def main(): size_of_learn_sample = int(len(x) * 0.9) print(size_of_learn_sample) nn = Network(x, y, 0.5) nn.train() nn.printer()
from data_loader import DataLoader from neural_network import Network from training_parameters import LAYER_SIZES, MINI_BATCH_SIZE, EPOCHS, LEARNING_RATE from config import VERBOSE, TRAIN_IMAGE_DIR, TRAIN_LABEL_DIR,\ TRAINING_DATA_SKIP_FOOTER, TEST_IMAGE_DIR, TEST_LABEL_DIR, TEST_DATA_SKIP_FOOTER, TEST_PREDICTIONS_FILENAME if VERBOSE: print('loading training data...') training_data = DataLoader.get_training_data(TRAIN_IMAGE_DIR, TRAIN_LABEL_DIR, TRAINING_DATA_SKIP_FOOTER) if VERBOSE: print('loading test data...') test_data = DataLoader.get_test_data(TEST_IMAGE_DIR, TEST_LABEL_DIR, TEST_DATA_SKIP_FOOTER) if VERBOSE: print('begin training...') neural_network = Network(LAYER_SIZES) neural_network.train(training_data, test_data, MINI_BATCH_SIZE, EPOCHS, LEARNING_RATE, VERBOSE, TEST_PREDICTIONS_FILENAME)
if __name__ == '__main__': # Load data X, y = load_data('./data/expert_q.txt') # Initialize the model nn = Network(hidden_layers=(256, 256, 256, 256), lr=9e-2, epoch=600, bias=True, batch_size=64) try: nn.load_weights() except: print('WARNING: No pre-trained networks!') # Training nn.train(X, y) nn.save_weights() # Display accuracy accuracy(nn, X, y) print() # Simulate 1,000 games N = 1000 environment = pm.PongModel(0.5, 0.5, 0.03, 0.01, 0.4) window = gfx.GFX() window.fps = 4e16 avg_score = simulate_game(N, window, environment, nn) print('Avg score of {0} games:{1:.3f}'.format(N, avg_score))
from neural_network import Network network = Network(training_iteration=500000, learning_rate=0.3, error_threshold=0.0001) network.add_layer(3, 2) network.add_layer(1) network.train([ [[0, 0], [0]], [[0, 1], [1]], [[1, 0], [1]], [[1, 1], [1]], ]) output = network.process([0, 0]) print('0 OR 0 = {}'.format(output)) output = network.process([0, 1]) print('0 OR 1 = {}'.format(output)) output = network.process([1, 0]) print('1 OR 0 = {}'.format(output)) output = network.process([1, 1]) print('1 OR 1 = {}'.format(output))