Python tensorflow.python.ops.array_ops.boolean_mask() Examples

def _make_auc_histograms(boolean_labels, scores, score_range, nbins):
  """Create histogram tensors from one batch of labels/scores."""

  with variable_scope.variable_scope(
      None, 'make_auc_histograms', [boolean_labels, scores, nbins]):
    # Histogram of scores for records in this batch with True label.
    hist_true = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, boolean_labels),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_true')
    # Histogram of scores for records in this batch with False label.
    hist_false = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, math_ops.logical_not(boolean_labels)),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_false')
    return hist_true, hist_false 

def _make_auc_histograms(boolean_labels, scores, score_range, nbins):
  """Create histogram tensors from one batch of labels/scores."""

  with variable_scope.variable_scope(
      None, 'make_auc_histograms', [boolean_labels, scores, nbins]):
    # Histogram of scores for records in this batch with True label.
    hist_true = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, boolean_labels),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_true')
    # Histogram of scores for records in this batch with False label.
    hist_false = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, math_ops.logical_not(boolean_labels)),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_false')
    return hist_true, hist_false 

def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: Additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    input_tensor = input_tensors[0]
    mask = input_tensors[1]
    if mask.get_shape().ndims > 1:
      mask = array_ops.squeeze(mask)

    if isinstance(input_tensor, sparse_tensor_py.SparseTensor):
      mask_fn = sparse_boolean_mask
    else:
      mask_fn = array_ops.boolean_mask

    # pylint: disable=not-callable
    return self.return_type(mask_fn(input_tensor, mask)) 

def insert(self, ids, scores):
    """Insert the ids and scores into the TopN."""
    with ops.control_dependencies(self.last_ops):
      scatter_op = state_ops.scatter_update(self.id_to_score, ids, scores)
      larger_scores = math_ops.greater(scores, self.sl_scores[0])

      def shortlist_insert():
        larger_ids = array_ops.boolean_mask(
            math_ops.to_int64(ids), larger_scores)
        larger_score_values = array_ops.boolean_mask(scores, larger_scores)
        shortlist_ids, new_ids, new_scores = tensor_forest_ops.top_n_insert(
            self.sl_ids, self.sl_scores, larger_ids, larger_score_values)
        u1 = state_ops.scatter_update(self.sl_ids, shortlist_ids, new_ids)
        u2 = state_ops.scatter_update(self.sl_scores, shortlist_ids, new_scores)
        return control_flow_ops.group(u1, u2)

      # We only need to insert into the shortlist if there are any
      # scores larger than the threshold.
      cond_op = control_flow_ops.cond(
          math_ops.reduce_any(larger_scores), shortlist_insert,
          control_flow_ops.no_op)
      with ops.control_dependencies([cond_op]):
        self.last_ops = [scatter_op, cond_op] 

def CheckVersusNumpy(self, ndims_mask, arr_shape, make_mask=None):
    """Check equivalence between boolean_mask and numpy masking."""
    if make_mask is None:
      make_mask = lambda shape: np.random.randint(0, 2, size=shape).astype(bool)
    arr = np.random.rand(*arr_shape)
    mask = make_mask(arr_shape[: ndims_mask])
    masked_arr = arr[mask]
    with self.test_session():
      masked_tensor = array_ops.boolean_mask(arr, mask)
      np.testing.assert_allclose(
          masked_arr,
          masked_tensor.eval(),
          err_msg="masked_arr:\n%s\n\nmasked_tensor:\n%s" % (
              masked_arr, masked_tensor.eval()))
      masked_tensor.get_shape().assert_is_compatible_with(masked_arr.shape)
      self.assertSequenceEqual(
          masked_tensor.get_shape()[1:].as_list(),
          masked_arr.shape[1:],
          msg="shape information lost %s -> %s" % (
              masked_arr.shape, masked_tensor.get_shape())) 

def _make_auc_histograms(boolean_labels, scores, score_range, nbins):
  """Create histogram tensors from one batch of labels/scores."""

  with variable_scope.variable_scope(
      None, 'make_auc_histograms', [boolean_labels, scores, nbins]):
    # Histogram of scores for records in this batch with True label.
    hist_true = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, boolean_labels),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_true')
    # Histogram of scores for records in this batch with False label.
    hist_false = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, math_ops.logical_not(boolean_labels)),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_false')
    return hist_true, hist_false 

def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: Additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    input_tensor = input_tensors[0]
    mask = input_tensors[1]
    if mask.get_shape().ndims > 1:
      mask = array_ops.squeeze(mask)

    if isinstance(input_tensor, sparse_tensor_py.SparseTensor):
      mask_fn = sparse_boolean_mask
    else:
      mask_fn = array_ops.boolean_mask

    # pylint: disable=not-callable
    return self.return_type(mask_fn(input_tensor, mask)) 

def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: Additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    input_tensor = input_tensors[0]
    mask = input_tensors[1]
    if mask.get_shape().ndims > 1:
      mask = array_ops.squeeze(mask)

    if isinstance(input_tensor, sparse_tensor_py.SparseTensor):
      mask_fn = sparse_boolean_mask
    else:
      mask_fn = array_ops.boolean_mask

    # pylint: disable=not-callable
    return self.return_type(mask_fn(input_tensor, mask)) 

def insert(self, ids, scores):
    """Insert the ids and scores into the TopN."""
    with ops.control_dependencies(self.last_ops):
      scatter_op = state_ops.scatter_update(self.id_to_score, ids, scores)
      larger_scores = math_ops.greater(scores, self.sl_scores[0])

      def shortlist_insert():
        larger_ids = array_ops.boolean_mask(
            math_ops.to_int64(ids), larger_scores)
        larger_score_values = array_ops.boolean_mask(scores, larger_scores)
        shortlist_ids, new_ids, new_scores = tensor_forest_ops.top_n_insert(
            self.sl_ids, self.sl_scores, larger_ids, larger_score_values)
        u1 = state_ops.scatter_update(self.sl_ids, shortlist_ids, new_ids)
        u2 = state_ops.scatter_update(self.sl_scores, shortlist_ids, new_scores)
        return control_flow_ops.group(u1, u2)

      # We only need to insert into the shortlist if there are any
      # scores larger than the threshold.
      cond_op = control_flow_ops.cond(
          math_ops.reduce_any(larger_scores), shortlist_insert,
          control_flow_ops.no_op)
      with ops.control_dependencies([cond_op]):
        self.last_ops = [scatter_op, cond_op] 

def _make_auc_histograms(boolean_labels, scores, score_range, nbins):
  """Create histogram tensors from one batch of labels/scores."""

  with variable_scope.variable_scope(
      None, 'make_auc_histograms', [boolean_labels, scores, nbins]):
    # Histogram of scores for records in this batch with True label.
    hist_true = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, boolean_labels),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_true')
    # Histogram of scores for records in this batch with False label.
    hist_false = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, math_ops.logical_not(boolean_labels)),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_false')
    return hist_true, hist_false 

def _make_auc_histograms(boolean_labels, scores, score_range, nbins):
  """Create histogram tensors from one batch of labels/scores."""

  with variable_scope.variable_scope(
      None, 'make_auc_histograms', [boolean_labels, scores, nbins]):
    # Histogram of scores for records in this batch with True label.
    hist_true = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, boolean_labels),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_true')
    # Histogram of scores for records in this batch with False label.
    hist_false = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, math_ops.logical_not(boolean_labels)),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_false')
    return hist_true, hist_false 

def report_uninitialized_resources(resource_list=None,
                                   name="report_uninitialized_resources"):
  """Returns the names of all uninitialized resources in resource_list.

  If the returned tensor is empty then all resources have been initialized.

  Args:
   resource_list: resources to check. If None, will use shared_resources() +
    local_resources().
   name: name for the resource-checking op.

  Returns:
   Tensor containing names of the handles of all resources which have not
   yet been initialized.

  """
  if resource_list is None:
    resource_list = shared_resources() + local_resources()
  with ops.name_scope(name):
    # Run all operations on CPU
    with ops.device("/cpu:0"):
      if not resource_list:
        # Return an empty tensor so we only need to check for returned tensor
        # size being 0 as an indication of model ready.
        return array_ops.constant([], dtype=dtypes.string)
      # Get a 1-D boolean tensor listing whether each resource is initialized.
      variables_mask = math_ops.logical_not(
          array_ops.stack([r.is_initialized for r in resource_list]))
      # Get a 1-D string tensor containing all the resource names.
      variable_names_tensor = array_ops.constant(
          [s.handle.name for s in resource_list])
      # Return a 1-D tensor containing all the names of uninitialized resources.
      return array_ops.boolean_mask(variable_names_tensor, variables_mask) 

def report_uninitialized_resources(resource_list=None,
                                   name="report_uninitialized_resources"):
  """Returns the names of all uninitialized resources in resource_list.

  If the returned tensor is empty then all resources have been initialized.

  Args:
   resource_list: resources to check. If None, will use shared_resources() +
    local_resources().
   name: name for the resource-checking op.

  Returns:
   Tensor containing names of the handles of all resources which have not
   yet been initialized.

  """
  if resource_list is None:
    resource_list = shared_resources() + local_resources()
  with ops.name_scope(name):
    if not resource_list:
      # Return an empty tensor so we only need to check for returned tensor
      # size being 0 as an indication of model ready.
      return array_ops.constant([], dtype=dtypes.string)
    # Get a 1-D boolean tensor listing whether each resource is initialized.
    variables_mask = math_ops.logical_not(array_ops.pack(
        [r.is_initialized for r in resource_list]))
    # Get a 1-D string tensor containing all the resource names.
    variable_names_tensor = array_ops.constant(
        [s.handle.name for s in resource_list])
    # Return a 1-D tensor containing all the names of uninitialized resources.
    return array_ops.boolean_mask(variable_names_tensor, variables_mask) 

def _mask_activations_and_labels(activations, labels, sequence_lengths):
  """Remove entries outside `sequence_lengths` and returned flattened results.

  Args:
    activations: output of the RNN, shape `[batch_size, padded_length, k]`.
    labels: label values, shape `[batch_size, padded_length]`.
    sequence_lengths: a `Tensor` of shape `[batch_size]` with the unpadded
      length of each sequence. If `None`, then each sequence is unpadded.

  Returns:
    activations_masked: `logit` values with those beyond `sequence_lengths`
    removed for each batch. Batches are then concatenated. Shape
      `[tf.sum(sequence_lengths), k]` if `sequence_lengths` is not `None` and
      shape `[batch_size * padded_length, k]` otherwise.
    labels_masked: label values after removing unneeded entries. Shape
      `[tf.sum(sequence_lengths)]` if `sequence_lengths` is not `None` and shape
      `[batch_size * padded_length]` otherwise.
  """
  with ops.name_scope('mask_activations_and_labels',
                      values=[activations, labels, sequence_lengths]):
    labels_shape = array_ops.shape(labels)
    batch_size = labels_shape[0]
    padded_length = labels_shape[1]
    if sequence_lengths is None:
      flattened_dimension = padded_length * batch_size
      activations_masked = array_ops.reshape(activations,
                                             [flattened_dimension, -1])
      labels_masked = array_ops.reshape(labels, [flattened_dimension])
    else:
      mask = _padding_mask(sequence_lengths, padded_length)
      activations_masked = array_ops.boolean_mask(activations, mask)
      labels_masked = array_ops.boolean_mask(labels, mask)
    return activations_masked, labels_masked 

def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
  """Boolean mask for `SparseTensor`s.

  Args:
    sparse_tensor: a `SparseTensor`.
    mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
      of `sparse_tensor`.
    name: optional name for this operation.
  Returns:
    A `SparseTensor` that contains row `k` of `sparse_tensor` iff `mask[k]` is
    `True`.
  """
  # TODO(jamieas): consider mask dimension > 1 for symmetry with `boolean_mask`.
  with ops.name_scope(name, values=[sparse_tensor, mask]):
    mask = ops.convert_to_tensor(mask)
    mask_rows = array_ops.where(mask)
    first_indices = array_ops.squeeze(array_ops.slice(sparse_tensor.indices,
                                                      [0, 0], [-1, 1]))

    # Identify indices corresponding to the rows identified by mask_rows.
    sparse_entry_matches = functional_ops.map_fn(
        lambda x: math_ops.equal(first_indices, x),
        mask_rows,
        dtype=dtypes.bool)
    # Combine the rows of index_matches to form a mask for the sparse indices
    # and values.
    to_retain = array_ops.reshape(
        functional_ops.foldl(math_ops.logical_or, sparse_entry_matches), [-1])

    return sparse_ops.sparse_retain(sparse_tensor, to_retain) 

def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: Additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    d = input_tensors[0]

    if self.strip_value is np.nan:
      strip_hot = math_ops.is_nan(d)
    else:
      strip_hot = math_ops.equal(d,
                                 array_ops.constant([self.strip_value],
                                                    dtype=d.dtype))
    keep_hot = math_ops.logical_not(strip_hot)

    length = array_ops.reshape(array_ops.shape(d), [])
    indices = array_ops.boolean_mask(math_ops.range(length), keep_hot)
    values = array_ops.boolean_mask(d, keep_hot)

    sparse_indices = array_ops.reshape(
        math_ops.cast(indices, dtypes.int64), [-1, 1])
    shape = math_ops.cast(array_ops.shape(d), dtypes.int64)

    # pylint: disable=not-callable
    return self.return_type(
        sparse_tensor.SparseTensor(sparse_indices, values, shape)) 

def report_uninitialized_variables(var_list=None,
                                   name="report_uninitialized_variables"):
  """Adds ops to list the names of uninitialized variables.

  When run, it returns a 1-D tensor containing the names of uninitialized
  variables if there are any, or an empty array if there are none.

  Args:
    var_list: List of `Variable` objects to check. Defaults to the
      value of `global_variables() + local_variables()`
    name: Optional name of the `Operation`.

  Returns:
    A 1-D tensor containing names of the uninitialized variables, or an empty
    1-D tensor if there are no variables or no uninitialized variables.
  """
  if var_list is None:
    var_list = global_variables() + local_variables()
    # Backwards compatibility for old-style variables. TODO(touts): remove.
    if not var_list:
      var_list = []
      for op in ops.get_default_graph().get_operations():
        if op.type in ["Variable", "AutoReloadVariable"]:
          var_list.append(op.outputs[0])
  with ops.name_scope(name):
    if not var_list:
      # Return an empty tensor so we only need to check for returned tensor
      # size being 0 as an indication of model ready.
      return array_ops.constant([], dtype=dtypes.string)
    else:
      # Get a 1-D boolean tensor listing whether each variable is initialized.
      variables_mask = math_ops.logical_not(array_ops.pack(
          [state_ops.is_variable_initialized(v) for v in var_list]))
      # Get a 1-D string tensor containing all the variable names.
      variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
      # Return a 1-D tensor containing all the names of uninitialized variables.
      return array_ops.boolean_mask(variable_names_tensor, variables_mask)

# pylint: disable=protected-access 

def testMaskDimensionsSetToNoneRaises(self):
    # The rank of the mask tensor must be specified. This is explained
    # in the docstring as well.
    with self.test_session():
      tensor = array_ops.placeholder(dtypes.int32, shape=[None, 2])
      mask = array_ops.placeholder(dtypes.bool, shape=None)
      with self.assertRaisesRegexp(ValueError, "dimensions must be specified"):
        array_ops.boolean_mask(tensor, mask) 

def report_uninitialized_resources(resource_list=None,
                                   name="report_uninitialized_resources"):
  """Returns the names of all uninitialized resources in resource_list.

  If the returned tensor is empty then all resources have been initialized.

  Args:
   resource_list: resources to check. If None, will use shared_resources() +
    local_resources().
   name: name for the resource-checking op.

  Returns:
   Tensor containing names of the handles of all resources which have not
   yet been initialized.

  """
  if resource_list is None:
    resource_list = shared_resources() + local_resources()
  with ops.name_scope(name):
    if not resource_list:
      # Return an empty tensor so we only need to check for returned tensor
      # size being 0 as an indication of model ready.
      return array_ops.constant([], dtype=dtypes.string)
    # Get a 1-D boolean tensor listing whether each resource is initialized.
    variables_mask = math_ops.logical_not(
        array_ops.stack([r.is_initialized for r in resource_list]))
    # Get a 1-D string tensor containing all the resource names.
    variable_names_tensor = array_ops.constant(
        [s.handle.name for s in resource_list])
    # Return a 1-D tensor containing all the names of uninitialized resources.
    return array_ops.boolean_mask(variable_names_tensor, variables_mask) 

def report_uninitialized_variables(var_list=None,
                                   name="report_uninitialized_variables"):
  """Adds ops to list the names of uninitialized variables.

  When run, it returns a 1-D tensor containing the names of uninitialized
  variables if there are any, or an empty array if there are none.

  Args:
    var_list: List of `Variable` objects to check. Defaults to the
      value of `global_variables() + local_variables()`
    name: Optional name of the `Operation`.

  Returns:
    A 1-D tensor containing names of the uninitialized variables, or an empty
    1-D tensor if there are no variables or no uninitialized variables.
  """
  if var_list is None:
    var_list = global_variables() + local_variables()
    # Backwards compatibility for old-style variables. TODO(touts): remove.
    if not var_list:
      var_list = []
      for op in ops.get_default_graph().get_operations():
        if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
          var_list.append(op.outputs[0])
  with ops.name_scope(name):
    if not var_list:
      # Return an empty tensor so we only need to check for returned tensor
      # size being 0 as an indication of model ready.
      return array_ops.constant([], dtype=dtypes.string)
    else:
      # Get a 1-D boolean tensor listing whether each variable is initialized.
      variables_mask = math_ops.logical_not(
          array_ops.stack(
              [state_ops.is_variable_initialized(v) for v in var_list]))
      # Get a 1-D string tensor containing all the variable names.
      variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
      # Return a 1-D tensor containing all the names of uninitialized variables.
      return array_ops.boolean_mask(variable_names_tensor, variables_mask)

# pylint: disable=protected-access 

def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: Additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    d = input_tensors[0]

    if self.strip_value is np.nan:
      strip_hot = math_ops.is_nan(d)
    else:
      strip_hot = math_ops.equal(d,
                                 array_ops.constant([self.strip_value],
                                                    dtype=d.dtype))
    keep_hot = math_ops.logical_not(strip_hot)

    length = array_ops.reshape(array_ops.shape(d), [])
    indices = array_ops.boolean_mask(math_ops.range(length), keep_hot)
    values = array_ops.boolean_mask(d, keep_hot)

    sparse_indices = array_ops.reshape(
        math_ops.cast(indices, dtypes.int64), [-1, 1])
    shape = math_ops.cast(array_ops.shape(d), dtypes.int64)

    # pylint: disable=not-callable
    return self.return_type(
        sparse_tensor.SparseTensor(sparse_indices, values, shape)) 

def _get_examples(file_name_queue, reader, num_threads, read_batch_size,
                  filter_fn, parse_fn):
  with ops.name_scope('read'):
    example_list = []
    for _ in range(num_threads):
      if read_batch_size > 1:
        keys, examples_proto = reader().read_up_to(file_name_queue,
                                                   read_batch_size)
      else:
        keys, examples_proto = reader().read(file_name_queue)
      if filter_fn:
        mask = filter_fn(keys, examples_proto)
        keys = array_ops.boolean_mask(keys, mask)
        examples_proto = array_ops.boolean_mask(examples_proto, mask)
      if parse_fn:
        parsed_examples = parse_fn(examples_proto)
        # Map keys into example map because batch_join doesn't support
        # tuple of Tensor + dict.
        if isinstance(parsed_examples, dict):
          parsed_examples[KEY_FEATURE_NAME] = keys
          example_list.append(parsed_examples)
        else:
          example_list.append((keys, parsed_examples))
      else:
        example_list.append((keys, examples_proto))
    return example_list 

def mask_activations_and_labels(activations, labels, sequence_lengths):
  """Remove entries outside `sequence_lengths` and returned flattened results.

  Args:
    activations: Output of the RNN, shape `[batch_size, padded_length, k]`.
    labels: Label values, shape `[batch_size, padded_length]`.
    sequence_lengths: A `Tensor` of shape `[batch_size]` with the unpadded
      length of each sequence. If `None`, then each sequence is unpadded.

  Returns:
    activations_masked: `logit` values with those beyond `sequence_lengths`
      removed for each batch. Batches are then concatenated. Shape
      `[tf.sum(sequence_lengths), k]` if `sequence_lengths` is not `None` and
      shape `[batch_size * padded_length, k]` otherwise.
    labels_masked: Label values after removing unneeded entries. Shape
      `[tf.sum(sequence_lengths)]` if `sequence_lengths` is not `None` and shape
      `[batch_size * padded_length]` otherwise.
  """
  with ops.name_scope('mask_activations_and_labels',
                      values=[activations, labels, sequence_lengths]):
    labels_shape = array_ops.shape(labels)
    batch_size = labels_shape[0]
    padded_length = labels_shape[1]
    if sequence_lengths is None:
      flattened_dimension = padded_length * batch_size
      activations_masked = array_ops.reshape(activations,
                                             [flattened_dimension, -1])
      labels_masked = array_ops.reshape(labels, [flattened_dimension])
    else:
      mask = array_ops.sequence_mask(sequence_lengths, padded_length)
      activations_masked = array_ops.boolean_mask(activations, mask)
      labels_masked = array_ops.boolean_mask(labels, mask)
    return activations_masked, labels_masked 

def boolean_mask(labeled_tensor, mask, name=None):
  """Apply a boolean mask to a labeled tensor.

  Unlike `tf.boolean_mask`, this currently only works on 1-dimensional masks.
  The mask is applied to the first axis of `labeled_tensor`. Labels on the first
  axis are removed, because True indices in `mask` may not be known dynamically.

  Args:
    labeled_tensor: The input tensor.
    mask: The type of the returned tensor.
    name: Optional op name.

  Returns:
    The masked labeled tensor.

  Raises:
    ValueError: if the first axis of the mask
  """
  with ops.name_scope(name, 'lt_boolean_mask', [labeled_tensor, mask]) as scope:
    labeled_tensor = core.convert_to_labeled_tensor(labeled_tensor)
    mask = core.convert_to_labeled_tensor(mask)

    if len(mask.axes) > 1:
      raise NotImplementedError(
          "LabeledTensor's boolean_mask currently only supports 1D masks")
    mask_axis = list(mask.axes.values())[0]
    lt_axis = list(labeled_tensor.axes.values())[0]
    if mask_axis != lt_axis:
      raise ValueError('the first axis of the labeled tensor and the mask '
                       'are not equal:\n%r\n%r' % (lt_axis, mask_axis))
    op = array_ops.boolean_mask(labeled_tensor.tensor, mask.tensor, name=scope)
    # TODO(shoyer): attempt to infer labels for the masked values, by calling
    # tf.contrib.util.constant_value on the mask?
    axes = [lt_axis.name] + list(labeled_tensor.axes.values())[1:]
    return core.LabeledTensor(op, axes) 

def test_name(self):
    mask = core.LabeledTensor(math_ops.range(7) > 3, [self.a0])
    masked_lt = ops.boolean_mask(self.original_lt, mask)
    self.assertIn('lt_boolean_mask', masked_lt.name) 

def test_invalid_rank(self):
    mask = core.LabeledTensor(array_ops.ones((7, 3)) > 3, [self.a0, self.a1])
    with self.assertRaises(NotImplementedError):
      ops.boolean_mask(self.original_lt, mask) 

def test_mismatched_axis(self):
    mask = core.LabeledTensor(math_ops.range(7) > 3, ['foo'])
    with self.assertRaisesRegexp(ValueError, 'not equal'):
      ops.boolean_mask(self.original_lt, mask) 

def mask_activations_and_labels(activations, labels, sequence_lengths):
  """Remove entries outside `sequence_lengths` and returned flattened results.

  Args:
    activations: Output of the RNN, shape `[batch_size, padded_length, k]`.
    labels: Label values, shape `[batch_size, padded_length]`.
    sequence_lengths: A `Tensor` of shape `[batch_size]` with the unpadded
      length of each sequence. If `None`, then each sequence is unpadded.

  Returns:
    activations_masked: `logit` values with those beyond `sequence_lengths`
      removed for each batch. Batches are then concatenated. Shape
      `[tf.sum(sequence_lengths), k]` if `sequence_lengths` is not `None` and
      shape `[batch_size * padded_length, k]` otherwise.
    labels_masked: Label values after removing unneeded entries. Shape
      `[tf.sum(sequence_lengths)]` if `sequence_lengths` is not `None` and shape
      `[batch_size * padded_length]` otherwise.
  """
  with ops.name_scope('mask_activations_and_labels',
                      values=[activations, labels, sequence_lengths]):
    labels_shape = array_ops.shape(labels)
    batch_size = labels_shape[0]
    padded_length = labels_shape[1]
    if sequence_lengths is None:
      flattened_dimension = padded_length * batch_size
      activations_masked = array_ops.reshape(activations,
                                             [flattened_dimension, -1])
      labels_masked = array_ops.reshape(labels, [flattened_dimension])
    else:
      mask = array_ops.sequence_mask(sequence_lengths, padded_length)
      activations_masked = array_ops.boolean_mask(activations, mask)
      labels_masked = array_ops.boolean_mask(labels, mask)
    return activations_masked, labels_masked 

def report_uninitialized_variables(var_list=None,
                                   name="report_uninitialized_variables"):
  """Adds ops to list the names of uninitialized variables.

  When run, it returns a 1-D tensor containing the names of uninitialized
  variables if there are any, or an empty array if there are none.

  Args:
    var_list: List of `Variable` objects to check. Defaults to the
      value of `global_variables() + local_variables()`
    name: Optional name of the `Operation`.

  Returns:
    A 1-D tensor containing names of the uninitialized variables, or an empty
    1-D tensor if there are no variables or no uninitialized variables.
  """
  if var_list is None:
    var_list = global_variables() + local_variables()
    # Backwards compatibility for old-style variables. TODO(touts): remove.
    if not var_list:
      var_list = []
      for op in ops.get_default_graph().get_operations():
        if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
          var_list.append(op.outputs[0])
  with ops.name_scope(name):
    if not var_list:
      # Return an empty tensor so we only need to check for returned tensor
      # size being 0 as an indication of model ready.
      return array_ops.constant([], dtype=dtypes.string)
    else:
      # Get a 1-D boolean tensor listing whether each variable is initialized.
      variables_mask = math_ops.logical_not(
          array_ops.stack(
              [state_ops.is_variable_initialized(v) for v in var_list]))
      # Get a 1-D string tensor containing all the variable names.
      variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
      # Return a 1-D tensor containing all the names of uninitialized variables.
      return array_ops.boolean_mask(variable_names_tensor, variables_mask)

# pylint: disable=protected-access 

def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: Additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    d = input_tensors[0]

    if self.strip_value is np.nan:
      strip_hot = math_ops.is_nan(d)
    else:
      strip_hot = math_ops.equal(d,
                                 array_ops.constant([self.strip_value],
                                                    dtype=d.dtype))
    keep_hot = math_ops.logical_not(strip_hot)

    length = array_ops.reshape(array_ops.shape(d), [])
    indices = array_ops.boolean_mask(math_ops.range(length), keep_hot)
    values = array_ops.boolean_mask(d, keep_hot)

    sparse_indices = array_ops.reshape(
        math_ops.cast(indices, dtypes.int64), [-1, 1])
    shape = math_ops.cast(array_ops.shape(d), dtypes.int64)

    # pylint: disable=not-callable
    return self.return_type(
        sparse_tensor.SparseTensor(sparse_indices, values, shape))