def testSampleFromDiscretizedMixLogistic(self):
batch = 2
height = 4
width = 4
num_mixtures = 5
seed = 42
logits = tf.concat( # assign all probability mass to first component
[tf.ones([batch, height, width, 1]) * 1e8,
tf.zeros([batch, height, width, num_mixtures - 1])],
axis=-1)
locs = tf.random_uniform([batch, height, width, num_mixtures * 3],
minval=-.9, maxval=.9)
log_scales = tf.ones([batch, height, width, num_mixtures * 3]) * -1e8
coeffs = tf.atanh(tf.zeros([batch, height, width, num_mixtures * 3]))
pred = tf.concat([logits, locs, log_scales, coeffs], axis=-1)
locs_0 = locs[..., :3]
expected_sample = tf.clip_by_value(locs_0, -1., 1.)
actual_sample = common_layers.sample_from_discretized_mix_logistic(
pred, seed=seed)
actual_sample_val, expected_sample_val = self.evaluate(
[actual_sample, expected_sample])
# Use a low tolerance: samples numerically differ, as the actual
# implementation clips log-scales so they always contribute to sampling.
self.assertAllClose(actual_sample_val, expected_sample_val, atol=1e-2)
def testSampleFromDiscretizedMixLogistic(self):
batch = 2
height = 4
width = 4
num_mixtures = 5
seed = 42
logits = tf.concat( # assign all probability mass to first component
[
tf.ones([batch, height, width, 1]) * 1e8,
tf.zeros([batch, height, width, num_mixtures - 1])
],
axis=-1)
locs = tf.random_uniform([batch, height, width, num_mixtures * 3],
minval=-.9,
maxval=.9)
log_scales = tf.ones([batch, height, width, num_mixtures * 3]) * -1e8
coeffs = tf.atanh(tf.zeros([batch, height, width, num_mixtures * 3]))
pred = tf.concat([logits, locs, log_scales, coeffs], axis=-1)
locs_0 = locs[..., :3]
expected_sample = tf.clip_by_value(locs_0, -1., 1.)
actual_sample = common_layers.sample_from_discretized_mix_logistic(
pred, seed=seed)
actual_sample_val, expected_sample_val = self.evaluate(
[actual_sample, expected_sample])
# Use a low tolerance: samples numerically differ, as the actual
# implementation clips log-scales so they always contribute to sampling.
self.assertAllClose(actual_sample_val, expected_sample_val, atol=1e-2)
def sample(self, features):
"""Run the model and extract samples.
Args:
features: an map of string to `Tensor`.
Returns:
samples: an integer `Tensor`.
logits: a list of `Tensor`s, one per datashard.
losses: a dictionary: {loss-name (string): floating point `Scalar`}.
"""
logits, losses = self(features) # pylint: disable=not-callable
samples = common_layers.sample_from_discretized_mix_logistic(logits,
seed=None)
return samples, logits, losses
def sample(self, features):
"""Run the model and extract samples.
Args:
features: an map of string to `Tensor`.
Returns:
samples: an integer `Tensor`.
logits: a list of `Tensor`s, one per datashard.
losses: a dictionary: {loss-name (string): floating point `Scalar`}.
"""
if self._hparams.likelihood == cia.DistributionType.DMOL:
logits, losses = self(features) # pylint: disable=not-callable
samples = common_layers.sample_from_discretized_mix_logistic(
logits, seed=None)
return samples, logits, losses
return super(Imagetransformer, self).sample(features)
def sample(self, features):
"""Run the model and extract samples.
Args:
features: an map of string to `Tensor`.
Returns:
samples: an integer `Tensor`.
logits: a list of `Tensor`s, one per datashard.
losses: a dictionary: {loss-name (string): floating point `Scalar`}.
"""
if self._hparams.likelihood == cia.DistributionType.DMOL:
logits, losses = self(features) # pylint: disable=not-callable
samples = common_layers.sample_from_discretized_mix_logistic(
logits, seed=None)
return samples, logits, losses
return super(Imagetransformer, self).sample(features)
