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Declarative Machine Learning

There is a newer version: 1.2.0

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/*
 * Rectified Linear Unit (ReLU) nonlinearity layer.
 */

forward = function(matrix[double] X)
    return (matrix[double] out) {
  /*
   * Computes the forward pass for a ReLU nonlinearity layer.
   *
   * Performs an element-wise evaluation of `f(input) = max(0, input)`.
   *
   * Inputs:
   *  - X: Inputs, of shape (any, any).
   *
   * Outputs:
   *  - out: Outputs, of same shape as `X`.
   */
  out = max(0, X)
}

backward = function(matrix[double] dout, matrix[double] X)
    return (matrix[double] dX) {
  /*
   * Computes the backward pass for a ReLU nonlinearity layer.
   *
   * Essentially performs a pass-through of the upstream gradient
   * for cells > 0.
   *
   * Inputs:
   *  - dout: Gradient wrt `out` from upstream, of same shape as `X`.
   *  - X: Previous input data matrix, of shape (any, any).
   *
   * Outputs:
   *  - dX: Gradient wrt `X`, of same shape as `X`.
   */
   dX = (X > 0) * dout
}