def plot():
plot_sample_grid(5, decoder_params, (21, 21), gaussian_decoder)
plt.savefig('pendulum.png')
def plot():
plot_sample_grid(5, decoder_params, (21, 21), gaussian_decoder)
plt.savefig('pendulum.png')
def plot():
plot_sample_grid(10, decoder_params, (30, 30), gaussian_decoder)
plt.savefig('mice.png')
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()
def plot():
plot_sample_grid(10, decoder_params, (30, 30), gaussian_decoder)
plt.savefig('mice.png')
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()
