| #------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
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| # 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 |
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| # |
| # 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 |
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| # KIND, either express or implied. See the License for the |
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| # |
| #------------------------------------------------------------- |
| |
| /* |
| * 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, SystemDS 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 |
| } |
| |