Flux.jl/docs/src/models/losses.md

Loss Functions

Flux provides a large number of common loss functions used for training machine learning models.

Loss functions for supervised learning typically expect as inputs a target y, and a prediction ŷ. In Flux's convention, the order of the arguments is the following

loss(ŷ, y)

Most loss functions in Flux have an optional argument agg, denoting the type of aggregation performed over the batch:

loss(ŷ, y)                         # defaults to `mean`
loss(ŷ, y, agg=sum)                # use `sum` for reduction
loss(ŷ, y, agg=x->sum(x, dims=2))  # partial reduction
loss(ŷ, y, agg=x->mean(w .* x))    # weighted mean
loss(ŷ, y, agg=identity)           # no aggregation. 

Losses Reference

Flux.mae
Flux.mse
Flux.msle
Flux.huber_loss
Flux.crossentropy
Flux.logitcrossentropy
Flux.binarycrossentropy
Flux.logitbinarycrossentropy
Flux.kldivergence
Flux.poisson_loss
Flux.hinge
Flux.squared_hinge
Flux.dice_coeff_loss
Flux.tversky_loss