Experimenting with training nn

3 files changed, 93 insertions, 34 deletions

diff --git a/data/extract.sh b/extract.sh
index 285be26..4ded1c0 100755
--- a/data/extract.sh
+++ b/extract.sh
@@ -9,9 +9,10 @@ and (result != '1/2-1/2');"
 }
 
 extract() {
+		cd data
 		for size in 5 6; do
 				echo Extracing games of size "$size"...
-				sqlite3 "$db_file" "$(query $1 $size)" > "playtak-$size"
+				sqlite3 "$db_file" "$(query $1 $size)" | shuf > "playtak-$size"
 		done
 }
 
@@ -22,7 +23,6 @@ fi
 extract notation,result
 
 echo Preparing pptdb
-cd ..
 if [ ! -f "pptdb" ]; then
 	 make pptdb
 fi
@@ -41,3 +41,7 @@ for i in 5 6; do
 		mv "data/shuf-$i.csv" "data/training-$i.csv"
 		echo
 done
+
+
+shuf -n50000 data/good-playtak-5 > data/smalltak-5 && ./pptdb 5 data/smalltak-5 generate && \
+head -n1 data/training-5.csv > t && tail -n+2 data/training-5.csv > test.csv && shuf test.csv >> t && mv t test.csv
diff --git a/src/pptdb.c b/src/pptdb.c
index 34056c4..712aabc 100644
--- a/src/pptdb.c
+++ b/src/pptdb.c
@@ -4,35 +4,43 @@
 
 #include <tak.h>
 
+float max_flats;
 uint64_t heights[16];
+int result, generate;
 FILE *training_fh = NULL;
-int wc_col, bc_col, wc_row, bc_row, result, generate;
 
 static void
 write_input(void) {
 	// Four numbers for capstone coords (col,row)
-	fprintf(training_fh,"%d,%d,%d,%d,",wc_col,wc_row,bc_col,bc_row);
 	// Two numbers for flats remaining
-	fprintf(training_fh,"%d,%d,",white_count & 127, black_count & 127);
+	fprintf(training_fh,"%.6f,%.6f,",
+					(float)(white_count & 127)/max_flats,
+					(float)(black_count & 127)/max_flats);
 	// Two layers of board_size * board_size:
-	int h, t; uint16_t mask;
+	float t; int h; uint16_t mask;
 	for (int k = 0; k < board_size * board_size; k++) {
-		// stacks encoded as balanced ternary without capstones and walls
-		t = 0; mask = 1;
+		// stacks encoded as balanced ternary, without caps and walls
+		t = 0;
 		h = COUNT_AT(k);
-		if (STONE_AT(k)) h--;
-		while (h-->0) {
-			t *= 3;
-			t += (colours[k] & mask) ? +1 : -1;
-			mask <<= 1;
+		if (h>0) {
+			mask = 1<<(h-1);
+			/* if (STONE_AT(k) != STONE_FLAT) h--; */
+			while (h-->0) {
+				t += (colours[k] & mask) ? +1.0 : -1.0;
+				t /= 3;
+				mask >>= 1;
+			}
 		}
-		fprintf(training_fh,"%d,",t);
+		fprintf(training_fh,"%.6f,",t);
 	}
+	int val;
 	for (int k = 0; k < board_size * board_size; k++) {
-		// walls with +- 1
-		if (COUNT_AT(k) && STONE_AT(k) == STONE_STANDING)
-			fprintf(training_fh,"%d,",(colours[k] & 1) ? +1 : -1);
-		else fputs("0,", training_fh);
+		val = 0;
+		if (COUNT_AT(k)) {
+			if (STONE_AT(k) == STONE_STANDING) val = (colours[k] & 1) ? +1 : -1;
+			else if (STONE_AT(k) == STONE_CAPSTONE) val = (colours[k] & 1) ? +2 : -2;
+		}
+		fprintf(training_fh,"%d,",val);
 	}
 }
 
@@ -51,17 +59,7 @@ parse_line(const char *pt, const ssize_t read) {
 			if (idx + 3 < read) {
 				switch (pt[idx+3]) {
 					case 'W': { stone = STONE_STANDING; break; }
-					case 'C': {
-						if (current_colour == C_BLACK) {
-							bc_col = col + 1;
-							bc_row = row + 1;
-						} else {
-							wc_col = col + 1;
-							wc_row = row + 1;
-						}
-						stone = STONE_CAPSTONE;
-						break;
-					}
+					case 'C': { stone = STONE_CAPSTONE; break; }
 					default:  { stone = STONE_FLAT;     break; }
 				}
 			} else {
@@ -102,7 +100,7 @@ parse_line(const char *pt, const ssize_t read) {
 		// Generate training data
 		if (generate) {
 			write_input();
-			fprintf(training_fh,"%d,%d\n", result, 1-result);
+			fprintf(training_fh,"%d\n", result);
 		}
 		// Parse next action
 		while (idx<read && pt[idx++]!=',');
@@ -135,19 +133,25 @@ main(int argc, char **argv) {
 
 	if (argc > 3 && (!strncmp("generate", argv[3], 8))) {
 		generate=1;
+		max_flats = (size == 5) ? 21.0 : 30.0;
 		snprintf(td_fn, 64, "data/training-%d.csv",size);
 		training_fh = fopen(td_fn, "w");
 		if (training_fh == NULL) exit(EXIT_FAILURE);
+		// Write header
+		fputs("\"White flats\",\"Black flats\",",training_fh);
+		for (int k = 0; k < size*size; k++)
+			fprintf(training_fh,"\"Stack %d\",",k);
+		for (int k = 0; k < size*size; k++)
+			fprintf(training_fh,"\"Wall %d\",",k);
+		fputs("\"Outcome\"\n",training_fh);
 	} else generate=0;
 
 	while ((read = getline(&line, &len, playtak_fh)) != -1) {
 		// Reset everything
 		reset_state(size);
-		bc_col = 0; wc_col = 0;
-		bc_row = 0; wc_row = 0;
 		// Store the result of this game
-		if (line[read-4] == '0') result = 1;
-		else result = 0;
+		if (line[read-4] == '0') result = 0;
+		else result = 1;
 		// Parse the line
 		r = parse_line(line,read-4);
 		// Adjust counts if we're not generating training data
diff --git a/src/train5.py b/src/train5.py
new file mode 100644
index 0000000..225068b
--- /dev/null
+++ b/src/train5.py
@@ -0,0 +1,51 @@
+import tensorflow as tf
+import pandas as pd
+import numpy as np
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.layers import Conv2D
+from tensorflow.keras.layers import MaxPooling2D
+from tensorflow.keras.layers import Activation
+from tensorflow.keras.layers import Flatten
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.layers import Dense
+
+model = Sequential([
+    Conv2D(5, kernel_size=3, padding='same', input_shape=(5, 5, 2)),
+    MaxPooling2D(pool_size=(2, 2), strides=None),
+    Activation("relu"),
+    Flatten(),
+    Dense(10, activation="relu"),
+    Dense(8, activation="relu"),
+    Dense(1, activation="sigmoid")
+
+    # Dense(40, activation="relu", input_shape=(52,)),
+    # Dense(10, activation="relu"),
+    # Dense(5, activation="relu"),
+    # Dense(1, activation="sigmoid")
+])
+
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+model.summary()
+
+csv = pd.read_csv("data/test.csv")
+
+training_data = csv.copy().head(5000)
+training_outcome = training_data.pop('Outcome')
+
+training_input = training_data.copy()
+training_input = training_input.drop(columns=training_data.keys()[0:2])
+train = np.array(training_input)
+train = train.reshape((training_input.shape[0], 5, 5, 2))
+
+model.fit(train, training_outcome, epochs=20)
+
+test_data = csv.copy().head(10000)
+test_data = test_data.drop(columns=test_data.keys()[0:2])
+test_outcome = test_data.pop('Outcome')
+test_input = np.array(test_data)
+test_input.reshape((test_data.shape[0], 5, 5, 2))
+test_loss, test_acc = model.evaluate(test_input,  test_outcome, verbose=2)
+print('\nTest accuracy:', test_acc)