CategoricalCrossentropy - LostTech.TensorFlow Documentation

Type CategoricalCrossentropy

Namespace tensorflow.keras.metrics

Computes the crossentropy metric between the labels and predictions.

This is the crossentropy metric class to be used when there are multiple label classes (2 or more). Here we assume that labels are given as a `one_hot` representation. eg., When labels values are [2, 0, 1], `y_true` = [[0, 0, 1], [1, 0, 0], [0, 1, 0]].

Usage: Usage with tf.keras API:

Show Example

m = tf.keras.metrics.CategoricalCrossentropy()
            m.update_state([[0, 1, 0], [0, 0, 1]],
                           [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])  # EPSILON = 1e-7, y = y_true, y` = y_pred
# y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
# y` = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]  # xent = -sum(y * log(y'), axis = -1)
#      = -((log 0.95), (log 0.1))
#      = [0.051, 2.302]
# Reduced xent = (0.051 + 2.302) / 2 
 print('Final result: ', m.result().numpy())  # Final result: 1.176

Methods

NewDyn

Properties

Public static methods

CategoricalCrossentropy NewDyn(ImplicitContainer<T> name, object dtype, ImplicitContainer<T> from_logits, ImplicitContainer<T> label_smoothing)

Creates a `MeanMetricWrapper` instance.

Parameters

ImplicitContainer<T> name: (Optional) string name of the metric instance.
object dtype: (Optional) data type of the metric result.
ImplicitContainer<T> from_logits
ImplicitContainer<T> label_smoothing

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

bool built get; set;

object count 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<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;

string reduction get; set;

bool stateful get; set;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

object total 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;