Python tensorflow.python.framework.ops.add_to_collections() Examples

def collect_named_outputs(collections, alias, outputs):
  """Add `Tensor` outputs tagged with alias to collections.

  It is useful to collect end-points or tags for summaries. Example of usage:

  logits = collect_named_outputs('end_points', 'inception_v3/logits', logits)
  assert 'inception_v3/logits' in logits.aliases

  Args:
    collections: A collection or list of collections. If None skip collection.
    alias: String to append to the list of aliases of outputs, for example,
           'inception_v3/conv1'.
    outputs: Tensor, an output tensor to collect

  Returns:
    The outputs Tensor to allow inline call.
  """
  if collections:
    append_tensor_alias(outputs, alias)
    ops.add_to_collections(collections, outputs)
  return outputs 

def collect_named_outputs(collections, alias, outputs):
  """Add `Tensor` outputs tagged with alias to collections.

  It is useful to collect end-points or tags for summaries. Example of usage:

  logits = collect_named_outputs('end_points', 'inception_v3/logits', logits)
  assert 'inception_v3/logits' in logits.aliases

  Args:
    collections: A collection or list of collections. If None skip collection.
    alias: String to append to the list of aliases of outputs, for example,
           'inception_v3/conv1'.
    outputs: Tensor, an output tensor to collect

  Returns:
    The outputs Tensor to allow inline call.
  """
  append_tensor_alias(outputs, alias)
  if collections:
    ops.add_to_collections(collections, outputs)
  return outputs 

def collect_named_outputs(collections, alias, outputs):
  """Add `Tensor` outputs tagged with alias to collections.

  It is useful to collect end-points or tags for summaries. Example of usage:

  logits = collect_named_outputs('end_points', 'inception_v3/logits', logits)
  assert logits.alias == 'inception_v3/logits'

  Args:
    collections: A collection or list of collections. If None skip collection.
    alias: String, alias to name the outputs, ex. 'inception_v3/conv1'
    outputs: Tensor, an output tensor to collect

  Returns:
    The outputs Tensor to allow inline call.
  """
  # Remove ending '/' if present.
  if alias[-1] == '/':
    alias = alias[:-1]
  outputs.alias = alias
  if collections:
    ops.add_to_collections(collections, outputs)
  return outputs 

def collect_named_outputs(collections, alias, outputs):
  """Add `Tensor` outputs tagged with alias to collections.

  It is useful to collect end-points or tags for summaries. Example of usage:

  logits = collect_named_outputs('end_points', 'inception_v3/logits', logits)
  assert 'inception_v3/logits' in logits.aliases

  Args:
    collections: A collection or list of collections. If None skip collection.
    alias: String to append to the list of aliases of outputs, for example,
           'inception_v3/conv1'.
    outputs: Tensor, an output tensor to collect

  Returns:
    The outputs Tensor to allow inline call.
  """
  if collections:
    append_tensor_alias(outputs, alias)
    ops.add_to_collections(collections, outputs)
  return outputs 

def collect_named_outputs(collections, alias, outputs):
  """Add `Tensor` outputs tagged with alias to collections.

  It is useful to collect end-points or tags for summaries. Example of usage:

  logits = collect_named_outputs('end_points', 'inception_v3/logits', logits)
  assert 'inception_v3/logits' in logits.aliases

  Args:
    collections: A collection or list of collections. If None skip collection.
    alias: String to append to the list of aliases of outputs, for example,
           'inception_v3/conv1'.
    outputs: Tensor, an output tensor to collect

  Returns:
    The outputs Tensor to allow inline call.
  """
  append_tensor_alias(outputs, alias)
  if collections:
    ops.add_to_collections(collections, outputs)
  return outputs 

def collect_named_outputs(collections, alias, outputs):
  """Add `Tensor` outputs tagged with alias to collections.

  It is useful to collect end-points or tags for summaries. Example of usage:

  logits = collect_named_outputs('end_points', 'inception_v3/logits', logits)
  assert 'inception_v3/logits' in logits.aliases

  Args:
    collections: A collection or list of collections. If None skip collection.
    alias: String to append to the list of aliases of outputs, for example,
           'inception_v3/conv1'.
    outputs: Tensor, an output tensor to collect

  Returns:
    The outputs Tensor to allow inline call.
  """
  if collections:
    append_tensor_alias(outputs, alias)
    ops.add_to_collections(collections, outputs)
  return outputs 

def _apply_activation(y, activation_fn, output_collections):
  if activation_fn is not None:
    y = activation_fn(y)
  ops.add_to_collections(
      list(output_collections or []) + [ops.GraphKeys.ACTIVATIONS], y)
  return y 

def _register_variable_read(read, collections, trainable):
  """Helper function to put a read from a variable in the collections."""
  if collections is None:
    collections = [ops.GraphKeys.GLOBAL_VARIABLES]
  if (trainable and ops.GraphKeys.TRAINABLE_VARIABLES
       not in collections):
    collections = (list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES])
    ops.add_to_collections(collections, read) 

def _apply_activation(y, activation_fn, output_collections):
  if activation_fn is not None:
    y = activation_fn(y)
  ops.add_to_collections(list(output_collections or []) +
                         [ops.GraphKeys.ACTIVATIONS], y)
  return y 

def _apply_activation(y, activation_fn, output_collections):
  if activation_fn is not None:
    y = activation_fn(y)
  ops.add_to_collections(list(output_collections or []) +
                         [ops.GraphKeys.ACTIVATIONS], y)
  return y 

def _apply_activation(y, activation_fn, output_collections):
  if activation_fn is not None:
    y = activation_fn(y)
  ops.add_to_collections(list(output_collections or []) +
                         [ops.GraphKeys.ACTIVATIONS], y)
  return y 

def _apply_activation(y, activation_fn, output_collections):
  if activation_fn is not None:
    y = activation_fn(y)
  ops.add_to_collections(
      list(output_collections or []) + [ops.GraphKeys.ACTIVATIONS], y)
  return y 

def _register_variable_read(read, collections, trainable):
  """Helper function to put a read from a variable in the collections."""
  if collections is None:
    collections = []
  if (trainable and
      ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES not in collections):
    collections = (list(collections) +
                   [ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES])
    ops.add_to_collections(collections, read) 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: An optional `Tensor` whose shape is broadcastable to `values`.
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A tensor representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    weights = math_ops.to_float(weights)
    values = math_ops.mul(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _apply_activation(y, activation_fn, output_collections):
  if activation_fn is not None:
    y = activation_fn(y)
  ops.add_to_collections(list(output_collections or []) +
                         [ops.GraphKeys.ACTIVATIONS], y)
  return y 

def _register_variable_read(read, collections, trainable):
  """Helper function to put a read from a variable in the collections."""
  if collections is None:
    collections = [ops.GraphKeys.GLOBAL_VARIABLES]
  if (trainable and ops.GraphKeys.TRAINABLE_VARIABLES
       not in collections):
    collections = (list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES])
    ops.add_to_collections(collections, read) 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `values` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    with ops.control_dependencies((
        check_ops.assert_rank_in(weights, (0, array_ops.rank(values))),)):
      weights = math_ops.to_float(weights)
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions
      must be either `1`, or the same as the corresponding `values`
      dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    weights = math_ops.to_float(weights)
    with ops.control_dependencies((_assert_weights_rank(weights, values),)):
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `values` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    with ops.control_dependencies((
        check_ops.assert_rank_in(weights, (0, array_ops.rank(values))),)):
      weights = math_ops.to_float(weights)
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def true_positives_at_thresholds(labels, predictions, thresholds, weights=None,
                                 metrics_collections=None,
                                 updates_collections=None,
                                 name=None):
  """Computes true positives at provided threshold values.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    labels: A `Tensor` whose shape matches `predictions`. Will be cast to
      `bool`.
    predictions: A floating point `Tensor` of arbitrary shape and whose values
      are in the range `[0, 1]`.
    thresholds: A python list or tuple of float thresholds in `[0, 1]`.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `labels` dimension).
    metrics_collections: An optional list of collections that `true_positives`
      should be added to.
    updates_collections: An optional list of collections that `update_op` should
      be added to.
    name: An optional variable_scope name.

  Returns:
    true_positives:  A float `Tensor` of shape `[len(thresholds)]`.
    update_op: An operation that updates the `true_positives` variable and
      returns its current value.

  Raises:
    ValueError: If `predictions` and `labels` have mismatched shapes, or if
      `weights` is not `None` and its shape doesn't match `predictions`, or if
      either `metrics_collections` or `updates_collections` are not a list or
      tuple.
  """
  with variable_scope.variable_scope(name, 'true_positives',
                                     (predictions, labels, weights)):
    values, update_ops = _confusion_matrix_at_thresholds(
        labels, predictions, thresholds, weights=weights, includes=('tp',))

    if metrics_collections:
      ops.add_to_collections(metrics_collections, values['tp'])

    if updates_collections:
      ops.add_to_collections(updates_collections, update_ops['tp'])

    return values['tp'], update_ops['tp'] 

def true_negatives_at_thresholds(labels, predictions, thresholds, weights=None,
                                 metrics_collections=None,
                                 updates_collections=None,
                                 name=None):
  """Computes true negatives at provided threshold values.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    labels: A `Tensor` whose shape matches `predictions`. Will be cast to
      `bool`.
    predictions: A floating point `Tensor` of arbitrary shape and whose values
      are in the range `[0, 1]`.
    thresholds: A python list or tuple of float thresholds in `[0, 1]`.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `labels` dimension).
    metrics_collections: An optional list of collections that `true_negatives`
      should be added to.
    updates_collections: An optional list of collections that `update_op` should
      be added to.
    name: An optional variable_scope name.

  Returns:
    true_negatives:  A float `Tensor` of shape `[len(thresholds)]`.
    update_op: An operation that updates the `true_negatives` variable and
      returns its current value.

  Raises:
    ValueError: If `predictions` and `labels` have mismatched shapes, or if
      `weights` is not `None` and its shape doesn't match `predictions`, or if
      either `metrics_collections` or `updates_collections` are not a list or
      tuple.
  """
  with variable_scope.variable_scope(name, 'true_negatives',
                                     (predictions, labels, weights)):
    values, update_ops = _confusion_matrix_at_thresholds(
        labels, predictions, thresholds, weights=weights, includes=('tn',))

    if metrics_collections:
      ops.add_to_collections(metrics_collections, values['tn'])

    if updates_collections:
      ops.add_to_collections(updates_collections, update_ops['tn'])

    return values['tn'], update_ops['tn'] 

def false_positives_at_thresholds(labels, predictions, thresholds, weights=None,
                                  metrics_collections=None,
                                  updates_collections=None,
                                  name=None):
  """Computes false positives at provided threshold values.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    labels: A `Tensor` whose shape matches `predictions`. Will be cast to
      `bool`.
    predictions: A floating point `Tensor` of arbitrary shape and whose values
      are in the range `[0, 1]`.
    thresholds: A python list or tuple of float thresholds in `[0, 1]`.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `labels` dimension).
    metrics_collections: An optional list of collections that `false_positives`
      should be added to.
    updates_collections: An optional list of collections that `update_op` should
      be added to.
    name: An optional variable_scope name.

  Returns:
    false_positives:  A float `Tensor` of shape `[len(thresholds)]`.
    update_op: An operation that updates the `false_positives` variable and
      returns its current value.

  Raises:
    ValueError: If `predictions` and `labels` have mismatched shapes, or if
      `weights` is not `None` and its shape doesn't match `predictions`, or if
      either `metrics_collections` or `updates_collections` are not a list or
      tuple.
  """
  with variable_scope.variable_scope(name, 'false_positives',
                                     (predictions, labels, weights)):
    values, update_ops = _confusion_matrix_at_thresholds(
        labels, predictions, thresholds, weights=weights, includes=('fp',))

    if metrics_collections:
      ops.add_to_collections(metrics_collections, values['fp'])

    if updates_collections:
      ops.add_to_collections(updates_collections, update_ops['fp'])

    return values['fp'], update_ops['fp'] 

def false_negatives_at_thresholds(labels, predictions, thresholds, weights=None,
                                  metrics_collections=None,
                                  updates_collections=None,
                                  name=None):
  """Computes false negatives at provided threshold values.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    labels: A `Tensor` whose shape matches `predictions`. Will be cast to
      `bool`.
    predictions: A floating point `Tensor` of arbitrary shape and whose values
      are in the range `[0, 1]`.
    thresholds: A python list or tuple of float thresholds in `[0, 1]`.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `labels` dimension).
    metrics_collections: An optional list of collections that `false_negatives`
      should be added to.
    updates_collections: An optional list of collections that `update_op` should
      be added to.
    name: An optional variable_scope name.

  Returns:
    false_negatives:  A float `Tensor` of shape `[len(thresholds)]`.
    update_op: An operation that updates the `false_negatives` variable and
      returns its current value.

  Raises:
    ValueError: If `predictions` and `labels` have mismatched shapes, or if
      `weights` is not `None` and its shape doesn't match `predictions`, or if
      either `metrics_collections` or `updates_collections` are not a list or
      tuple.
  """
  with variable_scope.variable_scope(name, 'false_negatives',
                                     (predictions, labels, weights)):
    values, update_ops = _confusion_matrix_at_thresholds(
        labels, predictions, thresholds, weights=weights, includes=('fn',))

    if metrics_collections:
      ops.add_to_collections(metrics_collections, values['fn'])

    if updates_collections:
      ops.add_to_collections(updates_collections, update_ops['fn'])

    return values['fn'], update_ops['fn'] 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `values` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    with ops.control_dependencies((
        check_ops.assert_rank_in(weights, (0, array_ops.rank(values))),)):
      weights = math_ops.to_float(weights)
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions
      must be either `1`, or the same as the corresponding `values`
      dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    weights = math_ops.to_float(weights)
    with ops.control_dependencies((_assert_weights_rank(weights, values),)):
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions
      must be either `1`, or the same as the corresponding `values`
      dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    weights = math_ops.to_float(weights)
    with ops.control_dependencies((_assert_weights_rank(weights, values),)):
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `values` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    with ops.control_dependencies((
        check_ops.assert_rank_in(weights, (0, array_ops.rank(values))),)):
      weights = math_ops.to_float(weights)
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op