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[optimization] Using adapted learning rate in stochastic gradient descent (if lambda > 0)

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Al
2016-01-17 20:59:47 -05:00
parent 7b300639f1
commit 7d727fc8f0
1 changed files with 5 additions and 2 deletions
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7
src/stochastic_gradient_descent.c
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@@ -103,7 +103,9 @@ bool stochastic_gradient_descent_regularize_weights(matrix_t *theta, uint32_arra
uint32_t row = rows[i]; uint32_t row = rows[i];
double *theta_i = matrix_get_row(theta, row); double *theta_i = matrix_get_row(theta, row);
uint32_t last_updated = updates[row]; uint32_t last_updated = updates[row];
regularize_row(theta_i, n, lambda, last_updated, t, gamma_0);
double gamma_t = stochastic_gradient_descent_gamma_t(gamma_0, lambda, t - last_updated);
regularize_row(theta_i, n, lambda, last_updated, t, gamma_t);
updates[row] = t; updates[row] = t;
} }
@@ -121,8 +123,9 @@ inline bool stochastic_gradient_descent_finalize_weights(matrix_t *theta, uint32
for (size_t i = 0; i < m; i++) { for (size_t i = 0; i < m; i++) {
double *theta_i = matrix_get_row(theta, i); double *theta_i = matrix_get_row(theta, i);
uint32_t last_updated = updates[i]; uint32_t last_updated = updates[i];
regularize_row(theta_i, n, lambda, last_updated, t, gamma_0);
double gamma_t = stochastic_gradient_descent_gamma_t(gamma_0, lambda, t - last_updated);
regularize_row(theta_i, n, lambda, last_updated, t, gamma_t);
updates[i] = t; updates[i] = t;
} }
} }