From 0e81096d5ecb6027814e7aae10b461e774f96407 Mon Sep 17 00:00:00 2001
From: tslil clingman <tslil@posteo.de>
Date: Sat, 21 Jan 2023 19:30:45 +0100
Subject: might as well enable size 6

Same network architecture, same training principle. Predictably this is
too slow.

Also statically allocate state in driver programmes.
---
 include/nn.c | 124 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 124 insertions(+)
 create mode 100644 include/nn.c

(limited to 'include/nn.c')

diff --git a/include/nn.c b/include/nn.c
new file mode 100644
index 0000000..a837a59
--- /dev/null
+++ b/include/nn.c
@@ -0,0 +1,124 @@
+/*
+  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 "nn.h"
+#include "tak.h"
+#include "weights_5.h"
+#include "weights_6.h"
+
+// ===================================================================
+// Helpers
+// ===================================================================
+
+#define RELU(x) ((x) = ((x) < 0) ? 0 : (x))
+
+static inline void prepare_input(float *cur_board, tak_state_p stat);
+
+// ===================================================================
+// Implementation of small neural networks
+// ===================================================================
+
+float nn1986_evaluate_black_win(tak_state_p state) {
+  static float cur_board[FIVE_INP_NUM];
+  static float dense1[FIVE_DENSE_NUM];
+  static float output[2];
+
+  prepare_input(cur_board, state);
+  /* ------------------ *
+   *  First dense layer *
+   * ------------------ */
+  for (unsigned int d1 = 0; d1 < FIVE_DENSE_NUM; d1++) {
+    dense1[d1] = five_dense_biases[d1];
+    for (unsigned int fl = 0; fl < FIVE_INP_NUM; fl++) {
+      dense1[d1] += cur_board[fl] * five_dense_weights[d1][fl];
+    }
+    RELU(dense1[d1]);
+  }
+  /* ------------- *
+   *  Output layer *
+   * ------------- */
+  for (uint8_t k = 0; k < 2; k++) {
+    output[k] = five_output_bias[k];
+    for (unsigned int d2 = 0; d2 < FIVE_DENSE_NUM; d2++) {
+      output[k] += dense1[d2] * five_output_weights[k][d2];
+    }
+    RELU(output[k]);
+  }
+  const float norm = output[0] + output[1];
+  return (2 * output[0] / norm) - 1;
+}
+
+float nn2690_evaluate_black_win(tak_state_p state) {
+  static float cur_board[SIX_INP_NUM];
+  static float dense1[SIX_DENSE_NUM];
+  static float output[2];
+
+  prepare_input(cur_board, state);
+  /* ------------------ *
+   *  First dense layer *
+   * ------------------ */
+  for (unsigned int d1 = 0; d1 < SIX_DENSE_NUM; d1++) {
+    dense1[d1] = six_dense_biases[d1];
+    for (unsigned int fl = 0; fl < SIX_INP_NUM; fl++) {
+      dense1[d1] += cur_board[fl] * six_dense_weights[d1][fl];
+    }
+    RELU(dense1[d1]);
+  }
+  /* ------------- *
+   *  Output layer *
+   * ------------- */
+  for (uint8_t k = 0; k < 2; k++) {
+    output[k] = six_output_bias[k];
+    for (unsigned int d2 = 0; d2 < SIX_DENSE_NUM; d2++) {
+      output[k] += dense1[d2] * six_output_weights[k][d2];
+    }
+    RELU(output[k]);
+  }
+  const float norm = output[0] + output[1];
+  return (2 * output[0] / norm) - 1;
+}
+
+// ===================================================================
+// Helper implementation
+// ===================================================================
+
+static inline void prepare_input(float *cur_board, tak_state_p state) {
+  /* --------------- *
+   *  Populate input *
+   * --------------- */
+  for (unsigned int y = 0; y < state->board_size; y++) {
+    for (unsigned int x = 0; x < state->board_size; x++) {
+      const unsigned int loc = x + y * state->board_size;
+      const unsigned int count = COUNT_AT(state, loc);
+      float lookup = 0;
+      if (count > 0) {
+        if (STONE_AT(state, loc) == STONE_STANDING) {
+          lookup = (state->colours[loc] & 1) ? +0.25 : -0.25;
+        } else if (STONE_AT(state, loc) == STONE_CAPSTONE) {
+          lookup = (state->colours[loc] & 1) ? +1.00 : -1.00;
+        } else {
+          lookup = (state->colours[loc] & 1) ? +0.50 : -0.50;
+        }
+      }
+      cur_board[3 + loc] = lookup;
+    }
+  }
+  // Add input of flat counts and ply parity
+  cur_board[0] = (state->ply & 1) ? 1 : -1;
+  cur_board[1] = (float)(state->white_count & 127) / 21.0;
+  cur_board[2] = (float)(state->black_count & 127) / 21.0;
+}
-- 
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