From a96ec5a74093eb800d14e143b81302d3e0905b85 Mon Sep 17 00:00:00 2001
From: tslil <tslil@posteo.de>
Date: Tue, 12 Jan 2021 15:51:48 -0500
Subject: one output (of course), Pade approximant of logistic for activation

---
 include/ct1973.c | 89 --------------------------------------------------------
 1 file changed, 89 deletions(-)
 delete mode 100644 include/ct1973.c

(limited to 'include/ct1973.c')

diff --git a/include/ct1973.c b/include/ct1973.c
deleted file mode 100644
index 592b0b1..0000000
--- a/include/ct1973.c
+++ /dev/null
@@ -1,89 +0,0 @@
-#include "ct1973.h"
-
-float flattened[CONV_NUM+2];
-float dense1[DENSE1_NUM];
-float dense2[DENSE2_NUM];
-float output[OUTPUT_NUM];
-
-#define RELU(x) ((x) = ((x)<0)?0:(x))
-
-float
-evaluate_black_win(void) {
-	/* ------------------ *
-	 *  Convolution layer *
-	 * ------------------ */
-	// for each kernel
-	for (uint8_t kern = 0; kern < KERN_NUM; kern++) {
-		// the stride is 1, march across the board
-		for (uint8_t bx = 0; bx < KERN_OSIZE; bx++) {
-			for (uint8_t by = 0; by < KERN_OSIZE; by++) {
-				flattened[kern+KERN_NUM*(bx+by*KERN_OSIZE)] =
-					conv2d_biases[kern];
-				// Compute the convolution for this position
-				for (uint8_t ky = 0; ky < KERN_SIZE; ky++) {
-					for (uint8_t kx = 0; kx < KERN_SIZE; kx++) {
-						for (uint8_t c = 0; c < KERN_CHAN; c++) {
-							// Where we are on the board
-							const uint8_t loc = kx+by+(ky+bx)*board_size;
-							// Look up what's on the board at this location, and
-							// multiply it. For c=0 we have to do some extra work
-							float lookup = 0;
-							if (COUNT_AT(loc)>c) {
-								if (c==0) {
-									if (STONE_AT(loc) == STONE_STANDING) {
-										lookup = (colours[loc] & 1) ? +0.25 : -0.25;
-									} else if (STONE_AT(loc) == STONE_CAPSTONE) {
-										lookup = (colours[loc] & 1) ? +1.00 : -1.00;
-									} else {
-										lookup = (colours[loc] & 1) ? +0.50 : -0.50;
-									}
-								} else {
-									lookup = (colours[loc] & (1<<c)) ? +0.50 : -0.50;
-								}
-							}
-							flattened[kern+KERN_NUM*(bx+by*KERN_OSIZE)]
-								+= lookup*conv2d_weights[kern][ky][kx][c];
-						}
-					}
-				}
-				RELU(flattened[kern+KERN_NUM*(bx+by*KERN_OSIZE)]);
-			}
-		}
-	}
-	// Add input of flat counts
-	flattened[CONV_NUM]   = (float)(white_count & 127)/21.0;
-	flattened[CONV_NUM+1] = (float)(black_count & 127)/21.0;
-	/* ------------------ *
-	 *  First dense layer *
-	 * ------------------ */
-	for (uint8_t d1 = 0; d1 < DENSE1_NUM; d1++) {
-		dense1[d1] = dense1_biases[d1];
-		for (uint8_t fl = 0; fl < CONV_NUM+2; fl++) {
-			dense1[d1] += flattened[fl]*dense1_weights[d1][fl];
-		}
-		RELU(dense1[d1]);
-	}
-	/* ------------------- *
-	 *  Second dense layer *
-	 * ------------------- */
-	for (uint8_t d2 = 0; d2 < DENSE2_NUM; d2++) {
-		dense2[d2] = dense2_biases[d2];
-		for (uint8_t d1 = 0; d1 < DENSE1_NUM; d1++) {
-			dense2[d2] += dense1[d1]*dense2_weights[d2][d1];
-		}
-		RELU(dense2[d2]);
-	}
-	/* ------------- *
-	 *  Output layer *
-	 * ------------- */
-	float norm = 0;
-	for (uint8_t out = 0; out < OUTPUT_NUM; out++) {
-		output[out] = output_biases[out];
-		for (uint8_t d2 = 0; d2 < DENSE2_NUM; d2++) {
-			output[out] += dense2[d2]*output_weights[out][d2];
-		}
-		output[out] = exp(output[out]);
-		norm += output[out];
-	}
-	return output[1]/norm;
-}
-- 
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