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# tfa.losses.ContrastiveLoss

Computes the contrastive loss between `y_true` and `y_pred`.

This loss encourages the embedding to be close to each other for the samples of the same label and the embedding to be far apart at least by the margin constant for the samples of different labels.

We expect labels `y_true` to be provided as 1-D integer `Tensor` with shape `[batch_size]` of binary integer labels. And `y_pred` must be 1-D float `Tensor` with shape `[batch_size]` of distances between two embedding matrices.

The euclidean distances `y_pred` between two embedding matrices `a` and `b` with shape `[batch_size, hidden_size]` can be computed as follows:

````a = tf.constant([[1, 2],`
`                [3, 4],[5, 6]], dtype=tf.float16)`
`b = tf.constant([[5, 9],`
`                [3, 6],[1, 8]], dtype=tf.float16)`
`y_pred = tf.linalg.norm(a - b, axis=1)`
`y_pred`
`<tf.Tensor: shape=(3,), dtype=float16, numpy=array([8.06 , 2.   , 4.473],`
`dtype=float16)>`
```

<... Note: constants a & b have been used purely for example purposes and have no significant value ...>

`margin` `Float`, margin term in the loss definition. Default value is 1.0.
`reduction` (Optional) Type of `tf.keras.losses.Reduction` to apply. Default value is `SUM_OVER_BATCH_SIZE`.
`name` (Optional) name for the loss.

## Methods

### `from_config`

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__`

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.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]