# tf.keras.losses.CosineSimilarity

Computes the cosine similarity between labels and predictions.

Inherits From: `Loss`

Note that it is a negative quantity between -1 and 0, where 0 indicates orthogonality and values closer to -1 indicate greater similarity. 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))`

#### Standalone usage:

````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))
``````

`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.

## Methods

### `from_config`

View source

Instantiates a `Loss` from its config (output of `get_config()`).

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

Returns
A `Loss` instance.

### `get_config`

View source

Returns the config dictionary for a `Loss` instance.

### `__call__`

View source

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.