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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.
#
#-------------------------------------------------------------
/*
* Sigmoid nonlinearity layer.
*/
forward = function(matrix[double] X)
return (matrix[double] out) {
/*
* Computes the forward pass for a sigmoid nonlinearity layer.
*
* `sigmoid(x) = 1 / (1 + e^-x)`
*
* If `X` contains a single feature column, the output of a sigmoid
* layer can be interpreted as a predicted probability of a true
* class when paired with a log loss function in a binary
* classification problem.
*
* Inputs:
* - X: Inputs, of shape (any, any).
*
* Outputs:
* - out: Outputs, of same shape as `X`.
*/
out = 1 / (1+exp(-X))
}
backward = function(matrix[double] dout, matrix[double] X)
return (matrix[double] dX) {
/*
* Computes the backward pass for a sigmoid nonlinearity layer.
*
* Inputs:
* - dout: Gradient wrt `out` from upstream, of same shape as `X`.
* - X: Inputs, of shape (any, any).
*
* Outputs:
* - dX: Gradient wrt `X`, of same shape as `X`.
*/
out = 1 / (1+exp(-X))
dX = out * (1-out) * dout
}