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Declarative Machine Learning
#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
#
#-------------------------------------------------------------
/*
* Dropout layer.
*/
forward = function(matrix[double] X, double p, int seed)
return (matrix[double] out, matrix[double] mask) {
/*
* Computes the forward pass for an inverted dropout layer.
*
* Drops the inputs element-wise with a probability p, and divides
* by p to maintain the expected values of those inputs (which are
* the outputs of neurons) at test time.
*
* Inputs:
* - X: Inputs, of shape (any, any).
* - p: Probability of keeping a neuron output.
* - seed: [Optional: -1] Random number generator seed to allow for
* deterministic evaluation. Set to -1 for a random seed.
*
* Outputs:
* - out: Outputs, of same shape as `X`.
* - mask: Dropout mask used to compute the output.
*/
# Normally, we might use something like
# `mask = rand(rows=nrow(X), cols=ncol(X), min=0, max=1, seed=seed) <= p`
# to create a dropout mask. Fortunately, SystemML has a `sparsity` parameter on
# the `rand` function that allows use to create a mask directly.
mask = ifelse(seed == -1,
rand(rows=nrow(X), cols=ncol(X), min=1, max=1, sparsity=p),
rand(rows=nrow(X), cols=ncol(X), min=1, max=1, sparsity=p, seed=seed));
out = X * mask / p
}
backward = function(matrix[double] dout, matrix[double] X, double p, matrix[double] mask)
return (matrix[double] dX) {
/*
* Computes the backward pass for an inverted dropout layer.
*
* Applies the mask to the upstream gradient, and divides by p to
* maintain the expected values at test time.
*
* Inputs:
* - dout: Gradient wrt `out`, of same shape as `X`.
* - X: Inputs, of shape (any, any).
* - p: Probability of keeping a neuron output.
* - mask: Dropout mask used to compute the output.
*
* Outputs:
* - dX: Gradient wrt `X`, of same shape as `X`.
*/
dX = mask / p * dout
}