tf.keras.metrics.CosineSimilarity

View source on GitHub

Computes the cosine similarity between the labels and predictions.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.CosineSimilarity, `tf.compat.v2.keras.metrics.CosineSimilarity`, `tf.compat.v2.metrics.CosineSimilarity`

tf.keras.metrics.CosineSimilarity(
    name='cosine_similarity', dtype=None, axis=-1
)

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)])

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()

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

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.

Returns
Update op.