def main(_):
console.suppress_logging()
FLAGS.train = True
FLAGS.overwrite = True
# Start
console.start('MNIST VANILLA VAE')
# Get model
model = models.vanilla('vanilla_00')
if FLAGS.train:
mnist = load_mnist('../../data/MNIST',
flatten=True,
validation_size=0,
one_hot=True)
model.train(training_set=mnist[pedia.training],
epoch=1000,
batch_size=128,
print_cycle=50,
snapshot_cycle=200)
else:
samples = model.generate(sample_num=16)
console.show_status('{} samples generated'.format(samples.shape[0]))
imtool.gan_grid_plot(samples, show=True)
# End
console.end()
def main(_):
console.suppress_logging()
FLAGS.train = True
FLAGS.overwrite = False
# Start
console.start("MNIST DCGAN DEMO")
# Get model
model = models.dcgan('dcgan_002')
# model = models.dcgan_h3_rs_nbn()
# Train or test
if FLAGS.train:
mnist = load_mnist('../../data/MNIST', flatten=False, validation_size=0,
one_hot=True)
model.train(training_set=mnist[pedia.training], epoch=10, batch_size=128,
print_cycle=20, snapshot_cycle=200, D_times=1, G_times=1)
else:
samples = model.generate(sample_num=16)
console.show_status('{} samples generated'.format(samples.shape[0]))
imtool.gan_grid_plot(samples, show=True)
# End
console.end()
def main(_):
console.suppress_logging()
FLAGS.overwrite = True
FLAGS.train = True
# Start
console.start()
# Get or define model
model = models.vanilla('vanilla_nov9_02_h2_c', bn=False)
# model = models.dcgan('dcgan_c00')
# model = models.vanilla_h3_rs_nbn('vanilla_nov9_01_h3_nbn_opdef')
# return
# Train or test
if FLAGS.train:
mnist = load_mnist('../../data/MNIST',
flatten=True,
validation_size=0,
one_hot=True)
model.train(training_set=mnist[pedia.training],
epoch=1000,
batch_size=128,
print_cycle=20,
snapshot_cycle=150,
sample_num=25)
else:
samples = model.generate(sample_num=16)
console.show_status('{} samples generated'.format(samples.shape[0]))
imtool.gan_grid_plot(samples, show=True)
# End
console.end()
def main(_):
console.suppress_logging()
FLAGS.train = True
FLAGS.overwrite = False
# Start
console.start('CIFAR-10 DCGAN')
# Get model
model = models.dcgan('dcgan_00')
if FLAGS.train:
cifar10 = load_cifar10('../../data/CIFAR-10',
validation_size=0,
one_hot=True)
model.train(training_set=cifar10[pedia.training],
epoch=20000,
batch_size=128,
print_cycle=20,
snapshot_cycle=2000)
else:
samples = model.generate(sample_num=16)
console.show_status('{} samples generated'.format(samples.shape[0]))
imtool.gan_grid_plot(samples, show=True)
# End
console.end()
def interpolate(self, z1=None, z2=None, inter_num=8, via='spherical'):
z1 = self._random_z(1) if z1 is None else z1
z2 = self._random_z(1) if z2 is None else z2
zs = np.stack((z1,)*(inter_num + 2))
zs[-1] = z2
# Interpolate z
if via in ['great_circle', 'circle', 'spherical']:
interp = lambda mu: interpolations.slerp(mu, z1, z2)
elif via in ['straight_line', 'line', 'linear']:
interp = lambda mu: z1 + mu * (z2 - z1)
else:
raise ValueError("Can not resolve '{}'".format(via))
for i in range(inter_num):
pct = 1.0 * (i + 1) / (inter_num + 1)
zs[i+1] = interp(pct)
# Generate samples
samples = self.generate(zs)
# Plot samples
fig = imtool.gan_grid_plot(samples, h=1)
return fig
def _default_snapshot_function(self):
assert isinstance(self, VAE)
z = self._random_z(self._sample_num)
feed_dict = {}
feed_dict[self.P.default_input_tensor] = z
feed_dict.update(self._get_status_feed_dict(is_training=False))
samples = self._outputs.eval(feed_dict)
# Plot samples
fig = imtool.gan_grid_plot(samples)
return fig
def _default_snapshot_function(self):
assert isinstance(self, GAN)
# Generate samples
feed_dict = {}
if self._conditional:
z, one_hot = self._random_z(self._sample_num, True)
feed_dict[self._targets] = one_hot
else:
z = self._random_z(self._sample_num)
feed_dict[self.G.default_input_tensor] = z
feed_dict.update(self._get_status_feed_dict(is_training=False))
samples = self._outputs.eval(feed_dict)
# Plot samples
fig = imtool.gan_grid_plot(samples)
return fig