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Computes the cosine similarity between the labels and predictions.

tf.keras.metrics.CosineSimilarity( name='cosine_similarity', dtype=None, axis=-1 )

`cosine similarity = (a . b) / ||a|| ||b||`

See: Cosine Similarity.

This metric keeps the average cosine similarity between `predictions`

and `labels`

over a stream of data.

Args | |
---|---|

`name` | (Optional) string name of the metric instance. |

`dtype` | (Optional) data type of the metric result. |

`axis` | (Optional) Defaults to -1. The dimension along which the cosine similarity is computed. |

# 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 m = tf.keras.metrics.CosineSimilarity(axis=1) m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]]) m.result().numpy() 0.49999997

m.reset_states() m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]], sample_weight=[0.3, 0.7]) m.result().numpy() 0.6999999

Usage with `compile()`

API:

model.compile( optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.CosineSimilarity(axis=1)])

`reset_states`

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

`result`

result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

`update_state`

update_state( y_true, y_pred, sample_weight=None )

Accumulates metric statistics.

`y_true`

and `y_pred`

should have the same shape.

Args | |
---|---|

`y_true` | Ground truth values. shape = `[batch_size, d0, .. dN]` . |

`y_pred` | The predicted values. shape = `[batch_size, d0, .. dN]` . |

`sample_weight` | Optional `sample_weight` acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]` , then the metric for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each metric element of `y_pred` is scaled by the corresponding value of `sample_weight` . (Note on `dN-1` : all metric functions reduce by 1 dimension, usually the last axis (-1)). |

Returns | |
---|---|

Update op. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/metrics/CosineSimilarity