~tslil/ctak

c9b108b767de285037e408e6fc88b91469da803e — tslil clingman 8 months ago f3b67eb 
might as well enable size 6

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

Also statically allocate state in driver programmes.
patch browse .tar.gz 
17 files changed, 1857 insertions(+), 143 deletions(-)

M include/negamax.c
M include/negamax.h
R include/{nn1986.c => nn.c}
R include/{nn1986.h => nn.h}
M include/tak.h
M include/tps.c
D include/weights.c
A include/weights_5.c
R include/{weights.h => weights_5.h}
A include/weights_6.c
A include/weights_6.h
M resources/extract.sh
M src/ct1986.c
M src/ctlm.c
M src/cttei.c
M src/geminict.c
M src/nn_train.py

M include/negamax.c => include/negamax.c +8 -1
@@ 26,6 26,7 @@ const float infty = 3.0;
char negamax_ptn[9];
uint8_t negamax_search_depth = 3;
static uint64_t negamax_best_action;
static nn_function evaluate;

// ===================================================================
// Helper declarations


@@ 43,6 44,12 @@ void negamax_init(const uint8_t new_board_size) {
  action_list_init(new_board_size);
  zobrist_init(new_board_size);
  tt_init();

  if (new_board_size == 5) {
    evaluate = &nn1986_evaluate_black_win;
  } else {
    evaluate = &nn2690_evaluate_black_win;
  }
}

void negamax_free(void) { zobrist_free(); }


@@ 132,7 139,7 @@ static float negamax(tak_state_p state, const uint8_t cur_depth,
      node_value = -negamax(state, cur_depth - 1, init_depth, -beta, -alpha,
                            -colour, zobrist_compute(state));
    } else {
      node_value = colour * nn1986_evaluate_black_win(state);
      node_value = colour * evaluate(state);
    }
    action_undo(state, node->action);



M include/negamax.h => include/negamax.h +1 -1
@@ 19,7 19,7 @@
#include <stdint.h>

#include <actions.h>
#include <nn1986.h>
#include <nn.h>
#include <tak.h>
#include <tt_llcht.h>
#include <zobrist.h>


R include/nn1986.c => include/nn.c +77 -34
@@ 15,20 15,88 @@
  along with ct. If not, see <https://www.gnu.org/licenses/>.
*/

#include "nn1986.h"
#include "nn.h"
#include "tak.h"
#include "weights.h"
#include "weights_5.h"
#include "weights_6.h"

// ===================================================================
// Implementation of a small neural network
// Helpers
// ===================================================================

static float cur_board[INP_NUM];
static float dense1[DENSE_NUM];
static float output[2];

#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 *
   * --------------- */


@@ 38,9 106,9 @@ float nn1986_evaluate_black_win(tak_state_p state) {
      const unsigned int count = COUNT_AT(state, loc);
      float lookup = 0;
      if (count > 0) {
          if (STONE_AT(state, loc) == STONE_STANDING) {
        if (STONE_AT(state, loc) == STONE_STANDING) {
          lookup = (state->colours[loc] & 1) ? +0.25 : -0.25;
          } else if (STONE_AT(state, loc) == STONE_CAPSTONE) {
        } 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;


@@ 49,33 117,8 @@ float nn1986_evaluate_black_win(tak_state_p state) {
      cur_board[3 + loc] = lookup;
    }
  }
  /* ------------------ *
   *  Convolution layer *
   * ------------------ */
  // 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;
  /* ------------------ *
   *  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;
}


R include/nn1986.h => include/nn.h +3 -0
@@ 17,4 17,7 @@

#include <tak.h>

typedef float (*nn_function)(tak_state_p);

float nn1986_evaluate_black_win(tak_state_p state);
float nn2690_evaluate_black_win(tak_state_p state);


M include/tak.h => include/tak.h +1 -1
@@ 55,7 55,7 @@ typedef struct tak_state_s {
  enum WIN_TYPE won;
  enum COLOUR current_colour;
  uint8_t white_count, black_count, ply, board_size;
} * tak_state_p;
} *tak_state_p;

#define NUM_SHIFT 4
#define NUM_MASK (0xF << NUM_SHIFT) // 0b11110000


M include/tps.c => include/tps.c +5 -5
@@ 76,7 76,7 @@ enum TPS_RESULT load_tps(tak_state_p state, char *tps) {
      break;
    }
    default: {
        const int l = THE_COORDS(state->board_size, col, row);
      const int l = THE_COORDS(state->board_size, col, row);
      uint8_t num_read = 0, reading = 1;
      // Read in a stack of colours, optionally terminated by an S
      // or C to change the top stone type


@@ 121,7 121,7 @@ enum TPS_RESULT load_tps(tak_state_p state, char *tps) {
        }
        case 'S': {
          // Have we already read a stone type?
            if (STONE_AT(state, l) != STONE_FLAT)
          if (STONE_AT(state, l) != STONE_FLAT)
            return TPS_INVALID;
          state->celldat[l] |= STONE_STANDING;
          tps++;


@@ 129,7 129,7 @@ enum TPS_RESULT load_tps(tak_state_p state, char *tps) {
          break;
        }
        case 'C': {
            if (STONE_AT(state, l) != STONE_FLAT)
          if (STONE_AT(state, l) != STONE_FLAT)
            return TPS_INVALID;
          state->celldat[l] |= STONE_CAPSTONE;
          tps++;


@@ 207,8 207,8 @@ void generate_tps(tak_state_p state, char *out_tps) {

  for (int8_t row = state->board_size - 1; row >= 0; row--) {
    for (int8_t col = 0; col < state->board_size; col++) {
        const int8_t l = THE_COORDS(state->board_size, col, row);
        const uint8_t count = COUNT_AT(state, l);
      const int8_t l = THE_COORDS(state->board_size, col, row);
      const uint8_t count = COUNT_AT(state, l);
      if (count) {
        colour_stack_t c = state->colours[l], s = 1 << (count - 1);
        for (int k = 0; k < count; k++, s >>= 1, out_tps++) {


D include/weights.c => include/weights.c +0 -33
@@ 1,33 0,0 @@
/*
 * Model: "model"
 * _________________________________________________________________
 *  Layer (type)                Output Shape              Param #   
 * =================================================================
 *  input_1 (InputLayer)        [(None, 28)]              0         
 *                                                                  
 *  dense (Dense)               (None, 64)                1856      
 *                                                                  
 *  dense_1 (Dense)             (None, 2)                 130       
 *                                                                  
 * =================================================================
 * Total params: 1,986
 * Trainable params: 1,986
 * Non-trainable params: 0
 * _________________________________________________________________
 * ([0.5177317261695862, 0.755382776260376], [0.5241230130195618, 0.7530876398086548])
*/

#include "weights.h"

const float dense1_weights[DENSE_NUM][INP_NUM] =
{{0.0335812121629715, -0.21365489065647125, -0.653753936290741, -0.13572198152542114, 0.18898768723011017, 0.06430475413799286, 0.1521165519952774, -0.6386323571205139, 0.5113293528556824, 0.3224753439426422, 0.3006350100040436, 0.4426257014274597, -0.4259096086025238, 0.34491172432899475, 0.26074907183647156, 0.30206671357154846, 0.2114274799823761, -0.5099963545799255, 0.38777005672454834, 0.23917558789253235, 0.32478395104408264, 0.2528369724750519, -0.5045477747917175, 0.10504062473773956, 0.34629005193710327, 0.28095799684524536, 0.31814444065093994, -0.7089307904243469}, {0.0565570704638958, -0.4527612030506134, 0.22363899648189545, -0.4776171147823334, -0.4429882764816284, 0.09122249484062195, 0.08279165625572205, 0.29376256465911865, -0.23718500137329102, -0.5789054036140442, 0.28780052065849304, 0.6442199945449829, 0.03741508722305298, -0.220468670129776, -0.644933819770813, -0.13106265664100647, 0.5042467713356018, 0.43313512206077576, 0.5279864072799683, -0.5684875249862671, -0.8156968355178833, -0.5465362668037415, -0.5424111485481262, 0.04707803204655647, 0.2047167718410492, -0.3449491560459137, -0.26257622241973877, -0.4042203426361084}, {0.002476541558280587, -0.21414169669151306, -0.11842579394578934, 0.14450429379940033, -0.01525128073990345, -0.12563736736774445, -0.27338773012161255, 0.009645791724324226, -0.25494587421417236, -0.2797246277332306, -0.2301362156867981, -0.40248778462409973, -0.2902604341506958, 0.19436009228229523, -0.11030247807502747, 0.08137191087007523, -0.1867382973432541, -0.7496097683906555, -0.008727616630494595, 0.22033251821994781, 0.19767463207244873, 0.11307194083929062, -0.8412720561027527, 0.2657962143421173, -0.07668104767799377, 0.06086883693933487, 0.3835994601249695, -1.091362476348877}, {0.05102699622511864, -0.8378904461860657, 0.4566768705844879, -0.2630152404308319, -0.23932738602161407, -0.19857573509216309, 0.30443575978279114, -0.14038261771202087, -0.14678728580474854, -0.40179580450057983, 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-0.05247826129198074, -0.04454374313354492, 0.22061742842197418, 0.2703307569026947, 0.2895837426185608, -0.10765346139669418, -0.1715518683195114}, {-0.08773751556873322, 0.1905992329120636, -0.1878463476896286, -0.06142798811197281, -0.07157573848962784, -0.15859191119670868, 0.2888740599155426, 0.29501354694366455, -0.18804210424423218, 0.03719957917928696, 0.04348141327500343, 0.43497395515441895, 0.3348860740661621, 0.046619195491075516, -0.012931774370372295, 0.43330755829811096, 0.783429741859436, 0.7513718605041504, 0.42782992124557495, 0.4602666199207306, 0.8619009256362915, 0.21070736646652222, -0.6946675181388855, 0.36241528391838074, 0.5762568712234497, 0.41478705406188965, -0.7064434289932251, -0.7981423139572144}, {0.0158368032425642, -0.46186450123786926, -0.04968561232089996, 0.08248921483755112, 0.09503590315580368, -0.02358037419617176, -0.31207606196403503, 0.5838796496391296, -0.015739191323518753, 0.14295169711112976, 0.06756915897130966, -0.23699864745140076, -0.031251370906829834, 0.044411782175302505, 0.07595864683389664, -0.06131885573267937, -0.2911010682582855, -0.03894522413611412, 0.26580068469047546, 0.10324547439813614, -0.22770865261554718, -0.43839356303215027, -0.10662800073623657, -1.1684633493423462, -0.6824188828468323, -0.5405550003051758, -0.3048187494277954, 0.12504103779792786}};

const float dense1_biases[DENSE_NUM] =
{-0.1444406658411026, -0.9969395995140076, -0.6437257528305054, -0.5090100169181824, -0.4562266170978546, -0.7947681546211243, -0.66095370054245, -0.0757993534207344, -0.6305758357048035, -0.6522586345672607, -0.6137698292732239, 0.06795938313007355, -0.5988752841949463, -0.022649021819233894, -0.559893012046814, -0.34122252464294434, 0.21291229128837585, -0.9544443488121033, -0.5839042663574219, -0.3325875997543335, -0.8768086433410645, -0.3371303975582123, -0.804802417755127, -0.7296857833862305, -0.24707458913326263, -0.9100144505500793, -0.7247095704078674, -0.6324146389961243, -0.42117181420326233, -1.2145949602127075, -0.7118635177612305, -0.9112556576728821, -0.21369697153568268, -0.10298735648393631, -0.8274878263473511, -0.6126794219017029, -0.7786099314689636, -0.5083696246147156, -0.38434338569641113, -0.468181848526001, -0.5246707201004028, -0.6266580820083618, -0.3932459354400635, -0.6446056365966797, -0.8690927028656006, -0.7806448936462402, -0.7514670491218567, -0.4992736577987671, -0.4725331962108612, -0.8359085321426392, -0.3995131254196167, -0.701408326625824, 0.13735836744308472, -0.7701425552368164, 0.37747254967689514, -0.8496323227882385, -0.41388845443725586, -0.3626680374145508, -0.4750637412071228, -1.0076007843017578, -0.4247843325138092, -0.8280375599861145, -0.8584521412849426, -0.49181294441223145};

const float output_weights[2][DENSE_NUM] =
{{-0.10164133459329605, -0.06545510143041611, -0.12559004127979279, -0.09770263731479645, 0.020511239767074585, -0.11956804245710373, 0.0725698173046112, 0.09884587675333023, 0.03334106504917145, -0.07531746476888657, -0.04404358193278313, 0.04667624458670616, -0.10733920335769653, 0.11603111028671265, -0.09832630306482315, -0.029710080474615097, 0.6112914681434631, 0.09875980764627457, 0.04528747498989105, 0.04975724592804909, 0.05296019837260246, -0.1494731307029724, 0.06372635811567307, 0.05117323249578476, -0.4960876405239105, 0.08172430098056793, 0.09049712121486664, 0.16494503617286682, 0.12463495880365372, 0.10889994353055954, 0.0901939794421196, -0.07425925135612488, -0.09327994287014008, 0.07225991785526276, 0.0995996817946434, 0.1002214103937149, 0.13605892658233643, -0.2738678455352783, 0.07213583588600159, 0.031051557511091232, -0.09413432329893112, -0.05657240375876427, 0.023954149335622787, -0.11132869869470596, 0.0918613076210022, 0.1731780767440796, -0.048148252069950104, -0.08594620227813721, -0.07079077512025833, 0.028347637504339218, 0.04007003828883171, -0.08521876484155655, -0.13736766576766968, 0.0973486453294754, -0.3953056037425995, -0.09303829818964005, 0.0926792323589325, 0.08662958443164825, 0.07239469885826111, 0.12995171546936035, -0.08624651283025742, -0.13703757524490356, 0.1074426919221878, -0.05400789529085159}, {0.09475231915712357, 0.10763916373252869, 0.10490848124027252, 0.051702577620744705, -0.025973767042160034, 0.1243276372551918, -0.09184729307889938, -0.09110323339700699, -0.10927686840295792, 0.07648954540491104, 0.08314546942710876, -0.054432936012744904, 0.11420167982578278, -0.14501862227916718, 0.07449985295534134, 0.04114171117544174, -0.5540419816970825, -0.11702275276184082, -0.057106584310531616, -0.11389349400997162, -0.04799086973071098, 0.11528362333774567, -0.10561501979827881, -0.06099977344274521, 0.5406088829040527, -0.07843243330717087, -0.07121624052524567, -0.15534670650959015, -0.10739737004041672, -0.10599866509437561, -0.03090781159698963, 0.1024080142378807, 0.11164399981498718, -0.10347762703895569, -0.09762325882911682, -0.044070787727832794, -0.1382112205028534, 0.2635824978351593, -0.013381495140492916, -0.017522230744361877, 0.09075458347797394, 0.08058229833841324, -0.03840004280209541, 0.08596818149089813, -0.10802465677261353, -0.17988164722919464, 0.08185803890228271, 0.09076226502656937, 0.042497776448726654, -0.0991385206580162, -0.03188726678490639, 0.0789727196097374, 0.12080144137144089, -0.12002957612276077, 0.3941708207130432, 0.07739747315645218, -0.04954647645354271, -0.10626203566789627, -0.054530855268239975, -0.19077910482883453, 0.07939524203538895, 0.10724455863237381, -0.09315766394138336, 0.07806982845067978}};

const float output_bias[2] =
{0.3762611150741577, 0.6172211170196533};



A include/weights_5.c => include/weights_5.c +735 -0
@@ 0,0 1,735 @@
/*
 * Model: "model"
 * _________________________________________________________________
 *  Layer (type)                Output Shape              Param #
 * =================================================================
 *  input_1 (InputLayer)        [(None, 28)]              0
 *
 *  dense (Dense)               (None, 64)                1856
 *
 *  dense_1 (Dense)             (None, 2)                 130
 *
 * =================================================================
 * Total params: 1,986
 * Trainable params: 1,986
 * Non-trainable params: 0
 * _________________________________________________________________
 * ([0.5177317261695862, 0.755382776260376], [0.5241230130195618,
 * 0.7530876398086548])
 */

#include "weights_5.h"

const float five_dense_weights[FIVE_DENSE_NUM][FIVE_INP_NUM] = {
    {0.0335812121629715,   -0.21365489065647125, -0.653753936290741,
     -0.13572198152542114, 0.18898768723011017,  0.06430475413799286,
     0.1521165519952774,   -0.6386323571205139,  0.5113293528556824,
     0.3224753439426422,   0.3006350100040436,   0.4426257014274597,
     -0.4259096086025238,  0.34491172432899475,  0.26074907183647156,
     0.30206671357154846,  0.2114274799823761,   -0.5099963545799255,
     0.38777005672454834,  0.23917558789253235,  0.32478395104408264,
     0.2528369724750519,   -0.5045477747917175,  0.10504062473773956,
     0.34629005193710327,  0.28095799684524536,  0.31814444065093994,
     -0.7089307904243469},
    {0.0565570704638958,   -0.4527612030506134, 0.22363899648189545,
     -0.4776171147823334,  -0.4429882764816284, 0.09122249484062195,
     0.08279165625572205,  0.29376256465911865, -0.23718500137329102,
     -0.5789054036140442,  0.28780052065849304, 0.6442199945449829,
     0.03741508722305298,  -0.220468670129776,  -0.644933819770813,
     -0.13106265664100647, 0.5042467713356018,  0.43313512206077576,
     0.5279864072799683,   -0.5684875249862671, -0.8156968355178833,
     -0.5465362668037415,  -0.5424111485481262, 0.04707803204655647,
     0.2047167718410492,   -0.3449491560459137, -0.26257622241973877,
     -0.4042203426361084},
    {0.002476541558280587,  -0.21414169669151306, -0.11842579394578934,
     0.14450429379940033,   -0.01525128073990345, -0.12563736736774445,
     -0.27338773012161255,  0.009645791724324226, -0.25494587421417236,
     -0.2797246277332306,   -0.2301362156867981,  -0.40248778462409973,
     -0.2902604341506958,   0.19436009228229523,  -0.11030247807502747,
     0.08137191087007523,   -0.1867382973432541,  -0.7496097683906555,
     -0.008727616630494595, 0.22033251821994781,  0.19767463207244873,
     0.11307194083929062,   -0.8412720561027527,  0.2657962143421173,
     -0.07668104767799377,  0.06086883693933487,  0.3835994601249695,
     -1.091362476348877},
    {0.05102699622511864,  -0.8378904461860657,  0.4566768705844879,
     -0.2630152404308319,  -0.23932738602161407, -0.19857573509216309,
     0.30443575978279114,  -0.14038261771202087, -0.14678728580474854,
     -0.40179580450057983, -0.35921597480773926, -0.14929337799549103,
     0.12302257865667343,  0.10777170211076736,  -0.12337932735681534,
     -0.33727583289146423, -0.28758835792541504, 0.37838035821914673,
     0.3109462559223175,   0.15170936286449432,  0.17238374054431915,
     -0.7253879308700562,  -0.23942553997039795, 0.11120962351560593,
     0.2859588861465454,   0.4778689444065094,   -0.679128885269165,
     -0.226375013589859},
    {0.03254811838269234,   -0.20261096954345703,   -0.07562973350286484,
     -0.10781332850456238,  -0.10312259197235107,   -0.07764437049627304,
     -0.04054751992225647,  -0.07112596929073334,   -0.06557303667068481,
     -0.12047363817691803,  -0.062015969306230545,  -0.026542047038674355,
     -0.01934126764535904,  -0.09312712401151657,   -0.03113151155412197,
     -0.051008112728595734, -0.05768309533596039,   -0.10491270571947098,
     -0.0717678889632225,   -0.04455398768186569,   -0.02723315730690956,
     -0.057788874953985214, -0.0033781195525079966, -0.03603564202785492,
     -0.006476488430052996, -0.021566126495599747,  -0.04043968394398689,
     0.007654271088540554},
    {-0.08095347136259079, -0.26414069533348083, -0.09814666211605072,
     -0.04380079358816147, -0.5849603414535522,  -0.7097421288490295,
     -0.9047000408172607,  -1.3424301147460938,  -0.31041404604911804,
     -0.25597554445266724, -0.11997552961111069, 0.20276427268981934,
     0.36256903409957886,  0.019292958080768585, -0.041115161031484604,
     0.12530303001403809,  0.20739392936229706,  0.3235205411911011,
     -0.05591806769371033, -0.05841316655278206, 0.038499265909194946,
     0.18055571615695953,  0.293590784072876,    0.16785693168640137,
     0.24048064649105072,  0.07176923751831055,  0.08983936905860901,
     0.22106173634529114},
    {-0.017707331106066704, 0.12255413830280304,  -0.6240278482437134,
     -0.04464518278837204,  -0.1846076399087906,  0.6399872899055481,
     0.12881889939308167,   0.22656285762786865,  -0.1679985672235489,
     -0.28858682513237,     0.5312493443489075,   0.049816571176052094,
     -0.24046465754508972,  -0.1774766743183136,  -0.09163064509630203,
     0.7954617142677307,    -0.39598366618156433, -0.4399476945400238,
     0.6123018860816956,    0.45216771960258484,  0.6593272089958191,
     -0.07644930481910706,  -0.4521935284137726,  0.012832398526370525,
     0.028779083862900734,  0.6450971961021423,   0.575737714767456,
     0.6349318027496338},
    {-0.03650929406285286,  -0.14473311603069305, -0.5584736466407776,
     -0.029627997428178787, -0.1539306640625,     -0.12314235419034958,
     0.08387640863656998,   -0.0337577685713768,  -0.17351587116718292,
     -0.19245818257331848,  -0.16416308283805847, 0.016272591426968575,
     0.36373433470726013,   0.04089183360338211,  0.02371835894882679,
     -0.22523774206638336,  -0.15100933611392975, 0.45591863989830017,
     -0.1123512014746666,   0.061778921633958817, -0.13473178446292877,
     -0.40654733777046204,  0.4156742990016937,   -0.03619186207652092,
     0.24843069911003113,   0.29947373270988464,  0.36726078391075134,
     1.2590346336364746},
    {-0.1366739124059677, 0.41514185070991516,  -0.2774442732334137,
     -0.3785100281238556, -0.43781572580337524, 0.0857686847448349,
     0.548991322517395,   0.3008037805557251,   -0.710213303565979,
     0.02195434831082821, 0.5724306702613831,   0.5335829854011536,
     0.09624887257814407, 1.017358660697937,    0.8264562487602234,
     0.4335733652114868,  0.2976129651069641,   -0.07695961743593216,
     0.08250663429498672, 0.12967929244041443,  -0.04427557811141014,
     0.24989163875579834, 0.08119788765907288,  0.05688076093792915,
     0.14414691925048828, -0.15780726075172424, 0.12778723239898682,
     0.17664006352424622},
    {0.045846302062273026, -0.4202306270599365,  0.24253332614898682,
     0.058551233261823654, 0.13127459585666656,  -0.08259733766317368,
     -0.5951005220413208,  0.010317057371139526, 0.32319802045822144,
     -0.2788604497909546,  -0.10889868438243866, -0.6505680084228516,
     0.0621410608291626,   -0.22810311615467072, -0.0803237184882164,
     0.4647519290447235,   -0.6627072691917419,  0.20077340304851532,
     0.1278313249349594,   0.021397558972239494, -0.017719708383083344,
     -0.65326988697052,    0.03247045353055,     -0.003151470795273781,
     -0.089555524289608,   -0.21280834078788757, -0.18042130768299103,
     -0.13124220073223114},
    {0.08920250833034515,  -0.4041590988636017,  0.11126764863729477,
     -0.38825124502182007, -0.37751445174217224, 0.3841644823551178,
     0.13915617763996124,  -0.427076131105423,   -0.42071813344955444,
     -0.8185845017433167,  -0.6395564675331116,  -0.3830700218677521,
     -0.34343987703323364, 0.14143605530261993,  0.2065681666135788,
     -0.5982153415679932,  -0.2753662168979645,  -0.5253153443336487,
     0.1136728972196579,   0.303848534822464,    -0.3599269390106201,
     -0.48723894357681274, 0.1170220747590065,   0.1082770973443985,
     0.2027408331632614,   -0.6000553369522095,  -0.2399190366268158,
     0.21266664564609528},
    {-0.044174984097480774, 0.10084715485572815,  -0.7766823768615723,
     0.5814716219902039,    0.2989753484725952,   -0.04704820364713669,
     -0.23142951726913452,  0.16222569346427917,  0.026166614145040512,
     0.3819707930088043,    0.0457681305706501,   -0.0551467202603817,
     0.09645840525627136,   -0.28990161418914795, 0.11402436345815659,
     0.445557564496994,     0.3328436315059662,   0.6563848853111267,
     -0.22550760209560394,  0.1100253015756607,   -0.29362085461616516,
     0.35912737250328064,   0.07314564287662506,  0.3241541087627411,
     -0.04152493178844452,  -0.18172766268253326, 0.3779054582118988,
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     -0.24474677443504333,  -0.20014965534210205,  -0.2710004448890686,
     0.5048877596855164},
    {0.06318517029285431,   -0.06626387685537338,  0.09102978557348251,
     0.2806074619293213,    -0.5871695280075073,   0.01448263693600893,
     -0.17774152755737305,  -0.015175321139395237, 0.26823174953460693,
     -0.5144285559654236,   -0.10162042826414108,  -0.11051122844219208,
     -0.056919533759355545, -0.0963352620601654,   -0.292245477437973,
     0.07747644931077957,   0.049891501665115356,  0.04416292905807495,
     -0.10346058011054993,  -0.27070391178131104,  0.16639839112758636,
     0.13697180151939392,   -0.03080388717353344,  -0.3958549499511719,
     -0.08085988461971283,  -0.15026159584522247,  -0.2458583563566208,
     -0.1183728277683258},
    {-0.031412992626428604, -0.5603559017181396,  0.23181116580963135,
     0.13550354540348053,   0.1384330689907074,   0.466655433177948,
     0.013126186095178127,  -0.1315859854221344,  0.19236598908901215,
     0.18724070489406586,   0.3651496469974518,   -0.2081764191389084,
     -0.21705558896064758,  -0.8947001695632935,  -0.8844435811042786,
     -0.9548132419586182,   -0.8363487720489502,  -0.43193918466567993,
     0.14401763677597046,   0.17948108911514282,  0.22730839252471924,
     -0.05247826129198074,  -0.04454374313354492, 0.22061742842197418,
     0.2703307569026947,    0.2895837426185608,   -0.10765346139669418,
     -0.1715518683195114},
    {-0.08773751556873322, 0.1905992329120636,   -0.1878463476896286,
     -0.06142798811197281, -0.07157573848962784, -0.15859191119670868,
     0.2888740599155426,   0.29501354694366455,  -0.18804210424423218,
     0.03719957917928696,  0.04348141327500343,  0.43497395515441895,
     0.3348860740661621,   0.046619195491075516, -0.012931774370372295,
     0.43330755829811096,  0.783429741859436,    0.7513718605041504,
     0.42782992124557495,  0.4602666199207306,   0.8619009256362915,
     0.21070736646652222,  -0.6946675181388855,  0.36241528391838074,
     0.5762568712234497,   0.41478705406188965,  -0.7064434289932251,
     -0.7981423139572144},
    {0.0158368032425642,    -0.46186450123786926, -0.04968561232089996,
     0.08248921483755112,   0.09503590315580368,  -0.02358037419617176,
     -0.31207606196403503,  0.5838796496391296,   -0.015739191323518753,
     0.14295169711112976,   0.06756915897130966,  -0.23699864745140076,
     -0.031251370906829834, 0.044411782175302505, 0.07595864683389664,
     -0.06131885573267937,  -0.2911010682582855,  -0.03894522413611412,
     0.26580068469047546,   0.10324547439813614,  -0.22770865261554718,
     -0.43839356303215027,  -0.10662800073623657, -1.1684633493423462,
     -0.6824188828468323,   -0.5405550003051758,  -0.3048187494277954,
     0.12504103779792786}};

const float five_dense_biases[FIVE_DENSE_NUM] = {
    -0.1444406658411026,  -0.9969395995140076,   -0.6437257528305054,
    -0.5090100169181824,  -0.4562266170978546,   -0.7947681546211243,
    -0.66095370054245,    -0.0757993534207344,   -0.6305758357048035,
    -0.6522586345672607,  -0.6137698292732239,   0.06795938313007355,
    -0.5988752841949463,  -0.022649021819233894, -0.559893012046814,
    -0.34122252464294434, 0.21291229128837585,   -0.9544443488121033,
    -0.5839042663574219,  -0.3325875997543335,   -0.8768086433410645,
    -0.3371303975582123,  -0.804802417755127,    -0.7296857833862305,
    -0.24707458913326263, -0.9100144505500793,   -0.7247095704078674,
    -0.6324146389961243,  -0.42117181420326233,  -1.2145949602127075,
    -0.7118635177612305,  -0.9112556576728821,   -0.21369697153568268,
    -0.10298735648393631, -0.8274878263473511,   -0.6126794219017029,
    -0.7786099314689636,  -0.5083696246147156,   -0.38434338569641113,
    -0.468181848526001,   -0.5246707201004028,   -0.6266580820083618,
    -0.3932459354400635,  -0.6446056365966797,   -0.8690927028656006,
    -0.7806448936462402,  -0.7514670491218567,   -0.4992736577987671,
    -0.4725331962108612,  -0.8359085321426392,   -0.3995131254196167,
    -0.701408326625824,   0.13735836744308472,   -0.7701425552368164,
    0.37747254967689514,  -0.8496323227882385,   -0.41388845443725586,
    -0.3626680374145508,  -0.4750637412071228,   -1.0076007843017578,
    -0.4247843325138092,  -0.8280375599861145,   -0.8584521412849426,
    -0.49181294441223145};

const float five_output_weights[2][FIVE_DENSE_NUM] = {
    {-0.10164133459329605,  -0.06545510143041611,  -0.12559004127979279,
     -0.09770263731479645,  0.020511239767074585,  -0.11956804245710373,
     0.0725698173046112,    0.09884587675333023,   0.03334106504917145,
     -0.07531746476888657,  -0.04404358193278313,  0.04667624458670616,
     -0.10733920335769653,  0.11603111028671265,   -0.09832630306482315,
     -0.029710080474615097, 0.6112914681434631,    0.09875980764627457,
     0.04528747498989105,   0.04975724592804909,   0.05296019837260246,
     -0.1494731307029724,   0.06372635811567307,   0.05117323249578476,
     -0.4960876405239105,   0.08172430098056793,   0.09049712121486664,
     0.16494503617286682,   0.12463495880365372,   0.10889994353055954,
     0.0901939794421196,    -0.07425925135612488,  -0.09327994287014008,
     0.07225991785526276,   0.0995996817946434,    0.1002214103937149,
     0.13605892658233643,   -0.2738678455352783,   0.07213583588600159,
     0.031051557511091232,  -0.09413432329893112,  -0.05657240375876427,
     0.023954149335622787,  -0.11132869869470596,  0.0918613076210022,
     0.1731780767440796,    -0.048148252069950104, -0.08594620227813721,
     -0.07079077512025833,  0.028347637504339218,  0.04007003828883171,
     -0.08521876484155655,  -0.13736766576766968,  0.0973486453294754,
     -0.3953056037425995,   -0.09303829818964005,  0.0926792323589325,
     0.08662958443164825,   0.07239469885826111,   0.12995171546936035,
     -0.08624651283025742,  -0.13703757524490356,  0.1074426919221878,
     -0.05400789529085159},
    {0.09475231915712357,   0.10763916373252869,   0.10490848124027252,
     0.051702577620744705,  -0.025973767042160034, 0.1243276372551918,
     -0.09184729307889938,  -0.09110323339700699,  -0.10927686840295792,
     0.07648954540491104,   0.08314546942710876,   -0.054432936012744904,
     0.11420167982578278,   -0.14501862227916718,  0.07449985295534134,
     0.04114171117544174,   -0.5540419816970825,   -0.11702275276184082,
     -0.057106584310531616, -0.11389349400997162,  -0.04799086973071098,
     0.11528362333774567,   -0.10561501979827881,  -0.06099977344274521,
     0.5406088829040527,    -0.07843243330717087,  -0.07121624052524567,
     -0.15534670650959015,  -0.10739737004041672,  -0.10599866509437561,
     -0.03090781159698963,  0.1024080142378807,    0.11164399981498718,
     -0.10347762703895569,  -0.09762325882911682,  -0.044070787727832794,
     -0.1382112205028534,   0.2635824978351593,    -0.013381495140492916,
     -0.017522230744361877, 0.09075458347797394,   0.08058229833841324,
     -0.03840004280209541,  0.08596818149089813,   -0.10802465677261353,
     -0.17988164722919464,  0.08185803890228271,   0.09076226502656937,
     0.042497776448726654,  -0.0991385206580162,   -0.03188726678490639,
     0.0789727196097374,    0.12080144137144089,   -0.12002957612276077,
     0.3941708207130432,    0.07739747315645218,   -0.04954647645354271,
     -0.10626203566789627,  -0.054530855268239975, -0.19077910482883453,
     0.07939524203538895,   0.10724455863237381,   -0.09315766394138336,
     0.07806982845067978}};

const float five_output_bias[2] = {0.3762611150741577, 0.6172211170196533};


R include/weights.h => include/weights_5.h +6 -6
@@ 15,10 15,10 @@
  along with ct. If not, see <https://www.gnu.org/licenses/>.
*/

#define INP_NUM 3 + 5 * 5
#define DENSE_NUM 64
#define FIVE_INP_NUM 3 + 5 * 5
#define FIVE_DENSE_NUM 64

extern const float dense1_weights[DENSE_NUM][INP_NUM];
extern const float dense1_biases[DENSE_NUM];
extern const float output_weights[2][DENSE_NUM];
extern const float output_bias[2];
extern const float five_dense_weights[FIVE_DENSE_NUM][FIVE_INP_NUM];
extern const float five_dense_biases[FIVE_DENSE_NUM];
extern const float five_output_weights[2][FIVE_DENSE_NUM];
extern const float five_output_bias[2];


A include/weights_6.c => include/weights_6.c +927 -0
@@ 0,0 1,927 @@
/*
 * Model: "model"
 * _________________________________________________________________
 *  Layer (type)                Output Shape              Param #
 * =================================================================
 *  input_1 (InputLayer)        [(None, 39)]              0
 *
 *  dense (Dense)               (None, 64)                2560
 *
 *  dense_1 (Dense)             (None, 2)                 130
 *
 * =================================================================
 * Total params: 2,690
 * Trainable params: 2,690
 * Non-trainable params: 0
 * _________________________________________________________________
 * ([0.43337419629096985, 0.8247458338737488], [0.439096063375473,
 * 0.8229411244392395])
 */

#include "weights_6.h"

const float six_dense_weights[SIX_DENSE_NUM][SIX_INP_NUM] = {
    {0.0938403531908989,   -0.1767289787530899,  -0.2594544291496277,
     0.0691506490111351,   -0.3403625190258026,  0.2910526692867279,
     0.2831748127937317,   0.09303940087556839,  0.2586047351360321,
     0.07098056375980377,  -0.3649413287639618,  0.04592672362923622,
     0.2833174765110016,   0.009526161476969719, -0.14935271441936493,
     -0.10905721783638,    -0.09543487429618835, -0.1335684210062027,
     0.279418408870697,    0.21970123052597046,  0.03924116864800453,
     0.2525206208229065,   -0.07575967162847519, 0.003592552850022912,
     -0.3410177528858185,  -0.2946719229221344,  -0.42420369386672974,
     0.25425875186920166,  0.09701071679592133,  0.048269227147102356,
     -0.12546728551387787, -0.13410994410514832, -0.021736539900302887,
     0.08405961841344833,  0.2916780710220337,   -0.2674121856689453,
     0.11115898936986923,  0.15194286406040192,  0.05519171059131622},
    {0.03854886814951897,   0.047170158475637436, -0.5652578473091125,
     -0.1501774787902832,   0.19765394926071167,  0.49258941411972046,
     0.442907452583313,     0.6037927865982056,   0.7828888297080994,
     0.7239607572555542,    0.5416994690895081,   0.3247818350791931,
     0.1419774442911148,    0.07112716138362885,  -0.48371824622154236,
     0.03897036612033844,   0.16006605327129364,  -0.19231346249580383,
     -0.11129150539636612,  -0.04494927451014519, -0.2814842164516449,
     -0.009567379020154476, 0.12357877939939499,  -0.08806543052196503,
     -0.3350891172885895,   -0.21195265650749207, 0.0829022154211998,
     0.153797909617424,     0.13344310224056244,  -0.23193320631980896,
     -0.3241296708583832,   -0.2072569876909256,  -0.08946460485458374,
     0.19245360791683197,   0.06570465862751007,  -0.07437288016080856,
     -0.2344023883342743,   -0.1934940367937088,  -0.3033899962902069},
    {0.09298650920391083,  0.1298256516456604,    -0.26803141832351685,
     0.10323704034090042,  0.31999361515045166,   0.10086508840322495,
     -0.25076261162757874, -0.24714693427085876,  -0.07469244301319122,
     -0.1501404047012329,  0.08652600646018982,   0.14937622845172882,
     -0.20169629156589508, -0.2770548462867737,   -0.19903460144996643,
     -0.1816127896308899,  0.2517884373664856,    0.3161589205265045,
     0.5760500431060791,   0.6995037198066711,    1.0056625604629517,
     -0.10604166984558105, -0.029168294742703438, -0.013238683342933655,
     0.28708288073539734,  -0.5204426646232605,   -0.2549469769001007,
     -0.11407356709241867, 0.00998044852167368,   0.03189884498715401,
     0.05421498417854309,  -0.1683892011642456,   -0.1772622913122177,
     0.1778106987476349,   -0.06838139146566391,  0.08891639113426208,
     0.046210043132305145, -0.03662918508052826,  -0.14595364034175873},
    {-0.09094150364398956, 0.34463661909103394,    0.09116946905851364,
     -0.06449760496616364, -0.3147732615470886,    0.1268700808286667,
     0.1254865974187851,   -0.0012171438429504633, 0.11133856326341629,
     0.16736876964569092,  0.438774973154068,      0.3511490225791931,
     0.37927722930908203,  0.38935965299606323,    -0.0079469820484519,
     0.3172787129878998,   0.2990153729915619,     -0.09792278707027435,
     -0.10387258976697922, 0.41493046283721924,    0.0964023545384407,
     0.26486533880233765,  0.15529265999794006,    -0.2944813370704651,
     -0.34798750281333923, 0.5233692526817322,     0.1444278061389923,
     0.26051023602485657,  0.36869293451309204,    0.21834968030452728,
     0.26130184531211853,  0.6258428692817688,     -0.23855987191200256,
     0.022854013368487358, 0.2632879316806793,     0.37072518467903137,
     0.1523171216249466,   0.25519171357154846,    -0.16038407385349274},
    {-0.032467667013406754, 0.7357418537139893,   -0.045072030276060104,
     -0.24760432541370392,  0.23623232543468475,  0.0744364857673645,
     0.14080816507339478,   0.1414225548505783,   -0.06911414116621017,
     0.07192256301641464,   0.16631357371807098,  0.2408289909362793,
     0.17106670141220093,   0.2528420686721802,   0.10969230532646179,
     0.02076885662972927,   0.20825524628162384,  0.2517687976360321,
     0.015450780279934406,  0.2909148037433624,   0.28733131289482117,
     0.11145968735218048,   0.1526435911655426,   0.2711062729358673,
     0.1014212965965271,    0.3045900762081146,   0.25306183099746704,
     -0.008300194516777992, 0.44009920954704285,  0.24647784233093262,
     0.1009436771273613,    0.27297350764274597,  -0.030720824375748634,
     -0.11540747433900833,  0.14160208404064178,  0.12604549527168274,
     0.2626807987689972,    0.008236709982156754, -0.18862208724021912},
    {-0.023633155971765518, 0.031894486397504807, -0.4481360912322998,
     -0.24594591557979584,  0.20171336829662323,  -0.18470190465450287,
     0.2863655686378479,    0.6400246024131775,   -0.0826200544834137,
     0.02861976809799671,   0.017064129933714867, -0.25840842723846436,
     -0.30897530913352966,  0.5295916199684143,   -0.16908574104309082,
     0.1952582597732544,    -0.16432832181453705, -0.03602570295333862,
     -0.04704880714416504,  0.3191918134689331,   -0.1255466639995575,
     -0.15681274235248566,  0.13616108894348145,  0.00557664642110467,
     0.16074173152446747,   0.19923390448093414,  -0.3232885003089905,
     0.09312762320041656,   0.1622481346130371,   0.3235580325126648,
     0.4518657922744751,    0.49210718274116516,  1.1623464822769165,
     0.6036497950553894,    0.41296878457069397,  0.32431092858314514,
     0.012712906114757061,  -0.3247729539871216,  0.07957495748996735},
    {0.09208795428276062,   -0.45911091566085815, -0.4499244689941406,
     0.4439805746078491,    0.2955973446369171,   0.3163372576236725,
     0.3850560784339905,    0.4180153012275696,   0.1610139012336731,
     -0.23070935904979706,  -0.15921248495578766, -0.09720172733068466,
     -0.005319203715771437, 0.042631376534700394, 0.011843227781355381,
     -0.2977549433708191,   -0.3696579337120056,  -0.34377896785736084,
     -0.2770437002182007,   -0.0684071034193039,  -0.12180585414171219,
     -0.3018224239349365,   -0.3641885817050934,  -0.4096143841743469,
     -0.2906401455402374,   -0.19256781041622162, -0.06858377903699875,
     -0.3312127888202667,   -0.2699941396713257,  -0.29128533601760864,
     -0.389739990234375,    -0.16568920016288757, -0.15711554884910583,
     -0.2741237282752991,   -0.351249098777771,   -0.382339209318161,
     -0.22146785259246826,  -0.2833898663520813,  0.23953834176063538},
    {-0.03820355236530304, 0.2552051544189453,   -0.41925331950187683,
     0.03598763793706894,  0.10766711086034775,  -0.02723642811179161,
     0.13639701902866364,  0.09994057565927505,  0.143342986702919,
     0.7588191032409668,   0.2866367697715759,   0.17419223487377167,
     0.24415870010852814,  0.21330080926418304,  0.004984943196177483,
     0.04004876688122749,  0.14586566388607025,  0.48024749755859375,
     0.03794018179178238,  -0.07516377419233322, 0.12968669831752777,
     -0.15804070234298706, 0.07177191227674484,  0.6032015681266785,
     -0.15146808326244354, -0.03773435950279236, 0.1546255350112915,
     -0.05635025352239609, -0.2109125256538391,  0.827411949634552,
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     -0.36970844864845276,  -0.1581408679485321,  0.2836640179157257,
     -0.2969404458999634,   -0.26580333709716797, -0.30694854259490967,
     -0.20928078889846802,  -0.06659498065710068, 0.06262539327144623,
     -0.28173190355300903,  -0.5303751826286316,  -0.18141047656536102,
     -0.08660170435905457,  0.06916430592536926,  0.16866236925125122,
     -0.27958157658576965,  -0.34707725048065186, -0.31698885560035706,
     -0.21292375028133392,  0.05616316571831703,  0.16938413679599762,
     0.10741657018661499,   0.168862447142601,    0.23488201200962067,
     0.015468287281692028,  -0.03646502271294594, -0.2648639380931854,
     -0.2527640461921692,   -0.14823690056800842, -0.06456912308931351},
    {0.04210243001580238,  0.45387983322143555,   -0.44824910163879395,
     -0.11651042848825455, 0.0024438428226858377, 0.07898260653018951,
     0.14961649477481842,  0.127238467335701,     -0.26453179121017456,
     0.030821237713098526, -0.0625578761100769,   0.0073285470716655254,
     0.1412707269191742,   0.02810809388756752,   0.24902337789535522,
     0.1821228265762329,   0.4875296950340271,    0.009102149866521358,
     0.2329307645559311,   0.20934829115867615,   0.06645675748586655,
     0.26638221740722656,  0.11245708167552948,   0.0901046171784401,
     0.09851130098104477,  0.26848214864730835,   0.20406296849250793,
     0.2567901313304901,   0.2970711290836334,    0.250925213098526,
     0.19565463066101074,  0.1478186994791031,    0.10656914114952087,
     0.12078160047531128,  0.2093629390001297,    0.18492747843265533,
     0.05206095799803734,  0.18785063922405243,   0.14206662774085999},
    {-0.04112589359283447,  0.39927321672439575,   -0.19951465725898743,
     -0.35009706020355225,  -0.09900064766407013,  0.7361993193626404,
     -0.18245524168014526,  0.2941736578941345,    0.0583181232213974,
     -0.12379385530948639,  -0.36719080805778503,  0.21095477044582367,
     0.09004422277212143,   -0.10622923076152802,  0.08987374603748322,
     -0.18611569702625275,  -0.258846253156662,    0.3114860951900482,
     0.30926087498664856,   0.18416263163089752,   0.3564222753047943,
     0.12976090610027313,   0.5256466269493103,    0.6567301154136658,
     0.13811467587947845,   0.11827583611011505,   -0.20710952579975128,
     0.5976359844207764,    0.4360915422439575,    0.1256430596113205,
     -0.047309305518865585, -0.03823299705982208,  -0.11956600099802017,
     0.2341434359550476,    0.3158662021160126,    -0.050737254321575165,
     -0.08090120553970337,  -0.047405995428562164, -0.03023303672671318},
    {0.10158651322126389,   -0.5521547794342041,  0.6178520917892456,
     -0.1693457067012787,   -0.2557540237903595,  -0.2649931311607361,
     -0.3328563868999481,   -0.20785102248191833, -0.1229957640171051,
     -0.09505331516265869,  -0.17078441381454468, -0.2452021837234497,
     -0.18229705095291138,  -0.23286975920200348, -0.1692340075969696,
     -0.1465841829776764,   -0.3245551884174347,  -0.28645777702331543,
     -0.2977093458175659,   -0.25731781125068665, -0.17705601453781128,
     -0.1440218687057495,   -0.17916043102741241, -0.22442571818828583,
     -0.20666608214378357,  -0.29822927713394165, -0.21789993345737457,
     -0.06410680711269379,  -0.1657690405845642,  -0.24488262832164764,
     -0.2960679531097412,   -0.32856786251068115, -0.14379723370075226,
     -0.055669285356998444, -0.15729306638240814, -0.06751980632543564,
     -0.15515989065170288,  -0.18442828953266144, -0.10962274670600891},
    {-0.1334318369626999,  0.3918937146663666,   0.1863659918308258,
     -0.2498036026954651,  -0.15366065502166748, -0.09301190078258514,
     0.41642525792121887,  0.06160397455096245,  -0.04211530461907387,
     -0.1072499081492424,  0.26343709230422974,  -0.17040394246578217,
     0.3029728829860687,   0.36989137530326843,  0.24300719797611237,
     0.07592061907052994,  0.5085697174072266,   0.48401325941085815,
     0.5596055388450623,   0.16305021941661835,  0.11628902703523636,
     0.206899031996727,    0.314456045627594,    0.23190821707248688,
     0.2607883810997009,   -0.18294231593608856, 0.11955120414495468,
     0.08547386527061462,  0.3099992871284485,   0.21271653473377228,
     -0.0970039963722229,  0.21062302589416504,  -0.12806379795074463,
     -0.21578791737556458, 0.7068646550178528,   -0.14216339588165283,
     0.01908116042613983,  -0.13280142843723297, -0.1397237330675125}};

const float six_dense_biases[SIX_DENSE_NUM] = {
    -0.06967716664075851, -0.44022783637046814,   -0.5417478084564209,
    -0.9069390892982483,  -0.5126388072967529,    -0.6354130506515503,
    -0.10972009599208832, -0.562232494354248,     -0.6675834655761719,
    -0.6644823551177979,  -0.8808755278587341,    -0.3119734525680542,
    -0.7456325888633728,  -0.5520071983337402,    -0.35148730874061584,
    -0.4321696162223816,  0.20877626538276672,    -0.39401814341545105,
    -0.34259599447250366, -0.05571189150214195,   -0.20628471672534943,
    -0.6253416538238525,  -0.06269057095050812,   -0.5957340002059937,
    -0.3175065815448761,  -0.3733671009540558,    -0.4160494804382324,
    -0.7057332396507263,  -0.0038920114748179913, -0.22997768223285675,
    -0.6255137324333191,  -0.7110783457756042,    -0.1551521271467209,
    -0.24990537762641907, -0.6163766980171204,    -0.4247187077999115,
    -0.7986910939216614,  -0.4614383280277252,    -0.3101421296596527,
    -0.26032987236976624, -0.6711196899414062,    -0.5886708498001099,
    -0.46851369738578796, -0.5316239595413208,    -0.6672037243843079,
    -0.5897069573402405,  -0.6752277612686157,    -0.2791188359260559,
    0.08230648934841156,  -0.3046773374080658,    -0.20194388926029205,
    -0.20666947960853577, -0.332783579826355,     -0.6711490750312805,
    -0.32295775413513184, -0.17244867980480194,   -0.37504926323890686,
    -0.28200626373291016, -0.5700741410255432,    -0.363629549741745,
    -0.5895812511444092,  -0.7540486454963684,    -0.5940008163452148,
    -0.6399450302124023};

const float six_output_weights[2][SIX_DENSE_NUM] = {
    {-0.07302232086658478,  0.13510112464427948,   0.1300254911184311,
     0.1799755096435547,    0.04949653893709183,   0.07790026068687439,
     0.1633823662996292,    0.18218885362148285,   0.14318706095218658,
     0.12348434329032898,   -0.059174586087465286, 0.1362956464290619,
     0.12713192403316498,   -0.11747836321592331,  0.04890844225883484,
     -0.10521009564399719,  -0.1504904329776764,   -0.06776679307222366,
     -0.011088703759014606, -0.1176120936870575,   0.11910095065832138,
     0.17112720012664795,   0.40426141023635864,   0.117193304002285,
     0.03174498677253723,   0.2071460783481598,    0.1914520561695099,
     -0.10132134705781937,  0.038214121013879776,  0.0016146935522556305,
     -0.06785089522600174,  -0.16885392367839813,  0.22577765583992004,
     -0.02406524494290352,  -0.107661671936512,    -0.03207802027463913,
     0.14433519542217255,   -0.10634109377861023,  0.11385593563318253,
     -0.09148751199245453,  -0.12748983502388,     0.09944026917219162,
     -0.11984292417764664,  -0.06870798766613007,  0.07336993515491486,
     -0.06733305752277374,  -0.2037811577320099,   0.08543498069047928,
     -0.08490556478500366,  -0.02513868920505047,  -0.22716110944747925,
     -0.11461621522903442,  0.09373198449611664,   -0.19322800636291504,
     -0.1750950962305069,   -0.12388377636671066,  -0.13034087419509888,
     0.17253483831882477,   -0.11518114805221558,  0.11116097122430801,
     -0.10767245292663574,  0.1428501009941101,    0.21024677157402039,
     0.09458904713392258},
    {-0.04867282137274742,  -0.12133938074111938, -0.12863431870937347,
     -0.22873400151729584,  -0.10455438494682312, -0.0994892343878746,
     -0.12702268362045288,  -0.10685571283102036, -0.11664576828479767,
     -0.18876326084136963,  0.024635830894112587, -0.16811606287956238,
     -0.1487940102815628,   0.07733053714036942,  -0.04051893949508667,
     0.14585408568382263,   0.15599925816059113,  -0.029253050684928894,
     -0.056079715490341187, 0.1628628373146057,   -0.15175913274288177,
     -0.09187500923871994,  -0.4156719446182251,  -0.13169778883457184,
     -0.027643941342830658, -0.155934140086174,   -0.02847992070019245,
     0.12021031230688095,   0.03525504097342491,  0.037111807614564896,
     -0.08677919209003448,  0.09230437129735947,  -0.17035338282585144,
     -0.028646811842918396, 0.19761770963668823,  0.048951324075460434,
     -0.16993199288845062,  0.009519846178591251, -0.05790598317980766,
     0.13865339756011963,   0.17961318790912628,  -0.1378742903470993,
     0.11909746378660202,   0.13531020283699036,  -0.07563496381044388,
     0.1344599574804306,    0.10990381985902786,  -0.06671777367591858,
     -0.005469973664730787, 0.23519796133041382,  0.22312238812446594,
     0.1941014975309372,    -0.06822828948497772, 0.14889642596244812,
     0.11146791279315948,   0.09140922129154205,  0.1110391691327095,
     -0.12873628735542297,  0.13196496665477753,  -0.10414397716522217,
     0.06405720114707947,   -0.0693216398358345,  -0.09506542235612869,
     -0.03883719816803932}};

const float six_output_bias[2] = {0.39013445377349854, 0.4827316999435425};


A include/weights_6.h => include/weights_6.h +24 -0
@@ 0,0 1,24 @@
/*
  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/>.
*/

#define SIX_INP_NUM (3 + 6 * 6)
#define SIX_DENSE_NUM 64

extern const float six_dense_weights[SIX_DENSE_NUM][SIX_INP_NUM];
extern const float six_dense_biases[SIX_DENSE_NUM];
extern const float six_output_weights[2][SIX_DENSE_NUM];
extern const float six_output_bias[2];


M resources/extract.sh => resources/extract.sh +1 -1
@@ 60,7 60,7 @@ fi

make pptdb

for size in 5; do
for size in 5 6; do
    echo
    extract $size notation,result
    prepare $size


M src/ct1986.c => src/ct1986.c +2 -1
@@ 30,7 30,8 @@
static int human;
static char *gamelog = 0;

tak_state_p state;
static struct tak_state_s state_s;
static tak_state_p state = &state_s;

static void new_game(uint8_t size) {
  reset_state(state, size);


M src/ctlm.c => src/ctlm.c +47 -42
@@ 36,6 36,9 @@ static const char *und = "\033[4m", *rst = "\033[0m";
#define CHAR_STN '/'
#define CHAR_CAP '*'

static struct tak_state_s state_s;
static tak_state_p state = &state_s;

static void put_stone(const enum STONE_VARIANT stone, const enum COLOUR colour,
                      const uint8_t top, const uint8_t beyond_carry_limit) {
  if (beyond_carry_limit) {


@@ 64,8 67,8 @@ static void put_stone(const enum STONE_VARIANT stone, const enum COLOUR colour,
  fputs(rst, stdout);
}

static void print_cell_line(tak_state_p state, const uint8_t line,
                            const uint8_t col, const uint8_t row) {
static void print_cell_line(const uint8_t line, const uint8_t col,
                            const uint8_t row) {

  const uint8_t location = THE_COORDS(state->board_size, col, row),
                stack_size = COUNT_AT(state, location);


@@ 97,7 100,7 @@ static void print_cell_line(tak_state_p state, const uint8_t line,

// It takes board_size*(SQUARE_H+1)+1 lines to print the board, they
// may be requested in any order and at any time
static void print_board_line(tak_state_p state, const uint8_t line) {
static void print_board_line(const uint8_t line) {
  const uint8_t mod = line % (SQUARE_H + 1),
                row = state->board_size - line / (SQUARE_H + 1) - 1;



@@ 121,7 124,7 @@ static void print_board_line(tak_state_p state, const uint8_t line) {
    if (line < state->board_size * (SQUARE_H + 1)) {
      for (uint8_t x = 0; x < state->board_size; x++) {
        putchar('|');
        print_cell_line(state, mod - 1, x, row);
        print_cell_line(mod - 1, x, row);
      }
      puts("|");
    } else {


@@ 139,15 142,14 @@ static void print_board_line(tak_state_p state, const uint8_t line) {
}

// Simple wrapper to print the whole board in one go
static void print_board(tak_state_p state) {
static void print_board(void) {
  for (uint8_t k = 0; k < state->board_size * (SQUARE_H + 1) + 2; k++) {
    print_board_line(state, k);
    print_board_line(k);
  }
}

// Print the contents of a single square
static void print_square(tak_state_p state, const uint8_t col,
                         const uint8_t row) {
static void print_square(const uint8_t col, const uint8_t row) {
  if (row < state->board_size && col < state->board_size) {
    printf("%c%c: ", 'a' + col, '1' + row);
    const uint8_t stack_size =


@@ 172,7 174,7 @@ static void print_square(tak_state_p state, const uint8_t col,
  }
}

static void print_info(tak_state_p state) {
static void print_info() {
  printf("Turn: %2d, %s%s%s%s\n", state->ply / 2 + 1,
         (state->ply & 1) ? blk : wht, (state->ply & 1) ? "Black" : "White",
         rst, (state->ply < 2) ? " (counter-play start)" : "");


@@ 185,8 187,7 @@ static int human;
static char *gamelog = NULL;
static uint8_t auto_board = 0xFF, auto_info = 0xFF;

static int append_to_gamelog(tak_state_p state, const char *line,
                             const uint8_t win_line) {
static int append_to_gamelog(const char *line, const uint8_t win_line) {
  // I _could_ dynamically compute the size but ... don't let
  // `perfect' be the enemy of `good' ?



@@ 214,16 215,16 @@ static int append_to_gamelog(tak_state_p state, const char *line,
  return EXIT_SUCCESS;
}

static void end_game(tak_state_p state, char *line, char *win) {
  append_to_gamelog(state, line, 0);
  append_to_gamelog(state, win, 1);
  print_board(state);
static void end_game(char *line, char *win) {
  append_to_gamelog(line, 0);
  append_to_gamelog(win, 1);
  print_board();
  puts("Game over:");
  puts(gamelog);
  putchar('\n');
}

static int handle_turn(tak_state_p state, char *line) {
static int handle_turn(char *line) {
  // Track win state
  uint8_t new_win = (state->won == 0xFF);
  switch (do_ptn(state, line)) {


@@ 246,23 247,23 @@ static int handle_turn(tak_state_p state, char *line) {
    if (new_win) {
      switch (state->won) {
      case WIN_DRAW: {
        end_game(state, line, "1/2-1/2");
        end_game(line, "1/2-1/2");
        break;
      }
      case WIN_FLAT_BLACK: {
        end_game(state, line, "0-F");
        end_game(line, "0-F");
        break;
      }
      case WIN_FLAT_WHITE: {
        end_game(state, line, "F-0");
        end_game(line, "F-0");
        break;
      }
      case WIN_ROAD_BLACK: {
        end_game(state, line, "0-R");
        end_game(line, "0-R");
        break;
      }
      case WIN_ROAD_WHITE: {
        end_game(state, line, "R-0");
        end_game(line, "R-0");
        break;
      }
      }


@@ 274,18 275,18 @@ static int handle_turn(tak_state_p state, char *line) {
  }
  // Valid, append to game log
  case ACT_OK: {
    append_to_gamelog(state, line, 0);
    append_to_gamelog(line, 0);
    if (auto_board)
      print_board(state);
      print_board();
    if (auto_info)
      print_info(state);
      print_info();
    break;
  }
  }
  return EXIT_SUCCESS;
}

static void new_game(tak_state_p state, uint8_t size) {
static void new_game(uint8_t size) {
  reset_state(state, size);
  printf("New %dx%d game! negamax at search depth %d.\n", size, size,
         negamax_search_depth);


@@ 296,7 297,7 @@ static void new_game(tak_state_p state, uint8_t size) {
  gamelog[0] = 0;
}

static int load_ptn(tak_state_p state, const char *fn) {
static int load_ptn(const char *fn) {
  FILE *fh = NULL;

  fh = fopen(fn, "r");


@@ 325,14 326,14 @@ static int load_ptn(tak_state_p state, const char *fn) {
          space2++;
        line[space2] = 0;
        // Try the first piece we found
        r = handle_turn(state, line + space1);
        r = handle_turn(line + space1);
        if (r) {
          printf("Error on: %s\n", line + space1);
          break;
        }
        // If there's a second piece, try it
        if (space2 + 1 < read) {
          r = handle_turn(state, line + space2 + 1);
          r = handle_turn(line + space2 + 1);
          if (r) {
            printf("Error on: %s", line + space2 + 1);
            break;


@@ 384,21 385,21 @@ static int negamax_turn(tak_state_p state) {
      puts("Opponent concedes!");
    printf("Result: %s (%.2f, checked %.1e)\n", negamax_ptn, minimax * 100.0,
           num_check);
    return handle_turn(state, negamax_ptn);
    return handle_turn(negamax_ptn);
  } else {
    return EXIT_FAILURE;
  }
}

static int input_is_not_turn(tak_state_p state, const char *line) {
static int input_is_not_turn(const char *line) {
  if (!strcmp(line, "help")) {
    puts("Valid commands: auto (board|info), board, depth [0-9], eval, \
help, info, load <file.ptn>, log, new, play (b|w), self-play, square\
<col><row>, tps, <PTN>.");
  } else if (!strcmp(line, "board")) {
    print_board(state);
    print_board();
  } else if (!strcmp(line, "info")) {
    print_info(state);
    print_info();
  } else if (!strcmp(line, "eval")) {
    float eval = nn1986_evaluate_black_win(state) * 100;
    if (state->ply & 1) {


@@ 408,8 409,14 @@ help, info, load <file.ptn>, log, new, play (b|w), self-play, square\
    }
  } else if (!strcmp(line, "log")) {
    puts(gamelog);
  } else if (!strcmp(line, "new")) {
    new_game(state, 5);
  } else if (!strncmp(line, "new", 3)) {
    if (strnlen(line, 5) == 5 && line[4] >= '5' && line[4] <= '6') {
      new_game(line[4] - '0');
      negamax_free();
      negamax_init(state->board_size);
    } else {
      puts("Usage: size [56].");
    }
  } else if (!strcmp(line, "tps")) {
    char buf[1000];
    generate_tps(state, buf);


@@ 426,7 433,7 @@ help, info, load <file.ptn>, log, new, play (b|w), self-play, square\
    }
  } else if (!strncmp(line, "load", 4)) {
    if (strnlen(line, 6) >= 6) {
      if (load_ptn(state, line + 5)) {
      if (load_ptn(line + 5)) {
        printf("Errors in file %s\n", line);
      }
    } else {


@@ 448,7 455,7 @@ help, info, load <file.ptn>, log, new, play (b|w), self-play, square\
    if (strnlen(line, 10) == 9 && line[7] >= 'a' &&
        line[7] <= '`' + state->board_size && line[8] >= '1' &&
        line[8] <= '0' + state->board_size) {
      print_square(state, line[7] - 'a', line[8] - '1');
      print_square(line[7] - 'a', line[8] - '1');
    } else {
      printf("Usage: square [a-%c][1-%c].\n", '`' + state->board_size,
             '0' + state->board_size);


@@ 458,7 465,7 @@ help, info, load <file.ptn>, log, new, play (b|w), self-play, square\
                                  (line[5] == 'w' || line[5] == 'W'))) {
      // 'b' is even :)
      human = 1 - (line[5] & 1);
      new_game(state, 5);
      new_game(5);
    } else {
      puts("Usage: play (b|w).");
    }


@@ 480,10 487,8 @@ int main(int argc, char **argv) {

  puts(license);

  tak_state_p state = new_tak_state(5);

  negamax_search_depth = 5;
  new_game(state, 5);
  new_game(5);
  negamax_init(5);

  char *line = NULL;


@@ 498,8 503,8 @@ int main(int argc, char **argv) {
      read = getline(&line, &alloc_size, stdin);
      if (read > 0) {
        line[read - 1] = 0;
        if (input_is_not_turn(state, line)) {
          int r = handle_turn(state, line);
        if (input_is_not_turn(line)) {
          int r = handle_turn(line);
          if (r == EXIT_SUCCESS && state->won == 0xFF) {
            human = 1;
          }


M src/cttei.c => src/cttei.c +2 -1
@@ 137,7 137,8 @@ int main(int argc, char **argv) {
  fflush(stdout);

  // Set default option
  tak_state_p state = new_tak_state(5);
  struct tak_state_s state_s;
  tak_state_p state = &state_s;
  negamax_search_depth = 4;
  negamax_init(5);
  reset_state(state, 5);


M src/geminict.c => src/geminict.c +2 -2
@@ 39,7 39,8 @@
#define STR_CHF_BLK "b"
#define STR_CHF_WHT "w"

static tak_state_p state;
struct tak_state_s state_s;
static tak_state_p state = &state_s;

static void put_stone(const enum STONE_VARIANT stone, const enum COLOUR colour,
                      const uint8_t top, const uint8_t beyond_carry_limit) {


@@ 306,7 307,6 @@ int main(int argc, char **argv) {
    return EXIT_FAILURE;
  };

  state = new_tak_state(5);
  negamax_search_depth = 5;
  new_game(5);
  negamax_init(5);


M src/nn_train.py => src/nn_train.py +16 -15
@@ 56,7 56,7 @@ def train(size, model, data, iterations=1, epochs=10, batch=None):
        tra_res = model.evaluate(tra_input, tra_outcome, verbose=False)
        results.append((tra_res, val_res))
        print(val_res)
        write_weights(model, str(i+1).zfill(len(str(iterations))), (tra_res, val_res))
        write_weights(size, model, str(i+1).zfill(len(str(iterations))), (tra_res, val_res))
    print("\nScores")
    for i, data in enumerate(results):
        print(f"Iteration {i+1}: {data}")


@@ 74,33 74,34 @@ def make_model(size, magic):
    return model


def write_weights(model, iteration, performance):
def write_weights(size, model, iteration, performance):
    def fix(val):
        string = str(np.array(val).tolist())
        string = string.replace("[", "{").replace("]", "}")
        return string
    dense1_weights = transpose(model.trainable_variables[0], perm=[1, 0])
    dense1_biases = model.trainable_variables[1]
    size_str = "five" if size==5 else "six"
    dense_weights = transpose(model.trainable_variables[0], perm=[1, 0])
    dense_biases = model.trainable_variables[1]
    output_weights = transpose(model.trainable_variables[2], perm=[1, 0])
    output_bias = model.trainable_variables[3]
    # Prepare output
    to_output = [("dense1_weights[DENSE_NUM][INP_NUM]", dense1_weights),
                 ("dense1_biases[DENSE_NUM]", dense1_biases),
                 ("output_weights[2][DENSE_NUM]", output_weights),
                 ("output_bias[2]", output_bias)]
    to_output = [(f"{size_str}_dense_weights[{size_str.upper()}_DENSE_NUM][{size_str.upper()}_INP_NUM]", dense_weights),
                 (f"{size_str}_dense_biases[{size_str.upper()}_DENSE_NUM]", dense_biases),
                 (f"{size_str}_output_weights[2][{size_str.upper()}_DENSE_NUM]", output_weights),
                 (f"{size_str}_output_bias[2]", output_bias)]
    # Write to file
    f = open("weights-"+iteration+".txt", "w")
    f = open(f"{size_str}_weights-"+iteration+".txt", "w")
    f.write("/*\n")
    model.summary(print_fn=lambda l: f.write(" * "+l+"\n"))
    f.write(" * "+str(performance)+"\n*/\n\n")
    f.write("#include \"weights.h\"\n\n")
    f.write(f"#include \"weights_{size}.h\"\n\n")
    for (name, val) in to_output:
        f.write("const float "+name+" =\n"+fix(val)+";\n\n")
    f.close()


data = load_data(5)
model = make_model(5, 64)

print("Before training", model.evaluate(data[1][0], data[1][1], verbose=False, batch_size=16))
results = train(5, model, data, iterations=5, epochs=10, batch=128)
for size in (5,6):
    data = load_data(size)
    model = make_model(size, 64)
    print("Before training", model.evaluate(data[1][0], data[1][1], verbose=False, batch_size=16))
    results = train(size, model, data, iterations=5, epochs=10, batch=128)