Python network_units.Layer方法代碼示例

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

在下文中一共展示了network_units.Layer方法的5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: create_hidden_layers

# 需要導入模塊: from dragnn.python import network_units [as 別名]
# 或者: from dragnn.python.network_units import Layer [as 別名]
def create_hidden_layers(self, component, hidden_layer_sizes):
    """See base class."""
    # Construct the layer meta info for the DRAGNN builder. Note that the order
    # of h and c are reversed compared to the vanilla DRAGNN LSTM cell, as
    # this is the standard in tf.contrib.rnn.
    #
    # NB: The h activations of the last LSTM must be the last layer, in order
    # for _append_base_layers() to work.
    layers = []
    for index, num_units in enumerate(hidden_layer_sizes):
      layers.append(
          dragnn.Layer(component, name='state_c_%d' % index, dim=num_units))
      layers.append(
          dragnn.Layer(component, name='state_h_%d' % index, dim=num_units))
    context_layers = list(layers)  # copy |layers|, don't alias it
    return layers, context_layers

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:18，代碼來源:wrapped_units.py

示例2: init

# 需要導入模塊: from dragnn.python import network_units [as 別名]
# 或者: from dragnn.python.network_units import Layer [as 別名]
def __init__(self, component):
    super(PairwiseBilinearLabelNetwork, self).__init__(component)
    parameters = component.spec.network_unit.parameters

    self._num_labels = int(parameters['num_labels'])

    self._source_dim = self._linked_feature_dims['sources']
    self._target_dim = self._linked_feature_dims['targets']

    self._weights = []
    self._weights.append(
        network_units.add_var_initialized('bilinear',
                                          [self._source_dim,
                                           self._num_labels,
                                           self._target_dim],
                                          'xavier'))

    self._params.extend(self._weights)
    self._regularized_weights.extend(self._weights)
    self._layers.append(network_units.Layer(component,
                                            name='bilinear_scores',
                                            dim=self._num_labels))

開發者ID:rky0930，項目名稱:yolo_v2，代碼行數:24，代碼來源:transformer_units.py

示例3: init

# 需要導入模塊: from dragnn.python import network_units [as 別名]
# 或者: from dragnn.python.network_units import Layer [as 別名]
def __init__(self, component):
    """Initializes weights and layers.

    Args:
      component: Parent ComponentBuilderBase object.
    """
    super(BiaffineDigraphNetwork, self).__init__(component)

    check.Eq(len(self._fixed_feature_dims.items()), 0,
             'Expected no fixed features')
    check.Eq(len(self._linked_feature_dims.items()), 2,
             'Expected two linked features')

    check.In('sources', self._linked_feature_dims,
             'Missing required linked feature')
    check.In('targets', self._linked_feature_dims,
             'Missing required linked feature')
    self._source_dim = self._linked_feature_dims['sources']
    self._target_dim = self._linked_feature_dims['targets']

    # TODO(googleuser): Make parameter initialization configurable.
    self._weights = []
    self._weights.append(tf.get_variable(
        'weights_arc', [self._source_dim, self._target_dim], tf.float32,
        tf.random_normal_initializer(stddev=1e-4)))
    self._weights.append(tf.get_variable(
        'weights_source', [self._source_dim], tf.float32,
        tf.random_normal_initializer(stddev=1e-4)))
    self._weights.append(tf.get_variable(
        'root', [self._source_dim], tf.float32,
        tf.random_normal_initializer(stddev=1e-4)))

    self._params.extend(self._weights)
    self._regularized_weights.extend(self._weights)

    # Negative Layer.dim indicates that the dimension is dynamic.
    self._layers.append(network_units.Layer(self, 'adjacency', -1))

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:39，代碼來源:biaffine_units.py

示例4: init

# 需要導入模塊: from dragnn.python import network_units [as 別名]
# 或者: from dragnn.python.network_units import Layer [as 別名]
def __init__(self, component):
    """Initializes weights and layers.

    Args:
      component: Parent ComponentBuilderBase object.
    """
    super(BiaffineDigraphNetwork, self).__init__(component)

    check.Eq(len(self._fixed_feature_dims.items()), 0,
             'Expected no fixed features')
    check.Eq(len(self._linked_feature_dims.items()), 2,
             'Expected two linked features')

    check.In('sources', self._linked_feature_dims,
             'Missing required linked feature')
    check.In('targets', self._linked_feature_dims,
             'Missing required linked feature')
    self._source_dim = self._linked_feature_dims['sources']
    self._target_dim = self._linked_feature_dims['targets']

    # TODO(googleuser): Make parameter initialization configurable.
    self._weights = []
    self._weights.append(tf.get_variable(
        'weights_arc', [self._source_dim, self._target_dim], tf.float32,
        tf.random_normal_initializer(stddev=1e-4)))
    self._weights.append(tf.get_variable(
        'weights_source', [self._source_dim], tf.float32,
        tf.random_normal_initializer(stddev=1e-4)))
    self._weights.append(tf.get_variable(
        'root', [self._source_dim], tf.float32,
        tf.random_normal_initializer(stddev=1e-4)))

    self._params.extend(self._weights)
    self._regularized_weights.extend(self._weights)

    # Negative Layer.dim indicates that the dimension is dynamic.
    self._layers.append(network_units.Layer(component, 'adjacency', -1))

開發者ID:rky0930，項目名稱:yolo_v2，代碼行數:39，代碼來源:biaffine_units.py

示例5: init

# 需要導入模塊: from dragnn.python import network_units [as 別名]
# 或者: from dragnn.python.network_units import Layer [as 別名]
def __init__(self, component):
    """Initializes weights and layers.

    Args:
      component: Parent ComponentBuilderBase object.
    """
    super(BiaffineDigraphNetwork, self).__init__(component)

    check.Eq(len(self._fixed_feature_dims.items()), 0,
             'Expected no fixed features')
    check.Eq(len(self._linked_feature_dims.items()), 2,
             'Expected two linked features')

    check.In('sources', self._linked_feature_dims,
             'Missing required linked feature')
    check.In('targets', self._linked_feature_dims,
             'Missing required linked feature')
    self._source_dim = self._linked_feature_dims['sources']
    self._target_dim = self._linked_feature_dims['targets']

    self._weights = []
    self._weights.append(
        tf.get_variable('weights_arc', [self._source_dim, self._target_dim],
                        tf.float32, tf.orthogonal_initializer()))
    self._weights.append(
        tf.get_variable('weights_source', [self._source_dim], tf.float32,
                        tf.zeros_initializer()))
    self._weights.append(
        tf.get_variable('root', [self._source_dim], tf.float32,
                        tf.zeros_initializer()))

    self._params.extend(self._weights)
    self._regularized_weights.extend(self._weights)

    # Add runtime hooks for pre-computed weights.
    self._derived_params.append(self._get_root_weights)
    self._derived_params.append(self._get_root_bias)

    # Negative Layer.dim indicates that the dimension is dynamic.
    self._layers.append(network_units.Layer(component, 'adjacency', -1))

開發者ID:generalized-iou，項目名稱:g-tensorflow-models，代碼行數:42，代碼來源:biaffine_units.py

注：本文中的dragnn.python.network_units.Layer方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。

示例1: create_hidden_layers

示例2: __init__

示例3: __init__

示例4: __init__

示例5: __init__

示例2: init

示例3: init

示例4: init

示例5: init