Python math_ops.to_double方法代码示例

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _sparse_true_positive_at_k(predictions_idx,
                               labels,
                               class_id=None,
                               weights=None,
                               name=None):
  """Calculates true positives for recall@k and precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels_sparse`.

  Args:
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose shape is broadcastable to the the first [D1, ... DN]
      dimensions of `predictions_idx` and `labels`.
    name: Name of operation.

  Returns:
    A [D1, ... DN] `Tensor` of true positive counts.
  """
  with ops.name_scope(name, 'true_positives', (predictions_idx, labels)):
    labels, predictions_idx = _maybe_select_class_id(
        labels, predictions_idx, class_id)
    tp = set_ops.set_size(set_ops.set_intersection(predictions_idx, labels))
    tp = math_ops.to_double(tp)
    if weights is not None:
      weights = math_ops.to_double(weights)
      tp = math_ops.mul(tp, weights)
    return tp 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _sparse_false_positive_at_k(predictions_idx,
                                labels,
                                class_id=None,
                                weights=None):
  """Calculates false positives for precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels_sparse`.

  Args:
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose shape is broadcastable to the the first [D1, ... DN]
      dimensions of `predictions_idx` and `labels`.

  Returns:
    A [D1, ... DN] `Tensor` of false positive counts.
  """
  with ops.name_scope(None, 'false_positives', (predictions_idx, labels)):
    labels, predictions_idx = _maybe_select_class_id(labels,
                                                     predictions_idx,
                                                     class_id)
    fp = set_ops.set_size(set_ops.set_difference(
        predictions_idx, labels, aminusb=True))
    fp = math_ops.to_double(fp)
    if weights is not None:
      weights = math_ops.to_double(weights)
      fp = math_ops.mul(fp, weights)
    return fp 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _sparse_false_negative_at_k(predictions_idx,
                                labels,
                                class_id=None,
                                weights=None):
  """Calculates false negatives for recall@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels_sparse`.

  Args:
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose shape is broadcastable to the the first [D1, ... DN]
      dimensions of `predictions_idx` and `labels`.

  Returns:
    A [D1, ... DN] `Tensor` of false negative counts.
  """
  with ops.name_scope(None, 'false_negatives', (predictions_idx, labels)):
    labels, predictions_idx = _maybe_select_class_id(labels,
                                                     predictions_idx,
                                                     class_id)
    fn = set_ops.set_size(set_ops.set_difference(predictions_idx,
                                                 labels,
                                                 aminusb=False))
    fn = math_ops.to_double(fn)
    if weights is not None:
      weights = math_ops.to_double(weights)
      fn = math_ops.mul(fn, weights)
    return fn 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _sparse_true_positive_at_k(labels,
                               predictions_idx,
                               class_id=None,
                               weights=None,
                               name=None):
  """Calculates true positives for recall@k and precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels_sparse`.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    name: Name of operation.

  Returns:
    A [D1, ... DN] `Tensor` of true positive counts.
  """
  with ops.name_scope(
      name, 'true_positives', (predictions_idx, labels, weights)):
    labels, predictions_idx = _maybe_select_class_id(
        labels, predictions_idx, class_id)
    tp = sets.set_size(sets.set_intersection(predictions_idx, labels))
    tp = math_ops.to_double(tp)
    if weights is not None:
      with ops.control_dependencies((
          weights_broadcast_ops.assert_broadcastable(weights, tp),)):
        weights = math_ops.to_double(weights)
        tp = math_ops.multiply(tp, weights)
    return tp 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _streaming_sparse_true_positive_at_k(labels,
                                         predictions_idx,
                                         k=None,
                                         class_id=None,
                                         weights=None,
                                         name=None):
  """Calculates weighted per step true positives for recall@k and precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels`.

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

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    k: Integer, k for @k metric. This is only used for default op name.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    name: Name of new variable, and namespace for other dependent ops.

  Returns:
    A tuple of `Variable` and update `Operation`.

  Raises:
    ValueError: If `weights` is not `None` and has an incompatible shape.
  """
  with ops.name_scope(
      name, _at_k_name('true_positive', k, class_id=class_id),
      (predictions_idx, labels, weights)) as scope:
    tp = _sparse_true_positive_at_k(
        predictions_idx=predictions_idx, labels=labels, class_id=class_id,
        weights=weights)
    batch_total_tp = math_ops.to_double(math_ops.reduce_sum(tp))

    var = _local_variable(array_ops.zeros([], dtype=dtypes.float64), name=scope)
    return var, state_ops.assign_add(var, batch_total_tp, name='update') 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _sparse_false_negative_at_k(labels,
                                predictions_idx,
                                class_id=None,
                                weights=None):
  """Calculates false negatives for recall@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels_sparse`.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).

  Returns:
    A [D1, ... DN] `Tensor` of false negative counts.
  """
  with ops.name_scope(
      None, 'false_negatives', (predictions_idx, labels, weights)):
    labels, predictions_idx = _maybe_select_class_id(labels,
                                                     predictions_idx,
                                                     class_id)
    fn = sets.set_size(sets.set_difference(predictions_idx,
                                           labels,
                                           aminusb=False))
    fn = math_ops.to_double(fn)
    if weights is not None:
      with ops.control_dependencies((
          weights_broadcast_ops.assert_broadcastable(weights, fn),)):
        weights = math_ops.to_double(weights)
        fn = math_ops.multiply(fn, weights)
    return fn 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _streaming_sparse_false_negative_at_k(labels,
                                          predictions_idx,
                                          k,
                                          class_id=None,
                                          weights=None,
                                          name=None):
  """Calculates weighted per step false negatives for recall@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels`.

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

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    k: Integer, k for @k metric. This is only used for default op name.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    name: Name of new variable, and namespace for other dependent ops.

  Returns:
    A tuple of `Variable` and update `Operation`.

  Raises:
    ValueError: If `weights` is not `None` and has an incompatible shape.
  """
  with ops.name_scope(
      name, _at_k_name('false_negative', k, class_id=class_id),
      (predictions_idx, labels, weights)) as scope:
    fn = _sparse_false_negative_at_k(
        predictions_idx=predictions_idx, labels=labels, class_id=class_id,
        weights=weights)
    batch_total_fn = math_ops.to_double(math_ops.reduce_sum(fn))

    var = _local_variable(array_ops.zeros([], dtype=dtypes.float64), name=scope)
    return var, state_ops.assign_add(var, batch_total_fn, name='update') 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _sparse_false_positive_at_k(labels,
                                predictions_idx,
                                class_id=None,
                                weights=None):
  """Calculates false positives for precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels_sparse`.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).

  Returns:
    A [D1, ... DN] `Tensor` of false positive counts.
  """
  with ops.name_scope(
      None, 'false_positives', (predictions_idx, labels, weights)):
    labels, predictions_idx = _maybe_select_class_id(labels,
                                                     predictions_idx,
                                                     class_id)
    fp = sets.set_size(sets.set_difference(
        predictions_idx, labels, aminusb=True))
    fp = math_ops.to_double(fp)
    if weights is not None:
      with ops.control_dependencies((
          weights_broadcast_ops.assert_broadcastable(weights, fp),)):
        weights = math_ops.to_double(weights)
        fp = math_ops.multiply(fp, weights)
    return fp 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _streaming_sparse_true_positive_at_k(labels,
                                         predictions_idx,
                                         k=None,
                                         class_id=None,
                                         weights=None,
                                         name=None):
  """Calculates weighted per step true positives for recall@k and precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels`.

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

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    k: Integer, k for @k metric. This is only used for default op name.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    name: Name of new variable, and namespace for other dependent ops.

  Returns:
    A tuple of `Variable` and update `Operation`.

  Raises:
    ValueError: If `weights` is not `None` and has an incomptable shape.
  """
  with ops.name_scope(
      name, _at_k_name('true_positive', k, class_id=class_id),
      (predictions_idx, labels, weights)) as scope:
    tp = _sparse_true_positive_at_k(
        predictions_idx=predictions_idx, labels=labels, class_id=class_id,
        weights=weights)
    batch_total_tp = math_ops.to_double(math_ops.reduce_sum(tp))

    var = _local_variable(array_ops.zeros([], dtype=dtypes.float64), name=scope)
    return var, state_ops.assign_add(var, batch_total_tp, name='update') 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _streaming_sparse_false_negative_at_k(labels,
                                          predictions_idx,
                                          k,
                                          class_id=None,
                                          weights=None,
                                          name=None):
  """Calculates weighted per step false negatives for recall@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels`.

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

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    k: Integer, k for @k metric. This is only used for default op name.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    name: Name of new variable, and namespace for other dependent ops.

  Returns:
    A tuple of `Variable` and update `Operation`.

  Raises:
    ValueError: If `weights` is not `None` and has an incomptable shape.
  """
  with ops.name_scope(
      name, _at_k_name('false_negative', k, class_id=class_id),
      (predictions_idx, labels, weights)) as scope:
    fn = _sparse_false_negative_at_k(
        predictions_idx=predictions_idx, labels=labels, class_id=class_id,
        weights=weights)
    batch_total_fn = math_ops.to_double(math_ops.reduce_sum(fn))

    var = _local_variable(array_ops.zeros([], dtype=dtypes.float64), name=scope)
    return var, state_ops.assign_add(var, batch_total_fn, name='update') 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _sparse_true_positive_at_k(predictions_idx,
                               labels,
                               class_id=None,
                               weights=None,
                               name=None):
  """Calculates true positives for recall@k and precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels_sparse`.

  Args:
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    name: Name of operation.

  Returns:
    A [D1, ... DN] `Tensor` of true positive counts.
  """
  with ops.name_scope(
      name, 'true_positives', (predictions_idx, labels, weights)):
    labels, predictions_idx = _maybe_select_class_id(
        labels, predictions_idx, class_id)
    tp = set_ops.set_size(set_ops.set_intersection(predictions_idx, labels))
    tp = math_ops.to_double(tp)
    if weights is not None:
      weights = math_ops.to_double(weights)
      with ops.control_dependencies((_assert_weights_rank(weights, tp),)):
        tp = math_ops.multiply(tp, weights)
    return tp 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _streaming_sparse_true_positive_at_k(predictions_idx,
                                         labels,
                                         k=None,
                                         class_id=None,
                                         weights=None,
                                         name=None):
  """Calculates weighted per step true positives for recall@k and precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels`.

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

  Args:
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    k: Integer, k for @k metric. This is only used for default op name.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    name: Name of new variable, and namespace for other dependent ops.

  Returns:
    A tuple of `Variable` and update `Operation`.

  Raises:
    ValueError: If `weights` is not `None` and has an incomptable shape.
  """
  default_name = _at_k_name('true_positive', k, class_id=class_id)
  with ops.name_scope(
      name, default_name, (predictions_idx, labels, weights)) as scope:
    tp = _sparse_true_positive_at_k(
        predictions_idx=predictions_idx, labels=labels, class_id=class_id,
        weights=weights)
    batch_total_tp = math_ops.to_double(math_ops.reduce_sum(tp))

    var = contrib_variables.local_variable(
        array_ops.zeros([], dtype=dtypes.float64), name=scope)
    return var, state_ops.assign_add(var, batch_total_tp, name='update') 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _sparse_false_positive_at_k(predictions_idx,
                                labels,
                                class_id=None,
                                weights=None):
  """Calculates false positives for precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels_sparse`.

  Args:
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).

  Returns:
    A [D1, ... DN] `Tensor` of false positive counts.
  """
  with ops.name_scope(
      None, 'false_positives', (predictions_idx, labels, weights)):
    labels, predictions_idx = _maybe_select_class_id(labels,
                                                     predictions_idx,
                                                     class_id)
    fp = set_ops.set_size(set_ops.set_difference(
        predictions_idx, labels, aminusb=True))
    fp = math_ops.to_double(fp)
    if weights is not None:
      weights = math_ops.to_double(weights)
      with ops.control_dependencies((_assert_weights_rank(weights, fp),)):
        fp = math_ops.multiply(fp, weights)
    return fp 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _streaming_sparse_false_positive_at_k(predictions_idx,
                                          labels,
                                          k=None,
                                          class_id=None,
                                          weights=None,
                                          name=None):
  """Calculates weighted per step false positives for precision@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels`.

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

  Args:
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    k: Integer, k for @k metric. This is only used for default op name.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    name: Name of new variable, and namespace for other dependent ops.

  Returns:
    A tuple of `Variable` and update `Operation`.

  Raises:
    ValueError: If `weights` is not `None` and has an incomptable shape.
  """
  with ops.name_scope(
      name, _at_k_name('false_positive', k, class_id=class_id),
      (predictions_idx, labels, weights)) as scope:
    fp = _sparse_false_positive_at_k(
        predictions_idx=predictions_idx, labels=labels, class_id=class_id,
        weights=weights)
    batch_total_fp = math_ops.to_double(math_ops.reduce_sum(fp))

    var = contrib_variables.local_variable(
        array_ops.zeros([], dtype=dtypes.float64), name=scope)
    return var, state_ops.assign_add(var, batch_total_fp, name='update') 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import to_double [as 别名]
def _sparse_false_negative_at_k(predictions_idx,
                                labels,
                                class_id=None,
                                weights=None):
  """Calculates false negatives for recall@k.

  If `class_id` is specified, calculate binary true positives for `class_id`
      only.
  If `class_id` is not specified, calculate metrics for `k` predicted vs
      `n` label classes, where `n` is the 2nd dimension of `labels_sparse`.

  Args:
    predictions_idx: 1-D or higher `int64` `Tensor` with last dimension `k`,
      top `k` predicted classes. For rank `n`, the first `n-1` dimensions must
      match `labels`.
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    class_id: Class for which we want binary metrics.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).

  Returns:
    A [D1, ... DN] `Tensor` of false negative counts.
  """
  with ops.name_scope(
      None, 'false_negatives', (predictions_idx, labels, weights)):
    labels, predictions_idx = _maybe_select_class_id(labels,
                                                     predictions_idx,
                                                     class_id)
    fn = set_ops.set_size(set_ops.set_difference(predictions_idx,
                                                 labels,
                                                 aminusb=False))
    fn = math_ops.to_double(fn)
    if weights is not None:
      weights = math_ops.to_double(weights)
      with ops.control_dependencies((_assert_weights_rank(weights, fn),)):
        fn = math_ops.multiply(fn, weights)
    return fn