easy_rec/python/core/easyrec_metrics/distribute_metrics_impl_pai.py [482:551]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - return mean(is_correct, weights, metrics_collections, updates_collections, name or 'accuracy') def _confusion_matrix_at_thresholds(labels, predictions, thresholds, weights=None, includes=None): """Computes true_positives, false_negatives, true_negatives, false_positives. This function creates up to four local variables, `true_positives`, `true_negatives`, `false_positives` and `false_negatives`. `true_positive[i]` is defined as the total weight of values in `predictions` above `thresholds[i]` whose corresponding entry in `labels` is `True`. `false_negatives[i]` is defined as the total weight of values in `predictions` at most `thresholds[i]` whose corresponding entry in `labels` is `True`. `true_negatives[i]` is defined as the total weight of values in `predictions` at most `thresholds[i]` whose corresponding entry in `labels` is `False`. `false_positives[i]` is defined as the total weight of values in `predictions` above `thresholds[i]` whose corresponding entry in `labels` is `False`. For estimation of these metrics over a stream of data, for each metric the function respectively creates an `update_op` operation that updates the variable and returns its value. If `weights` is `None`, weights default to 1. Use weights of 0 to mask values. Args: labels: A `Tensor` whose shape matches `predictions`. Will be cast to `bool`. predictions: A floating point `Tensor` of arbitrary shape and whose values are in the range `[0, 1]`. thresholds: A python list or tuple of float thresholds in `[0, 1]`. 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). includes: Tuple of keys to return, from 'tp', 'fn', 'tn', fp'. If `None`, default to all four. Returns: values: Dict of variables of shape `[len(thresholds)]`. Keys are from `includes`. update_ops: Dict of operations that increments the `values`. Keys are from `includes`. Raises: ValueError: If `predictions` and `labels` have mismatched shapes, or if `weights` is not `None` and its shape doesn't match `predictions`, or if `includes` contains invalid keys. """ all_includes = ('tp', 'fn', 'tn', 'fp') if includes is None: includes = all_includes else: for include in includes: if include not in all_includes: raise ValueError('Invalid key: %s.' % include) with ops.control_dependencies([ check_ops.assert_greater_equal( predictions, math_ops.cast(0.0, dtype=predictions.dtype), message='predictions must be in [0, 1]'), check_ops.assert_less_equal( predictions, math_ops.cast(1.0, dtype=predictions.dtype), message='predictions must be in [0, 1]') ]): predictions, labels, weights = _remove_squeezable_dimensions( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - easy_rec/python/core/easyrec_metrics/distribute_metrics_impl_tf.py [464:533]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - return mean(is_correct, weights, metrics_collections, updates_collections, name or 'accuracy') def _confusion_matrix_at_thresholds(labels, predictions, thresholds, weights=None, includes=None): """Computes true_positives, false_negatives, true_negatives, false_positives. This function creates up to four local variables, `true_positives`, `true_negatives`, `false_positives` and `false_negatives`. `true_positive[i]` is defined as the total weight of values in `predictions` above `thresholds[i]` whose corresponding entry in `labels` is `True`. `false_negatives[i]` is defined as the total weight of values in `predictions` at most `thresholds[i]` whose corresponding entry in `labels` is `True`. `true_negatives[i]` is defined as the total weight of values in `predictions` at most `thresholds[i]` whose corresponding entry in `labels` is `False`. `false_positives[i]` is defined as the total weight of values in `predictions` above `thresholds[i]` whose corresponding entry in `labels` is `False`. For estimation of these metrics over a stream of data, for each metric the function respectively creates an `update_op` operation that updates the variable and returns its value. If `weights` is `None`, weights default to 1. Use weights of 0 to mask values. Args: labels: A `Tensor` whose shape matches `predictions`. Will be cast to `bool`. predictions: A floating point `Tensor` of arbitrary shape and whose values are in the range `[0, 1]`. thresholds: A python list or tuple of float thresholds in `[0, 1]`. 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). includes: Tuple of keys to return, from 'tp', 'fn', 'tn', fp'. If `None`, default to all four. Returns: values: Dict of variables of shape `[len(thresholds)]`. Keys are from `includes`. update_ops: Dict of operations that increments the `values`. Keys are from `includes`. Raises: ValueError: If `predictions` and `labels` have mismatched shapes, or if `weights` is not `None` and its shape doesn't match `predictions`, or if `includes` contains invalid keys. """ all_includes = ('tp', 'fn', 'tn', 'fp') if includes is None: includes = all_includes else: for include in includes: if include not in all_includes: raise ValueError('Invalid key: %s.' % include) with ops.control_dependencies([ check_ops.assert_greater_equal( predictions, math_ops.cast(0.0, dtype=predictions.dtype), message='predictions must be in [0, 1]'), check_ops.assert_less_equal( predictions, math_ops.cast(1.0, dtype=predictions.dtype), message='predictions must be in [0, 1]') ]): predictions, labels, weights = _remove_squeezable_dimensions( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -