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Python digraph_ops.ArcSourcePotentialsFromTokens方法代码示例

本文整理汇总了Python中dragnn.python.digraph_ops.ArcSourcePotentialsFromTokens方法的典型用法代码示例。如果您正苦于以下问题:Python digraph_ops.ArcSourcePotentialsFromTokens方法的具体用法?Python digraph_ops.ArcSourcePotentialsFromTokens怎么用?Python digraph_ops.ArcSourcePotentialsFromTokens使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在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个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: testArcSourcePotentialsFromTokens

# 需要导入模块: from dragnn.python import digraph_ops [as 别名]
# 或者: from dragnn.python.digraph_ops import ArcSourcePotentialsFromTokens [as 别名]
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]]]) 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:20,代码来源:digraph_ops_test.py

示例2: testArcSourcePotentialsFromTokens

# 需要导入模块: from dragnn.python import digraph_ops [as 别名]
# 或者: from dragnn.python.digraph_ops import ArcSourcePotentialsFromTokens [as 别名]
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 
开发者ID:generalized-iou,项目名称:g-tensorflow-models,代码行数:20,代码来源:digraph_ops_test.py

示例3: create

# 需要导入模块: from dragnn.python import digraph_ops [as 别名]
# 或者: from dragnn.python.digraph_ops import ArcSourcePotentialsFromTokens [as 别名]
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])] 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:45,代码来源:biaffine_units.py

示例4: create

# 需要导入模块: from dragnn.python import digraph_ops [as 别名]
# 或者: from dragnn.python.digraph_ops import ArcSourcePotentialsFromTokens [as 别名]
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])] 
开发者ID:generalized-iou,项目名称:g-tensorflow-models,代码行数:50,代码来源:biaffine_units.py


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