# 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.
# ==============================================================================
"""Implementation of the Keras API, the high-level API of TensorFlow.
Detailed documentation and user guides are available at
[keras.io](https://keras.io).
"""
# pylint: disable=unused-import
from tensorflow.python import tf2
from keras import distribute
from keras import models
from keras.engine.input_layer import Input
from keras.engine.sequential import Sequential
from keras.engine.training import Model
from tensorflow.python.util.tf_export import keras_export
__version__ = '2.9.0'
keras_export('keras.__version__').export_constant(__name__, '__version__')
maxlen: Optional Int, maximum length of all sequences. If not provided,
sequences will be padded to the length of the longest individual
sequence.
dtype: (Optional, defaults to int32). Type of the output sequences.
To pad sequences with variable length strings, you can use `object`.
padding: String, 'pre' or 'post' (optional, defaults to 'pre'):
pad either before or after each sequence.
truncating: String, 'pre' or 'post' (optional, defaults to 'pre'):
remove values from sequences larger than
`maxlen`, either at the beginning or at the end of the sequences.
value: Float or String, padding value. (Optional, defaults to 0.)
Returns:
Numpy array with shape `(len(sequences), maxlen)`
Raises:
ValueError: In case of invalid values for `truncating` or `padding`,
or in case of invalid shape for a `sequences` entry.
"""
return sequence.pad_sequences(sequences,
maxlen=maxlen,
dtype=dtype,
padding=padding,
truncating=truncating,
value=value)
keras_export('keras.preprocessing.sequence.make_sampling_table')(
make_sampling_table)
keras_export('keras.preprocessing.sequence.skipgrams')(skipgrams)
Arguments:
input_text: Input text (string).
n: int. Size of vocabulary.
filters: list (or concatenation) of characters to filter out, such as
punctuation. Default:
```
'!"#$%&()*+,-./:;<=>?@[\]^_`{|}~\t\n
```,
includes basic punctuation, tabs, and newlines.
lower: boolean. Whether to set the text to lowercase.
split: str. Separator for word splitting.
Returns:
List of integers in `[1, n]`. Each integer encodes a word
(unicity non-guaranteed).
"""
return text.one_hot(input_text, n, filters=filters, lower=lower, split=split)
# text.tokenizer_from_json is only available if keras_preprocessing >= 1.1.0
try:
tokenizer_from_json = text.tokenizer_from_json
keras_export('keras.preprocessing.text.tokenizer_from_json', allow_multiple_exports=True)(
tokenizer_from_json)
except AttributeError:
pass
keras_export('keras.preprocessing.text.hashing_trick', allow_multiple_exports=True)(hashing_trick)
keras_export('keras.preprocessing.text.Tokenizer', allow_multiple_exports=True)(Tokenizer)
width_shift_range=width_shift_range,
height_shift_range=height_shift_range,
brightness_range=brightness_range,
shear_range=shear_range,
zoom_range=zoom_range,
channel_shift_range=channel_shift_range,
fill_mode=fill_mode,
cval=cval,
horizontal_flip=horizontal_flip,
vertical_flip=vertical_flip,
rescale=rescale,
preprocessing_function=preprocessing_function,
data_format=data_format,
validation_split=validation_split,
**kwargs)
keras_export('keras.preprocessing.image.random_rotation')(random_rotation)
keras_export('keras.preprocessing.image.random_shift')(random_shift)
keras_export('keras.preprocessing.image.random_shear')(random_shear)
keras_export('keras.preprocessing.image.random_zoom')(random_zoom)
keras_export(
'keras.preprocessing.image.apply_channel_shift')(apply_channel_shift)
keras_export(
'keras.preprocessing.image.random_channel_shift')(random_channel_shift)
keras_export(
'keras.preprocessing.image.apply_brightness_shift')(apply_brightness_shift)
keras_export('keras.preprocessing.image.random_brightness')(random_brightness)
keras_export(
'keras.preprocessing.image.apply_affine_transform')(apply_affine_transform)
keras_export('keras.preprocessing.image.load_img')(load_img)
# pylint: disable=protected-access
"""Utilities related to loss functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from keras import backend as K
from keras.engine import keras_tensor
from tensorflow.python.ops.losses import loss_reduction
from tensorflow.python.util.tf_export import keras_export
# TODO(joshl/psv): Update references to ReductionV2 to point to its
# new location.
ReductionV2 = loss_reduction.ReductionV2
keras_export('keras.losses.Reduction', v1=[],
allow_multiple_exports=True)(loss_reduction.ReductionV2)
def remove_squeezable_dimensions(labels,
predictions,
expected_rank_diff=0,
name=None):
"""Squeeze last dim if ranks differ from expected by exactly 1.
In the common case where we expect shapes to match, `expected_rank_diff`
defaults to 0, and we squeeze the last dimension of the larger rank if they
differ by 1.
But, for example, if `labels` contains class IDs and `predictions` contains 1
probability per class, we expect `predictions` to have 1 more dimension than
`labels`, so `expected_rank_diff` would be 1. In this case, we'd squeeze
# 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.
# ==============================================================================
"""Utilities for text input preprocessing.
"""
# pylint: disable=invalid-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from keras_preprocessing import text
from tensorflow.python.util.tf_export import keras_export
text_to_word_sequence = text.text_to_word_sequence
one_hot = text.one_hot
hashing_trick = text.hashing_trick
Tokenizer = text.Tokenizer
tokenizer_from_json = text.tokenizer_from_json
keras_export(
'keras.preprocessing.text.text_to_word_sequence')(text_to_word_sequence)
keras_export('keras.preprocessing.text.one_hot')(one_hot)
keras_export('keras.preprocessing.text.hashing_trick')(hashing_trick)
keras_export('keras.preprocessing.text.Tokenizer')(Tokenizer)
keras_export('keras.preprocessing.text.tokenizer_from_json')(
tokenizer_from_json)
from tensorflow.python.distribute import distribution_strategy_context
from tensorflow.python.framework import ops
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.engine import keras_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.losses import loss_reduction
from tensorflow.python.util.tf_export import keras_export
# TODO(joshl/psv): Update references to ReductionV2 to point to its
# new location.
ReductionV2 = loss_reduction.ReductionV2
keras_export('keras.losses.Reduction', v1=[])(loss_reduction.ReductionV2)
def remove_squeezable_dimensions(
labels, predictions, expected_rank_diff=0, name=None):
"""Squeeze last dim if ranks differ from expected by exactly 1.
In the common case where we expect shapes to match, `expected_rank_diff`
defaults to 0, and we squeeze the last dimension of the larger rank if they
differ by 1.
But, for example, if `labels` contains class IDs and `predictions` contains 1
probability per class, we expect `predictions` to have 1 more dimension than
`labels`, so `expected_rank_diff` would be 1. In this case, we'd squeeze
`labels` if `rank(predictions) - rank(labels) == 0`, and
`predictions` if `rank(predictions) - rank(labels) == 2`.
sequences will be padded to the length of the longest individual
sequence.
dtype: (Optional, defaults to int32). Type of the output sequences.
To pad sequences with variable length strings, you can use `object`.
padding: String, 'pre' or 'post' (optional, defaults to 'pre'):
pad either before or after each sequence.
truncating: String, 'pre' or 'post' (optional, defaults to 'pre'):
remove values from sequences larger than
`maxlen`, either at the beginning or at the end of the sequences.
value: Float or String, padding value. (Optional, defaults to 0.)
Returns:
Numpy array with shape `(len(sequences), maxlen)`
Raises:
ValueError: In case of invalid values for `truncating` or `padding`,
or in case of invalid shape for a `sequences` entry.
"""
return sequence.pad_sequences(sequences,
maxlen=maxlen,
dtype=dtype,
padding=padding,
truncating=truncating,
value=value)
keras_export('keras.preprocessing.sequence.make_sampling_table',
allow_multiple_exports=True)(make_sampling_table)
keras_export('keras.preprocessing.sequence.skipgrams',
allow_multiple_exports=True)(skipgrams)
# 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.
# ==============================================================================
"""Implementation of the Keras API, the high-level API of TensorFlow.
Detailed documentation and user guides are available at
[keras.io](https://keras.io).
"""
from keras import distribute
from keras import models
from keras.engine.input_layer import Input
from keras.engine.sequential import Sequential
from keras.engine.training import Model
# isort: off
from tensorflow.python import tf2
from tensorflow.python.util.tf_export import keras_export
__version__ = "2.10.0"
keras_export("keras.__version__").export_constant(__name__, "__version__")
filters: list (or concatenation) of characters to filter out, such as
punctuation. Default:
```
'!"#$%&()*+,-./:;<=>?@[\]^_`{|}~\t\n
```,
includes basic punctuation, tabs, and newlines.
lower: boolean. Whether to set the text to lowercase.
split: str. Separator for word splitting.
Returns:
List of integers in `[1, n]`. Each integer encodes a word
(unicity non-guaranteed).
"""
return text.one_hot(input_text,
n,
filters=filters,
lower=lower,
split=split)
# text.tokenizer_from_json is only available if keras_preprocessing >= 1.1.0
try:
tokenizer_from_json = text.tokenizer_from_json
keras_export('keras.preprocessing.text.tokenizer_from_json')(
tokenizer_from_json)
except AttributeError:
pass
keras_export('keras.preprocessing.text.hashing_trick')(hashing_trick)
keras_export('keras.preprocessing.text.Tokenizer')(Tokenizer)
import tensorflow.compat.v2 as tf
# isort: off
from tensorflow.python.util.tf_export import keras_export
_v1_zeros_initializer = tf.compat.v1.zeros_initializer
_v1_ones_initializer = tf.compat.v1.ones_initializer
_v1_constant_initializer = tf.compat.v1.constant_initializer
_v1_variance_scaling_initializer = tf.compat.v1.variance_scaling_initializer
_v1_orthogonal_initializer = tf.compat.v1.orthogonal_initializer
_v1_identity = tf.compat.v1.initializers.identity
_v1_glorot_uniform_initializer = tf.compat.v1.glorot_uniform_initializer
_v1_glorot_normal_initializer = tf.compat.v1.glorot_normal_initializer
keras_export(
v1=["keras.initializers.Zeros", "keras.initializers.zeros"],
allow_multiple_exports=True,
)(_v1_zeros_initializer)
keras_export(
v1=["keras.initializers.Ones", "keras.initializers.ones"],
allow_multiple_exports=True,
)(_v1_ones_initializer)
keras_export(
v1=["keras.initializers.Constant", "keras.initializers.constant"],
allow_multiple_exports=True,
)(_v1_constant_initializer)
keras_export(v1=["keras.initializers.VarianceScaling"],
allow_multiple_exports=True)(_v1_variance_scaling_initializer)
keras_export(
v1=["keras.initializers.Orthogonal", "keras.initializers.orthogonal"],
allow_multiple_exports=True,
)(_v1_orthogonal_initializer)
#
# 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.
# ==============================================================================
"""Utilities for text input preprocessing.
"""
# pylint: disable=invalid-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from keras_preprocessing import text
from tensorflow.python.util.tf_export import keras_export
text_to_word_sequence = text.text_to_word_sequence
one_hot = text.one_hot
hashing_trick = text.hashing_trick
Tokenizer = text.Tokenizer
keras_export(
'keras.preprocessing.text.text_to_word_sequence')(text_to_word_sequence)
keras_export('keras.preprocessing.text.one_hot')(one_hot)
keras_export('keras.preprocessing.text.hashing_trick')(hashing_trick)
keras_export('keras.preprocessing.text.Tokenizer')(Tokenizer)
from tensorflow.python.keras import backend
from tensorflow.python.keras import callbacks
from tensorflow.python.keras import callbacks_v1
from tensorflow.python.keras import constraints
from tensorflow.python.keras import datasets
from tensorflow.python.keras import estimator
from tensorflow.python.keras import initializers
from tensorflow.python.keras import layers
from tensorflow.python.keras import losses
from tensorflow.python.keras import metrics
from tensorflow.python.keras import models
from tensorflow.python.keras import ops
from tensorflow.python.keras import optimizers
from tensorflow.python.keras import preprocessing
from tensorflow.python.keras import regularizers
from tensorflow.python.keras import utils
from tensorflow.python.keras import wrappers
from tensorflow.python.keras.layers import Input
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.models import Sequential
from tensorflow.python.util.tf_export import keras_export
__version__ = '2.2.4-tf'
keras_export('keras.__version__').export_constant(__name__, '__version__')
del absolute_import
del division
del print_function
from tensorflow.python.distribute import distribution_strategy_context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.keras import backend as K
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import confusion_matrix
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import weights_broadcast_ops
from tensorflow.python.ops.losses import loss_reduction
from tensorflow.python.util.tf_export import keras_export
# TODO(joshl/psv): Update references to ReductionV2 to point to its
# new location.
ReductionV2 = keras_export( # pylint: disable=invalid-name
'keras.losses.Reduction', v1=[])(loss_reduction.ReductionV2)
def squeeze_or_expand_dimensions(y_pred, y_true, sample_weight):
"""Squeeze or expand last dimension if needed.
1. Squeezes last dim of `y_pred` or `y_true` if their rank differs by 1
(using `confusion_matrix.remove_squeezable_dimensions`).
2. Squeezes or expands last dim of `sample_weight` if its rank differs by 1
from the new rank of `y_pred`.
If `sample_weight` is scalar, it is kept scalar.
This will use static shape if available. Otherwise, it will add graph
operations, which could result in a performance hit.
Args:
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Module for exporting TensorFlow ops under tf.keras.*."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import init_ops_v2
from tensorflow.python.ops.losses import losses_impl
from tensorflow.python.util.tf_export import keras_export
# pylint: disable=bad-continuation
keras_export(v1=["keras.initializers.Initializer"])(init_ops.Initializer)
keras_export(v1=["keras.initializers.Zeros", "keras.initializers.zeros"])(
init_ops.Zeros)
keras_export(v1=["keras.initializers.Ones", "keras.initializers.ones"])(
init_ops.Ones)
keras_export(
v1=["keras.initializers.Constant", "keras.initializers.constant"])(
init_ops.Constant)
keras_export(v1=["keras.initializers.VarianceScaling"])(
init_ops.VarianceScaling)
keras_export(
v1=["keras.initializers.Orthogonal", "keras.initializers.orthogonal"])(
init_ops.Orthogonal)
keras_export(
v1=["keras.initializers.Identity", "keras.initializers.identity"])(
init_ops.Identity)
from __future__ import print_function
from tensorflow.python.distribute import distribution_strategy_context
from tensorflow.python.framework import ops
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.engine import keras_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.losses import loss_reduction
from tensorflow.python.ops.losses import util as tf_losses_utils
from tensorflow.python.util.tf_export import keras_export
# TODO(joshl/psv): Update references to ReductionV2 to point to its
# new location.
ReductionV2 = keras_export( # pylint: disable=invalid-name
'keras.losses.Reduction', v1=[])(loss_reduction.ReductionV2)
def remove_squeezable_dimensions(labels,
predictions,
expected_rank_diff=0,
name=None):
"""Squeeze last dim if ranks differ from expected by exactly 1.
In the common case where we expect shapes to match, `expected_rank_diff`
defaults to 0, and we squeeze the last dimension of the larger rank if they
differ by 1.
But, for example, if `labels` contains class IDs and `predictions` contains 1
probability per class, we expect `predictions` to have 1 more dimension than
`labels`, so `expected_rank_diff` would be 1. In this case, we'd squeeze
target_size=target_size,
color_mode=color_mode,
classes=classes,
class_mode=class_mode,
data_format=self.data_format,
batch_size=batch_size,
shuffle=shuffle,
seed=seed,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
subset=subset,
interpolation=interpolation,
validate_filenames=validate_filenames)
keras_export('keras.preprocessing.image.random_rotation', allow_multiple_exports=True)(random_rotation)
keras_export('keras.preprocessing.image.random_shift', allow_multiple_exports=True)(random_shift)
keras_export('keras.preprocessing.image.random_shear', allow_multiple_exports=True)(random_shear)
keras_export('keras.preprocessing.image.random_zoom', allow_multiple_exports=True)(random_zoom)
keras_export(
'keras.preprocessing.image.apply_channel_shift', allow_multiple_exports=True)(apply_channel_shift)
keras_export(
'keras.preprocessing.image.random_channel_shift', allow_multiple_exports=True)(random_channel_shift)
keras_export(
'keras.preprocessing.image.apply_brightness_shift', allow_multiple_exports=True)(apply_brightness_shift)
keras_export('keras.preprocessing.image.random_brightness', allow_multiple_exports=True)(random_brightness)
keras_export(
'keras.preprocessing.image.apply_affine_transform', allow_multiple_exports=True)(apply_affine_transform)
keras_export('keras.preprocessing.image.load_img', allow_multiple_exports=True)(load_img)
"""Keras initializers for TF 1."""
# pylint:disable=g-classes-have-attributes
import tensorflow.compat.v2 as tf
from tensorflow.python.util.tf_export import keras_export
_v1_zeros_initializer = tf.compat.v1.zeros_initializer
_v1_ones_initializer = tf.compat.v1.ones_initializer
_v1_constant_initializer = tf.compat.v1.constant_initializer
_v1_variance_scaling_initializer = tf.compat.v1.variance_scaling_initializer
_v1_orthogonal_initializer = tf.compat.v1.orthogonal_initializer
_v1_identity = tf.compat.v1.initializers.identity
_v1_glorot_uniform_initializer = tf.compat.v1.glorot_uniform_initializer
_v1_glorot_normal_initializer = tf.compat.v1.glorot_normal_initializer
keras_export(v1=['keras.initializers.Zeros', 'keras.initializers.zeros'],
allow_multiple_exports=True)(_v1_zeros_initializer)
keras_export(v1=['keras.initializers.Ones', 'keras.initializers.ones'],
allow_multiple_exports=True)(_v1_ones_initializer)
keras_export(v1=['keras.initializers.Constant', 'keras.initializers.constant'],
allow_multiple_exports=True)(_v1_constant_initializer)
keras_export(v1=['keras.initializers.VarianceScaling'],
allow_multiple_exports=True)(_v1_variance_scaling_initializer)
keras_export(
v1=['keras.initializers.Orthogonal', 'keras.initializers.orthogonal'],
allow_multiple_exports=True)(_v1_orthogonal_initializer)
keras_export(v1=['keras.initializers.Identity', 'keras.initializers.identity'],
allow_multiple_exports=True)(_v1_identity)
keras_export(v1=['keras.initializers.glorot_uniform'],
allow_multiple_exports=True)(_v1_glorot_uniform_initializer)
keras_export(v1=['keras.initializers.glorot_normal'],
allow_multiple_exports=True)(_v1_glorot_normal_initializer)
# limitations under the License.
# ==============================================================================
"""Module for exporting TensorFlow ops under tf.keras.*."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import init_ops_v2
from tensorflow.python.ops.losses import losses_impl
from tensorflow.python.util.tf_export import keras_export
# pylint: disable=bad-continuation
keras_export(v1=["keras.initializers.Initializer"])(
init_ops.Initializer)
keras_export(v1=["keras.initializers.Zeros", "keras.initializers.zeros"])(
init_ops.Zeros)
keras_export(v1=["keras.initializers.Ones", "keras.initializers.ones"])(
init_ops.Ones)
keras_export(v1=["keras.initializers.Constant", "keras.initializers.constant"])(
init_ops.Constant)
keras_export(v1=["keras.initializers.VarianceScaling"])(
init_ops.VarianceScaling)
keras_export(v1=["keras.initializers.Orthogonal",
"keras.initializers.orthogonal"])(
init_ops.Orthogonal)
keras_export(v1=["keras.initializers.Identity",
"keras.initializers.identity"])(
init_ops.Identity)
keras_export(v1=["keras.initializers.glorot_uniform"])(
height_shift_range=height_shift_range,
brightness_range=brightness_range,
shear_range=shear_range,
zoom_range=zoom_range,
channel_shift_range=channel_shift_range,
fill_mode=fill_mode,
cval=cval,
horizontal_flip=horizontal_flip,
vertical_flip=vertical_flip,
rescale=rescale,
preprocessing_function=preprocessing_function,
data_format=data_format,
validation_split=validation_split,
**kwargs)
keras_export('keras.preprocessing.image.random_rotation')(random_rotation)
keras_export('keras.preprocessing.image.random_shift')(random_shift)
keras_export('keras.preprocessing.image.random_shear')(random_shear)
keras_export('keras.preprocessing.image.random_zoom')(random_zoom)
keras_export('keras.preprocessing.image.apply_channel_shift')(
apply_channel_shift)
keras_export('keras.preprocessing.image.random_channel_shift')(
random_channel_shift)
keras_export('keras.preprocessing.image.apply_brightness_shift')(
apply_brightness_shift)
keras_export('keras.preprocessing.image.random_brightness')(random_brightness)
keras_export('keras.preprocessing.image.apply_affine_transform')(
apply_affine_transform)
keras_export('keras.preprocessing.image.load_img')(load_img)
batch_size: Number of timeseries samples in each batch
(except maybe the last one).
# Returns
A [Sequence](/utils/#sequence) instance.
# Examples
```python
from keras.preprocessing.sequence import TimeseriesGenerator
import numpy as np
data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])
data_gen = TimeseriesGenerator(data, targets,
length=10, sampling_rate=2,
batch_size=2)
assert len(data_gen) == 20
batch_0 = data_gen[0]
x, y = batch_0
assert np.array_equal(x,
np.array([[[0], [2], [4], [6], [8]],
[[1], [3], [5], [7], [9]]]))
assert np.array_equal(y,
np.array([[10], [11]]))
```
"""
pass
keras_export('keras.preprocessing.sequence.pad_sequences')(pad_sequences)
keras_export(
'keras.preprocessing.sequence.make_sampling_table')(make_sampling_table)
keras_export('keras.preprocessing.sequence.skipgrams')(skipgrams)
