Module keras.legacy_tf_layers.convolutional
Contains the convolutional layer classes and their functional aliases.
Expand source code
# 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.
# =============================================================================
# pylint: disable=g-classes-have-attributes
"""Contains the convolutional layer classes and their functional aliases."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v2 as tf
import warnings
from keras import layers as keras_layers
from keras.legacy_tf_layers import base
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.Conv1D'])
@tf_export(v1=['layers.Conv1D'])
class Conv1D(keras_layers.Conv1D, base.Layer):
"""1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
outputs. If `use_bias` is True (and a `bias_initializer` is provided),
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: An integer or tuple/list of a single integer, specifying the
length of the 1D convolution window.
strides: An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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)`.
dilation_rate: An integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv1D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
conv = tf.compat.v1.layers.Conv1D(filters=3, kernel_size=3)
```
After:
```python
conv = tf.keras.layers.Conv1D(filters=3, kernels_size=3)
```
@end_compatibility
"""
def __init__(self, filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(Conv1D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name, **kwargs)
@keras_export(v1=['keras.__internal__.legacy.layers.conv1d'])
@tf_export(v1=['layers.conv1d'])
def conv1d(inputs,
filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for 1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
outputs. If `use_bias` is True (and a `bias_initializer` is provided),
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
Args:
inputs: Tensor input.
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: An integer or tuple/list of a single integer, specifying the
length of the 1D convolution window.
strides: An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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)`.
dilation_rate: An integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv1D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.conv1d(x, filters=3, kernel_size=3)
```
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.Conv1D(filters=3, kernels_size=3)(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.conv1d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv1D` instead.')
layer = Conv1D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)
@keras_export(v1=['keras.__internal__.legacy.layers.Conv2D'])
@tf_export(v1=['layers.Conv2D'])
class Conv2D(keras_layers.Conv2D, base.Layer):
"""2D convolution layer (e.g. spatial convolution over images).
This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
outputs. If `use_bias` is True (and a `bias_initializer` is provided),
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the
height and width of the 2D convolution 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 convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
dilation_rate: An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv2D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
conv = tf.compat.v1.layers.Conv2D(filters=3, kernel_size=3)
```
After:
```python
conv = tf.keras.layers.Conv2D(filters=3, kernels_size=3)
```
@end_compatibility
"""
def __init__(self, filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format='channels_last',
dilation_rate=(1, 1),
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(Conv2D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name, **kwargs)
@keras_export(v1=['keras.__internal__.legacy.layers.conv2d'])
@tf_export(v1=['layers.conv2d'])
def conv2d(inputs,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format='channels_last',
dilation_rate=(1, 1),
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for the 2D convolution layer.
This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
outputs. If `use_bias` is True (and a `bias_initializer` is provided),
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
Args:
inputs: Tensor input.
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the
height and width of the 2D convolution 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 convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
dilation_rate: An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv2D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.conv2d(x, filters=3, kernel_size=3)
```
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.Conv2D(filters=3, kernels_size=3)(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.conv2d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv2D` instead.')
layer = Conv2D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)
@keras_export(v1=['keras.__internal__.legacy.layers.Conv3D'])
@tf_export(v1=['layers.Conv3D'])
class Conv3D(keras_layers.Conv3D, base.Layer):
"""3D convolution layer (e.g. spatial convolution over volumes).
This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
outputs. If `use_bias` is True (and a `bias_initializer` is provided),
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: An integer or tuple/list of 3 integers, specifying the
depth, height and width of the 3D convolution 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 convolution along the depth,
height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
dilation_rate: An integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv3D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
conv = tf.compat.v1.layers.Conv3D(filters=3, kernel_size=3)
```
After:
```python
conv = tf.keras.layers.Conv3D(filters=3, kernels_size=3)
```
@end_compatibility
"""
def __init__(self, filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format='channels_last',
dilation_rate=(1, 1, 1),
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(Conv3D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name, **kwargs)
@keras_export(v1=['keras.__internal__.legacy.layers.conv3d'])
@tf_export(v1=['layers.conv3d'])
def conv3d(inputs,
filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format='channels_last',
dilation_rate=(1, 1, 1),
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for the 3D convolution layer.
This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
outputs. If `use_bias` is True (and a `bias_initializer` is provided),
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
Args:
inputs: Tensor input.
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: An integer or tuple/list of 3 integers, specifying the
depth, height and width of the 3D convolution 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 convolution along the depth,
height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
dilation_rate: An integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv3D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.conv3d(x, filters=3, kernel_size=3)
```
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.Conv3D(filters=3, kernels_size=3)(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.conv3d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv3D` instead.')
layer = Conv3D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)
@keras_export(v1=['keras.__internal__.legacy.layers.SeparableConv1D'])
@tf_export(v1=['layers.SeparableConv1D'])
class SeparableConv1D(keras_layers.SeparableConv1D, base.Layer):
"""Depthwise separable 1D convolution.
This layer performs a depthwise convolution that acts separately on
channels, followed by a pointwise convolution that mixes channels.
If `use_bias` is True and a bias initializer is provided,
it adds a bias vector to the output.
It then optionally applies an activation function to produce the final output.
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: A single integer specifying the spatial
dimensions of the filters.
strides: A single integer specifying the strides
of the convolution.
Specifying any `stride` value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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)`.
dilation_rate: A single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
depth_multiplier: The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to `num_filters_in * depth_multiplier`.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
depthwise_initializer: An initializer for the depthwise convolution kernel.
pointwise_initializer: An initializer for the pointwise convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
depthwise_regularizer: Optional regularizer for the depthwise
convolution kernel.
pointwise_regularizer: Optional regularizer for the pointwise
convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
depthwise_constraint: Optional projection function to be applied to the
depthwise kernel after being updated by an `Optimizer` (e.g. used for
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
pointwise_constraint: Optional projection function to be applied to the
pointwise kernel after being updated by an `Optimizer`.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.SeparableConv1D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
conv = tf.compat.v1.layers.SeparableConv1D(filters=3, kernel_size=3)
```
After:
```python
conv = tf.keras.layers.SeparableConv1D(filters=3, kernels_size=3)
```
@end_compatibility
"""
def __init__(self, filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer=None,
pointwise_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(SeparableConv1D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
depth_multiplier=depth_multiplier,
activation=activation,
use_bias=use_bias,
depthwise_initializer=depthwise_initializer,
pointwise_initializer=pointwise_initializer,
bias_initializer=bias_initializer,
depthwise_regularizer=depthwise_regularizer,
pointwise_regularizer=pointwise_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
depthwise_constraint=depthwise_constraint,
pointwise_constraint=pointwise_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
**kwargs)
@keras_export(v1=['keras.__internal__.legacy.layers.SeparableConv2D'])
@tf_export(v1=['layers.SeparableConv2D'])
class SeparableConv2D(keras_layers.SeparableConv2D, base.Layer):
"""Depthwise separable 2D convolution.
This layer performs a depthwise convolution that acts separately on
channels, followed by a pointwise convolution that mixes channels.
If `use_bias` is True and a bias initializer is provided,
it adds a bias vector to the output.
It then optionally applies an activation function to produce the final output.
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: A tuple or list of 2 integers specifying the spatial
dimensions of the filters. Can be a single integer to specify the same
value for all spatial dimensions.
strides: A tuple or list of 2 positive integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any `stride` value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
dilation_rate: An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
depth_multiplier: The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to `num_filters_in * depth_multiplier`.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
depthwise_initializer: An initializer for the depthwise convolution kernel.
pointwise_initializer: An initializer for the pointwise convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
depthwise_regularizer: Optional regularizer for the depthwise
convolution kernel.
pointwise_regularizer: Optional regularizer for the pointwise
convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
depthwise_constraint: Optional projection function to be applied to the
depthwise kernel after being updated by an `Optimizer` (e.g. used for
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
pointwise_constraint: Optional projection function to be applied to the
pointwise kernel after being updated by an `Optimizer`.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.SeparableConv2D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
conv = tf.compat.v1.layers.SeparableConv2D(filters=3, kernel_size=3)
```
After:
```python
conv = tf.keras.layers.SeparableConv2D(filters=3, kernels_size=3)
```
@end_compatibility
"""
def __init__(self, filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format='channels_last',
dilation_rate=(1, 1),
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer=None,
pointwise_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(SeparableConv2D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
depth_multiplier=depth_multiplier,
activation=activation,
use_bias=use_bias,
depthwise_initializer=depthwise_initializer,
pointwise_initializer=pointwise_initializer,
bias_initializer=bias_initializer,
depthwise_regularizer=depthwise_regularizer,
pointwise_regularizer=pointwise_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
depthwise_constraint=depthwise_constraint,
pointwise_constraint=pointwise_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
**kwargs)
@keras_export(v1=['keras.__internal__.legacy.layers.separable_conv1d'])
@tf_export(v1=['layers.separable_conv1d'])
def separable_conv1d(inputs,
filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer=None,
pointwise_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for the depthwise separable 1D convolution layer.
This layer performs a depthwise convolution that acts separately on
channels, followed by a pointwise convolution that mixes channels.
If `use_bias` is True and a bias initializer is provided,
it adds a bias vector to the output.
It then optionally applies an activation function to produce the final output.
Args:
inputs: Input tensor.
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: A single integer specifying the spatial
dimensions of the filters.
strides: A single integer specifying the strides
of the convolution.
Specifying any `stride` value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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)`.
dilation_rate: A single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
depth_multiplier: The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to `num_filters_in * depth_multiplier`.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
depthwise_initializer: An initializer for the depthwise convolution kernel.
pointwise_initializer: An initializer for the pointwise convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
depthwise_regularizer: Optional regularizer for the depthwise
convolution kernel.
pointwise_regularizer: Optional regularizer for the pointwise
convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
depthwise_constraint: Optional projection function to be applied to the
depthwise kernel after being updated by an `Optimizer` (e.g. used for
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
pointwise_constraint: Optional projection function to be applied to the
pointwise kernel after being updated by an `Optimizer`.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.SeparableConv1D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.separable_conv1d(x, filters=3, kernel_size=3)
```
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.SeparableConv1D(filters=3, kernels_size=3)(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.separable_conv1d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.SeparableConv1D` instead.')
layer = SeparableConv1D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
depth_multiplier=depth_multiplier,
activation=activation,
use_bias=use_bias,
depthwise_initializer=depthwise_initializer,
pointwise_initializer=pointwise_initializer,
bias_initializer=bias_initializer,
depthwise_regularizer=depthwise_regularizer,
pointwise_regularizer=pointwise_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
depthwise_constraint=depthwise_constraint,
pointwise_constraint=pointwise_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)
@keras_export(v1=['keras.__internal__.legacy.layers.separable_conv2d'])
@tf_export(v1=['layers.separable_conv2d'])
def separable_conv2d(inputs,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format='channels_last',
dilation_rate=(1, 1),
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer=None,
pointwise_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for the depthwise separable 2D convolution layer.
This layer performs a depthwise convolution that acts separately on
channels, followed by a pointwise convolution that mixes channels.
If `use_bias` is True and a bias initializer is provided,
it adds a bias vector to the output.
It then optionally applies an activation function to produce the final output.
Args:
inputs: Input tensor.
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: A tuple or list of 2 integers specifying the spatial
dimensions of the filters. Can be a single integer to specify the same
value for all spatial dimensions.
strides: A tuple or list of 2 positive integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any `stride` value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
dilation_rate: An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
depth_multiplier: The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to `num_filters_in * depth_multiplier`.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
depthwise_initializer: An initializer for the depthwise convolution kernel.
pointwise_initializer: An initializer for the pointwise convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
depthwise_regularizer: Optional regularizer for the depthwise
convolution kernel.
pointwise_regularizer: Optional regularizer for the pointwise
convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
depthwise_constraint: Optional projection function to be applied to the
depthwise kernel after being updated by an `Optimizer` (e.g. used for
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
pointwise_constraint: Optional projection function to be applied to the
pointwise kernel after being updated by an `Optimizer`.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.SeparableConv2D`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.separable_conv2d(x, filters=3, kernel_size=3)
```
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.SeparableConv2D(filters=3, kernels_size=3)(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.separable_conv2d` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.SeparableConv2D` instead.')
layer = SeparableConv2D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
depth_multiplier=depth_multiplier,
activation=activation,
use_bias=use_bias,
depthwise_initializer=depthwise_initializer,
pointwise_initializer=pointwise_initializer,
bias_initializer=bias_initializer,
depthwise_regularizer=depthwise_regularizer,
pointwise_regularizer=pointwise_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
depthwise_constraint=depthwise_constraint,
pointwise_constraint=pointwise_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)
@keras_export(v1=['keras.__internal__.legacy.layers.Conv2DTranspose'])
@tf_export(v1=['layers.Conv2DTranspose'])
class Conv2DTranspose(keras_layers.Conv2DTranspose, base.Layer):
"""Transposed 2D convolution layer (sometimes called 2D Deconvolution).
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: A tuple or list of 2 positive integers specifying the spatial
dimensions of the filters. Can be a single integer to specify the same
value for all spatial dimensions.
strides: A tuple or list of 2 positive integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
padding: one of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv2DTranspose`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
conv = tf.compat.v1.layers.Conv2DTranspose(filters=3, kernel_size=3)
```
After:
```python
conv = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)
```
@end_compatibility
"""
def __init__(self, filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format='channels_last',
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(Conv2DTranspose, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
**kwargs)
@keras_export(v1=['keras.__internal__.legacy.layers.conv2d_transpose'])
@tf_export(v1=['layers.conv2d_transpose'])
def conv2d_transpose(inputs,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format='channels_last',
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for transposed 2D convolution layer.
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
Args:
inputs: Input tensor.
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: A tuple or list of 2 positive integers specifying the spatial
dimensions of the filters. Can be a single integer to specify the same
value for all spatial dimensions.
strides: A tuple or list of 2 positive integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
padding: one of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
activation: Activation function. Set it to `None` to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If `None`, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv2DTranspose`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3)
```
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.Conv2DTranspose(filters=3, kernels_size=3)(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.conv2d_transpose` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv2DTranspose` instead.')
layer = Conv2DTranspose(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)
@keras_export(v1=['keras.__internal__.legacy.layers.Conv3DTranspose'])
@tf_export(v1=['layers.Conv3DTranspose'])
class Conv3DTranspose(keras_layers.Conv3DTranspose, base.Layer):
"""Transposed 3D convolution layer (sometimes called 3D Deconvolution).
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: An integer or tuple/list of 3 integers, specifying the
depth, height and width of the 3D convolution 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 convolution along the depth, height and width.
Can be a single integer to specify the same value for all spatial
dimensions.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
activation: Activation function. Set it to `None` to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If `None`, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv3DTranspose`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3)
```
After:
```python
conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)
```
@end_compatibility
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format='channels_last',
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(Conv3DTranspose, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
**kwargs)
@keras_export(v1=['keras.__internal__.legacy.layers.conv3d_transpose'])
@tf_export(v1=['layers.conv3d_transpose'])
def conv3d_transpose(inputs,
filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format='channels_last',
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for transposed 3D convolution layer.
Args:
inputs: Input tensor.
filters: Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
kernel_size: A tuple or list of 3 positive integers specifying the spatial
dimensions of the filters. Can be a single integer to specify the same
value for all spatial dimensions.
strides: A tuple or list of 3 positive integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
padding: one of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
activation: Activation function. Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: An initializer for the convolution kernel.
bias_initializer: An initializer for the bias vector. If None, the default
initializer will be used.
kernel_regularizer: Optional regularizer for the convolution kernel.
bias_regularizer: Optional regularizer for the bias vector.
activity_regularizer: Optional regularizer function for the output.
kernel_constraint: Optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: Optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: A string, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.Conv3DTranspose`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3)
```
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.Conv3DTranspose(filters=3, kernels_size=3)(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.conv3d_transpose` is deprecated and '
'will be removed in a future version. '
'Please Use `tf.keras.layers.Conv3DTranspose` instead.')
layer = Conv3DTranspose(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)
# Aliases
Convolution1D = Conv1D
Convolution2D = Conv2D
Convolution3D = Conv3D
SeparableConvolution2D = SeparableConv2D
Convolution2DTranspose = Deconvolution2D = Deconv2D = Conv2DTranspose
Convolution3DTranspose = Deconvolution3D = Deconv3D = Conv3DTranspose
convolution1d = conv1d
convolution2d = conv2d
convolution3d = conv3d
separable_convolution2d = separable_conv2d
convolution2d_transpose = deconvolution2d = deconv2d = conv2d_transpose
convolution3d_transpose = deconvolution3d = deconv3d = conv3d_transpose
Functions
def conv1d(inputs, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for 1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
inputs
- Tensor input.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
strides
- An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, length, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, length)
. dilation_rate
- An integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying anystrides
value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv1D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv1d(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv1D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv1d']) @tf_export(v1=['layers.conv1d']) def conv1d(inputs, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for 1D convolution layer (e.g. temporal convolution). This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: inputs: Tensor input. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window. strides: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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)`. dilation_rate: An integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv1d(x, filters=3, kernel_size=3) ``` 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.Conv1D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv1d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv1D` instead.') layer = Conv1D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for the 2D convolution layer.
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
inputs
- Tensor input.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution 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 convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. dilation_rate
- An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv2d(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv2D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv2d']) @tf_export(v1=['layers.conv2d']) def conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for the 2D convolution layer. This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: inputs: Tensor input. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution 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 convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv2d(x, filters=3, kernel_size=3) ``` 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.Conv2D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv2d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv2D` instead.') layer = Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def conv2d_transpose(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for transposed 2D convolution layer.
The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. activation
- Activation function. Set it to
None
to maintain a linear activation. use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If
None
, the default initializer will be used. kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv2d_transpose']) @tf_export(v1=['layers.conv2d_transpose']) def conv2d_transpose(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for transposed 2D convolution layer. The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. activation: Activation function. Set it to `None` to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If `None`, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3) ``` 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.Conv2DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv2d_transpose` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv2DTranspose` instead.') layer = Conv2DTranspose( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def conv3d(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for the 3D convolution layer.
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
inputs
- Tensor input.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth,
height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. dilation_rate
- An integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv3d(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv3D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv3d']) @tf_export(v1=['layers.conv3d']) def conv3d(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for the 3D convolution layer. This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: inputs: Tensor input. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. dilation_rate: An integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv3d(x, filters=3, kernel_size=3) ``` 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.Conv3D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv3d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv3D` instead.') layer = Conv3D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def conv3d_transpose(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for transposed 3D convolution layer.
Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 3 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv3d_transpose']) @tf_export(v1=['layers.conv3d_transpose']) def conv3d_transpose(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for transposed 3D convolution layer. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 3 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3) ``` 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.Conv3DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv3d_transpose` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv3DTranspose` instead.') layer = Conv3DTranspose( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def convolution1d(inputs, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for 1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
inputs
- Tensor input.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
strides
- An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, length, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, length)
. dilation_rate
- An integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying anystrides
value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv1D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv1d(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv1D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv1d']) @tf_export(v1=['layers.conv1d']) def conv1d(inputs, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for 1D convolution layer (e.g. temporal convolution). This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: inputs: Tensor input. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window. strides: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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)`. dilation_rate: An integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv1d(x, filters=3, kernel_size=3) ``` 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.Conv1D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv1d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv1D` instead.') layer = Conv1D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def convolution2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for the 2D convolution layer.
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
inputs
- Tensor input.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution 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 convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. dilation_rate
- An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv2d(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv2D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv2d']) @tf_export(v1=['layers.conv2d']) def conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for the 2D convolution layer. This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: inputs: Tensor input. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution 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 convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv2d(x, filters=3, kernel_size=3) ``` 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.Conv2D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv2d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv2D` instead.') layer = Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def convolution2d_transpose(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for transposed 2D convolution layer.
The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. activation
- Activation function. Set it to
None
to maintain a linear activation. use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If
None
, the default initializer will be used. kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv2d_transpose']) @tf_export(v1=['layers.conv2d_transpose']) def conv2d_transpose(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for transposed 2D convolution layer. The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. activation: Activation function. Set it to `None` to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If `None`, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3) ``` 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.Conv2DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv2d_transpose` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv2DTranspose` instead.') layer = Conv2DTranspose( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def convolution3d(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for the 3D convolution layer.
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
inputs
- Tensor input.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth,
height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. dilation_rate
- An integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv3d(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv3D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv3d']) @tf_export(v1=['layers.conv3d']) def conv3d(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for the 3D convolution layer. This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: inputs: Tensor input. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. dilation_rate: An integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv3d(x, filters=3, kernel_size=3) ``` 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.Conv3D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv3d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv3D` instead.') layer = Conv3D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def convolution3d_transpose(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for transposed 3D convolution layer.
Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 3 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv3d_transpose']) @tf_export(v1=['layers.conv3d_transpose']) def conv3d_transpose(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for transposed 3D convolution layer. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 3 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3) ``` 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.Conv3DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv3d_transpose` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv3DTranspose` instead.') layer = Conv3DTranspose( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def deconv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for transposed 2D convolution layer.
The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. activation
- Activation function. Set it to
None
to maintain a linear activation. use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If
None
, the default initializer will be used. kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv2d_transpose']) @tf_export(v1=['layers.conv2d_transpose']) def conv2d_transpose(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for transposed 2D convolution layer. The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. activation: Activation function. Set it to `None` to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If `None`, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3) ``` 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.Conv2DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv2d_transpose` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv2DTranspose` instead.') layer = Conv2DTranspose( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def deconv3d(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for transposed 3D convolution layer.
Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 3 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv3d_transpose']) @tf_export(v1=['layers.conv3d_transpose']) def conv3d_transpose(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for transposed 3D convolution layer. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 3 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3) ``` 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.Conv3DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv3d_transpose` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv3DTranspose` instead.') layer = Conv3DTranspose( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def deconvolution2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for transposed 2D convolution layer.
The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. activation
- Activation function. Set it to
None
to maintain a linear activation. use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If
None
, the default initializer will be used. kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv2d_transpose']) @tf_export(v1=['layers.conv2d_transpose']) def conv2d_transpose(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for transposed 2D convolution layer. The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. activation: Activation function. Set it to `None` to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If `None`, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv2d_transpose(x, filters=3, kernel_size=3) ``` 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.Conv2DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv2d_transpose` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv2DTranspose` instead.') layer = Conv2DTranspose( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def deconvolution3d(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for transposed 3D convolution layer.
Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 3 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.conv3d_transpose']) @tf_export(v1=['layers.conv3d_transpose']) def conv3d_transpose(inputs, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for transposed 3D convolution layer. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 3 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3) ``` 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.Conv3DTranspose(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.conv3d_transpose` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.Conv3DTranspose` instead.') layer = Conv3DTranspose( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def separable_conv1d(inputs, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for the depthwise separable 1D convolution layer.
This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If
use_bias
is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A single integer specifying the spatial dimensions of the filters.
strides
- A single integer specifying the strides
of the convolution.
Specifying any
stride
value != 1 is incompatible with specifying anydilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, length, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, length)
. dilation_rate
- A single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. depth_multiplier
- The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to
num_filters_in * depth_multiplier
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
depthwise_initializer
- An initializer for the depthwise convolution kernel.
pointwise_initializer
- An initializer for the pointwise convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
depthwise_regularizer
- Optional regularizer for the depthwise convolution kernel.
pointwise_regularizer
- Optional regularizer for the pointwise convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
depthwise_constraint
- Optional projection function to be applied to the
depthwise kernel after being updated by an
Optimizer
(e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint
- Optional projection function to be applied to the
pointwise kernel after being updated by an
Optimizer
. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.SeparableConv1D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.separable_conv1d(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.SeparableConv1D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.separable_conv1d']) @tf_export(v1=['layers.separable_conv1d']) def separable_conv1d(inputs, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for the depthwise separable 1D convolution layer. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If `use_bias` is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A single integer specifying the spatial dimensions of the filters. strides: A single integer specifying the strides of the convolution. Specifying any `stride` value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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)`. dilation_rate: A single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `num_filters_in * depth_multiplier`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. depthwise_initializer: An initializer for the depthwise convolution kernel. pointwise_initializer: An initializer for the pointwise convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. depthwise_regularizer: Optional regularizer for the depthwise convolution kernel. pointwise_regularizer: Optional regularizer for the pointwise convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. depthwise_constraint: Optional projection function to be applied to the depthwise kernel after being updated by an `Optimizer` (e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint: Optional projection function to be applied to the pointwise kernel after being updated by an `Optimizer`. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.SeparableConv1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.separable_conv1d(x, filters=3, kernel_size=3) ``` 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.SeparableConv1D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.separable_conv1d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.SeparableConv1D` instead.') layer = SeparableConv1D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, depth_multiplier=depth_multiplier, activation=activation, use_bias=use_bias, depthwise_initializer=depthwise_initializer, pointwise_initializer=pointwise_initializer, bias_initializer=bias_initializer, depthwise_regularizer=depthwise_regularizer, pointwise_regularizer=pointwise_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, depthwise_constraint=depthwise_constraint, pointwise_constraint=pointwise_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def separable_conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for the depthwise separable 2D convolution layer.
This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If
use_bias
is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any
stride
value != 1 is incompatible with specifying anydilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. dilation_rate
- An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. depth_multiplier
- The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to
num_filters_in * depth_multiplier
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
depthwise_initializer
- An initializer for the depthwise convolution kernel.
pointwise_initializer
- An initializer for the pointwise convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
depthwise_regularizer
- Optional regularizer for the depthwise convolution kernel.
pointwise_regularizer
- Optional regularizer for the pointwise convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
depthwise_constraint
- Optional projection function to be applied to the
depthwise kernel after being updated by an
Optimizer
(e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint
- Optional projection function to be applied to the
pointwise kernel after being updated by an
Optimizer
. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.SeparableConv2D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.separable_conv2d(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.SeparableConv2D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.separable_conv2d']) @tf_export(v1=['layers.separable_conv2d']) def separable_conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for the depthwise separable 2D convolution layer. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If `use_bias` is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any `stride` value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `num_filters_in * depth_multiplier`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. depthwise_initializer: An initializer for the depthwise convolution kernel. pointwise_initializer: An initializer for the pointwise convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. depthwise_regularizer: Optional regularizer for the depthwise convolution kernel. pointwise_regularizer: Optional regularizer for the pointwise convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. depthwise_constraint: Optional projection function to be applied to the depthwise kernel after being updated by an `Optimizer` (e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint: Optional projection function to be applied to the pointwise kernel after being updated by an `Optimizer`. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.SeparableConv2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.separable_conv2d(x, filters=3, kernel_size=3) ``` 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.SeparableConv2D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.separable_conv2d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.SeparableConv2D` instead.') layer = SeparableConv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, depth_multiplier=depth_multiplier, activation=activation, use_bias=use_bias, depthwise_initializer=depthwise_initializer, pointwise_initializer=pointwise_initializer, bias_initializer=bias_initializer, depthwise_regularizer=depthwise_regularizer, pointwise_regularizer=pointwise_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, depthwise_constraint=depthwise_constraint, pointwise_constraint=pointwise_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
def separable_convolution2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None)
-
Functional interface for the depthwise separable 2D convolution layer.
This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If
use_bias
is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.Args
inputs
- Input tensor.
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any
stride
value != 1 is incompatible with specifying anydilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. dilation_rate
- An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. depth_multiplier
- The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to
num_filters_in * depth_multiplier
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
depthwise_initializer
- An initializer for the depthwise convolution kernel.
pointwise_initializer
- An initializer for the pointwise convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
depthwise_regularizer
- Optional regularizer for the depthwise convolution kernel.
pointwise_regularizer
- Optional regularizer for the pointwise convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
depthwise_constraint
- Optional projection function to be applied to the
depthwise kernel after being updated by an
Optimizer
(e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint
- Optional projection function to be applied to the
pointwise kernel after being updated by an
Optimizer
. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor.
Raises
ValueError
- if eager execution is enabled.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.SeparableConv2D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.separable_conv2d(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.SeparableConv2D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.separable_conv2d']) @tf_export(v1=['layers.separable_conv2d']) def separable_conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None): """Functional interface for the depthwise separable 2D convolution layer. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If `use_bias` is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output. Args: inputs: Input tensor. filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any `stride` value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `num_filters_in * depth_multiplier`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. depthwise_initializer: An initializer for the depthwise convolution kernel. pointwise_initializer: An initializer for the pointwise convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. depthwise_regularizer: Optional regularizer for the depthwise convolution kernel. pointwise_regularizer: Optional regularizer for the pointwise convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. depthwise_constraint: Optional projection function to be applied to the depthwise kernel after being updated by an `Optimizer` (e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint: Optional projection function to be applied to the pointwise kernel after being updated by an `Optimizer`. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. Raises: ValueError: if eager execution is enabled. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.SeparableConv2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.separable_conv2d(x, filters=3, kernel_size=3) ``` 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.SeparableConv2D(filters=3, kernels_size=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.separable_conv2d` is deprecated and ' 'will be removed in a future version. ' 'Please Use `tf.keras.layers.SeparableConv2D` instead.') layer = SeparableConv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, depth_multiplier=depth_multiplier, activation=activation, use_bias=use_bias, depthwise_initializer=depthwise_initializer, pointwise_initializer=pointwise_initializer, bias_initializer=bias_initializer, depthwise_regularizer=depthwise_regularizer, pointwise_regularizer=pointwise_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, depthwise_constraint=depthwise_constraint, pointwise_constraint=pointwise_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, _reuse=reuse, _scope=name) return layer.apply(inputs)
Classes
class Conv1D (filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
strides
- An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, length, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, length)
. dilation_rate
- An integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying anystrides
value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv1D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv1D(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv1D(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv1D(keras_layers.Conv1D, base.Layer): """1D convolution layer (e.g. temporal convolution). This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window. strides: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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)`. dilation_rate: An integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv1D(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv1D(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv1D, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv1D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class Convolution1D (filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
strides
- An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, length, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, length)
. dilation_rate
- An integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying anystrides
value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv1D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv1D(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv1D(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv1D(keras_layers.Conv1D, base.Layer): """1D convolution layer (e.g. temporal convolution). This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window. strides: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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)`. dilation_rate: An integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv1D(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv1D(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv1D, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv1D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Conv1D
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
class Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
2D convolution layer (e.g. spatial convolution over images).
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution 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 convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. dilation_rate
- An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv2D(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv2D(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv2D(keras_layers.Conv2D, base.Layer): """2D convolution layer (e.g. spatial convolution over images). This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution 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 convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv2D(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv2D(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv2D, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv2D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class Convolution2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
2D convolution layer (e.g. spatial convolution over images).
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution 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 convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. dilation_rate
- An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv2D(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv2D(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv2D(keras_layers.Conv2D, base.Layer): """2D convolution layer (e.g. spatial convolution over images). This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution 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 convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv2D(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv2D(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv2D, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv2D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Conv2D
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
class Conv2DTranspose (filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Transposed 2D convolution layer (sometimes called 2D Deconvolution).
The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv2DTranspose(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv2DTranspose(keras_layers.Conv2DTranspose, base.Layer): """Transposed 2D convolution layer (sometimes called 2D Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv2DTranspose(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv2DTranspose, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv2DTranspose
- Conv2D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class Convolution2DTranspose (filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Transposed 2D convolution layer (sometimes called 2D Deconvolution).
The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv2DTranspose(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv2DTranspose(keras_layers.Conv2DTranspose, base.Layer): """Transposed 2D convolution layer (sometimes called 2D Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv2DTranspose(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv2DTranspose, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv2DTranspose
- Conv2D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class Deconvolution2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Transposed 2D convolution layer (sometimes called 2D Deconvolution).
The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv2DTranspose(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv2DTranspose(keras_layers.Conv2DTranspose, base.Layer): """Transposed 2D convolution layer (sometimes called 2D Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv2DTranspose(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv2DTranspose, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv2DTranspose
- Conv2D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class Deconv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Transposed 2D convolution layer (sometimes called 2D Deconvolution).
The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.
padding
- one of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv2DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv2DTranspose(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv2DTranspose(keras_layers.Conv2DTranspose, base.Layer): """Transposed 2D convolution layer (sometimes called 2D Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. padding: one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv2DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv2DTranspose(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv2DTranspose(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv2DTranspose, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv2DTranspose
- Conv2D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Conv2DTranspose
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
class Conv3D (filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
3D convolution layer (e.g. spatial convolution over volumes).
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth,
height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. dilation_rate
- An integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv3D(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv3D(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv3D(keras_layers.Conv3D, base.Layer): """3D convolution layer (e.g. spatial convolution over volumes). This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. dilation_rate: An integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv3D(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv3D(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv3D, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv3D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class Convolution3D (filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
3D convolution layer (e.g. spatial convolution over volumes).
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If
use_bias
is True (and abias_initializer
is provided), a bias vector is created and added to the outputs. Finally, ifactivation
is notNone
, it is applied to the outputs as well.Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth,
height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. dilation_rate
- An integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv3D(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv3D(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv3D(keras_layers.Conv3D, base.Layer): """3D convolution layer (e.g. spatial convolution over volumes). This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. dilation_rate: An integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv3D(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv3D(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv3D, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv3D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Conv3D
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
class Conv3DTranspose (filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Transposed 3D convolution layer (sometimes called 3D Deconvolution).
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions.
padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. activation
- Activation function. Set it to
None
to maintain a linear activation. use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If
None
, the default initializer will be used. kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv3DTranspose(keras_layers.Conv3DTranspose, base.Layer): """Transposed 3D convolution layer (sometimes called 3D Deconvolution). Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. activation: Activation function. Set it to `None` to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If `None`, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv3DTranspose, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv3DTranspose
- Conv3D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class Convolution3DTranspose (filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Transposed 3D convolution layer (sometimes called 3D Deconvolution).
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions.
padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. activation
- Activation function. Set it to
None
to maintain a linear activation. use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If
None
, the default initializer will be used. kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv3DTranspose(keras_layers.Conv3DTranspose, base.Layer): """Transposed 3D convolution layer (sometimes called 3D Deconvolution). Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. activation: Activation function. Set it to `None` to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If `None`, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv3DTranspose, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv3DTranspose
- Conv3D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class Deconvolution3D (filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Transposed 3D convolution layer (sometimes called 3D Deconvolution).
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions.
padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. activation
- Activation function. Set it to
None
to maintain a linear activation. use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If
None
, the default initializer will be used. kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv3DTranspose(keras_layers.Conv3DTranspose, base.Layer): """Transposed 3D convolution layer (sometimes called 3D Deconvolution). Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. activation: Activation function. Set it to `None` to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If `None`, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv3DTranspose, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv3DTranspose
- Conv3D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class Deconv3D (filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Transposed 3D convolution layer (sometimes called 3D Deconvolution).
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions.
padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, depth, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, depth, height, width)
. activation
- Activation function. Set it to
None
to maintain a linear activation. use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- An initializer for the convolution kernel.
bias_initializer
- An initializer for the bias vector. If
None
, the default initializer will be used. kernel_regularizer
- Optional regularizer for the convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
kernel_constraint
- Optional projection function to be applied to the
kernel after being updated by an
Optimizer
(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3DTranspose
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class Conv3DTranspose(keras_layers.Conv3DTranspose, base.Layer): """Transposed 3D convolution layer (sometimes called 3D Deconvolution). Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution 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 convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. activation: Activation function. Set it to `None` to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If `None`, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.Conv3DTranspose`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(Conv3DTranspose, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- Conv3DTranspose
- Conv3D
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Conv3DTranspose
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
class SeparableConv1D (filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Depthwise separable 1D convolution.
This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If
use_bias
is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A single integer specifying the spatial dimensions of the filters.
strides
- A single integer specifying the strides
of the convolution.
Specifying any
stride
value != 1 is incompatible with specifying anydilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, length, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, length)
. dilation_rate
- A single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. depth_multiplier
- The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to
num_filters_in * depth_multiplier
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
depthwise_initializer
- An initializer for the depthwise convolution kernel.
pointwise_initializer
- An initializer for the pointwise convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
depthwise_regularizer
- Optional regularizer for the depthwise convolution kernel.
pointwise_regularizer
- Optional regularizer for the pointwise convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
depthwise_constraint
- Optional projection function to be applied to the
depthwise kernel after being updated by an
Optimizer
(e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint
- Optional projection function to be applied to the
pointwise kernel after being updated by an
Optimizer
. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.SeparableConv1D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.SeparableConv1D(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.SeparableConv1D(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class SeparableConv1D(keras_layers.SeparableConv1D, base.Layer): """Depthwise separable 1D convolution. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If `use_bias` is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A single integer specifying the spatial dimensions of the filters. strides: A single integer specifying the strides of the convolution. Specifying any `stride` value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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)`. dilation_rate: A single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `num_filters_in * depth_multiplier`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. depthwise_initializer: An initializer for the depthwise convolution kernel. pointwise_initializer: An initializer for the pointwise convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. depthwise_regularizer: Optional regularizer for the depthwise convolution kernel. pointwise_regularizer: Optional regularizer for the pointwise convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. depthwise_constraint: Optional projection function to be applied to the depthwise kernel after being updated by an `Optimizer` (e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint: Optional projection function to be applied to the pointwise kernel after being updated by an `Optimizer`. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.SeparableConv1D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.SeparableConv1D(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.SeparableConv1D(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=1, depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(SeparableConv1D, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, depth_multiplier=depth_multiplier, activation=activation, use_bias=use_bias, depthwise_initializer=depthwise_initializer, pointwise_initializer=pointwise_initializer, bias_initializer=bias_initializer, depthwise_regularizer=depthwise_regularizer, pointwise_regularizer=pointwise_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, depthwise_constraint=depthwise_constraint, pointwise_constraint=pointwise_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- SeparableConv1D
- SeparableConv
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
SeparableConv1D
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
class SeparableConv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Depthwise separable 2D convolution.
This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If
use_bias
is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any
stride
value != 1 is incompatible with specifying anydilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. dilation_rate
- An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. depth_multiplier
- The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to
num_filters_in * depth_multiplier
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
depthwise_initializer
- An initializer for the depthwise convolution kernel.
pointwise_initializer
- An initializer for the pointwise convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
depthwise_regularizer
- Optional regularizer for the depthwise convolution kernel.
pointwise_regularizer
- Optional regularizer for the pointwise convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
depthwise_constraint
- Optional projection function to be applied to the
depthwise kernel after being updated by an
Optimizer
(e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint
- Optional projection function to be applied to the
pointwise kernel after being updated by an
Optimizer
. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.SeparableConv2D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.SeparableConv2D(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.SeparableConv2D(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class SeparableConv2D(keras_layers.SeparableConv2D, base.Layer): """Depthwise separable 2D convolution. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If `use_bias` is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any `stride` value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `num_filters_in * depth_multiplier`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. depthwise_initializer: An initializer for the depthwise convolution kernel. pointwise_initializer: An initializer for the pointwise convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. depthwise_regularizer: Optional regularizer for the depthwise convolution kernel. pointwise_regularizer: Optional regularizer for the pointwise convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. depthwise_constraint: Optional projection function to be applied to the depthwise kernel after being updated by an `Optimizer` (e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint: Optional projection function to be applied to the pointwise kernel after being updated by an `Optimizer`. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.SeparableConv2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.SeparableConv2D(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.SeparableConv2D(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(SeparableConv2D, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, depth_multiplier=depth_multiplier, activation=activation, use_bias=use_bias, depthwise_initializer=depthwise_initializer, pointwise_initializer=pointwise_initializer, bias_initializer=bias_initializer, depthwise_regularizer=depthwise_regularizer, pointwise_regularizer=pointwise_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, depthwise_constraint=depthwise_constraint, pointwise_constraint=pointwise_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- SeparableConv2D
- SeparableConv
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class SeparableConvolution2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs)
-
Depthwise separable 2D convolution.
This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If
use_bias
is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.Args
filters
- Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size
- A tuple or list of 2 integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides
- A tuple or list of 2 positive integers specifying the strides
of the convolution. Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any
stride
value != 1 is incompatible with specifying anydilation_rate
value != 1. padding
- One of
"valid"
or"same"
(case-insensitive)."valid"
means no padding."same"
results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. dilation_rate
- An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying any stride value != 1. depth_multiplier
- The number of depthwise convolution output channels for
each input channel. The total number of depthwise convolution output
channels will be equal to
num_filters_in * depth_multiplier
. activation
- Activation function. Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
depthwise_initializer
- An initializer for the depthwise convolution kernel.
pointwise_initializer
- An initializer for the pointwise convolution kernel.
bias_initializer
- An initializer for the bias vector. If None, the default initializer will be used.
depthwise_regularizer
- Optional regularizer for the depthwise convolution kernel.
pointwise_regularizer
- Optional regularizer for the pointwise convolution kernel.
bias_regularizer
- Optional regularizer for the bias vector.
activity_regularizer
- Optional regularizer function for the output.
depthwise_constraint
- Optional projection function to be applied to the
depthwise kernel after being updated by an
Optimizer
(e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint
- Optional projection function to be applied to the
pointwise kernel after being updated by an
Optimizer
. bias_constraint
- Optional projection function to be applied to the
bias after being updated by an
Optimizer
. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). name
- A string, the name of the layer.
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.SeparableConv2D
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.SeparableConv2D(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.SeparableConv2D(filters=3, kernels_size=3)
@end_compatibility
Expand source code
class SeparableConv2D(keras_layers.SeparableConv2D, base.Layer): """Depthwise separable 2D convolution. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If `use_bias` is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output. Args: filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size: A tuple or list of 2 integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. strides: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any `stride` value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `num_filters_in * depth_multiplier`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. depthwise_initializer: An initializer for the depthwise convolution kernel. pointwise_initializer: An initializer for the pointwise convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. depthwise_regularizer: Optional regularizer for the depthwise convolution kernel. pointwise_regularizer: Optional regularizer for the pointwise convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. depthwise_constraint: Optional projection function to be applied to the depthwise kernel after being updated by an `Optimizer` (e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. pointwise_constraint: Optional projection function to be applied to the pointwise kernel after being updated by an `Optimizer`. bias_constraint: Optional projection function to be applied to the bias after being updated by an `Optimizer`. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: A string, the name of the layer. @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.SeparableConv2D`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python conv = tf.compat.v1.layers.SeparableConv2D(filters=3, kernel_size=3) ``` After: ```python conv = tf.keras.layers.SeparableConv2D(filters=3, kernels_size=3) ``` @end_compatibility """ def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=tf.compat.v1.zeros_initializer(), depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs): super(SeparableConv2D, self).__init__( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, depth_multiplier=depth_multiplier, activation=activation, use_bias=use_bias, depthwise_initializer=depthwise_initializer, pointwise_initializer=pointwise_initializer, bias_initializer=bias_initializer, depthwise_regularizer=depthwise_regularizer, pointwise_regularizer=pointwise_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, depthwise_constraint=depthwise_constraint, pointwise_constraint=pointwise_constraint, bias_constraint=bias_constraint, trainable=trainable, name=name, **kwargs)
Ancestors
- SeparableConv2D
- SeparableConv
- Conv
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
SeparableConv2D
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights