easy_rec/python/core/easyrec_metrics/distribute_metrics_impl_pai.py [1948:2026]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def precision(labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None): """Computes the precision of the predictions with respect to the labels. The `precision` function creates two local variables, `true_positives` and `false_positives`, that are used to compute the precision. This value is ultimately returned as `precision`, an idempotent operation that simply divides `true_positives` by the sum of `true_positives` and `false_positives`. For estimation of the metric over a stream of data, the function creates an `update_op` operation that updates these variables and returns the `precision`. `update_op` weights each prediction by the corresponding value in `weights`. If `weights` is `None`, weights default to 1. Use weights of 0 to mask values. Args: labels: The ground truth values, a `Tensor` whose dimensions must match `predictions`. Will be cast to `bool`. predictions: The predicted values, a `Tensor` of arbitrary dimensions. Will be cast to `bool`. 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 `precision` 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: precision: Scalar float `Tensor` with the value of `true_positives` divided by the sum of `true_positives` and `false_positives`. update_op: `Operation` that increments `true_positives` and `false_positives` variables appropriately and whose value matches `precision`. 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. RuntimeError: If eager execution is enabled. """ if context.executing_eagerly(): raise RuntimeError('tf.metrics.precision is not ' 'supported when eager execution is enabled.') with variable_scope.variable_scope(name, 'precision', (predictions, labels, weights)): predictions, labels, weights = _remove_squeezable_dimensions( predictions=math_ops.cast(predictions, dtype=dtypes.bool), labels=math_ops.cast(labels, dtype=dtypes.bool), weights=weights) true_p, true_positives_update_op = true_positives( labels, predictions, weights, metrics_collections=None, updates_collections=None, name=None) false_p, false_positives_update_op = false_positives( labels, predictions, weights, metrics_collections=None, updates_collections=None, name=None) def compute_precision(tp, fp, name): return array_ops.where( math_ops.greater(tp + fp, 0), math_ops.div(tp, tp + fp), 0, name) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - easy_rec/python/core/easyrec_metrics/distribute_metrics_impl_tf.py [1960:2038]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def precision(labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None): """Computes the precision of the predictions with respect to the labels. The `precision` function creates two local variables, `true_positives` and `false_positives`, that are used to compute the precision. This value is ultimately returned as `precision`, an idempotent operation that simply divides `true_positives` by the sum of `true_positives` and `false_positives`. For estimation of the metric over a stream of data, the function creates an `update_op` operation that updates these variables and returns the `precision`. `update_op` weights each prediction by the corresponding value in `weights`. If `weights` is `None`, weights default to 1. Use weights of 0 to mask values. Args: labels: The ground truth values, a `Tensor` whose dimensions must match `predictions`. Will be cast to `bool`. predictions: The predicted values, a `Tensor` of arbitrary dimensions. Will be cast to `bool`. 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 `precision` 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: precision: Scalar float `Tensor` with the value of `true_positives` divided by the sum of `true_positives` and `false_positives`. update_op: `Operation` that increments `true_positives` and `false_positives` variables appropriately and whose value matches `precision`. 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. RuntimeError: If eager execution is enabled. """ if context.executing_eagerly(): raise RuntimeError('tf.metrics.precision is not ' 'supported when eager execution is enabled.') with variable_scope.variable_scope(name, 'precision', (predictions, labels, weights)): predictions, labels, weights = _remove_squeezable_dimensions( predictions=math_ops.cast(predictions, dtype=dtypes.bool), labels=math_ops.cast(labels, dtype=dtypes.bool), weights=weights) true_p, true_positives_update_op = true_positives( labels, predictions, weights, metrics_collections=None, updates_collections=None, name=None) false_p, false_positives_update_op = false_positives( labels, predictions, weights, metrics_collections=None, updates_collections=None, name=None) def compute_precision(tp, fp, name): return array_ops.where( math_ops.greater(tp + fp, 0), math_ops.div(tp, tp + fp), 0, name) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -