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Python metrics_impl._streaming_confusion_matrix方法代码示例

本文整理汇总了Python中tensorflow.python.ops.metrics_impl._streaming_confusion_matrix方法的典型用法代码示例。如果您正苦于以下问题:Python metrics_impl._streaming_confusion_matrix方法的具体用法?Python metrics_impl._streaming_confusion_matrix怎么用?Python metrics_impl._streaming_confusion_matrix使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在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个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: precision

# 需要导入模块: from tensorflow.python.ops import metrics_impl [as 别名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 别名]
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) 
开发者ID:fennuDetudou,项目名称:tudouNLP,代码行数:38,代码来源:tf_metrics.py

示例2: recall

# 需要导入模块: from tensorflow.python.ops import metrics_impl [as 别名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 别名]
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) 
开发者ID:fennuDetudou,项目名称:tudouNLP,代码行数:38,代码来源:tf_metrics.py

示例3: fbeta

# 需要导入模块: from tensorflow.python.ops import metrics_impl [as 别名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 别名]
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) 
开发者ID:fennuDetudou,项目名称:tudouNLP,代码行数:40,代码来源:tf_metrics.py

示例4: precision

# 需要导入模块: from tensorflow.python.ops import metrics_impl [as 别名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 别名]
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) 
开发者ID:guillaumegenthial,项目名称:tf_metrics,代码行数:40,代码来源:__init__.py

示例5: recall

# 需要导入模块: from tensorflow.python.ops import metrics_impl [as 别名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 别名]
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) 
开发者ID:guillaumegenthial,项目名称:tf_metrics,代码行数:40,代码来源:__init__.py

示例6: fbeta

# 需要导入模块: from tensorflow.python.ops import metrics_impl [as 别名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 别名]
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) 
开发者ID:guillaumegenthial,项目名称:tf_metrics,代码行数:42,代码来源:__init__.py

示例7: precision

# 需要导入模块: from tensorflow.python.ops import metrics_impl [as 别名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 别名]
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) 
开发者ID:tracy-talent,项目名称:curriculum,代码行数:11,代码来源:metrics.py

示例8: recall

# 需要导入模块: from tensorflow.python.ops import metrics_impl [as 别名]
# 或者: from tensorflow.python.ops.metrics_impl import _streaming_confusion_matrix [as 别名]
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) 
开发者ID:tracy-talent,项目名称:curriculum,代码行数:11,代码来源:metrics.py


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