|
**Token count output**
|
||
|
|
||
|
Configure the layer with `output_mode='count'`. As with multi_hot output,
|
||
|
the first `num_oov_indices` dimensions in the output represent OOV tokens.
|
||
|
|
||
|
>>> vocab = [12, 36, 1138, 42]
|
||
|
>>> data = tf.constant([[12, 1138, 42, 42],
|
||
|
... [42, 7, 36, 7]]) # Note OOV tokens
|
||
|
>>> layer = tf.keras.layers.IntegerLookup(
|
||
|
... vocabulary=vocab, output_mode='count')
|
||
|
>>> layer(data)
|
||
|
<tf.Tensor: shape=(2, 5), dtype=float32, numpy=
|
||
|
array([[0., 1., 0., 1., 2.],
|
||
|
[2., 0., 1., 0., 1.]], dtype=float32)>
|
||
|
|
||
|
**TF-IDF output**
|
||
|
|
||
|
Configure the layer with `output_mode='tf_idf'`. As with multi_hot output,
|
||
|
the first `num_oov_indices` dimensions in the output represent OOV tokens.
|
||
|
|
||
|
Each token bin will output `token_count * idf_weight`, where the idf weights
|
||
|
are the inverse document frequency weights per token. These should be
|
||
|
provided along with the vocabulary. Note that the `idf_weight` for OOV
|
||
|
tokens will default to the average of all idf weights passed in.
|
||
|
|
||
|
>>> vocab = [12, 36, 1138, 42]
|
||
|
>>> idf_weights = [0.25, 0.75, 0.6, 0.4]
|
||
|
>>> data = tf.constant([[12, 1138, 42, 42],
|
||
|
... [42, 7, 36, 7]]) # Note OOV tokens
|
||
|
>>> layer = tf.keras.layers.IntegerLookup(
|
||
|
... output_mode='tf_idf', vocabulary=vocab, idf_weights=idf_weights)
|
||
|
>>> layer(data)
|
||
|
<tf.Tensor: shape=(2, 5), dtype=float32, numpy=
|
||
|
array([[0. , 0.25, 0. , 0.6 , 0.8 ],
|
||
|
[1.0 , 0. , 0.75, 0. , 0.4 ]], dtype=float32)>
|
||
|
|
||
|
To specify the idf weights for oov tokens, you will need to pass the entire
|
||
|
vocabularly including the leading oov token.
|
||
|
|
||
|
>>> vocab = [-1, 12, 36, 1138, 42]
|
||
|
>>> idf_weights = [0.9, 0.25, 0.75, 0.6, 0.4]
|
||
|
>>> data = tf.constant([[12, 1138, 42, 42],
|
||
|
... [42, 7, 36, 7]]) # Note OOV tokens
|
||
|
>>> layer = tf.keras.layers.IntegerLookup(
|
||
|
... output_mode='tf_idf', vocabulary=vocab, idf_weights=idf_weights)
|
||
|
>>> layer(data)
|
||
|
<tf.Tensor: shape=(2, 5), dtype=float32, numpy=
|
||
|
array([[0. , 0.25, 0. , 0.6 , 0.8 ],
|
||
|
[1.8 , 0. , 0.75, 0. , 0.4 ]], dtype=float32)>
|
||
|
|
||
|
When adapting the layer in tf_idf mode, each input sample will be considered
|
||
|
a document, and idf weight per token will be calculated as
|
||
|
`log(1 + num_documents / (1 + token_document_count))`.
|
||
|
|
||
|
**Inverse lookup**
|
||
|
|
||
|
This example demonstrates how to map indices to tokens using this layer.
|
||
|
(You can also use `adapt()` with `inverse=True`, but for simplicity we'll
|
||
|
pass the vocab in this example.)
|
||
|
|
||
|
>>> vocab = [12, 36, 1138, 42]
|
||
|
>>> data = tf.constant([[1, 3, 4], [4, 0, 2]])
|
||
|
>>> layer = tf.keras.layers.IntegerLookup(vocabulary=vocab, invert=True)
|
||
|
>>> layer(data)
|
||
|
<tf.Tensor: shape=(2, 3), dtype=int64, numpy=
|
||
|
array([[ 12, 1138, 42],
|
||
|
[ 42, -1, 36]])>
|
||
|
|
||
|
Note that the first index correspond to the oov token by default.
|
||
|
|
||
|
|
||
|
**Forward and inverse lookup pairs**
|
||
|
|
||
|
This example demonstrates how to use the vocabulary of a standard lookup
|
||
|
layer to create an inverse lookup layer.
|
||
|
|
||
|
>>> vocab = [12, 36, 1138, 42]
|
||
|
>>> data = tf.constant([[12, 1138, 42], [42, 1000, 36]])
|
||
|
>>> layer = tf.keras.layers.IntegerLookup(vocabulary=vocab)
|
||
|
>>> i_layer = tf.keras.layers.IntegerLookup(
|
||
|
... vocabulary=layer.get_vocabulary(), invert=True)
|
||
|
>>> int_data = layer(data)
|
||
|
>>> i_layer(int_data)
|
||
|
<tf.Tensor: shape=(2, 3), dtype=int64, numpy=
|
||
|
array([[ 12, 1138, 42],
|
||
|
[ 42, -1, 36]])>
|
||
|
|
||
|
In this example, the input token 1000 resulted in an output of -1, since
|
||
|
1000 was not in the vocabulary - it got represented as an OOV, and all OOV
|
||
|
tokens are returned as -1 in the inverse layer. Also, note that for the
|
||
|
inverse to work, you must have already set the forward layer vocabulary
|
||
|
either directly or via `adapt()` before calling `get_vocabulary()`.
|
||
|
"""
|
||
|
|
||
|
def __init__(
|
||
|
self,
|
||
|
max_tokens=None,
|
||
|
num_oov_indices=1,
|
||
|
mask_token=None,
|
||
|
oov_token=-1,
|
||
|
vocabulary=None,
|
||
|
vocabulary_dtype="int64",
|
||
|
idf_weights=None,
|
||
|
invert=False,
|
||
|
output_mode="int",
|
||
|
sparse=False,
|
||
|
pad_to_max_tokens=False,
|
||
|
**kwargs,
|
||
|
):
|
||
|
if not tf.dtypes.as_dtype(vocabulary_dtype).is_integer:
|
||
|
raise ValueError(
|
||
|
"`vocabulary_dtype` must be an integer dtype. "
|
||
|
f"Received: {vocabulary_dtype}"
|
||
|
)
|
||
|
|
||
|
# Legacy versions of the IntegerLookup layer set layer dtype to int64,
|
||
|
# instead of the output type. If we see this and output mode is not
|
||
|
# "int", clear the setting so we don't switch types for old SavedModels.
|
||
|
if (
|
||
|
output_mode != "int"
|
||
|
and "dtype" in kwargs
|
||
|
and (kwargs["dtype"] == tf.int64 or kwargs["dtype"] == "int64")
|
||
|
):
|
||
|
del kwargs["dtype"]
|
||
|
|
||
|
# Support deprecated args for this layer.
|
||
|
if "max_values" in kwargs:
|
||
|
logging.log_first_n(
|
||
|
logging.WARN,
|
||
|
"max_values is deprecated, use max_tokens instead.",
|
||
|
1,
|
||
|
)
|
||
|
max_tokens = kwargs["max_values"]
|
||
|
del kwargs["max_values"]
|
||
|
if "mask_value" in kwargs:
|
||
|
logging.log_first_n(
|
||
|
logging.WARN,
|
||
|
"mask_value is deprecated, use mask_token instead.",
|
||
|
1,
|
||
|
)
|
||
|
mask_token = kwargs["mask_value"]
|
||
|
del kwargs["mask_value"]
|
||
|
if "oov_value" in kwargs:
|
||
|
logging.log_first_n(
|
||
|
logging.WARN,
|
||
|
"oov_value is deprecated, use oov_token instead.",
|
||
|
1,
|
||
|
)
|
||
|
oov_token = kwargs["oov_value"]
|
||
|
del kwargs["oov_value"]
|
||
|
|
||
|
# If max_tokens is set, the token must be greater than 1 - otherwise we
|
||
|
# are creating a 0-element vocab, which doesn't make sense.
|
||
|
if max_tokens is not None and max_tokens <= 1:
|
||
|
raise ValueError(
|
||
|
"If `max_tokens` is set for `IntegerLookup`, it must be "
|
||
|
f"greater than 1. Received: max_tokens={max_tokens}."
|
||
|
)
|
||
|
|
||
|
if num_oov_indices < 0:
|
||
|
raise ValueError(
|
||
|
"The value of `num_oov_indices` argument for `IntegerLookup` "
|
||
|
"must >= 0. Received num_oov_indices="
|
||
|
f"{num_oov_indices}."
|
||
|
)
|
||
|
|
||
|
# Make sure mask and oov are of the dtype we want.
|
||
|
mask_token = None if mask_token is None else np.int64(mask_token)
|
||
|
oov_token = None if oov_token is None else np.int64(oov_token)
|
||
|
|
||
|
super().__init__(
|
||
|
max_tokens=max_tokens,
|
||
|
num_oov_indices=num_oov_indices,
|
||
|
mask_token=mask_token,
|
||
|
oov_token=oov_token,
|
||
|
vocabulary=vocabulary,
|
||
|
vocabulary_dtype=vocabulary_dtype,
|
||
|
idf_weights=idf_weights,
|
||
|
invert=invert,
|
||
|
output_mode=output_mode,
|
||
|
sparse=sparse,
|
||
|
pad_to_max_tokens=pad_to_max_tokens,
|
||
|
**kwargs,
|
||
|
)
|
||
|
base_preprocessing_layer.keras_kpl_gauge.get_cell("IntegerLookup").set(
|
||
|
True
|
||
|
)
|
||
|
|
||
|
# We override this method solely to generate a docstring.
|
||
|
def adapt(self, data, batch_size=None, steps=None):
|
||
|
"""Computes a vocabulary of interger terms from tokens in a dataset.
|
||
|
|
||
|
Calling `adapt()` on an `IntegerLookup` layer is an alternative to
|
||
|
passing in a precomputed vocabulary on construction via the
|
||
|
`vocabulary` argument. An `IntegerLookup` layer should always be either
|
||
|
adapted over a dataset or supplied with a vocabulary.
|
||
|
|
||
|
During `adapt()`, the layer will build a vocabulary of all integer
|
||
|
tokens seen in the dataset, sorted by occurrence count, with ties broken
|
||
|
by sort order of the tokens (high to low). At the end of `adapt()`, if
|
||
|
`max_tokens` is set, the vocabulary wil be truncated to `max_tokens`
|
||
|
size. For example, adapting a layer with `max_tokens=1000` will compute
|
||
|
the 1000 most frequent tokens occurring in the input dataset. If
|
||
|
`output_mode='tf-idf'`, `adapt()` will also learn the document
|
||
|
frequencies of each token in the input dataset.
|
||
|
|
||
|
In order to make `StringLookup` efficient in any distribution context,
|
||
|
the vocabulary is kept static with respect to any compiled `tf.Graph`s
|
||
|
that call the layer. As a consequence, if the layer is adapted a second
|
||
|
time, any models using the layer should be re-compiled. For more
|
||
|
information see
|
||
|
`tf.keras.layers.experimental.preprocessing.PreprocessingLayer.adapt`.
|
||
|
|
||
|
`adapt()` is meant only as a single machine utility to compute layer
|
||
|
state. To analyze a dataset that cannot fit on a single machine, see
|
||
|
[Tensorflow Transform](
|
||
|
https://www.tensorflow.org/tfx/transform/get_started) for a
|
||
|
multi-machine, map-reduce solution.
|
||
|
|
||
|
Arguments:
|
||
|
data: The data to train on. It can be passed either as a
|
||
|
`tf.data.Dataset`, or as a numpy array.
|
||
|
batch_size: Integer or `None`.
|
||
|
Number of samples per state update.
|
||
|
If unspecified, `batch_size` will default to 32.
|
||
|
Do not specify the `batch_size` if your data is in the
|
||
|
form of datasets, generators, or `keras.utils.Sequence` instances
|
||
|
(since they generate batches).
|
||
|
steps: Integer or `None`.
|
||
|
Total number of steps (batches of samples)
|
||
|
When training with input tensors such as
|
||
|
TensorFlow data tensors, the default `None` is equal to
|
||
|
the number of samples in your dataset divided by
|
||
|
the batch size, or 1 if that cannot be determined. If x is a
|
||
|
`tf.data` dataset, and 'steps' is None, the epoch will run until
|
||
|
the input dataset is exhausted. When passing an infinitely
|
||
|
repeating dataset, you must specify the `steps` argument. This
|
||
|
argument is not supported with array inputs.
|
||
|
"""
|
||
|
super().adapt(data, batch_size=batch_size, steps=steps)
|