plot_sample_grid(5, decoder_params, (21, 21), gaussian_decoder)
plt.savefig('pendulum.png')
if __name__ == "__main__":
npr.seed(0)
trX = load_pendulum(100)
encoder_params, decoder_params, fit = \
make_gaussian_fitter(trX, 3, [200], [200]) # 2 also works well, 1 not quite as well
fit(1 * 500, 50, 1, adadelta())
plot()
fit(25 * 500, 50, 1, adadelta())
plot()
fit(50 * 500, 100, 1, rmsprop(1e-4))
plot()
fit(50 * 500, 100, 1, rmsprop(1e-5))
plot()
fit(50 * 500, 100, 1, rmsprop(1e-6))
plot()
params = get_ndarrays(encoder_params), get_ndarrays(decoder_params)
with gzip.open('pendulum_params.pkl.gz', 'w') as f:
pickle.dump(params, f, protocol=-1)
plt.show()
# TODO try adam
import logging.config
logging.config.fileConfig('logging.conf')
from vae.vae import make_gaussian_fitter
from vae.optimization import sgd, adagrad, rmsprop, adadelta, adam, \
momentum_sgd, nesterov
from vae.util import get_ndarrays
from load import load_mice
if __name__ == '__main__':
logging.info('\n\nStarting experiment!')
np.random.seed(0)
N = 750000 # 750k is about the memory limit on 3GB GPU
trX = load_mice(N)
encoder_params, decoder_params, fit = \
make_gaussian_fitter(trX, 20, [200, 200], [200, 200])
fit(1, 50, 1, adadelta())
fit(1, 250, 1, adadelta())
fit(10, 500, 1, rmsprop(1e-4))
fit(25, 500, 1, rmsprop(1e-5))
fit(25, 1000, 1, rmsprop(1e-5))
params = get_ndarrays(encoder_params), get_ndarrays(decoder_params)
with gzip.open('params.pkl.gz', 'w') as f:
pickle.dump(params, f, protocol=-1)
def plot():
plot_sample_grid(5, decoder_params, (21, 21), gaussian_decoder)
plt.savefig('pendulum.png')
if __name__ == "__main__":
npr.seed(0)
trX = load_pendulum(100)
encoder_params, decoder_params, fit = \
make_gaussian_fitter(trX, 3, [200], [200]) # 2 also works well, 1 not quite as well
fit(1*500, 50, 1, adadelta())
plot()
fit(25*500, 50, 1, adadelta())
plot()
fit(50*500, 100, 1, rmsprop(1e-4))
plot()
fit(50*500, 100, 1, rmsprop(1e-5))
plot()
fit(50*500, 100, 1, rmsprop(1e-6))
plot()
params = get_ndarrays(encoder_params), get_ndarrays(decoder_params)
with gzip.open('pendulum_params.pkl.gz', 'w') as f:
pickle.dump(params, f, protocol=-1)
plt.show()
# TODO try adam
def plot():
plot_sample_grid(10, decoder_params, (30, 30), gaussian_decoder)
plt.savefig('mice.png')
if __name__ == '__main__':
logging.info('\n\nStarting experiment!')
np.random.seed(0)
N = 750000 # 750k is about the memory limit on 3GB GPU
trX = load_mice(N)
encoder_params, decoder_params, fit = \
make_gaussian_fitter(trX, 20, [200, 200], [200, 200])
fit(1, 50, 1, adadelta())
plot()
fit(1, 250, 1, adadelta())
plot()
fit(10, 500, 1, rmsprop(1e-4))
plot()
fit(25, 500, 1, rmsprop(1e-5))
plot()
fit(25, 1000, 1, rmsprop(1e-5))
params = get_ndarrays(encoder_params), get_ndarrays(decoder_params)
with gzip.open('mice_params.pkl.gz', 'w') as f:
pickle.dump(params, f, protocol=-1)
import theano
from vae.vae import make_binary_fitter, binary_decoder
from vae.optimization import adadelta, rmsprop
from vae.util import get_ndarrays, floatX
from vae.viz import plot_sample_grid
from load import load_letters
if __name__ == "__main__":
npr.seed(0)
trX, labels = load_letters('f')
encoder_params, decoder_params, fit = make_binary_fitter(trX, 5, [200], [200])
fit(1, 50, 1, adadelta())
fit(3, 250, 1, adadelta())
fit(2000, 50, 1, rmsprop(1e-3))
fit(2000, 250, 10, rmsprop(1e-4))
params = get_ndarrays(encoder_params), get_ndarrays(decoder_params)
with gzip.open('letter_params.pkl.gz', 'w') as f:
pickle.dump(params, f, protocol=-1)
plot_sample_grid(5, decoder_params, (16, 8), binary_decoder)
plt.savefig('letters.png')
plt.show()
import gzip
import theano
from vae.vae import make_binary_fitter, binary_decoder
from vae.optimization import adadelta, rmsprop
from vae.util import get_ndarrays, floatX
from vae.viz import plot_sample_grid
from load import load_letters
if __name__ == "__main__":
npr.seed(0)
trX, labels = load_letters('f')
encoder_params, decoder_params, fit = make_binary_fitter(
trX, 5, [200], [200])
fit(1, 50, 1, adadelta())
fit(3, 250, 1, adadelta())
fit(2000, 50, 1, rmsprop(1e-3))
fit(2000, 250, 10, rmsprop(1e-4))
params = get_ndarrays(encoder_params), get_ndarrays(decoder_params)
with gzip.open('letter_params.pkl.gz', 'w') as f:
pickle.dump(params, f, protocol=-1)
plot_sample_grid(5, decoder_params, (16, 8), binary_decoder)
plt.savefig('letters.png')
plt.show()
