LostTech.TensorFlow : API Documentation

Type MeanIoU

Namespace tensorflow.keras.metrics

Parent Metric

Interfaces IMeanIoU

Computes the mean Intersection-Over-Union metric.

Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows: IOU = true_positive / (true_positive + false_positive + false_negative). The predictions are accumulated in a confusion matrix, weighted by `sample_weight` and the metric is then calculated from it.

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.MeanIoU(num_classes=2)
            m.update_state([0, 0, 1, 1], [0, 1, 0, 1]) 

# cm = [[1, 1], [1, 1]] # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1] # iou = true_positives / (sum_row + sum_col - true_positives)) # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33 print('Final result: ', m.result().numpy()) # Final result: 0.33


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;

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_classes 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;

bool stateful get; set;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

object total_cm 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;