TensorFlow 2 version | View source on GitHub |
Computes the cosine similarity between the labels and predictions.
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
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
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
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. |