Permalink
Find file Copy path
322 lines (255 sloc) 10.6 KB
# -*- coding: utf-8 -*-
"""Layers that act as activation functions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .. import activations
from .. import initializers
from .. import regularizers
from .. import constraints
from ..engine.base_layer import Layer
from ..engine.base_layer import InputSpec
from .. import backend as K
from ..legacy import interfaces
from ..utils.generic_utils import to_list
class LeakyReLU(Layer):
"""Leaky version of a Rectified Linear Unit.
It allows a small gradient when the unit is not active:
`f(x) = alpha * x for x < 0`,
`f(x) = x for x >= 0`.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as the input.
# Arguments
alpha: float >= 0. Negative slope coefficient.
# References
- [Rectifier Nonlinearities Improve Neural Network Acoustic Models](
https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf)
"""
def __init__(self, alpha=0.3, **kwargs):
super(LeakyReLU, self).__init__(**kwargs)
self.supports_masking = True
self.alpha = K.cast_to_floatx(alpha)
def call(self, inputs):
return K.relu(inputs, alpha=self.alpha)
def get_config(self):
config = {'alpha': float(self.alpha)}
base_config = super(LeakyReLU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
class PReLU(Layer):
"""Parametric Rectified Linear Unit.
It follows:
`f(x) = alpha * x for x < 0`,
`f(x) = x for x >= 0`,
where `alpha` is a learned array with the same shape as x.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as the input.
# Arguments
alpha_initializer: initializer function for the weights.
alpha_regularizer: regularizer for the weights.
alpha_constraint: constraint for the weights.
shared_axes: the axes along which to share learnable
parameters for the activation function.
For example, if the incoming feature maps
are from a 2D convolution
with output shape `(batch, height, width, channels)`,
and you wish to share parameters across space
so that each filter only has one set of parameters,
set `shared_axes=[1, 2]`.
# References
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification](https://arxiv.org/abs/1502.01852)
"""
@interfaces.legacy_prelu_support
def __init__(self, alpha_initializer='zeros',
alpha_regularizer=None,
alpha_constraint=None,
shared_axes=None,
**kwargs):
super(PReLU, self).__init__(**kwargs)
self.supports_masking = True
self.alpha_initializer = initializers.get(alpha_initializer)
self.alpha_regularizer = regularizers.get(alpha_regularizer)
self.alpha_constraint = constraints.get(alpha_constraint)
if shared_axes is None:
self.shared_axes = None
else:
self.shared_axes = to_list(shared_axes, allow_tuple=True)
def build(self, input_shape):
param_shape = list(input_shape[1:])
self.param_broadcast = [False] * len(param_shape)
if self.shared_axes is not None:
for i in self.shared_axes:
param_shape[i - 1] = 1
self.param_broadcast[i - 1] = True
self.alpha = self.add_weight(shape=param_shape,
name='alpha',
initializer=self.alpha_initializer,
regularizer=self.alpha_regularizer,
constraint=self.alpha_constraint)
# Set input spec
axes = {}
if self.shared_axes:
for i in range(1, len(input_shape)):
if i not in self.shared_axes:
axes[i] = input_shape[i]
self.input_spec = InputSpec(ndim=len(input_shape), axes=axes)
self.built = True
def call(self, inputs, mask=None):
pos = K.relu(inputs)
if K.backend() == 'theano':
neg = (K.pattern_broadcast(self.alpha, self.param_broadcast) *
(inputs - K.abs(inputs)) * 0.5)
else:
neg = -self.alpha * K.relu(-inputs)
return pos + neg
def get_config(self):
config = {
'alpha_initializer': initializers.serialize(self.alpha_initializer),
'alpha_regularizer': regularizers.serialize(self.alpha_regularizer),
'alpha_constraint': constraints.serialize(self.alpha_constraint),
'shared_axes': self.shared_axes
}
base_config = super(PReLU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
class ELU(Layer):
"""Exponential Linear Unit.
It follows:
`f(x) = alpha * (exp(x) - 1.) for x < 0`,
`f(x) = x for x >= 0`.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as the input.
# Arguments
alpha: scale for the negative factor.
# References
- [Fast and Accurate Deep Network Learning by Exponential Linear Units
(ELUs)](https://arxiv.org/abs/1511.07289v1)
"""
def __init__(self, alpha=1.0, **kwargs):
super(ELU, self).__init__(**kwargs)
self.supports_masking = True
self.alpha = K.cast_to_floatx(alpha)
def call(self, inputs):
return K.elu(inputs, self.alpha)
def get_config(self):
config = {'alpha': float(self.alpha)}
base_config = super(ELU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
class ThresholdedReLU(Layer):
"""Thresholded Rectified Linear Unit.
It follows:
`f(x) = x for x > theta`,
`f(x) = 0 otherwise`.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as the input.
# Arguments
theta: float >= 0. Threshold location of activation.
# References
- [Zero-Bias Autoencoders and the Benefits of Co-Adapting Features](
https://arxiv.org/abs/1402.3337)
"""
def __init__(self, theta=1.0, **kwargs):
super(ThresholdedReLU, self).__init__(**kwargs)
self.supports_masking = True
self.theta = K.cast_to_floatx(theta)
def call(self, inputs, mask=None):
return inputs * K.cast(K.greater(inputs, self.theta), K.floatx())
def get_config(self):
config = {'theta': float(self.theta)}
base_config = super(ThresholdedReLU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
class Softmax(Layer):
"""Softmax activation function.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as the input.
# Arguments
axis: Integer, axis along which the softmax normalization is applied.
"""
def __init__(self, axis=-1, **kwargs):
super(Softmax, self).__init__(**kwargs)
self.supports_masking = True
self.axis = axis
def call(self, inputs):
return activations.softmax(inputs, axis=self.axis)
def get_config(self):
config = {'axis': self.axis}
base_config = super(Softmax, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
class ReLU(Layer):
"""Rectified Linear Unit activation function.
With default values, it returns element-wise `max(x, 0)`.
Otherwise, it follows:
`f(x) = max_value` for `x >= max_value`,
`f(x) = x` for `threshold <= x < max_value`,
`f(x) = negative_slope * (x - threshold)` otherwise.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as the input.
# Arguments
max_value: float >= 0. Maximum activation value.
negative_slope: float >= 0. Negative slope coefficient.
threshold: float. Threshold value for thresholded activation.
"""
def __init__(self, max_value=None, negative_slope=0.,
threshold=0., **kwargs):
super(ReLU, self).__init__(**kwargs)
if max_value is not None and max_value < 0.:
raise ValueError('max_value of ReLU layer '
'cannot be negative value: %s' % str(max_value))
if negative_slope < 0.:
raise ValueError('negative_slope of ReLU layer cannot be '
'negative value: %s' % str(negative_slope))
self.supports_masking = True
if max_value is not None:
max_value = K.cast_to_floatx(max_value)
self.max_value = max_value
self.negative_slope = K.cast_to_floatx(negative_slope)
self.threshold = K.cast_to_floatx(threshold)
def call(self, inputs):
return K.relu(inputs,
alpha=self.negative_slope,
max_value=self.max_value,
threshold=self.threshold)
def get_config(self):
config = {
'max_value': self.max_value,
'negative_slope': self.negative_slope,
'threshold': self.threshold
}
base_config = super(ReLU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape