Deep13Go/DL_round2.py at master · thorsilver/Deep13Go · GitHub

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import h5py

from keras.models import load_model
from keras.callbacks import ModelCheckpoint
from dlgo import kerasutil
from dlgo.agent.predict import DeepLearningAgent, load_prediction_agent
from dlgo.data.parallel_processor import GoDataProcessor
from dlgo.encoders.sevenplane import SevenPlaneEncoder
from dlgo.httpfrontend import get_web_app


def main():
    go_board_rows, go_board_cols = 19, 19
    nb_classes = go_board_rows * go_board_cols
    num_games = 5000

    encoder = SevenPlaneEncoder((go_board_rows, go_board_cols))
    processor = GoDataProcessor(encoder=encoder.name())
    generator = processor.load_go_data('train', num_games, use_generator=True)
    test_generator = processor.load_go_data('test', num_games, use_generator=True)

    # input_shape = (encoder.num_planes, go_board_rows, go_board_cols)
    #model_file = h5py.File("checkpointsSeven/deep7_epoch_10.h5", "r")
    model = load_model('checkpointsSeven/deep7_run2-20k-train.h5')
    model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

    epochs = 5
    batch_size = 128
    model.fit_generator(generator=generator.generate(batch_size, nb_classes),
                            epochs=epochs,
                            steps_per_epoch=generator.get_num_samples() / batch_size,
                            validation_data=test_generator.generate(batch_size, nb_classes),
                            validation_steps=test_generator.get_num_samples() / batch_size,
                            callbacks=[ModelCheckpoint('checkpointsSeven/deep7_training_epoch_{epoch}.h5')])

    model.evaluate_generator(generator=test_generator.generate(batch_size, nb_classes),
                                steps=test_generator.get_num_samples() / batch_size)

    deep_learning_bot = DeepLearningAgent(model, encoder)
    deep_learning_bot.serialize('checkpointsSeven/deep7-run2-25k.h5')

    model_file = h5py.File("checkpointsSeven/deep7-run2-25k.h5", "r")
    bot_from_file = load_prediction_agent(model_file)

    web_app = get_web_app({'predict': bot_from_file})
    web_app.run()


if __name__ == '__main__':
    main()