class Actor: def __init__(self, game, layers=[], checkpoint=None, format='one_hot', optimizer='adam'): self.game = game self.format = format self.layers = layers self.optimizer = optimizer self.network = Network( [game.state_size(format)] + layers + [game.num_possible_moves()], [], minibatch_size=50, steps=1, loss_function='cross_entropy', validation_fraction=0, test_fraction=0, learning_rate=0.001, optimizer=optimizer, output_functions=[tf.nn.softmax] ) self.network.build() if checkpoint: self.load_checkpoint(checkpoint) def select_move(self, state, stochastic=False): possible_moves = self.game.get_moves(state) formatted_state = self.game.format_for_nn(state, format=self.format) predictions = self.network.predict([formatted_state])[0] predictions = predictions[:len(possible_moves)] if not stochastic: move = np.argmax(predictions) return possible_moves[move] predictions = np.array(predictions) ps = predictions.sum() if predictions.sum() == 0: move = np.random.choice(np.arange(0, len(predictions))) else: predictions = predictions / predictions.sum() move = np.random.choice(np.arange(0, len(predictions)), p=predictions) return possible_moves[move] def save_checkpoint(self, checkpoint): self.network.save(checkpoint) def load_checkpoint(self, checkpoint): self.network.load(checkpoint)
y_train = tf.keras.utils.to_categorical(y_train) x_train = (x_train / 255).astype('float32') x_test = (x_test / 255).astype('float32') net = Network() net.init(input_dimension=784, loss_function="cross entropy", layers=[{ "units": 128, "activation": "relu", "type": "dense" }, { "units": 64, "activation": "relu", "type": "dense" }, { "units": 10, "activation": "softmax", "type": "dense" }]) net.fit(x_train, y_train, epochs=10) y_pred = net.predict(x_test) y_pred = np.argmax(y_pred, axis=1) cmatrix = confusion_matrix(y_test, y_pred) print(cmatrix) print(f"Accuracy score: {metrics.accuracy_score(y_test, y_pred):10.5}")