LostTech.TensorFlow : API Documentation

Type FalsePositives

Namespace tensorflow.keras.metrics

Parent _ConfusionMatrixConditionCount

Interfaces IFalsePositives

Calculates the number of false positives.

For example, if `y_true` is [0, 1, 0, 0] and `y_pred` is [0, 0, 1, 1] then the false positives value is 2. If the weights were specified as [0, 0, 1, 0] then the false positives value would be 1.

If `sample_weight` is given, calculates the sum of the weights of false positives. This metric creates one local variable, `accumulator` that is used to keep track of the number of false positives.

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

Usage: Usage with tf.keras API:
Show Example 
m = tf.keras.metrics.FalsePositives()
            m.update_state([0, 1, 0, 0], [0, 0, 1, 1])
            print('Final result: ', m.result().numpy())  # Final result: 2

Properties

Public properties

object accumulator get; set;

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;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<double> init_thresholds get; set;

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;

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;

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;

IList<object> updates get;

object updates_dyn get;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;