def testLabelPotentialsFromTokenPairs(self):
with self.test_session():
sources = tf.constant([[[1, 2],
[3, 4],
[5, 6]],
[[6, 5],
[4, 3],
[2, 1]]], tf.float32)
targets = tf.constant([[[3, 4],
[5, 6],
[7, 8]],
[[8, 7],
[6, 5],
[4, 3]]], tf.float32)
weights = tf.constant([[[ 2, 3],
[ 5, 7]],
[[11, 13],
[17, 19]],
[[23, 29],
[31, 37]]], tf.float32)
labels = digraph_ops.LabelPotentialsFromTokenPairs(sources, targets,
weights)
self.assertAllEqual(labels.eval(),
[[[ 104, 339, 667],
[ 352, 1195, 2375],
[ 736, 2531, 5043]],
[[ 667, 2419, 4857],
[ 303, 1115, 2245],
[ 75, 291, 593]]])
def create(self,
fixed_embeddings,
linked_embeddings,
context_tensor_arrays,
attention_tensor,
during_training,
stride=None):
"""Requires |stride|; otherwise see base class."""
check.NotNone(
stride,
'BiaffineLabelNetwork requires "stride" and must be called '
'in the bulk feature extractor component.')
# TODO(googleuser): Add dropout during training.
del during_training
# Retrieve (possibly averaged) weights.
weights_pair = self._component.get_variable('weights_pair')
weights_source = self._component.get_variable('weights_source')
weights_target = self._component.get_variable('weights_target')
biases = self._component.get_variable('biases')
# Extract and shape the source and target token activations. Use |stride|
# to collapse batch and beam into a single dimension.
sources = network_units.lookup_named_tensor('sources',
linked_embeddings)
targets = network_units.lookup_named_tensor('targets',
linked_embeddings)
sources_bxnxs = tf.reshape(sources.tensor,
[stride, -1, self._source_dim])
targets_bxnxt = tf.reshape(targets.tensor,
[stride, -1, self._target_dim])
# Compute the pair, source, and target potentials.
pairs_bxnxl = digraph_ops.LabelPotentialsFromTokenPairs(
sources_bxnxs, targets_bxnxt, weights_pair)
sources_bxnxl = digraph_ops.LabelPotentialsFromTokens(
sources_bxnxs, weights_source)
targets_bxnxl = digraph_ops.LabelPotentialsFromTokens(
targets_bxnxt, weights_target)
# Combine them with the biases.
labels_bxnxl = pairs_bxnxl + sources_bxnxl + targets_bxnxl + biases
# Flatten out the batch dimension.
return [tf.reshape(labels_bxnxl, [-1, self._num_labels])]