Python metrics_impl._streaming_confusion_matrix方法代碼示例

本文整理匯總了Python中tensorflow.python.ops.metrics_impl._streaming_confusion_matrix方法的典型用法代碼示例。如果您正苦於以下問題：Python metrics_impl._streaming_confusion_matrix方法的具體用法？Python metrics_impl._streaming_confusion_matrix怎麽用？Python metrics_impl._streaming_confusion_matrix使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.ops.metrics_impl的用法示例。

在下文中一共展示了metrics_impl._streaming_confusion_matrix方法的8個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: precision

# 需要導入模塊: from tensorflow.python.ops import metrics_impl [as 別名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 別名]
def precision(labels, predictions, num_classes, pos_indices=None,
              weights=None, average='micro'):
    """Multi-class precision metric for Tensorflow
    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.
    Returns
    -------
    tuple of (scalar float Tensor, update_op)
    """
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    pr, _, _ = metrics_from_confusion_matrix(
        cm, pos_indices, average=average)
    op, _, _ = metrics_from_confusion_matrix(
        op, pos_indices, average=average)
    return (pr, op)

開發者ID:fennuDetudou，項目名稱:tudouNLP，代碼行數:38，代碼來源:tf_metrics.py

示例2: recall

# 需要導入模塊: from tensorflow.python.ops import metrics_impl [as 別名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 別名]
def recall(labels, predictions, num_classes, pos_indices=None, weights=None,
           average='micro'):
    """Multi-class recall metric for Tensorflow
    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.
    Returns
    -------
    tuple of (scalar float Tensor, update_op)
    """
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    _, re, _ = metrics_from_confusion_matrix(
        cm, pos_indices, average=average)
    _, op, _ = metrics_from_confusion_matrix(
        op, pos_indices, average=average)
    return (re, op)

開發者ID:fennuDetudou，項目名稱:tudouNLP，代碼行數:38，代碼來源:tf_metrics.py

示例3: fbeta

# 需要導入模塊: from tensorflow.python.ops import metrics_impl [as 別名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 別名]
def fbeta(labels, predictions, num_classes, pos_indices=None, weights=None,
          average='micro', beta=1):
    """Multi-class fbeta metric for Tensorflow
    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.
    beta : int, optional
        Weight of precision in harmonic mean
    Returns
    -------
    tuple of (scalar float Tensor, update_op)
    """
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    _, _, fbeta = metrics_from_confusion_matrix(
        cm, pos_indices, average=average, beta=beta)
    _, _, op = metrics_from_confusion_matrix(
        op, pos_indices, average=average, beta=beta)
    return (fbeta, op)

開發者ID:fennuDetudou，項目名稱:tudouNLP，代碼行數:40，代碼來源:tf_metrics.py

示例4: precision

# 需要導入模塊: from tensorflow.python.ops import metrics_impl [as 別名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 別名]
def precision(labels, predictions, num_classes, pos_indices=None,
              weights=None, average='micro'):
    """Multi-class precision metric for Tensorflow

    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.

    Returns
    -------
    tuple of (scalar float Tensor, update_op)
    """
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    pr, _, _ = metrics_from_confusion_matrix(
        cm, pos_indices, average=average)
    op, _, _ = metrics_from_confusion_matrix(
        op, pos_indices, average=average)
    return (pr, op)

開發者ID:guillaumegenthial，項目名稱:tf_metrics，代碼行數:40，代碼來源:__init__.py

示例5: recall

# 需要導入模塊: from tensorflow.python.ops import metrics_impl [as 別名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 別名]
def recall(labels, predictions, num_classes, pos_indices=None, weights=None,
           average='micro'):
    """Multi-class recall metric for Tensorflow

    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.

    Returns
    -------
    tuple of (scalar float Tensor, update_op)
    """
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    _, re, _ = metrics_from_confusion_matrix(
        cm, pos_indices, average=average)
    _, op, _ = metrics_from_confusion_matrix(
        op, pos_indices, average=average)
    return (re, op)

開發者ID:guillaumegenthial，項目名稱:tf_metrics，代碼行數:40，代碼來源:__init__.py

示例6: fbeta

# 需要導入模塊: from tensorflow.python.ops import metrics_impl [as 別名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 別名]
def fbeta(labels, predictions, num_classes, pos_indices=None, weights=None,
          average='micro', beta=1):
    """Multi-class fbeta metric for Tensorflow

    Parameters
    ----------
    labels : Tensor of tf.int32 or tf.int64
        The true labels
    predictions : Tensor of tf.int32 or tf.int64
        The predictions, same shape as labels
    num_classes : int
        The number of classes
    pos_indices : list of int, optional
        The indices of the positive classes, default is all
    weights : Tensor of tf.int32, optional
        Mask, must be of compatible shape with labels
    average : str, optional
        'micro': counts the total number of true positives, false
            positives, and false negatives for the classes in
            `pos_indices` and infer the metric from it.
        'macro': will compute the metric separately for each class in
            `pos_indices` and average. Will not account for class
            imbalance.
        'weighted': will compute the metric separately for each class in
            `pos_indices` and perform a weighted average by the total
            number of true labels for each class.
    beta : int, optional
        Weight of precision in harmonic mean

    Returns
    -------
    tuple of (scalar float Tensor, update_op)
    """
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    _, _, fbeta = metrics_from_confusion_matrix(
        cm, pos_indices, average=average, beta=beta)
    _, _, op = metrics_from_confusion_matrix(
        op, pos_indices, average=average, beta=beta)
    return (fbeta, op)

開發者ID:guillaumegenthial，項目名稱:tf_metrics，代碼行數:42，代碼來源:__init__.py

示例7: precision

# 需要導入模塊: from tensorflow.python.ops import metrics_impl [as 別名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 別名]
def precision(labels, predictions, num_classes, pos_indices=None,
              weights=None, average='micro'):
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    pr, _, _ = metrics_from_confusion_matrix(
        cm, pos_indices, average=average)
    op, _, _ = metrics_from_confusion_matrix(
        op, pos_indices, average=average)
    return (pr, op)

開發者ID:tracy-talent，項目名稱:curriculum，代碼行數:11，代碼來源:metrics.py

示例8: recall

# 需要導入模塊: from tensorflow.python.ops import metrics_impl [as 別名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 別名]
def recall(labels, predictions, num_classes, pos_indices=None, weights=None,
           average='micro'):
    cm, op = _streaming_confusion_matrix(
        labels, predictions, num_classes, weights)
    _, re, _ = metrics_from_confusion_matrix(
        cm, pos_indices, average=average)
    _, op, _ = metrics_from_confusion_matrix(
        op, pos_indices, average=average)
    return (re, op)

開發者ID:tracy-talent，項目名稱:curriculum，代碼行數:11，代碼來源:metrics.py

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