Cosine embedding loss — nn_cosine_embedding

Creates a criterion that measures the loss given input tensors $x_{1}$ , $x_{2}$ and a Tensor label $y$ with values 1 or -1. This is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning. The loss function for each sample is:

Usage

nn_cosine_embedding_loss(margin = 0, reduction = "mean")

Arguments

margin: (float, optional): Should be a number from $- 1$ to $1$ , $0$ to $0.5$ is suggested. If margin is missing, the default value is $0$ .
reduction: (string, optional): Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed.

Details

$loss (x, y) = \begin{array}{ll} 1 - \cos (x_{1}, x_{2}), & if y = 1 \\ max (0, \cos (x_{1}, x_{2}) - margin), & if y = - 1 \end{array}$