# tf.losses.cosine_distance tensorflow 损失函数 cosine 距离

## tensorflow 损失函数 cosine 距离

tf.losses.cosine_distance

cosine_distance(
labels,
predictions,
dim=None,
weights=1.0,
scope=None,
loss_collection=tf.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)


labels:Tensors 形状和 predictions 匹配
predictions：一个随意的矩阵
dim:cosine 距离计算的维度

Defined in tensorflow/python/ops/losses/losses_impl.py.

Adds a cosine-distance loss to the training procedure.

Note that the function assumes that predictions and labels are already unit-normalized.

Args:
labels: Tensor whose shape matches ‘predictions’
predictions: An arbitrary matrix.
dim: The dimension along which the cosine distance is computed.
weights: Optional Tensor whose rank is either 0, or the same rank as labels, and must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding losses dimension).
scope: The scope for the operations performed in computing the loss.
loss_collection: collection to which this loss will be added.
reduction: Type of reduction to apply to loss.
Returns:

Weighted loss float Tensor. If reduction is NONE, this has the same shape as labels; otherwise, it is scalar.

Raises:

ValueError: If predictions shape doesn’t match labels shape, or weights is None.