本文整理匯總了Python中dragnn.python.digraph_ops.ArcSourcePotentialsFromTokens方法的典型用法代碼示例。如果您正苦於以下問題：Python digraph_ops.ArcSourcePotentialsFromTokens方法的具體用法？Python digraph_ops.ArcSourcePotentialsFromTokens怎麽用？Python digraph_ops.ArcSourcePotentialsFromTokens使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類dragnn.python.digraph_ops的用法示例。
在下文中一共展示了digraph_ops.ArcSourcePotentialsFromTokens方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: testArcSourcePotentialsFromTokens
# 需要導入模塊: from dragnn.python import digraph_ops [as 別名]
# 或者: from dragnn.python.digraph_ops import ArcSourcePotentialsFromTokens [as 別名]
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]]]) 示例2: testArcSourcePotentialsFromTokens
# 需要導入模塊: from dragnn.python import digraph_ops [as 別名]
# 或者: from dragnn.python.digraph_ops import ArcSourcePotentialsFromTokens [as 別名]
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) # pyformat: disable
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]]]) # pyformat: disable 示例3: create
# 需要導入模塊: from dragnn.python import digraph_ops [as 別名]
# 或者: from dragnn.python.digraph_ops import ArcSourcePotentialsFromTokens [as 別名]
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)
# Combine them into a single matrix with the roots on the diagonal.
adjacency_bxnxn = digraph_ops.CombineArcAndRootPotentials(
arcs_bxnxn + sources_bxnxn, roots_bxn)
return [tf.reshape(adjacency_bxnxn, [-1, num_tokens])] 示例4: create
# 需要導入模塊: from dragnn.python import digraph_ops [as 別名]
# 或者: from dragnn.python.digraph_ops import ArcSourcePotentialsFromTokens [as 別名]
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])]