Just some #weightgoals ;)

It turns out that while i was training on a 0/1 classification
problem, i was using 2*eval - 1. Training using this function instead,
and on bot-dominated game choices (chosen_player in extract.sh) seems
to have given a better evaluation function. At the least, Morten's
swindle doesn't work anymore.

1 files changed, 4 insertions, 4 deletions

diff --git a/include/cnn1986.c b/include/cnn1986.c
index d4f6ad6..b95fca1 100644
--- a/include/cnn1986.c
+++ b/include/cnn1986.c
@@ -100,13 +100,13 @@ float cnn1986_evaluate_black_win(void) {
 	for (uint8_t d2 = 0; d2 < DENSE2_NUM; d2++) {
 		output += dense2[d2]*output_weights[d2];
 	}
-	// Truncated Pade approximant of logistic function
-	output = (12.0+output+50.0*output/(output*output+10.0))/24.0;
+	// 2*(clamped Pade approximant of logistic function) - 1
+	output = (12.0+output+50.0*output/(output*output+10.0))/12.0 - 1.0;
 	if (output > 1.0) {
 		return 1.0;
 	}
-	else if (output < 0.0) {
+	else if (output < -1.0) {
 		return -1.0;
 	}
-	return 2*output-1.0;
+	return output;
 }