def build(self, net):
config = self.config
gan = self.gan
ops = self.ops
discriminator = DCGANDiscriminator(gan, config)
discriminator.ops = ops
encoder = UniformDistribution(gan, gan.config.encoder)
# careful, this order matters
g2 = gan.generator.reuse(encoder.create())
double = tf.concat([net] + [g2, g2], axis=0)
original = discriminator.build(double)
d1 = self.split_batch(original, 4)
dg = ops.concat([d1[2], d1[3]], axis=0) # xs for baseline
#dx is a sampling of x twice
dx = ops.concat([d1[0], d1[0]], axis=0) # xs for baseline
xinput = ops.concat([d1[0], d1[1]], axis=0)
#dg is a sampling of g twice
# net is [x,g] (stacked)
error = self.f(xinput, dx, dg)
return error
def test_projection_gaussian(self):
config = {
"projections": ['identity', 'gaussian'],
"z": 2,
"min": 0,
"max": 1
}
subject = UniformDistribution(gan, config)
projections = subject.create()
assert int(projections.get_shape()[1]) == len(config['projections'])*config['z']
def test_projection(self):
config = {
"projections": [hg.distributions.uniform_distribution.identity],
"z": 2,
"min": 0,
"max": 1
}
subject = UniformDistribution(gan, config)
projections = subject.create()
assert subject.ops.shape(projections)[1] == 2
def test_projection_gaussian(self):
config = {
"projections": ['identity', 'gaussian'],
"z": 2,
"min": 0,
"max": 1
}
subject = UniformDistribution(gan, config)
with self.test_session():
projections = subject.create()
self.assertEqual(int(projections.get_shape()[1]), len(config['projections'])*config['z'])
def test_projection_twice(self):
config = {
"projections": ['identity', 'identity'],
"z": 2,
"min": 0,
"max": 1
}
subject = UniformDistribution(gan, config)
with self.test_session():
projections = subject.create()
self.assertEqual(int(projections.get_shape()[1]), len(config['projections'])*config['z'])
def test_projection(self):
config = {
"projections": [hg.distributions.uniform_distribution.identity],
"z": 2,
"min": 0,
"max": 1
}
subject = UniformDistribution(gan, config)
with self.test_session():
projections = subject.create()
self.assertEqual(subject.ops.shape(projections)[1], 2)