python类zeros_initializer()的实例源码

def _create_joint_embedding_lookup(columns_to_tensors,
                                   embedding_lookup_arguments,
                                   num_outputs,
                                   trainable,
                                   weight_collections):
  """Creates an embedding lookup for all columns sharing a single weight."""
  for arg in embedding_lookup_arguments:
    assert arg.weight_tensor is None, (
        'Joint sums for weighted sparse columns are not supported. '
        'Please use weighted_sum_from_feature_columns instead.')
    assert arg.combiner == 'sum', (
        'Combiners other than sum are not supported for joint sums. '
        'Please use weighted_sum_from_feature_columns instead.')
  assert len(embedding_lookup_arguments) >= 1, (
      'At least one column must be in the model.')
  prev_size = 0
  sparse_tensors = []
  for a in embedding_lookup_arguments:
    t = a.input_tensor
    values = t.values + prev_size
    prev_size += a.vocab_size
    sparse_tensors.append(
        sparse_tensor_py.SparseTensor(t.indices,
                                      values,
                                      t.shape))
  sparse_tensor = sparse_ops.sparse_concat(1, sparse_tensors)
  with variable_scope.variable_scope(
      None, default_name='linear_weights', values=columns_to_tensors.values()):
    variable = contrib_variables.model_variable(
        name='weights',
        shape=[prev_size, num_outputs],
        dtype=dtypes.float32,
        initializer=init_ops.zeros_initializer,
        trainable=trainable,
        collections=weight_collections)
    if isinstance(variable, variables.Variable):
      variable = [variable]
    else:
      variable = variable._get_variable_list()  # pylint: disable=protected-access
    predictions = embedding_ops.safe_embedding_lookup_sparse(
        variable,
        sparse_tensor,
        sparse_weights=None,
        combiner='sum',
        name='_weights')
    return variable, predictions

def weighted_moving_average(value,
                            decay,
                            weight,
                            truediv=True,
                            collections=None,
                            name=None):
  """Compute the weighted moving average of `value`.

  Conceptually, the weighted moving average is:
    `moving_average(value * weight) / moving_average(weight)`,
  where a moving average updates by the rule
    `new_value = decay * old_value + (1 - decay) * update`
  Internally, this Op keeps moving average variables of both `value * weight`
  and `weight`.

  Args:
    value: A numeric `Tensor`.
    decay: A float `Tensor` or float value.  The moving average decay.
    weight:  `Tensor` that keeps the current value of a weight.
      Shape should be able to multiply `value`.
    truediv:  Boolean, if `True`, dividing by `moving_average(weight)` is
      floating point division.  If `False`, use division implied by dtypes.
    collections:  List of graph collections keys to add the internal variables
      `value * weight` and `weight` to.  Defaults to `[GraphKeys.VARIABLES]`.
    name: Optional name of the returned operation.
      Defaults to "WeightedMovingAvg".

  Returns:
    An Operation that updates and returns the weighted moving average.
  """
  # Unlike assign_moving_average, the weighted moving average doesn't modify
  # user-visible variables. It is the ratio of two internal variables, which are
  # moving averages of the updates.  Thus, the signature of this function is
  # quite different than assign_moving_average.
  if collections is None:
    collections = [ops.GraphKeys.VARIABLES]
  with variable_scope.variable_op_scope(
      [value, weight, decay], name, "WeightedMovingAvg") as scope:
    value_x_weight_var = variable_scope.get_variable(
        "value_x_weight",
        initializer=init_ops.zeros_initializer(value.get_shape(),
                                               dtype=value.dtype),
        trainable=False,
        collections=collections)
    weight_var = variable_scope.get_variable(
        "weight",
        initializer=init_ops.zeros_initializer(weight.get_shape(),
                                               dtype=weight.dtype),
        trainable=False,
        collections=collections)
    numerator = assign_moving_average(value_x_weight_var, value * weight, decay)
    denominator = assign_moving_average(weight_var, weight, decay)

    if truediv:
      return math_ops.truediv(numerator, denominator, name=scope.name)
    else:
      return math_ops.div(numerator, denominator, name=scope.name)

def testInitFromCheckpointMissing(self):
    checkpoint_dir = self.get_temp_dir()
    with self.test_session() as session:
      _, _, _, _ = _create_checkpoints(session, checkpoint_dir)

    # New graph and session.
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g) as session:
        with variable_scope.variable_scope("some_scope"):
          _ = variable_scope.get_variable("my1", [10, 10])
          _ = variable_scope.get_variable(
              "my2", [1, 10],
              dtype=dtypes.int64,
              initializer=init_ops.zeros_initializer())

        # No directory.
        with self.assertRaises(errors_impl.OpError):
          checkpoint_utils.init_from_checkpoint("no_dir",
                                                {"var1": "some_scope/my1"})

        # No variable in checkpoint.
        with self.assertRaises(ValueError):
          checkpoint_utils.init_from_checkpoint(checkpoint_dir,
                                                {"no_var": "some_scope/my1"})

        # No variable in the graph.
        with self.assertRaises(ValueError):
          checkpoint_utils.init_from_checkpoint(checkpoint_dir,
                                                {"var3": "some_scope/no_var"})

        # Shape mismatch.
        with self.assertRaises(ValueError):
          checkpoint_utils.init_from_checkpoint(checkpoint_dir,
                                                {"var1": "some_scope/my1"})

        # Variable 'my1' and 'my2' are missing in given checkpoint scope.
        with self.assertRaises(ValueError):
          checkpoint_utils.init_from_checkpoint(
              checkpoint_dir, {"useful_scope/": "some_scope/"})

        # Mapping is not to scope name.
        with self.assertRaises(ValueError):
          checkpoint_utils.init_from_checkpoint(checkpoint_dir,
                                                {"useful_scope": "some_scope/"})

def _create_joint_embedding_lookup(columns_to_tensors,
                                   embedding_lookup_arguments,
                                   num_outputs,
                                   trainable,
                                   weight_collections):
  """Creates an embedding lookup for all columns sharing a single weight."""
  for arg in embedding_lookup_arguments:
    assert arg.weight_tensor is None, (
        'Joint sums for weighted sparse columns are not supported. '
        'Please use weighted_sum_from_feature_columns instead.')
    assert arg.combiner == 'sum', (
        'Combiners other than sum are not supported for joint sums. '
        'Please use weighted_sum_from_feature_columns instead.')
  assert len(embedding_lookup_arguments) >= 1, (
      'At least one column must be in the model.')
  prev_size = 0
  sparse_tensors = []
  for a in embedding_lookup_arguments:
    t = a.input_tensor
    values = t.values + prev_size
    prev_size += a.vocab_size
    sparse_tensors.append(
        sparse_tensor_py.SparseTensor(t.indices,
                                      values,
                                      t.dense_shape))
  sparse_tensor = sparse_ops.sparse_concat(1, sparse_tensors)
  with variable_scope.variable_scope(
      None, default_name='linear_weights', values=columns_to_tensors.values()):
    variable = contrib_variables.model_variable(
        name='weights',
        shape=[prev_size, num_outputs],
        dtype=dtypes.float32,
        initializer=init_ops.zeros_initializer(),
        trainable=trainable,
        collections=weight_collections)
    if isinstance(variable, variables.Variable):
      variable = [variable]
    else:
      variable = variable._get_variable_list()  # pylint: disable=protected-access
    predictions = embedding_ops.safe_embedding_lookup_sparse(
        variable,
        sparse_tensor,
        sparse_weights=None,
        combiner='sum',
        name='_weights')
    return variable, predictions