diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/pptdb.c | 17 | ||||
| -rw-r--r-- | src/train5.py | 38 |
2 files changed, 29 insertions, 26 deletions
diff --git a/src/pptdb.c b/src/pptdb.c index 712aabc..7a26ffd 100644 --- a/src/pptdb.c +++ b/src/pptdb.c @@ -19,28 +19,29 @@ write_input(void) { // Two layers of board_size * board_size: float t; int h; uint16_t mask; for (int k = 0; k < board_size * board_size; k++) { - // stacks encoded as balanced ternary, without caps and walls + // stacks encoded as balanced ternary t = 0; h = COUNT_AT(k); + 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; + t += (colours[k] & mask) ? +1 : -1; + t/=3; mask >>= 1; } } fprintf(training_fh,"%.6f,",t); } - int val; + float val; for (int k = 0; k < board_size * board_size; k++) { 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; + if (STONE_AT(k) == STONE_STANDING) val = (colours[k] & 1) ? +1/3 : -1/3; + else if (STONE_AT(k) == STONE_CAPSTONE) val = (colours[k] & 1) ? +1.0 : -1.0; + else val = (colours[k] & 1) ? +2/3 : -2/3; } - fprintf(training_fh,"%d,",val); + fprintf(training_fh,"%.6f,",val); } } diff --git a/src/train5.py b/src/train5.py index 225068b..3718fd0 100644 --- a/src/train5.py +++ b/src/train5.py @@ -12,40 +12,42 @@ 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,)), + # Conv2D(5, kernel_size=5, padding='same', input_shape=(5, 5, 2)), + # MaxPooling2D(pool_size=2, strides=2), + # Activation("relu"), + # Flatten(), + # Dense(25, activation="relu"), # Dense(10, activation="relu"), - # Dense(5, activation="relu"), # Dense(1, activation="sigmoid") + + Dense(30, activation="relu", input_shape=(52,)), # 30 + Dense(10, activation="relu"), # 10 + Dense(8, activation="relu"), # 8 + Dense(1, activation="sigmoid") ]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() -csv = pd.read_csv("data/test.csv") +csv = pd.read_csv("data/train5.csv") -training_data = csv.copy().head(5000) +training_data = csv.copy().head(40000) training_outcome = training_data.pop('Outcome') training_input = training_data.copy() -training_input = training_input.drop(columns=training_data.keys()[0:2]) +# 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)) +# train = train.reshape((training_input.shape[0], 5, 5, 2)) -model.fit(train, training_outcome, epochs=20) +model.fit(train, training_outcome, epochs=100) -test_data = csv.copy().head(10000) -test_data = test_data.drop(columns=test_data.keys()[0:2]) +test_data = csv.copy().tail(50000) +# 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_input = 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) + +# Test accuracy: 0.67448 |