ConcreteDropout 0.1.2

Concrete Dropout implementation for Tensorflow 2

ConcreteDropout-TF2

Concrete Dropout updated implementation for Tensorflow 2.0 following the original code from the paper.

Installation

To install this package, please use:

pip install concretedropout

Introduction

Concrete dropout allows for the dropout probability of a layer to become a trainable parameter. For more information, see the original paper: https://arxiv.org/abs/1705.07832

This package implements Concrete Dropout for the following layers:

• Dense - ConcreteDenseDropout
• Conv1D - ConcreteSpatialDropout1D
• Conv2D - ConcreteSpatialDropout2D
• Conv3D - ConcreteSpatialDropout2D
• DepthwiseConv1D - ConcreteSpatialDropoutDepthwise1D
• DepthwiseConv2D - ConcreteSpatialDropoutDepthwise2D
• DepthwiseConv3D - ConcreteSpatialDropoutDepthwise3D

Please notice that the dropout layer will be applied before the chosen layer.

Arguments

Each concrete dropout layer supports the following arguments:

• layer: an instance of the layer to which concrete dropout will be applied
• weight_regularizer=1e-6: A positive number which satisfies weight_regularizer = $l^2 / (\tau * N)$ with prior lengthscale l, model precision $\tau$ (inverse observation noise), and N the number of instances in the dataset. Note that kernel_regularizer is not needed. The appropriate weight_regularizer value can be computed with the utility function get_weight_regularizer(N, l, tau)
• dropout_regularizer=1e-5: A positive number which satisfies dropout_regularizer} = $2 / (\tau * N)$ with model precision $\tau$ (inverse observation noise) and N the number of instances in the dataset. Note the relation between dropout_regularizer and weight_regularizer: weight_regularizer / dropout_regularizer = $l^2 / 2$ with prior lengthscale l. Note also that the factor of two should be ignored for cross-entropy loss, and used only for the eculedian loss. The appropriate dropout_regularizer value can be computed with the utility function get_dropout_regularizer(N, tau, cross_entropy_loss=False). By default, a regression problem will be assumed.
• init_min=0.1: minimum value for the random initial dropout probability
• init_max=0.1: maximum value for the random initial dropout probability
• is_mc_dropout=False: enables Monte Carlo Dropout (i.e. dropout will remain active also at prediction time). Default: False.
• data_format=None: channels_last or channels_first. Defaults to channels_last.
• temperature: temperature of the concrete distribution. For more information see arXiv:1611.00712. Defaults to 0.1 for dense layers, and 2/3 for convolution layers.

Example

The suggested way to employ concrete dropout layers is the following:

import tensorflow as tf
from concretedropout import ConcreteDenseDropout 

#... import the dataset
Ns = x_train.shape[0]
# get the regularizers
wr = get_weight_regularizer(Ns, l=1e-2, tau=1.0) # tau is the inverse 
dr = get_dropout_regularizer(Ns, tau=1.0, cross_entropy_loss=True)

# ... a neural network with output x
dense1 = tf.keras.layers.Dense(N_neurons, weight_regularizer=wr, dropout_regularizer=dr)
x = ConcreteDenseDropout(dense1)(x)

For a practical example on how to use concrete dropout for the mnist dataset, see this example.

Bayesian neural network with MCDropout

You can find here an example on how to use MCDropout and Concrete Dropout to implement a Bayesian Neural Network with MCDropout. For more information, see arXiv:1506.02142.