Python tensorflow.python.ops.nn.in_top_k() Examples

def in_top_k(predictions, targets, k):
  """Returns whether the `targets` are in the top `k` `predictions`.

  Arguments:
      predictions: A tensor of shape `(batch_size, classes)` and type `float32`.
      targets: A 1D tensor of length `batch_size` and type `int32` or `int64`.
      k: An `int`, number of top elements to consider.

  Returns:
      A 1D tensor of length `batch_size` and type `bool`.
      `output[i]` is `True` if `predictions[i, targets[i]]` is within top-`k`
      values of `predictions[i]`.
  """
  return nn.in_top_k(predictions, targets, k)


# CONVOLUTIONS 

def in_top_k(predictions, targets, k):
  """Returns whether the `targets` are in the top `k` `predictions`.

  Arguments:
      predictions: A tensor of shape `(batch_size, classes)` and type `float32`.
      targets: A 1D tensor of length `batch_size` and type `int32` or `int64`.
      k: An `int`, number of top elements to consider.

  Returns:
      A 1D tensor of length `batch_size` and type `bool`.
      `output[i]` is `True` if `predictions[i, targets[i]]` is within top-`k`
      values of `predictions[i]`.
  """
  return nn.in_top_k(predictions, targets, k)


# CONVOLUTIONS 

def _top_k_generator(k):
  def _top_k(probabilities, targets):
    targets = math_ops.to_int32(targets)
    if targets.get_shape().ndims > 1:
      targets = array_ops.squeeze(targets, squeeze_dims=[1])
    return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets, k))
  return _top_k 

def _top_k_generator(k):
  def _top_k(probabilities, targets):
    targets = math_ops.to_int32(targets)
    if targets.get_shape().ndims > 1:
      targets = array_ops.squeeze(targets, squeeze_dims=[1])
    return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets, k))
  return _top_k 

def _top_k_generator(k):
  def _top_k(probabilities, targets):
    targets = math_ops.to_int32(targets)
    if targets.get_shape().ndims > 1:
      targets = array_ops.squeeze(targets, squeeze_dims=[1])
    return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets, k))
  return _top_k 

def streaming_recall_at_k(predictions, labels, k, weights=None,
                          metrics_collections=None, updates_collections=None,
                          name=None):
  """Computes the recall@k of the predictions with respect to dense labels.

  The `streaming_recall_at_k` function creates two local variables, `total` and
  `count`, that are used to compute the recall@k frequency. This frequency is
  ultimately returned as `recall_at_<k>`: an idempotent operation that simply
  divides `total` by `count`.

  For estimation of the metric over a stream of data, the function creates an
  `update_op` operation that updates these variables and returns the
  `recall_at_<k>`. Internally, an `in_top_k` operation computes a `Tensor` with
  shape [batch_size] whose elements indicate whether or not the corresponding
  label is in the top `k` `predictions`. Then `update_op` increments `total`
  with the reduced sum of `weights` where `in_top_k` is `True`, and it
  increments `count` with the reduced sum of `weights`.

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

  Args:
    predictions: A float `Tensor` of dimension [batch_size, num_classes].
    labels: A `Tensor` of dimension [batch_size] whose type is in `int32`,
      `int64`.
    k: The number of top elements to look at for computing recall.
    weights: `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 `recall_at_k`
      should be added to.
    updates_collections: An optional list of collections `update_op` should be
      added to.
    name: An optional variable_scope name.

  Returns:
    recall_at_k: A `Tensor` representing the recall@k, the fraction of labels
      which fall into the top `k` predictions.
    update_op: An operation that increments the `total` and `count` variables
      appropriately and whose value matches `recall_at_k`.

  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.
  """
  in_top_k = math_ops.to_float(nn.in_top_k(predictions, labels, k))
  return streaming_mean(in_top_k,
                        weights,
                        metrics_collections,
                        updates_collections,
                        name or _at_k_name('recall', k))


# TODO(ptucker): Validate range of values in labels? 

def streaming_recall_at_k(predictions, labels, k, weights=None,
                          metrics_collections=None, updates_collections=None,
                          name=None):
  """Computes the recall@k of the predictions with respect to dense labels.

  The `streaming_recall_at_k` function creates two local variables, `total` and
  `count`, that are used to compute the recall@k frequency. This frequency is
  ultimately returned as `recall_at_<k>`: an idempotent operation that simply
  divides `total` by `count`.

  For estimation of the metric over a stream of data, the function creates an
  `update_op` operation that updates these variables and returns the
  `recall_at_<k>`. Internally, an `in_top_k` operation computes a `Tensor` with
  shape [batch_size] whose elements indicate whether or not the corresponding
  label is in the top `k` `predictions`. Then `update_op` increments `total`
  with the reduced sum of `weights` where `in_top_k` is `True`, and it
  increments `count` with the reduced sum of `weights`.

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

  Args:
    predictions: A float `Tensor` of dimension [batch_size, num_classes].
    labels: A `Tensor` of dimension [batch_size] whose type is in `int32`,
      `int64`.
    k: The number of top elements to look at for computing recall.
    weights: `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 `recall_at_k`
      should be added to.
    updates_collections: An optional list of collections `update_op` should be
      added to.
    name: An optional variable_scope name.

  Returns:
    recall_at_k: A `Tensor` representing the recall@k, the fraction of labels
      which fall into the top `k` predictions.
    update_op: An operation that increments the `total` and `count` variables
      appropriately and whose value matches `recall_at_k`.

  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.
  """
  in_top_k = math_ops.to_float(nn.in_top_k(predictions, labels, k))
  return streaming_mean(in_top_k,
                        weights,
                        metrics_collections,
                        updates_collections,
                        name or _at_k_name('recall', k))


# TODO(ptucker): Validate range of values in labels? 

def streaming_recall_at_k(predictions,
                          labels,
                          k,
                          weights=None,
                          metrics_collections=None,
                          updates_collections=None,
                          name=None):
  """Computes the recall@k of the predictions with respect to dense labels.

  The `streaming_recall_at_k` function creates two local variables, `total` and
  `count`, that are used to compute the recall@k frequency. This frequency is
  ultimately returned as `recall_at_<k>`: an idempotent operation that simply
  divides `total` by `count`.

  For estimation of the metric over a stream of data, the function creates an
  `update_op` operation that updates these variables and returns the
  `recall_at_<k>`. Internally, an `in_top_k` operation computes a `Tensor` with
  shape [batch_size] whose elements indicate whether or not the corresponding
  label is in the top `k` `predictions`. Then `update_op` increments `total`
  with the reduced sum of `weights` where `in_top_k` is `True`, and it
  increments `count` with the reduced sum of `weights`.

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

  Args:
    predictions: A float `Tensor` of dimension [batch_size, num_classes].
    labels: A `Tensor` of dimension [batch_size] whose type is in `int32`,
      `int64`.
    k: The number of top elements to look at for computing recall.
    weights: `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 `recall_at_k`
      should be added to.
    updates_collections: An optional list of collections `update_op` should be
      added to.
    name: An optional variable_scope name.

  Returns:
    recall_at_k: A `Tensor` representing the recall@k, the fraction of labels
      which fall into the top `k` predictions.
    update_op: An operation that increments the `total` and `count` variables
      appropriately and whose value matches `recall_at_k`.

  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.
  """
  in_top_k = math_ops.cast(nn.in_top_k(predictions, labels, k), dtypes.float32)
  return streaming_mean(in_top_k, weights, metrics_collections,
                        updates_collections, name or _at_k_name('recall', k)) 

def streaming_recall_at_k(predictions, labels, k, weights=None,
                          metrics_collections=None, updates_collections=None,
                          name=None):
  """Computes the recall@k of the predictions with respect to dense labels.

  The `streaming_recall_at_k` function creates two local variables, `total` and
  `count`, that are used to compute the recall@k frequency. This frequency is
  ultimately returned as `recall_at_<k>`: an idempotent operation that simply
  divides `total` by `count`.

  For estimation of the metric over a stream of data, the function creates an
  `update_op` operation that updates these variables and returns the
  `recall_at_<k>`. Internally, an `in_top_k` operation computes a `Tensor` with
  shape [batch_size] whose elements indicate whether or not the corresponding
  label is in the top `k` `predictions`. Then `update_op` increments `total`
  with the reduced sum of `weights` where `in_top_k` is `True`, and it
  increments `count` with the reduced sum of `weights`.

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

  Args:
    predictions: A floating point tensor of dimension [batch_size, num_classes]
    labels: A tensor of dimension [batch_size] whose type is in `int32`,
      `int64`.
    k: The number of top elements to look at for computing recall.
    weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
    metrics_collections: An optional list of collections that `recall_at_k`
      should be added to.
    updates_collections: An optional list of collections `update_op` should be
      added to.
    name: An optional variable_scope name.

  Returns:
    recall_at_k: A tensor representing the recall@k, the fraction of labels
      which fall into the top `k` predictions.
    update_op: An operation that increments the `total` and `count` variables
      appropriately and whose value matches `recall_at_k`.

  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.
  """
  in_top_k = math_ops.to_float(nn.in_top_k(predictions, labels, k))
  return streaming_mean(in_top_k,
                        weights,
                        metrics_collections,
                        updates_collections,
                        name or _at_k_name('recall', k))


# TODO(ptucker): Validate range of values in labels? 

def streaming_recall_at_k(predictions, labels, k, weights=None,
                          metrics_collections=None, updates_collections=None,
                          name=None):
  """Computes the recall@k of the predictions with respect to dense labels.

  The `streaming_recall_at_k` function creates two local variables, `total` and
  `count`, that are used to compute the recall@k frequency. This frequency is
  ultimately returned as `recall_at_<k>`: an idempotent operation that simply
  divides `total` by `count`.

  For estimation of the metric over a stream of data, the function creates an
  `update_op` operation that updates these variables and returns the
  `recall_at_<k>`. Internally, an `in_top_k` operation computes a `Tensor` with
  shape [batch_size] whose elements indicate whether or not the corresponding
  label is in the top `k` `predictions`. Then `update_op` increments `total`
  with the reduced sum of `weights` where `in_top_k` is `True`, and it
  increments `count` with the reduced sum of `weights`.

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

  Args:
    predictions: A float `Tensor` of dimension [batch_size, num_classes].
    labels: A `Tensor` of dimension [batch_size] whose type is in `int32`,
      `int64`.
    k: The number of top elements to look at for computing recall.
    weights: `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 `recall_at_k`
      should be added to.
    updates_collections: An optional list of collections `update_op` should be
      added to.
    name: An optional variable_scope name.

  Returns:
    recall_at_k: A `Tensor` representing the recall@k, the fraction of labels
      which fall into the top `k` predictions.
    update_op: An operation that increments the `total` and `count` variables
      appropriately and whose value matches `recall_at_k`.

  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.
  """
  in_top_k = math_ops.to_float(nn.in_top_k(predictions, labels, k))
  return streaming_mean(in_top_k,
                        weights,
                        metrics_collections,
                        updates_collections,
                        name or _at_k_name('recall', k))


# TODO(ptucker): Validate range of values in labels?