1 files changed, 81 insertions, 0 deletions
diff --git a/include/nn1986.c b/include/nn1986.c
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+++ b/include/nn1986.c
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+/*
+  This file is part of ct.
+
+  This program is free software: you can redistribute it and/or modify
+  it under the terms of the GNU General Public License as published by
+  the Free Software Foundation, either version 3 of the License, or
+  (at your option) any later version.
+
+  This program is distributed in the hope that it will be useful, but
+  WITHOUT ANY WARRANTY; without even the implied warranty of
+  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+  General Public License for more details.
+
+  You should have received a copy of the GNU General Public License
+  along with ct. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+#include "nn1986.h"
+#include "tak.h"
+#include "weights.h"
+
+// ===================================================================
+// Implementation of a small neural network
+// ===================================================================
+
+static float cur_board[INP_NUM];
+static float dense1[DENSE_NUM];
+static float output[2];
+
+#define RELU(x) ((x) = ((x) < 0) ? 0 : (x))
+float nn1986_evaluate_black_win(void) {
+  /* --------------- *
+   *  Populate input *
+   * --------------- */
+  for (unsigned int y = 0; y < board_size; y++) {
+    for (unsigned int x = 0; x < board_size; x++) {
+      const unsigned int loc = x + y * board_size;
+      const unsigned int count = COUNT_AT(loc);
+      float lookup = 0;
+      if (count > 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;
+        }
+      }
+      cur_board[3 + loc] = lookup;
+    }
+  }
+  /* ------------------ *
+   *  Convolution layer *
+   * ------------------ */
+  // Add input of flat counts and ply parity
+  cur_board[0] = (ply & 1) ? 1 : -1;
+  cur_board[1] = (float)(white_count & 127) / 21.0;
+  cur_board[2] = (float)(black_count & 127) / 21.0;
+  /* ------------------ *
+   *  First dense layer *
+   * ------------------ */
+  for (unsigned int d1 = 0; d1 < DENSE_NUM; d1++) {
+    dense1[d1] = dense1_biases[d1];
+    for (unsigned int fl = 0; fl < 3 + 5 * 5; fl++) {
+      dense1[d1] += cur_board[fl] * dense1_weights[d1][fl];
+    }
+    RELU(dense1[d1]);
+  }
+  /* ------------- *
+   *  Output layer *
+   * ------------- */
+  for (uint8_t k = 0; k < 2; k++) {
+    output[k] = output_bias[k];
+    for (unsigned int d2 = 0; d2 < DENSE_NUM; d2++) {
+      output[k] += dense1[d2] * output_weights[k][d2];
+    }
+    RELU(output[k]);
+  }
+  const float norm = output[0] + output[1];
+  return (2 * output[0] / norm) - 1;
+}