LostTech.TensorFlow : API Documentation

Type Accuracy

Namespace tensorflow.keras.metrics

Parent MeanMetricWrapper

Interfaces IAccuracy

Calculates how often predictions matches labels.

For example, if `y_true` is [1, 2, 3, 4] and `y_pred` is [0, 2, 3, 4] then the accuracy is 3/4 or.75. If the weights were specified as [1, 1, 0, 0] then the accuracy would be 1/2 or.5.

This metric creates two local variables, `total` and `count` that are used to compute the frequency with which `y_pred` matches `y_true`. This frequency is ultimately returned as `binary accuracy`: 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:

m = tf.keras.metrics.Accuracy()
            m.update_state([1, 2, 3, 4], [0, 2, 3, 4])
            print('Final result: ', m.result().numpy())  # Final result: 0.75

Methods

Properties

Public instance methods

object update_state(IEnumerable<IGraphNodeBase> y_true, IEnumerable<object> y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

IEnumerable<IGraphNodeBase> y_true: The ground truth values.
IEnumerable<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(IEnumerable<IGraphNodeBase> y_true, int y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

IEnumerable<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(IEnumerable<IGraphNodeBase> y_true, IGraphNodeBase y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

IEnumerable<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(IEnumerable<IGraphNodeBase> y_true, object y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

IEnumerable<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(ValueTuple<PythonClassContainer, PythonClassContainer> y_true, IEnumerable<object> y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

ValueTuple<PythonClassContainer, PythonClassContainer> y_true: The ground truth values.
IEnumerable<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(ValueTuple<PythonClassContainer, PythonClassContainer> y_true, int y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

ValueTuple<PythonClassContainer, PythonClassContainer> 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(ValueTuple<PythonClassContainer, PythonClassContainer> y_true, IGraphNodeBase y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

ValueTuple<PythonClassContainer, PythonClassContainer> 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(ValueTuple<PythonClassContainer, PythonClassContainer> y_true, object y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

ValueTuple<PythonClassContainer, PythonClassContainer> 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(int y_true, IEnumerable<object> y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

int y_true: The ground truth values.
IEnumerable<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(int y_true, int y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

int 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(int y_true, IGraphNodeBase y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

int 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(int y_true, object y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

int 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(IGraphNodeBase y_true, IEnumerable<object> y_pred, object sample_weight)

Accumulates root mean squared error statistics.

Parameters

IGraphNodeBase y_true: The ground truth values.
IEnumerable<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;

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;