本文整理匯總了Python中dragnn.python.digraph_ops.LabelPotentialsFromTokenPairs方法的典型用法代碼示例。如果您正苦於以下問題：Python digraph_ops.LabelPotentialsFromTokenPairs方法的具體用法？Python digraph_ops.LabelPotentialsFromTokenPairs怎麽用？Python digraph_ops.LabelPotentialsFromTokenPairs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.LabelPotentialsFromTokenPairs方法的3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: testLabelPotentialsFromTokenPairs
# 需要導入模塊: from dragnn.python import digraph_ops [as 別名]
# 或者: from dragnn.python.digraph_ops import LabelPotentialsFromTokenPairs [as 別名]
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]]]) 示例2: testLabelPotentialsFromTokenPairs
# 需要導入模塊: from dragnn.python import digraph_ops [as 別名]
# 或者: from dragnn.python.digraph_ops import LabelPotentialsFromTokenPairs [as 別名]
def testLabelPotentialsFromTokenPairs(self):
with self.test_session():
sources = tf.constant([[[1, 2],
[3, 4],
[5, 6]],
[[6, 5],
[4, 3],
[2, 1]]], tf.float32) # pyformat: disable
targets = tf.constant([[[3, 4],
[5, 6],
[7, 8]],
[[8, 7],
[6, 5],
[4, 3]]], tf.float32) # pyformat: disable
weights = tf.constant([[[ 2, 3],
[ 5, 7]],
[[11, 13],
[17, 19]],
[[23, 29],
[31, 37]]], tf.float32) # pyformat: disable
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]]]) # 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 LabelPotentialsFromTokenPairs [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,
'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])]