Module keras.legacy_tf_layers.core
Contains the core layers: Dense, Dropout.
Also contains 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 core layers: Dense, Dropout.
Also contains 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.Dense'])
@tf_export(v1=['layers.Dense'])
class Dense(keras_layers.Dense, base.Layer):
"""Densely-connected layer class.
This layer implements the operation:
`outputs = activation(inputs * kernel + bias)`
Where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Args:
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.compat.v1.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An 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: An 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: String, the name of the layer. Layers with the same name will
share weights, but to avoid mistakes we require reuse=True in such cases.
_reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Properties:
units: Python integer, dimensionality of the output space.
activation: Activation function (callable).
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer instance (or name) for the kernel matrix.
bias_initializer: Initializer instance (or name) for the bias.
kernel_regularizer: Regularizer instance for the kernel matrix (callable)
bias_regularizer: Regularizer instance for the bias (callable).
activity_regularizer: Regularizer instance for the output (callable)
kernel_constraint: Constraint function for the kernel matrix.
bias_constraint: Constraint function for the bias.
kernel: Weight matrix (TensorFlow variable or tensor).
bias: Bias vector, if applicable (TensorFlow variable or tensor).
@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.Dense`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
dense = tf.compat.v1.layers.Dense(units=3)
```
After:
```python
dense = tf.keras.layers.Dense(units=3)
```
@end_compatibility
"""
def __init__(self, units,
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(Dense, self).__init__(units=units,
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.dense'])
@tf_export(v1=['layers.dense'])
def dense(
inputs, units,
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 densely-connected layer.
This layer implements the operation:
`outputs = activation(inputs * kernel + bias)`
where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Args:
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.compat.v1.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An 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: An 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: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor the same shape as `inputs` except the last dimension is of
size `units`.
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.Dense`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.dense(x, units=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,))
y = tf.keras.layers.Dense(units=3)(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.dense` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.Dense` instead.')
layer = Dense(units,
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,
_scope=name,
_reuse=reuse)
return layer.apply(inputs)
@keras_export(v1=['keras.__internal__.legacy.layers.Dropout'])
@tf_export(v1=['layers.Dropout'])
class Dropout(keras_layers.Dropout, base.Layer):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Args:
rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed`.
for behavior.
name: The name of the layer (string).
@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.Dropout`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
dropout = tf.compat.v1.layers.Dropout()
```
After:
```python
dropout = tf.keras.layers.Dropout()
```
@end_compatibility
"""
def __init__(self, rate=0.5,
noise_shape=None,
seed=None,
name=None,
**kwargs):
super(Dropout, self).__init__(rate=rate,
noise_shape=noise_shape,
seed=seed,
name=name,
**kwargs)
def call(self, inputs, training=False):
return super(Dropout, self).call(inputs, training=training)
@keras_export(v1=['keras.__internal__.legacy.layers.dropout'])
@tf_export(v1=['layers.dropout'])
def dropout(inputs,
rate=0.5,
noise_shape=None,
seed=None,
training=False,
name=None):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Args:
inputs: Tensor input.
rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed`
for behavior.
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
(e.g. a placeholder). Whether to return the output in training mode
(apply dropout) or in inference mode (return the input untouched).
name: The name of the layer (string).
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.Dropout`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.dropout(x)
```
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.Dropout()(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.dropout` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.Dropout` instead.')
layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
return layer.apply(inputs, training=training)
@keras_export(v1=['keras.__internal__.legacy.layers.Flatten'])
@tf_export(v1=['layers.Flatten'])
class Flatten(keras_layers.Flatten, base.Layer):
"""Flattens an input tensor while preserving the batch axis (axis 0).
Args:
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, ..., channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, ...)`.
Examples:
```
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
y = Flatten()(x)
# now `y` has shape `(None, 16)`
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
y = Flatten()(x)
# now `y` has shape `(None, None)`
```
@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.Flatten`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
flatten = tf.compat.v1.layers.Flatten()
```
After:
```python
flatten = tf.keras.layers.Flatten()
```
@end_compatibility
"""
pass
@keras_export(v1=['keras.__internal__.legacy.layers.flatten'])
@tf_export(v1=['layers.flatten'])
def flatten(inputs, name=None, data_format='channels_last'):
"""Flattens an input tensor while preserving the batch axis (axis 0).
Args:
inputs: Tensor input.
name: The name of the layer (string).
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)`.
Returns:
Reshaped tensor.
Examples:
```
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, 16)`
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, None)`
```
@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.Flatten`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
y = tf.compat.v1.layers.flatten(x)
```
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.Flatten()(x)
model = tf.keras.Model(x, y)
```
@end_compatibility
"""
warnings.warn('`tf.layers.flatten` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.Flatten` instead.')
layer = Flatten(name=name, data_format=data_format)
return layer.apply(inputs)
# Aliases
FullyConnected = Dense
fully_connected = dense
Functions
def dense(inputs, units, 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 densely-connected layer.
This layer implements the operation:
outputs = activation(inputs * kernel + bias)
whereactivation
is the activation function passed as theactivation
argument (if notNone
),kernel
is a weights matrix created by the layer, andbias
is a bias vector created by the layer (only ifuse_bias
isTrue
).Args
inputs
- Tensor input.
units
- Integer or Long, dimensionality of the output space.
activation
- Activation function (callable). Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- Initializer function for the weight matrix.
If
None
(default), weights are initialized using the default initializer used bytf.compat.v1.get_variable
. bias_initializer
- Initializer function for the bias.
kernel_regularizer
- Regularizer function for the weight matrix.
bias_regularizer
- Regularizer function for the bias.
activity_regularizer
- Regularizer function for the output.
kernel_constraint
- An 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
- An 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
- String, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor the same shape as
inputs
except the last dimension is of sizeunits
.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.Dense
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.dense(x, units=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,)) y = tf.keras.layers.Dense(units=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.dense']) @tf_export(v1=['layers.dense']) def dense( inputs, units, 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 densely-connected layer. This layer implements the operation: `outputs = activation(inputs * kernel + bias)` where `activation` is the activation function passed as the `activation` argument (if not `None`), `kernel` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only if `use_bias` is `True`). Args: inputs: Tensor input. units: Integer or Long, dimensionality of the output space. activation: Activation function (callable). Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: Initializer function for the weight matrix. If `None` (default), weights are initialized using the default initializer used by `tf.compat.v1.get_variable`. bias_initializer: Initializer function for the bias. kernel_regularizer: Regularizer function for the weight matrix. bias_regularizer: Regularizer function for the bias. activity_regularizer: Regularizer function for the output. kernel_constraint: An 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: An 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: String, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor the same shape as `inputs` except the last dimension is of size `units`. 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.Dense`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.dense(x, units=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,)) y = tf.keras.layers.Dense(units=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.dense` is deprecated and ' 'will be removed in a future version. ' 'Please use `tf.keras.layers.Dense` instead.') layer = Dense(units, 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, _scope=name, _reuse=reuse) return layer.apply(inputs)
def dropout(inputs, rate=0.5, noise_shape=None, seed=None, training=False, name=None)
-
Applies Dropout to the input.
Dropout consists in randomly setting a fraction
rate
of input units to 0 at each update during training time, which helps prevent overfitting. The units that are kept are scaled by1 / (1 - rate)
, so that their sum is unchanged at training time and inference time.Args
inputs
- Tensor input.
rate
- The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out 10% of input units.
noise_shape
- 1D tensor of type
int32
representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape(batch_size, timesteps, features)
, and you want the dropout mask to be the same for all timesteps, you can usenoise_shape=[batch_size, 1, features]
. seed
- A Python integer. Used to create random seeds. See
tf.compat.v1.set_random_seed
for behavior. training
- Either a Python boolean, or a TensorFlow boolean scalar tensor (e.g. a placeholder). Whether to return the output in training mode (apply dropout) or in inference mode (return the input untouched).
name
- The name of the layer (string).
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.Dropout
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.dropout(x)
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.Dropout()(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.dropout']) @tf_export(v1=['layers.dropout']) def dropout(inputs, rate=0.5, noise_shape=None, seed=None, training=False, name=None): """Applies Dropout to the input. Dropout consists in randomly setting a fraction `rate` of input units to 0 at each update during training time, which helps prevent overfitting. The units that are kept are scaled by `1 / (1 - rate)`, so that their sum is unchanged at training time and inference time. Args: inputs: Tensor input. rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out 10% of input units. noise_shape: 1D tensor of type `int32` representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape `(batch_size, timesteps, features)`, and you want the dropout mask to be the same for all timesteps, you can use `noise_shape=[batch_size, 1, features]`. seed: A Python integer. Used to create random seeds. See `tf.compat.v1.set_random_seed` for behavior. training: Either a Python boolean, or a TensorFlow boolean scalar tensor (e.g. a placeholder). Whether to return the output in training mode (apply dropout) or in inference mode (return the input untouched). name: The name of the layer (string). 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.Dropout`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.dropout(x) ``` 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.Dropout()(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.dropout` is deprecated and ' 'will be removed in a future version. ' 'Please use `tf.keras.layers.Dropout` instead.') layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name) return layer.apply(inputs, training=training)
def flatten(inputs, name=None, data_format='channels_last')
-
Flattens an input tensor while preserving the batch axis (axis 0).
Args
inputs
- Tensor input.
name
- The name of the layer (string).
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)
.
Returns
Reshaped tensor. Examples:
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32') y = flatten(x) # now `y` has shape `(None, 16)` x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32') y = flatten(x) # now `y` has shape `(None, None)`
@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.Flatten
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.flatten(x)
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.Flatten()(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.flatten']) @tf_export(v1=['layers.flatten']) def flatten(inputs, name=None, data_format='channels_last'): """Flattens an input tensor while preserving the batch axis (axis 0). Args: inputs: Tensor input. name: The name of the layer (string). 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)`. Returns: Reshaped tensor. Examples: ``` x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32') y = flatten(x) # now `y` has shape `(None, 16)` x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32') y = flatten(x) # now `y` has shape `(None, None)` ``` @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.Flatten`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.flatten(x) ``` 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.Flatten()(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.flatten` is deprecated and ' 'will be removed in a future version. ' 'Please use `tf.keras.layers.Flatten` instead.') layer = Flatten(name=name, data_format=data_format) return layer.apply(inputs)
def fully_connected(inputs, units, 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 densely-connected layer.
This layer implements the operation:
outputs = activation(inputs * kernel + bias)
whereactivation
is the activation function passed as theactivation
argument (if notNone
),kernel
is a weights matrix created by the layer, andbias
is a bias vector created by the layer (only ifuse_bias
isTrue
).Args
inputs
- Tensor input.
units
- Integer or Long, dimensionality of the output space.
activation
- Activation function (callable). Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- Initializer function for the weight matrix.
If
None
(default), weights are initialized using the default initializer used bytf.compat.v1.get_variable
. bias_initializer
- Initializer function for the bias.
kernel_regularizer
- Regularizer function for the weight matrix.
bias_regularizer
- Regularizer function for the bias.
activity_regularizer
- Regularizer function for the output.
kernel_constraint
- An 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
- An 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
- String, the name of the layer.
reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Returns
Output tensor the same shape as
inputs
except the last dimension is of sizeunits
.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.Dense
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
y = tf.compat.v1.layers.dense(x, units=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,)) y = tf.keras.layers.Dense(units=3)(x) model = tf.keras.Model(x, y)
@end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.dense']) @tf_export(v1=['layers.dense']) def dense( inputs, units, 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 densely-connected layer. This layer implements the operation: `outputs = activation(inputs * kernel + bias)` where `activation` is the activation function passed as the `activation` argument (if not `None`), `kernel` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only if `use_bias` is `True`). Args: inputs: Tensor input. units: Integer or Long, dimensionality of the output space. activation: Activation function (callable). Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: Initializer function for the weight matrix. If `None` (default), weights are initialized using the default initializer used by `tf.compat.v1.get_variable`. bias_initializer: Initializer function for the bias. kernel_regularizer: Regularizer function for the weight matrix. bias_regularizer: Regularizer function for the bias. activity_regularizer: Regularizer function for the output. kernel_constraint: An 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: An 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: String, the name of the layer. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor the same shape as `inputs` except the last dimension is of size `units`. 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.Dense`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python y = tf.compat.v1.layers.dense(x, units=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,)) y = tf.keras.layers.Dense(units=3)(x) model = tf.keras.Model(x, y) ``` @end_compatibility """ warnings.warn('`tf.layers.dense` is deprecated and ' 'will be removed in a future version. ' 'Please use `tf.keras.layers.Dense` instead.') layer = Dense(units, 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, _scope=name, _reuse=reuse) return layer.apply(inputs)
Classes
class Dense (units, 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)
-
Densely-connected layer class.
This layer implements the operation:
outputs = activation(inputs * kernel + bias)
Whereactivation
is the activation function passed as theactivation
argument (if notNone
),kernel
is a weights matrix created by the layer, andbias
is a bias vector created by the layer (only ifuse_bias
isTrue
).Args
units
- Integer or Long, dimensionality of the output space.
activation
- Activation function (callable). Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- Initializer function for the weight matrix.
If
None
(default), weights are initialized using the default initializer used bytf.compat.v1.get_variable
. bias_initializer
- Initializer function for the bias.
kernel_regularizer
- Regularizer function for the weight matrix.
bias_regularizer
- Regularizer function for the bias.
activity_regularizer
- Regularizer function for the output.
kernel_constraint
- An 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
- An 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
- String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
_reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Properties
units: Python integer, dimensionality of the output space. activation: Activation function (callable). use_bias: Boolean, whether the layer uses a bias. kernel_initializer: Initializer instance (or name) for the kernel matrix. bias_initializer: Initializer instance (or name) for the bias. kernel_regularizer: Regularizer instance for the kernel matrix (callable) bias_regularizer: Regularizer instance for the bias (callable). activity_regularizer: Regularizer instance for the output (callable) kernel_constraint: Constraint function for the kernel matrix. bias_constraint: Constraint function for the bias. kernel: Weight matrix (TensorFlow variable or tensor). bias: Bias vector, if applicable (TensorFlow variable or tensor).
@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.Dense
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
dense = tf.compat.v1.layers.Dense(units=3)
After:
dense = tf.keras.layers.Dense(units=3)
@end_compatibility
Expand source code
class Dense(keras_layers.Dense, base.Layer): """Densely-connected layer class. This layer implements the operation: `outputs = activation(inputs * kernel + bias)` Where `activation` is the activation function passed as the `activation` argument (if not `None`), `kernel` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only if `use_bias` is `True`). Args: units: Integer or Long, dimensionality of the output space. activation: Activation function (callable). Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: Initializer function for the weight matrix. If `None` (default), weights are initialized using the default initializer used by `tf.compat.v1.get_variable`. bias_initializer: Initializer function for the bias. kernel_regularizer: Regularizer function for the weight matrix. bias_regularizer: Regularizer function for the bias. activity_regularizer: Regularizer function for the output. kernel_constraint: An 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: An 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: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. _reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Properties: units: Python integer, dimensionality of the output space. activation: Activation function (callable). use_bias: Boolean, whether the layer uses a bias. kernel_initializer: Initializer instance (or name) for the kernel matrix. bias_initializer: Initializer instance (or name) for the bias. kernel_regularizer: Regularizer instance for the kernel matrix (callable) bias_regularizer: Regularizer instance for the bias (callable). activity_regularizer: Regularizer instance for the output (callable) kernel_constraint: Constraint function for the kernel matrix. bias_constraint: Constraint function for the bias. kernel: Weight matrix (TensorFlow variable or tensor). bias: Bias vector, if applicable (TensorFlow variable or tensor). @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.Dense`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python dense = tf.compat.v1.layers.Dense(units=3) ``` After: ```python dense = tf.keras.layers.Dense(units=3) ``` @end_compatibility """ def __init__(self, units, 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(Dense, self).__init__(units=units, 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
- Dense
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
class FullyConnected (units, 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)
-
Densely-connected layer class.
This layer implements the operation:
outputs = activation(inputs * kernel + bias)
Whereactivation
is the activation function passed as theactivation
argument (if notNone
),kernel
is a weights matrix created by the layer, andbias
is a bias vector created by the layer (only ifuse_bias
isTrue
).Args
units
- Integer or Long, dimensionality of the output space.
activation
- Activation function (callable). Set it to None to maintain a linear activation.
use_bias
- Boolean, whether the layer uses a bias.
kernel_initializer
- Initializer function for the weight matrix.
If
None
(default), weights are initialized using the default initializer used bytf.compat.v1.get_variable
. bias_initializer
- Initializer function for the bias.
kernel_regularizer
- Regularizer function for the weight matrix.
bias_regularizer
- Regularizer function for the bias.
activity_regularizer
- Regularizer function for the output.
kernel_constraint
- An 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
- An 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
- String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
_reuse
- Boolean, whether to reuse the weights of a previous layer by the same name.
Properties
units: Python integer, dimensionality of the output space. activation: Activation function (callable). use_bias: Boolean, whether the layer uses a bias. kernel_initializer: Initializer instance (or name) for the kernel matrix. bias_initializer: Initializer instance (or name) for the bias. kernel_regularizer: Regularizer instance for the kernel matrix (callable) bias_regularizer: Regularizer instance for the bias (callable). activity_regularizer: Regularizer instance for the output (callable) kernel_constraint: Constraint function for the kernel matrix. bias_constraint: Constraint function for the bias. kernel: Weight matrix (TensorFlow variable or tensor). bias: Bias vector, if applicable (TensorFlow variable or tensor).
@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.Dense
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
dense = tf.compat.v1.layers.Dense(units=3)
After:
dense = tf.keras.layers.Dense(units=3)
@end_compatibility
Expand source code
class Dense(keras_layers.Dense, base.Layer): """Densely-connected layer class. This layer implements the operation: `outputs = activation(inputs * kernel + bias)` Where `activation` is the activation function passed as the `activation` argument (if not `None`), `kernel` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only if `use_bias` is `True`). Args: units: Integer or Long, dimensionality of the output space. activation: Activation function (callable). Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: Initializer function for the weight matrix. If `None` (default), weights are initialized using the default initializer used by `tf.compat.v1.get_variable`. bias_initializer: Initializer function for the bias. kernel_regularizer: Regularizer function for the weight matrix. bias_regularizer: Regularizer function for the bias. activity_regularizer: Regularizer function for the output. kernel_constraint: An 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: An 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: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. _reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Properties: units: Python integer, dimensionality of the output space. activation: Activation function (callable). use_bias: Boolean, whether the layer uses a bias. kernel_initializer: Initializer instance (or name) for the kernel matrix. bias_initializer: Initializer instance (or name) for the bias. kernel_regularizer: Regularizer instance for the kernel matrix (callable) bias_regularizer: Regularizer instance for the bias (callable). activity_regularizer: Regularizer instance for the output (callable) kernel_constraint: Constraint function for the kernel matrix. bias_constraint: Constraint function for the bias. kernel: Weight matrix (TensorFlow variable or tensor). bias: Bias vector, if applicable (TensorFlow variable or tensor). @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.Dense`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python dense = tf.compat.v1.layers.Dense(units=3) ``` After: ```python dense = tf.keras.layers.Dense(units=3) ``` @end_compatibility """ def __init__(self, units, 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(Dense, self).__init__(units=units, 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
- Dense
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Dense
: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 Dropout (rate=0.5, noise_shape=None, seed=None, name=None, **kwargs)
-
Applies Dropout to the input.
Dropout consists in randomly setting a fraction
rate
of input units to 0 at each update during training time, which helps prevent overfitting. The units that are kept are scaled by1 / (1 - rate)
, so that their sum is unchanged at training time and inference time.Args
rate
- The dropout rate, between 0 and 1. E.g.
rate=0.1
would drop out 10% of input units. noise_shape
- 1D tensor of type
int32
representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape(batch_size, timesteps, features)
, and you want the dropout mask to be the same for all timesteps, you can usenoise_shape=[batch_size, 1, features]
. seed
- A Python integer. Used to create random seeds. See
tf.compat.v1.set_random_seed
. for behavior. name
- The name of the layer (string).
@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.Dropout
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
dropout = tf.compat.v1.layers.Dropout()
After:
dropout = tf.keras.layers.Dropout()
@end_compatibility
Expand source code
class Dropout(keras_layers.Dropout, base.Layer): """Applies Dropout to the input. Dropout consists in randomly setting a fraction `rate` of input units to 0 at each update during training time, which helps prevent overfitting. The units that are kept are scaled by `1 / (1 - rate)`, so that their sum is unchanged at training time and inference time. Args: rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out 10% of input units. noise_shape: 1D tensor of type `int32` representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape `(batch_size, timesteps, features)`, and you want the dropout mask to be the same for all timesteps, you can use `noise_shape=[batch_size, 1, features]`. seed: A Python integer. Used to create random seeds. See `tf.compat.v1.set_random_seed`. for behavior. name: The name of the layer (string). @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.Dropout`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python dropout = tf.compat.v1.layers.Dropout() ``` After: ```python dropout = tf.keras.layers.Dropout() ``` @end_compatibility """ def __init__(self, rate=0.5, noise_shape=None, seed=None, name=None, **kwargs): super(Dropout, self).__init__(rate=rate, noise_shape=noise_shape, seed=seed, name=name, **kwargs) def call(self, inputs, training=False): return super(Dropout, self).call(inputs, training=training)
Ancestors
- Dropout
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Dropout
: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 Flatten (data_format=None, **kwargs)
-
Flattens an input tensor while preserving the batch axis (axis 0).
Args
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, …, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, …)
.
Examples:
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32') y = Flatten()(x) # now `y` has shape `(None, 16)` x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32') y = Flatten()(x) # now `y` has shape `(None, None)`
@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.Flatten
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
flatten = tf.compat.v1.layers.Flatten()
After:
flatten = tf.keras.layers.Flatten()
@end_compatibility
Expand source code
class Flatten(keras_layers.Flatten, base.Layer): """Flattens an input tensor while preserving the batch axis (axis 0). Args: 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, ..., channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, ...)`. Examples: ``` x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32') y = Flatten()(x) # now `y` has shape `(None, 16)` x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32') y = Flatten()(x) # now `y` has shape `(None, None)` ``` @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.Flatten`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python flatten = tf.compat.v1.layers.Flatten() ``` After: ```python flatten = tf.keras.layers.Flatten() ``` @end_compatibility """ pass
Ancestors
- Flatten
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Flatten
: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