tf.keras.losses.CosineSimilarity     View source on GitHub    Computes the cosine similarity between labels and predictions. Inherits From: Loss  View aliases  Main aliases 
tf.losses.CosineSimilarity Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.losses.CosineSimilarity  
tf.keras.losses.CosineSimilarity(
    axis=-1, reduction=losses_utils.ReductionV2.AUTO,
    name='cosine_similarity'
)
 Note that it is a number between -1 and 1. When it is a negative number between -1 and 0, 0 indicates orthogonality and values closer to -1 indicate greater similarity. The values closer to 1 indicate greater dissimilarity. This makes it usable as a loss function in a setting where you try to maximize the proximity between predictions and targets. If either y_true or y_pred is a zero vector, cosine similarity will be 0 regardless of the proximity between predictions and targets. loss = -sum(l2_norm(y_true) * l2_norm(y_pred)) Standalone usage: 
y_true = [[0., 1.], [1., 1.]]
y_pred = [[1., 0.], [1., 1.]]
# Using 'auto'/'sum_over_batch_size' reduction type.
cosine_loss = tf.keras.losses.CosineSimilarity(axis=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]]
# loss = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
#       = -((0. + 0.) +  (0.5 + 0.5)) / 2
cosine_loss(y_true, y_pred).numpy()
-0.5
 
# Calling with 'sample_weight'.
cosine_loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
-0.0999
 
# Using 'sum' reduction type.
cosine_loss = tf.keras.losses.CosineSimilarity(axis=1,
    reduction=tf.keras.losses.Reduction.SUM)
cosine_loss(y_true, y_pred).numpy()
-0.999
 
# Using 'none' reduction type.
cosine_loss = tf.keras.losses.CosineSimilarity(axis=1,
    reduction=tf.keras.losses.Reduction.NONE)
cosine_loss(y_true, y_pred).numpy()
array([-0., -0.999], dtype=float32)
 Usage with the compile() API: model.compile(optimizer='sgd', loss=tf.keras.losses.CosineSimilarity(axis=1))

 


 Args
  axis   (Optional) Defaults to -1. The dimension along which the cosine similarity is computed.  
  reduction   (Optional) Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see this custom training tutorial for more details.  
  name   Optional name for the op.    Methods from_config View source 
@classmethod
from_config(
    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 
get_config()
 Returns the config dictionary for a Loss instance. __call__ View source 
__call__(
    y_true, y_pred, sample_weight=None
)
 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 ondN-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.