1018 lines
32 KiB
Python
1018 lines
32 KiB
Python
|
# Copyright 2015 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.
|
||
|
|
# =============================================================================
|
||
|
|
|
||
|
|
"""Contains the pooling layer classes and their functional aliases."""
|
||
|
|
from __future__ import absolute_import
|
||
|
|
from __future__ import division
|
||
|
|
from __future__ import print_function
|
||
|
|
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
from keras import layers as keras_layers
|
||
|
|
from keras.legacy_tf_layers import base
|
||
|
|
|
||
|
|
# isort: off
|
||
|
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
from tensorflow.python.util.tf_export import tf_export
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.AveragePooling1D"])
|
||
|
|
@tf_export(v1=["layers.AveragePooling1D"])
|
||
|
|
class AveragePooling1D(keras_layers.AveragePooling1D, base.Layer):
|
||
|
|
"""Average Pooling layer for 1D inputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pool_size: An integer or tuple/list of a single integer,
|
||
|
|
representing the size of the pooling window.
|
||
|
|
strides: An integer or tuple/list of a single integer, specifying the
|
||
|
|
strides of the pooling operation.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string, one of `channels_last` (default) or
|
||
|
|
`channels_first`. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, length, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, length)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.AveragePooling1D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.compat.v1.layers.AveragePooling1D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.keras.layers.AveragePooling1D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
**kwargs
|
||
|
|
):
|
||
|
|
if strides is None:
|
||
|
|
raise ValueError("Argument `strides` must not be None.")
|
||
|
|
super().__init__(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
**kwargs
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.average_pooling1d"])
|
||
|
|
@tf_export(v1=["layers.average_pooling1d"])
|
||
|
|
def average_pooling1d(
|
||
|
|
inputs,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
):
|
||
|
|
"""Average Pooling layer for 1D inputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: The tensor over which to pool. Must have rank 3.
|
||
|
|
pool_size: An integer or tuple/list of a single integer,
|
||
|
|
representing the size of the pooling window.
|
||
|
|
strides: An integer or tuple/list of a single integer, specifying the
|
||
|
|
strides of the pooling operation.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string, one of `channels_last` (default) or
|
||
|
|
`channels_first`. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, length, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, length)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The output tensor, of rank 3.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.AveragePooling1D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
y = tf.compat.v1.layers.average_pooling1d(x, pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
To migrate code using TF1 functional layers use the [Keras Functional API]
|
||
|
|
(https://www.tensorflow.org/guide/keras/functional):
|
||
|
|
|
||
|
|
```python
|
||
|
|
x = tf.keras.Input((28, 28, 1))
|
||
|
|
y = tf.keras.layers.AveragePooling1D(pool_size=2, strides=2)(x)
|
||
|
|
model = tf.keras.Model(x, y)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
warnings.warn(
|
||
|
|
"`tf.layers.average_pooling1d` is deprecated and "
|
||
|
|
"will be removed in a future version. "
|
||
|
|
"Please use `tf.keras.layers.AveragePooling1D` instead.",
|
||
|
|
stacklevel=2,
|
||
|
|
)
|
||
|
|
layer = AveragePooling1D(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
)
|
||
|
|
return layer(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.MaxPooling1D"])
|
||
|
|
@tf_export(v1=["layers.MaxPooling1D"])
|
||
|
|
class MaxPooling1D(keras_layers.MaxPooling1D, base.Layer):
|
||
|
|
"""Max Pooling layer for 1D inputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pool_size: An integer or tuple/list of a single integer,
|
||
|
|
representing the size of the pooling window.
|
||
|
|
strides: An integer or tuple/list of a single integer, specifying the
|
||
|
|
strides of the pooling operation.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string, one of `channels_last` (default) or
|
||
|
|
`channels_first`. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, length, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, length)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.MaxPooling1D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.compat.v1.layers.MaxPooling1D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
**kwargs
|
||
|
|
):
|
||
|
|
if strides is None:
|
||
|
|
raise ValueError("Argument `strides` must not be None.")
|
||
|
|
super().__init__(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
**kwargs
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.max_pooling1d"])
|
||
|
|
@tf_export(v1=["layers.max_pooling1d"])
|
||
|
|
def max_pooling1d(
|
||
|
|
inputs,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
):
|
||
|
|
"""Max Pooling layer for 1D inputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: The tensor over which to pool. Must have rank 3.
|
||
|
|
pool_size: An integer or tuple/list of a single integer,
|
||
|
|
representing the size of the pooling window.
|
||
|
|
strides: An integer or tuple/list of a single integer, specifying the
|
||
|
|
strides of the pooling operation.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string, one of `channels_last` (default) or
|
||
|
|
`channels_first`. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, length, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, length)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The output tensor, of rank 3.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.MaxPooling1D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
y = tf.compat.v1.layers.max_pooling1d(x, pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
To migrate code using TF1 functional layers use the [Keras Functional API]
|
||
|
|
(https://www.tensorflow.org/guide/keras/functional):
|
||
|
|
|
||
|
|
```python
|
||
|
|
x = tf.keras.Input((28, 28, 1))
|
||
|
|
y = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2)(x)
|
||
|
|
model = tf.keras.Model(x, y)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
warnings.warn(
|
||
|
|
"`tf.layers.max_pooling1d` is deprecated and "
|
||
|
|
"will be removed in a future version. "
|
||
|
|
"Please use `tf.keras.layers.MaxPooling1D` instead.",
|
||
|
|
stacklevel=2,
|
||
|
|
)
|
||
|
|
layer = MaxPooling1D(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
)
|
||
|
|
return layer(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.AveragePooling2D"])
|
||
|
|
@tf_export(v1=["layers.AveragePooling2D"])
|
||
|
|
class AveragePooling2D(keras_layers.AveragePooling2D, base.Layer):
|
||
|
|
"""Average pooling layer for 2D inputs (e.g. images).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pool_size: An integer or tuple/list of 2 integers: (pool_height,
|
||
|
|
pool_width) specifying the size of the pooling window.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
strides: An integer or tuple/list of 2 integers,
|
||
|
|
specifying the strides of the pooling operation.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` (default) and `channels_first` are supported.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, height, width, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, height, width)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.AveragePooling2D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.compat.v1.layers.AveragePooling2D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.keras.layers.AveragePooling2D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
**kwargs
|
||
|
|
):
|
||
|
|
if strides is None:
|
||
|
|
raise ValueError("Argument `strides` must not be None.")
|
||
|
|
super().__init__(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
**kwargs
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.average_pooling2d"])
|
||
|
|
@tf_export(v1=["layers.average_pooling2d"])
|
||
|
|
def average_pooling2d(
|
||
|
|
inputs,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
):
|
||
|
|
"""Average pooling layer for 2D inputs (e.g. images).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: The tensor over which to pool. Must have rank 4.
|
||
|
|
pool_size: An integer or tuple/list of 2 integers: (pool_height,
|
||
|
|
pool_width) specifying the size of the pooling window.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
strides: An integer or tuple/list of 2 integers,
|
||
|
|
specifying the strides of the pooling operation.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` (default) and `channels_first` are supported.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, height, width, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, height, width)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.AveragePooling2D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
y = tf.compat.v1.layers.average_pooling2d(x, pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
To migrate code using TF1 functional layers use the [Keras Functional API]
|
||
|
|
(https://www.tensorflow.org/guide/keras/functional):
|
||
|
|
|
||
|
|
```python
|
||
|
|
x = tf.keras.Input((28, 28, 1))
|
||
|
|
y = tf.keras.layers.AveragePooling2D(pool_size=2, strides=2)(x)
|
||
|
|
model = tf.keras.Model(x, y)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
warnings.warn(
|
||
|
|
"`tf.layers.average_pooling2d` is deprecated and "
|
||
|
|
"will be removed in a future version. "
|
||
|
|
"Please use `tf.keras.layers.AveragePooling2D` instead.",
|
||
|
|
stacklevel=2,
|
||
|
|
)
|
||
|
|
layer = AveragePooling2D(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
)
|
||
|
|
return layer(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.MaxPooling2D"])
|
||
|
|
@tf_export(v1=["layers.MaxPooling2D"])
|
||
|
|
class MaxPooling2D(keras_layers.MaxPooling2D, base.Layer):
|
||
|
|
"""Max pooling layer for 2D inputs (e.g. images).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pool_size: An integer or tuple/list of 2 integers: (pool_height,
|
||
|
|
pool_width) specifying the size of the pooling window.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
strides: An integer or tuple/list of 2 integers,
|
||
|
|
specifying the strides of the pooling operation.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` (default) and `channels_first` are supported.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, height, width, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, height, width)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.MaxPooling2D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.compat.v1.layers.MaxPooling2D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.keras.layers.MaxPooling2D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
**kwargs
|
||
|
|
):
|
||
|
|
if strides is None:
|
||
|
|
raise ValueError("Argument `strides` must not be None.")
|
||
|
|
super().__init__(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
**kwargs
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.max_pooling2d"])
|
||
|
|
@tf_export(v1=["layers.max_pooling2d"])
|
||
|
|
def max_pooling2d(
|
||
|
|
inputs,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
):
|
||
|
|
"""Max pooling layer for 2D inputs (e.g. images).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: The tensor over which to pool. Must have rank 4.
|
||
|
|
pool_size: An integer or tuple/list of 2 integers: (pool_height,
|
||
|
|
pool_width) specifying the size of the pooling window.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
strides: An integer or tuple/list of 2 integers,
|
||
|
|
specifying the strides of the pooling operation.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` (default) and `channels_first` are supported.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, height, width, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, height, width)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.MaxPooling2D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
y = tf.compat.v1.layers.max_pooling2d(x, pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
To migrate code using TF1 functional layers use the [Keras Functional API]
|
||
|
|
(https://www.tensorflow.org/guide/keras/functional):
|
||
|
|
|
||
|
|
```python
|
||
|
|
x = tf.keras.Input((28, 28, 1))
|
||
|
|
y = tf.keras.layers.MaxPooling2D(pool_size=2, strides=2)(x)
|
||
|
|
model = tf.keras.Model(x, y)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
warnings.warn(
|
||
|
|
"`tf.layers.max_pooling2d` is deprecated and "
|
||
|
|
"will be removed in a future version. "
|
||
|
|
"Please use `tf.keras.layers.MaxPooling2D` instead.",
|
||
|
|
stacklevel=2,
|
||
|
|
)
|
||
|
|
layer = MaxPooling2D(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
)
|
||
|
|
return layer(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.AveragePooling3D"])
|
||
|
|
@tf_export(v1=["layers.AveragePooling3D"])
|
||
|
|
class AveragePooling3D(keras_layers.AveragePooling3D, base.Layer):
|
||
|
|
"""Average pooling layer for 3D inputs (e.g. volumes).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pool_size: An integer or tuple/list of 3 integers:
|
||
|
|
(pool_depth, pool_height, pool_width)
|
||
|
|
specifying the size of the pooling window.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
strides: An integer or tuple/list of 3 integers,
|
||
|
|
specifying the strides of the pooling operation.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` (default) and `channels_first` are supported.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, depth, height, width, channels)` while `channels_first`
|
||
|
|
corresponds to inputs with shape
|
||
|
|
`(batch, channels, depth, height, width)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.AveragePooling3D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.compat.v1.layers.AveragePooling3D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.keras.layers.AveragePooling3D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
**kwargs
|
||
|
|
):
|
||
|
|
if strides is None:
|
||
|
|
raise ValueError("Argument `strides` must not be None.")
|
||
|
|
super().__init__(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
**kwargs
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.average_pooling3d"])
|
||
|
|
@tf_export(v1=["layers.average_pooling3d"])
|
||
|
|
def average_pooling3d(
|
||
|
|
inputs,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
):
|
||
|
|
"""Average pooling layer for 3D inputs (e.g. volumes).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: The tensor over which to pool. Must have rank 5.
|
||
|
|
pool_size: An integer or tuple/list of 3 integers:
|
||
|
|
(pool_depth, pool_height, pool_width)
|
||
|
|
specifying the size of the pooling window.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
strides: An integer or tuple/list of 3 integers,
|
||
|
|
specifying the strides of the pooling operation.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` (default) and `channels_first` are supported.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, depth, height, width, channels)` while `channels_first`
|
||
|
|
corresponds to inputs with shape
|
||
|
|
`(batch, channels, depth, height, width)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.AveragePooling3D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
y = tf.compat.v1.layers.average_pooling3d(x, pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
To migrate code using TF1 functional layers use the [Keras Functional API]
|
||
|
|
(https://www.tensorflow.org/guide/keras/functional):
|
||
|
|
|
||
|
|
```python
|
||
|
|
x = tf.keras.Input((28, 28, 1))
|
||
|
|
y = tf.keras.layers.AveragePooling3D(pool_size=2, strides=2)(x)
|
||
|
|
model = tf.keras.Model(x, y)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
warnings.warn(
|
||
|
|
"`tf.layers.average_pooling3d` is deprecated and "
|
||
|
|
"will be removed in a future version. "
|
||
|
|
"Please use `tf.keras.layers.AveragePooling3D` instead.",
|
||
|
|
stacklevel=2,
|
||
|
|
)
|
||
|
|
layer = AveragePooling3D(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
)
|
||
|
|
return layer(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.MaxPooling3D"])
|
||
|
|
@tf_export(v1=["layers.MaxPooling3D"])
|
||
|
|
class MaxPooling3D(keras_layers.MaxPooling3D, base.Layer):
|
||
|
|
"""Max pooling layer for 3D inputs (e.g. volumes).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pool_size: An integer or tuple/list of 3 integers:
|
||
|
|
(pool_depth, pool_height, pool_width)
|
||
|
|
specifying the size of the pooling window.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
strides: An integer or tuple/list of 3 integers,
|
||
|
|
specifying the strides of the pooling operation.
|
||
|
|
Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` (default) and `channels_first` are supported.
|
||
|
|
`channels_last` corresponds to inputs with shape
|
||
|
|
`(batch, depth, height, width, channels)` while `channels_first`
|
||
|
|
corresponds to inputs with shape
|
||
|
|
`(batch, channels, depth, height, width)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.MaxPooling3D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.compat.v1.layers.MaxPooling3D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
```python
|
||
|
|
pooling = tf.keras.layers.MaxPooling3D(pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
**kwargs
|
||
|
|
):
|
||
|
|
if strides is None:
|
||
|
|
raise ValueError("Argument `strides` must not be None.")
|
||
|
|
super().__init__(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
**kwargs
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(v1=["keras.__internal__.legacy.layers.max_pooling3d"])
|
||
|
|
@tf_export(v1=["layers.max_pooling3d"])
|
||
|
|
def max_pooling3d(
|
||
|
|
inputs,
|
||
|
|
pool_size,
|
||
|
|
strides,
|
||
|
|
padding="valid",
|
||
|
|
data_format="channels_last",
|
||
|
|
name=None,
|
||
|
|
):
|
||
|
|
"""Max pooling layer for 3D inputs (e.g.
|
||
|
|
|
||
|
|
volumes).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: The tensor over which to pool. Must have rank 5.
|
||
|
|
pool_size: An integer or tuple/list of 3 integers: (pool_depth,
|
||
|
|
pool_height, pool_width) specifying the size of the pooling window. Can
|
||
|
|
be a single integer to specify the same value for all spatial
|
||
|
|
dimensions.
|
||
|
|
strides: An integer or tuple/list of 3 integers, specifying the strides of
|
||
|
|
the pooling operation. Can be a single integer to specify the same value
|
||
|
|
for all spatial dimensions.
|
||
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
||
|
|
Case-insensitive.
|
||
|
|
data_format: A string. The ordering of the dimensions in the inputs.
|
||
|
|
`channels_last` (default) and `channels_first` are supported.
|
||
|
|
`channels_last` corresponds to inputs with shape `(batch, depth, height,
|
||
|
|
width, channels)` while `channels_first` corresponds to inputs with
|
||
|
|
shape `(batch, channels, depth, height, width)`.
|
||
|
|
name: A string, the name of the layer.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
|
||
|
|
|
||
|
|
@compatibility(TF2)
|
||
|
|
This API is a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with Keras.
|
||
|
|
|
||
|
|
The corresponding TensorFlow v2 layer is
|
||
|
|
`tf.keras.layers.MaxPooling3D`.
|
||
|
|
|
||
|
|
|
||
|
|
#### Structural Mapping to Native TF2
|
||
|
|
|
||
|
|
None of the supported arguments have changed name.
|
||
|
|
|
||
|
|
Before:
|
||
|
|
|
||
|
|
```python
|
||
|
|
y = tf.compat.v1.layers.max_pooling3d(x, pool_size=2, strides=2)
|
||
|
|
```
|
||
|
|
|
||
|
|
After:
|
||
|
|
|
||
|
|
To migrate code using TF1 functional layers use the [Keras Functional API]
|
||
|
|
(https://www.tensorflow.org/guide/keras/functional):
|
||
|
|
|
||
|
|
```python
|
||
|
|
x = tf.keras.Input((28, 28, 1))
|
||
|
|
y = tf.keras.layers.MaxPooling3D(pool_size=2, strides=2)(x)
|
||
|
|
model = tf.keras.Model(x, y)
|
||
|
|
```
|
||
|
|
@end_compatibility
|
||
|
|
"""
|
||
|
|
warnings.warn(
|
||
|
|
"`tf.layers.max_pooling3d` is deprecated and "
|
||
|
|
"will be removed in a future version. "
|
||
|
|
"Please use `tf.keras.layers.MaxPooling3D` instead.",
|
||
|
|
stacklevel=2,
|
||
|
|
)
|
||
|
|
layer = MaxPooling3D(
|
||
|
|
pool_size=pool_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
name=name,
|
||
|
|
)
|
||
|
|
return layer(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
# Aliases
|
||
|
|
|
||
|
|
AvgPool2D = AveragePooling2D
|
||
|
|
MaxPool2D = MaxPooling2D
|
||
|
|
max_pool2d = max_pooling2d
|
||
|
|
avg_pool2d = average_pooling2d
|