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# 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.
# ==============================================================================
"""Built-in activation functions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.util.tf_export import tf_export
@tf_export('keras.activations.softmax')
def softmax(x, axis=-1):
"""Softmax activation function.
Arguments:
x : Input tensor.
axis: Integer, axis along which the softmax normalization is applied.
Returns:
Tensor, output of softmax transformation.
Raises:
ValueError: In case `dim(x) == 1`.
"""
ndim = K.ndim(x)
if ndim == 2:
return nn.softmax(x)
elif ndim > 2:
e = math_ops.exp(x - math_ops.reduce_max(x, axis=axis, keepdims=True))
s = math_ops.reduce_sum(e, axis=axis, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor that is 1D. '
'Received input: %s' % (x,))
@tf_export('keras.activations.elu')
def elu(x, alpha=1.0):
"""Exponential linear unit.
Arguments:
x: Input tensor.
alpha: A scalar, slope of negative section.
Returns:
The exponential linear activation: `x` if `x > 0` and
`alpha * (exp(x)-1)` if `x < 0`.
Reference:
- [Fast and Accurate Deep Network Learning by Exponential
Linear Units (ELUs)](https://arxiv.org/abs/1511.07289)
"""
return K.elu(x, alpha)
@tf_export('keras.activations.selu')
def selu(x):
"""Scaled Exponential Linear Unit (SELU).
SELU is equal to: `scale * elu(x, alpha)`, where alpha and scale
are pre-defined constants. The values of `alpha` and `scale` are
chosen so that the mean and variance of the inputs are preserved
between two consecutive layers as long as the weights are initialized
correctly (see `lecun_normal` initialization) and the number of inputs
is "large enough" (see references for more information).
Arguments:
x: A tensor or variable to compute the activation function for.
Returns:
The scaled exponential unit activation: `scale * elu(x, alpha)`.
# Note
- To be used together with the initialization "lecun_normal".
- To be used together with the dropout variant "AlphaDropout".
References:
- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
"""
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
return scale * K.elu(x, alpha)
@tf_export('keras.activations.softplus')
def softplus(x):
"""Softplus activation function.
Arguments:
x: Input tensor.
Returns:
The softplus activation: `log(exp(x) + 1)`.
"""
return nn.softplus(x)
@tf_export('keras.activations.softsign')
def softsign(x):
"""Softsign activation function.
Arguments:
x: Input tensor.
Returns:
The softplus activation: `x / (abs(x) + 1)`.
"""
return nn.softsign(x)
@tf_export('keras.activations.relu')
def relu(x, alpha=0., max_value=None):
"""Rectified Linear Unit.
Arguments:
x: Input tensor.
alpha: Slope of the negative part. Defaults to zero.
max_value: Maximum value for the output.
Returns:
The (leaky) rectified linear unit activation: `x` if `x > 0`,
`alpha * x` if `x < 0`. If `max_value` is defined, the result
is truncated to this value.
"""
return K.relu(x, alpha=alpha, max_value=max_value)
@tf_export('keras.activations.tanh')
def tanh(x):
return nn.tanh(x)
@tf_export('keras.activations.sigmoid')
def sigmoid(x):
return nn.sigmoid(x)
@tf_export('keras.activations.hard_sigmoid')
def hard_sigmoid(x):
"""Hard sigmoid activation function.
Faster to compute than sigmoid activation.
Arguments:
x: Input tensor.
Returns:
Hard sigmoid activation:
- `0` if `x < -2.5`
- `1` if `x > 2.5`
- `0.2 * x + 0.5` if `-2.5 <= x <= 2.5`.
"""
return K.hard_sigmoid(x)
@tf_export('keras.activations.linear')
def linear(x):
return x
@tf_export('keras.activations.serialize')
def serialize(activation):
return activation.__name__
@tf_export('keras.activations.deserialize')
def deserialize(name, custom_objects=None):
return deserialize_keras_object(
name,
module_objects=globals(),
custom_objects=custom_objects,
printable_module_name='activation function')
@tf_export('keras.activations.get')
def get(identifier):
if identifier is None:
return linear
if isinstance(identifier, six.string_types):
identifier = str(identifier)
return deserialize(identifier)
elif callable(identifier):
return identifier
else:
raise ValueError('Could not interpret '
'activation function identifier:', identifier)
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