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Computes the cosine similarity between labels and predictions.

Inherits From: `Loss`

tf.keras.losses.CosineSimilarity( axis=-1, reduction=losses_utils.ReductionV2.AUTO, name='cosine_similarity' )

Note that it is a number between -1 and 1. When it is a negative number between -1 and 0, 0 indicates orthogonality and values closer to -1 indicate greater similarity. The values closer to 1 indicate greater dissimilarity. This makes it usable as a loss function in a setting where you try to maximize the proximity between predictions and targets. If either `y_true`

or `y_pred`

is a zero vector, cosine similarity will be 0 regardless of the proximity between predictions and targets.

`loss = -sum(l2_norm(y_true) * l2_norm(y_pred))`

y_true = [[0., 1.], [1., 1.]] y_pred = [[1., 0.], [1., 1.]] # Using 'auto'/'sum_over_batch_size' reduction type. cosine_loss = tf.keras.losses.CosineSimilarity(axis=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]] # loss = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1)) # = -((0. + 0.) + (0.5 + 0.5)) / 2 cosine_loss(y_true, y_pred).numpy() -0.5

# Calling with 'sample_weight'. cosine_loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy() -0.0999

# Using 'sum' reduction type. cosine_loss = tf.keras.losses.CosineSimilarity(axis=1, reduction=tf.keras.losses.Reduction.SUM) cosine_loss(y_true, y_pred).numpy() -0.999

# Using 'none' reduction type. cosine_loss = tf.keras.losses.CosineSimilarity(axis=1, reduction=tf.keras.losses.Reduction.NONE) cosine_loss(y_true, y_pred).numpy() array([-0., -0.999], dtype=float32)

Usage with the `compile()`

API:

model.compile(optimizer='sgd', loss=tf.keras.losses.CosineSimilarity(axis=1))

Args | |
---|---|

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

`reduction` | (Optional) Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO` . `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE` . When used with `tf.distribute.Strategy` , outside of built-in training loops such as `tf.keras` `compile` and `fit` , using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training tutorial for more details. |

`name` | Optional name for the op. |

`from_config`

@classmethod from_config( config )

Instantiates a `Loss`

from its config (output of `get_config()`

).

Args | |
---|---|

`config` | Output of `get_config()` . |

Returns | |
---|---|

A `Loss` instance. |

`get_config`

get_config()

Returns the config dictionary for a `Loss`

instance.

`__call__`

__call__( y_true, y_pred, sample_weight=None )

Invokes the `Loss`

instance.

Args | |
---|---|

`y_true` | Ground truth values. shape = `[batch_size, d0, .. dN]` , except sparse loss functions such as sparse categorical crossentropy where shape = `[batch_size, d0, .. dN-1]` |

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

`sample_weight` | Optional `sample_weight` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]` , then the total loss 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 loss element of `y_pred` is scaled by the corresponding value of `sample_weight` . (Note on`dN-1` : all loss functions reduce by 1 dimension, usually axis=-1.) |

Returns | |
---|---|

Weighted loss float `Tensor` . If `reduction` is `NONE` , this has shape `[batch_size, d0, .. dN-1]` ; otherwise, it is scalar. (Note `dN-1` because all loss functions reduce by 1 dimension, usually axis=-1.) |

Raises | |
---|---|

`ValueError` | If the shape of `sample_weight` is invalid. |

© 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/r2.4/api_docs/python/tf/keras/losses/CosineSimilarity