def dmol_neg_log_perplexity(predictions,
labels,
weights_fn=None):
"""Average log-perplexity excluding padding 0s. No smoothing."""
del weights_fn # Unused
num, den = common_layers.dml_loss(
predictions, labels, reduce_sum=False)
return (-num, den)
def dmol_neg_log_perplexity(predictions,
labels,
weights_fn=None):
"""Average log-perplexity excluding padding 0s. No smoothing."""
del weights_fn # Unused
num, den = common_layers.dml_loss(
predictions, labels, reduce_sum=False)
return (-num, den)
def testDmlLoss(self, batch, height, width, num_mixtures, reduce_sum):
channels = 3
pred = tf.random_normal([batch, height, width, num_mixtures * 10])
labels = tf.random_uniform([batch, height, width, channels],
minval=0, maxval=256, dtype=tf.int32)
actual_loss_num, actual_loss_den = common_layers.dml_loss(
pred=pred, labels=labels, reduce_sum=reduce_sum)
actual_loss = actual_loss_num / actual_loss_den
real_labels = common_layers.convert_rgb_to_symmetric_real(labels)
expected_loss = common_layers.discretized_mix_logistic_loss(
pred=pred, labels=real_labels) / channels
if reduce_sum:
expected_loss = tf.reduce_mean(expected_loss)
actual_loss_val, expected_loss_val = self.evaluate(
[actual_loss, expected_loss])
self.assertAllClose(actual_loss_val, expected_loss_val)
def testDmlLoss(self, batch, height, width, num_mixtures, reduce_sum):
channels = 3
pred = tf.random_normal([batch, height, width, num_mixtures * 10])
labels = tf.random_uniform([batch, height, width, channels],
minval=0, maxval=256, dtype=tf.int32)
actual_loss_num, actual_loss_den = common_layers.dml_loss(
pred=pred, labels=labels, reduce_sum=reduce_sum)
actual_loss = actual_loss_num / actual_loss_den
real_labels = common_layers.convert_rgb_to_symmetric_real(labels)
expected_loss = common_layers.discretized_mix_logistic_loss(
pred=pred, labels=real_labels) / channels
if reduce_sum:
expected_loss = tf.reduce_mean(expected_loss)
actual_loss_val, expected_loss_val = self.evaluate(
[actual_loss, expected_loss])
self.assertAllClose(actual_loss_val, expected_loss_val)
def loss(self, logits, features):
if self._hparams.likelihood == cia.DistributionType.DMOL:
return common_layers.dml_loss(logits, features["targets"])
return super(Imagetransformer, self).loss(logits, features)
def loss(self, logits, features):
return common_layers.dml_loss(logits, features["targets"])
def loss(self, pred, features): return common_layers.dml_loss(pred, features["targets"])
def loss(self, logits, features):
if self._hparams.likelihood == cia.DistributionType.DMOL:
return common_layers.dml_loss(logits, features["targets"])
return super(Imagetransformer, self).loss(logits, features)
