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Python digraph_ops.RootPotentialsFromTokens方法代碼示例

本文整理匯總了Python中dragnn.python.digraph_ops.RootPotentialsFromTokens方法的典型用法代碼示例。如果您正苦於以下問題:Python digraph_ops.RootPotentialsFromTokens方法的具體用法?Python digraph_ops.RootPotentialsFromTokens怎麽用?Python digraph_ops.RootPotentialsFromTokens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dragnn.python.digraph_ops的用法示例。


在下文中一共展示了digraph_ops.RootPotentialsFromTokens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: testRootPotentialsFromTokens

# 需要導入模塊: from dragnn.python import digraph_ops [as 別名]
# 或者: from dragnn.python.digraph_ops import RootPotentialsFromTokens [as 別名]
def testRootPotentialsFromTokens(self):
    with self.test_session():
      root = tf.constant([1, 2], tf.float32)
      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],
                             [7, 11, 13]],
                            tf.float32)

      roots = digraph_ops.RootPotentialsFromTokens(root, tokens, weights)

      self.assertAllEqual(roots.eval(), [[375, 447, 519],
                                         [519, 447, 375]]) 
開發者ID:ringringyi,項目名稱:DOTA_models,代碼行數:19,代碼來源:digraph_ops_test.py

示例2: testRootPotentialsFromTokens

# 需要導入模塊: from dragnn.python import digraph_ops [as 別名]
# 或者: from dragnn.python.digraph_ops import RootPotentialsFromTokens [as 別名]
def testRootPotentialsFromTokens(self):
    with self.test_session():
      root = tf.constant([1, 2], tf.float32)
      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_arc = tf.constant([[2, 3, 5],
                                 [7, 11, 13]],
                                tf.float32)  # pyformat: disable
      weights_source = tf.constant([11, 10], tf.float32)

      roots = digraph_ops.RootPotentialsFromTokens(root, tokens, weights_arc,
                                                   weights_source)

      self.assertAllEqual(roots.eval(), [[406, 478, 550],
                                         [550, 478, 406]])  # pyformat: disable 
開發者ID:generalized-iou,項目名稱:g-tensorflow-models,代碼行數:21,代碼來源:digraph_ops_test.py

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 RootPotentialsFromTokens [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])] 
開發者ID:ringringyi,項目名稱:DOTA_models,代碼行數:45,代碼來源:biaffine_units.py

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 RootPotentialsFromTokens [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])] 
開發者ID:generalized-iou,項目名稱:g-tensorflow-models,代碼行數:50,代碼來源:biaffine_units.py


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