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| |
| /* |
| * RMSprop optimizer. |
| */ |
| |
| update = function(matrix[double] X, matrix[double] dX, double lr, double decay_rate, |
| double epsilon, matrix[double] cache) |
| return (matrix[double] X, matrix[double] cache) { |
| /* |
| * Performs an RMSprop update. |
| * |
| * This is an adaptive learning rate optimizer that can be viewed |
| * as an adjustment of the Adagrad method to use a moving average |
| * of the sum of squared gradients in order to improve convergence. |
| * |
| * Reference: |
| * - Neural Networks for Machine Learning, Lecture 6a, Hinton, |
| * slide 29. |
| * - http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf |
| * |
| * Inputs: |
| * - X: Parameters to update, of shape (any, any). |
| * - dX: Gradient wrt `X` of a loss function being optimized, of |
| * same shape as `X`. |
| * - lr: Learning rate. |
| * - decay_rate: Term controlling the rate of the moving average. |
| * Typical values are in the range of [0.9, 0.999]. |
| * - epsilon: Smoothing term to avoid divide by zero errors. |
| * Typical values are in the range of [1e-8, 1e-4]. |
| * - cache: State that maintains the moving average of the squared |
| * gradients, of same shape as `X`. |
| * |
| * Outputs: |
| * - X: Updated parameters `X`, of same shape as input `X`. |
| * - cache: Updated state that maintains the moving average of the |
| * squared gradients, of same shape as `X`. |
| */ |
| cache = decay_rate*cache + (1-decay_rate)*dX^2 |
| X = X - (lr * dX / (sqrt(cache)+epsilon)) |
| } |
| |
| init = function(matrix[double] X) |
| return (matrix[double] cache) { |
| /* |
| * Initialize the state for this optimizer. |
| * |
| * Note: This is just a convenience function, and state |
| * may be initialized manually if needed. |
| * |
| * Inputs: |
| * - X: Parameters to update, of shape (any, any). |
| * |
| * Outputs: |
| * - cache: State that maintains the moving average of the squared |
| * gradients, of same shape as `X`. |
| */ |
| cache = matrix(0, rows=nrow(X), cols=ncol(X)) |
| } |
| |