LostTech.TensorFlow : API Documentation

Type CosineSimilarity

Namespace tensorflow.keras.metrics

Parent MeanMetricWrapper

Interfaces ICosineSimilarity

Computes the cosine similarity between the labels and predictions.

cosine similarity = (a. b) / ||a|| ||b|| [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity)

For example, if `y_true` is [0, 1, 1], and `y_pred` is [1, 0, 1], the cosine similarity is 0.5.

This metric keeps the average cosine similarity between `predictions` and `labels` over a stream of data.

Usage: Usage with tf.keras API:
Show Example
m = tf.keras.metrics.CosineSimilarity(axis=1)
            m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]])
            # l2_norm(y_true) = [[0., 1.], [1./1.414], 1./1.414]]]
            # l2_norm(y_pred) = [[1., 0.], [1./1.414], 1./1.414]]]
            # l2_norm(y_true). l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]]
            # result = mean(sum(l2_norm(y_true). l2_norm(y_pred), axis=1))
                   = ((0. + 0.) +  (0.5 + 0.5)) / 2 

print('Final result: ', m.result().numpy()) # Final result: 0.5


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;