LostTech.TensorFlow : API Documentation

Type MeanRelativeError

Namespace tensorflow.keras.metrics

Parent Mean

Interfaces IMeanRelativeError

Computes the mean relative error by normalizing with the given values.

This metric creates two local variables, `total` and `count` that are used to compute the mean relative absolute error. This average is weighted by `sample_weight`, and it is ultimately returned as `mean_relative_error`: an idempotent operation that simply divides `total` by `count`.

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.MeanRelativeError(normalizer=[1, 3, 2, 3])
            m.update_state([1, 3, 2, 3], [2, 4, 6, 8]) 
 # metric = mean(|y_pred - y_true| / normalizer)
#        = mean([1, 1, 4, 5] / [1, 3, 2, 3]) = mean([1, 1/3, 2, 5/3])
#        = 5/4 = 1.25
print('Final result: ', m.result().numpy())  # Final result: 1.25

Methods

Properties

Public instance methods

object update_state(IGraphNodeBase y_true, int y_pred, object sample_weight)

Accumulates root mean squared error statistics.
Parameters
IGraphNodeBase y_true
The ground truth values.
int y_pred
The predicted values.
object sample_weight
Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.
Returns
object
Update op.

object update_state(IGraphNodeBase y_true, IGraphNodeBase y_pred, object sample_weight)

Accumulates root mean squared error statistics.
Parameters
IGraphNodeBase y_true
The ground truth values.
IGraphNodeBase y_pred
The predicted values.
object sample_weight
Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.
Returns
object
Update op.

object update_state(IGraphNodeBase y_true, object y_pred, object sample_weight)

Accumulates root mean squared error statistics.
Parameters
IGraphNodeBase y_true
The ground truth values.
object y_pred
The predicted values.
object sample_weight
Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.
Returns
object
Update op.

object update_state_dyn(object y_true, object y_pred, object sample_weight)

Accumulates root mean squared error statistics.
Parameters
object y_true
The ground truth values.
object y_pred
The predicted values.
object sample_weight
Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.
Returns
object
Update op.

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;

object normalizer 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;

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;