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# tf.keras.metrics.CosineSimilarity

## Class `CosineSimilarity`

Computes the cosine similarity between the labels and predictions.

### Aliases:

• Class `tf.compat.v1.keras.metrics.CosineSimilarity`
• Class `tf.compat.v2.keras.metrics.CosineSimilarity`
• Class `tf.compat.v2.metrics.CosineSimilarity`

cosine similarity = (a . b) / ||a|| ||b|| Cosine Similarity

For example, if `y_true` is [0, 1, 1], and `y_pred` is [1, 0, 1], the cosine similarity is 0.5.

This metric keeps the average cosine similarity between `predictions` and `labels` over a stream of data.

#### Usage:

``````m = tf.keras.metrics.CosineSimilarity(axis=1)
m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 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]]
# result = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
= ((0. + 0.) +  (0.5 + 0.5)) / 2

print('Final result: ', m.result().numpy())  # Final result: 0.5
``````

Usage with tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile(
'sgd',
loss='mse',
metrics=[tf.keras.metrics.CosineSimilarity(axis=1)])
``````

## `__init__`

View source

``````__init__(
name='cosine_similarity',
dtype=None,
axis=-1
)
``````

Creates a `CosineSimilarity` instance.

#### 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.

## Methods

### `reset_states`

View source

``````reset_states()
``````

Resets all of the metric state variables.

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

### `result`

View source

``````result()
``````

### `update_state`

View source

``````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`: The ground truth values.
• `y_pred`: The predicted values.
• `sample_weight`: Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.

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