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)
