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Added PELU Activation

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josephyearsley
2017-01-25 12:47:56 +00:00
parent 080f5996f0
commit 6f97693399
7 changed files with 342 additions and 0 deletions
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keras_contrib/layers/advanced_activations.py
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@@ -2,3 +2,75 @@ from .. import initializations
from keras.engine import Layer
from .. import backend as K
import numpy as np
class PELU(Layer):
"""Parametric Exponential Linear Unit.
It follows:
`f(x) = alphas * x for x < 0`,
`f(x) = x for x >= 0`,
where `alphas` 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
init: initialization function for the weights.
weights: initial weights, as a list of a single Numpy array.
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
- [PARAMETRIC EXPONENTIAL LINEAR UNIT FOR DEEP CONVOLUTIONAL NEURAL NETWORKS](https://arxiv.org/abs/1605.09332v3)
"""
def __init__(self, init='zero', weights=None, shared_axes=None, **kwargs):
self.supports_masking = True
self.init = initializations.get(init)
self.initial_weights = weights
if not isinstance(shared_axes, (list, tuple)):
self.shared_axes = [shared_axes]
else:
self.shared_axes = list(shared_axes)
super(PELU, self).__init__(**kwargs)
def build(self, input_shape):
param_shape = list(input_shape[1:])
self.param_broadcast = [False] * len(param_shape)
if self.shared_axes[0] is not None:
for i in self.shared_axes:
param_shape[i - 1] = 1
self.param_broadcast[i - 1] = True
# Initialised as ones to emulate the default ELU
self.alphas = K.ones(param_shape, dtype=K.floatx(),
name='{}_alphas'.format(self.name))
self.betas = K.ones(param_shape, dtype=K.floatx(),
name='{}_betas'.format(self.name))
self.trainable_weights = [self.alphas, self.betas]
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
def call(self, x, mask=None):
pos = K.relu(x) * (self.alphas / self.betas)
if K.backend() == 'theano':
neg = (K.pattern_broadcast(self.alphas, self.param_broadcast) *
(K.exp(x / self.betas) - 1))
else:
neg = self.alphas * (K.exp(x / self.betas) - 1)
return pos + neg
def get_config(self):
config = {'init': self.init.__name__}
base_config = super(PELU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))