LostTech.TensorFlow : API Documentation

Type SpecificityAtSensitivity

Namespace tensorflow.keras.metrics

Parent SensitivitySpecificityBase

Interfaces ISpecificityAtSensitivity

Computes the specificity at a given sensitivity.

`Sensitivity` measures the proportion of actual positives that are correctly identified as such (tp / (tp + fn)). `Specificity` measures the proportion of actual negatives that are correctly identified as such (tn / (tn + fp)).

This metric creates four local variables, `true_positives`, `true_negatives`, `false_positives` and `false_negatives` that are used to compute the specificity at the given sensitivity. The threshold for the given sensitivity value is computed and used to evaluate the corresponding specificity.

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

For additional information about specificity and sensitivity, see the following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity

Usage: Usage with tf.keras API:
Show Example 
m = tf.keras.metrics.SpecificityAtSensitivity(0.8, num_thresholds=1)
            m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
            print('Final result: ', m.result().numpy())  # Final result: 1.0

Properties

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

bool built get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

object false_negatives get; set;

object false_positives get; set;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

object input_dyn get;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

object input_spec get; set;

object input_spec_dyn get; set;

IList<object> losses get;

object losses_dyn get;

IList<object> metrics get;

object metrics_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

IList<object> non_trainable_variables get;

object non_trainable_variables_dyn get;

IList<object> non_trainable_weights get;

object non_trainable_weights_dyn get;

int num_thresholds get; set;

IList<object> outbound_nodes get;

object outbound_nodes_dyn get;

IList<object> output get;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_shape get;

object output_shape_dyn get;

object PythonObject get;

double sensitivity get; set;

bool stateful get; set;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

object thresholds get; set;

bool trainable get; set;

object trainable_dyn get; set;

object trainable_variables get;

object trainable_variables_dyn get;

IList<object> trainable_weights get;

object trainable_weights_dyn get;

object true_negatives get; set;

object true_positives get; set;

IList<object> updates get;

object updates_dyn get;

double value get; set;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;