background_color="white",
colormap="tab20").generate_from_frequencies(frequencies)
plt.clf()
plt.figure(figsize=(hsize, vsize))
plt.imshow(cloud, interpolation='bilinear', aspect="equal")
plt.axis("off")
plt.tight_layout()
plt.savefig(name + ".pdf", figsize=(5, vsize), dpi=400)
from scipy.misc import imsave
imsave(name + '.png', cloud)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
arg(parser, 'name', type=str, default="", help='name for plots')
arg(parser,
'raw',
type=bool,
default=True,
help='Use raw vectors rather than UMAP ones.')
arg(parser,
'alpha',
type=float,
default=1.,
help='Float for alpha in divergence.')
arg(parser,
'rbf',
type=bool,
default=False,
help='Use RBF rather than cosine similarity.')
import argparse
import glob
import os
import torch
from pylego.misc import add_argument as arg
from runners.fixedrunner import FixedRunner
from runners.advrunner import AdversarialRunner
if __name__ == '__main__':
parser = argparse.ArgumentParser()
arg(parser, 'name', type=str, required=True, help='name of the experiment')
arg(parser, 'model', type=str, default='fixed.fixed', help='model to use')
arg(parser, 'cuda', type=bool, default=True, help='enable CUDA')
arg(parser,
'double_precision',
type=bool,
default=False,
help='use double precision')
arg(parser,
'load_file',
type=str,
default='',
help='file to load model from')
arg(parser,
'save_file',
type=str,
default='model.dat',
help='model save file')
import argparse
import os
from pylego.misc import add_argument as arg
from runners.tdvaerunner import TDVAERunner
if __name__ == '__main__':
parser = argparse.ArgumentParser()
arg(parser, 'name', type=str, required=True, help='name of the experiment')
arg(parser, 'model', type=str, default='tdvae.tdvae', help='model to use')
arg(parser, 'cuda', type=bool, default=True, help='enable CUDA')
arg(parser,
'load_file',
type=str,
default='',
help='file to load model from')
arg(parser,
'save_file',
type=str,
default='model.dat',
help='model save file')
arg(parser,
'save_every',
type=int,
default=500,
help='save every these many global steps (-1 to disable saving)')
arg(parser, 'data_path', type=str, default='data/MNIST')
arg(parser, 'logs_path', type=str, default='logs')
arg(parser, 'force_logs', type=bool, default=False)
arg(parser, 'optimizer', type=str, default='adam', help='one of: adam')
import argparse
import glob
import os
from shutil import copyfile
import sys
from pylego.misc import add_argument as arg
if __name__ == '__main__':
parser = argparse.ArgumentParser()
arg(parser,
'logs_path',
type=str,
default='../logs',
help='input logs directory')
arg(parser, 'out_path', type=str, default='out', help='output directory')
arg(parser,
'name_pattern',
type=str,
default='*',
help='names to match in logs directory')
arg(parser,
'step',
type=int,
default=-1,
help='override comparison step if greater than 0')
arg(parser,
'min_step',
type=int,
default=-1,
help='minimum step to dump if greater than 0')
import argparse
import os
from pylego.misc import add_argument as arg
from runners.imgtdvae.tdvaerunner import TDVAERunner
from runners.conditional.gym_runner import GymRunner
from runners.rl.rl_runner import GymRLRunner
if __name__ == '__main__':
parser = argparse.ArgumentParser()
arg(parser, 'name', type=str, required=True, help='name of the experiment')
arg(parser,
'model',
type=str,
default='conditional.gymtdvae',
help='model to use')
arg(parser, 'cuda', type=bool, default=True, help='enable CUDA')
arg(parser,
'load_file',
type=str,
default='',
help='file to load model from')
arg(parser,
'save_file',
type=str,
default='model.dat',
help='model save file')
arg(parser,
'save_every',
type=int,