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# tf.keras.metrics.CosineSimilarity

Computes the cosine similarity between the labels and predictions.

`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.

`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.

#### Standalone usage:

````# 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)])
``````

## Methods

### `reset_states`

View source

Resets all of the metric state variables.

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

### `result`

View source

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`

View source

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.

[{ "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" }]