"""
Copyright (C) 2018 Patrick Schwab, ETH Zurich
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions
of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.
"""
import keras.backend as K
from keras.legacy import interfaces
from keras.layers import Layer
import tensorflow as tf
class PerSampleDropout(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.
# Arguments
rate: float between 0 and 1. Fraction of the input units to drop.
noise_shape: 1D integer tensor 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 to use as random seed.
# References
- [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
"""
@interfaces.legacy_dropout_support
def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
super(PerSampleDropout, self).__init__(**kwargs)
self.rate = rate
self.noise_shape = noise_shape
self.seed = seed
self.supports_masking = True
def _get_noise_shape(self, _):
return self.noise_shape
def call(self, inputs, training=None):
def dropped_inputs():
keep_prob = 1. - self.rate
tile_shape = tf.expand_dims(tf.shape(inputs)[-1], axis=0)
tiled_keep_prob = K.tile(keep_prob, tile_shape)
keep_prob = tf.transpose(K.reshape(tiled_keep_prob, [tile_shape[0], tf.shape(keep_prob)[0]]))
binary_tensor = tf.floor(keep_prob + K.random_uniform(shape=tf.shape(inputs)))
return inputs * binary_tensor
return K.in_train_phase(dropped_inputs, inputs,
training=training)
def get_config(self):
config = {}
base_config = super(PerSampleDropout, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
