def testArcSourcePotentialsFromTokens(self):
with self.test_session():
tokens = tf.constant([[[4, 5, 6], [5, 6, 7], [6, 7, 8]],
[[6, 7, 8], [5, 6, 7], [4, 5, 6]]],
tf.float32)
weights = tf.constant([2, 3, 5], tf.float32)
arcs = digraph_ops.ArcSourcePotentialsFromTokens(tokens, weights)
self.assertAllEqual(arcs.eval(),
[[[53, 53, 53], [63, 63, 63], [73, 73, 73]],
[[73, 73, 73], [63, 63, 63], [53, 53, 53]]])
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,
'BiaffineDigraphNetwork 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_arc = self._component.get_variable('weights_arc')
weights_source = self._component.get_variable('weights_source')
root = self._component.get_variable('root')
# Extract 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)
source_tokens_bxnxs = tf.reshape(sources.tensor,
[stride, -1, self._source_dim])
target_tokens_bxnxt = tf.reshape(targets.tensor,
[stride, -1, self._target_dim])
num_tokens = tf.shape(source_tokens_bxnxs)[1]
# Compute the arc, source, and root potentials.
arcs_bxnxn = digraph_ops.ArcPotentialsFromTokens(
source_tokens_bxnxs, target_tokens_bxnxt, weights_arc)
sources_bxnxn = digraph_ops.ArcSourcePotentialsFromTokens(
source_tokens_bxnxs, weights_source)
roots_bxn = digraph_ops.RootPotentialsFromTokens(
root, target_tokens_bxnxt, weights_arc, weights_source)
# Combine them into a single matrix with the roots on the diagonal.
adjacency_bxnxn = digraph_ops.CombineArcAndRootPotentials(
arcs_bxnxn + sources_bxnxn, roots_bxn)
# The adjacency matrix currently has sources on rows and targets on columns,
# but we want targets on rows so that maximizing within a row corresponds to
# selecting sources for a given target.
adjacency_bxnxn = tf.matrix_transpose(adjacency_bxnxn)
return [tf.reshape(adjacency_bxnxn, [-1, num_tokens])]