g = climate.add_group('MNIST Example') g.add_argument('--features', type=int, default=8, metavar='N', help='train a model using N^2 hidden-layer features') def main(args): train, valid, _ = load_mnist() e = theanets.Experiment(theanets.Autoencoder, layers=(784, args.features**2, 784)) e.train(train, valid) plot_layers([e.network.find(1, 0), e.network.find(2, 0)]) plt.tight_layout() plt.show() v = valid[:100] plot_images(v, 121, 'Sample data') plot_images(e.network.predict(v), 122, 'Reconstructed data') plt.tight_layout() plt.show() if __name__ == '__main__': climate.call(main)
def main(root='/tmp/measurements', output=None): data = [] for s in os.listdir(root): subject = [] for b in os.listdir(os.path.join(root, s)): block = [] bweight, bspeed, bhand, bpaths = b.split('-')[1:] for t in os.listdir(os.path.join(root, s, b)): thand, tspeed = re.search(r'(left|right)-speed_(\d\.\d+)', t).groups() config = np.tile([ C[bweight], C[bspeed], C[bhand], C[bpaths], C[thand], float(tspeed)], (120, 1)) block.append( np.hstack([ config, np.loadtxt(os.path.join(root, s, b, t), skiprows=1, delimiter=',')])) subject.append(block) if len(subject) == 3: data.append(subject) else: print('incorrect block count! discarding {}'.format(s)) data = np.array(data) logging.info('loaded data %s', data.shape) if output: np.save(output, data.astype('f')) if __name__ == '__main__': climate.call(main)
import logging import climate import yaml def tethbot_main(yaml_config_path): with open(yaml_config_path, "r") as fid: client = yaml.load(fid) client() # t.run_forever() if __name__ == "__main__": logging.basicConfig(level=logging.INFO) climate.call(tethbot_main)