Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CIRA Guide to Custom Loss Functions for Neural Networks in Environmental
  Sciences -- Version 1   Neural networks are increasingly used in environmental science applications.
Furthermore, neural network models are trained by minimizing a loss function,
and it is crucial to choose the loss function very carefully for environmental
science applications, as it determines what exactly is being optimized.
Standard loss functions do not cover all the needs of the environmental
sciences, which makes it important for scientists to be able to develop their
own custom loss functions so that they can implement many of the classic
performance measures already developed in environmental science, including
measures developed for spatial model verification. However, there are very few
resources available that cover the basics of custom loss function development
comprehensively, and to the best of our knowledge none that focus on the needs
of environmental scientists. This document seeks to fill this gap by providing
a guide on how to write custom loss functions targeted toward environmental
science applications. Topics include the basics of writing custom loss
functions, common pitfalls, functions to use in loss functions, examples such
as fractions skill score as loss function, how to incorporate physical
constraints, discrete and soft discretization, and concepts such as focal,
robust, and adaptive loss. While examples are currently provided in this guide
for Python with Keras and the TensorFlow backend, the basic concepts also apply
to other environments, such as Python with PyTorch. Similarly, while the sample
loss functions provided here are from meteorology, these are just examples of
how to create custom loss functions. Other fields in the environmental sciences
have very similar needs for custom loss functions, e.g., for evaluating spatial
forecasts effectively, and the concepts discussed here can be applied there as
well. All code samples are provided in a GitHub repository.

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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol