Flux.jl/previews/PR1150/models/losses/index.html

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Huber loss = |
             |  δ * (|ŷ - y| - 0.5 * δ), otherwise</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/bf9fe18c47e89df1f0f09df06be3b7f2c7925a3e/src/layers/losses.jl#L33-L42">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.crossentropy" href="#Flux.crossentropy"><code>Flux.crossentropy</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">crossentropy(ŷ, y; weight=nothing, dims=1, ϵ=eps(eltype(ŷ)), agg=mean)</code></pre><p>Return the cross entropy between the given probability distributions; calculated as</p><pre><code class="language-none">agg(.-sum(weight .* y .* log.(ŷ .+ ϵ); dims=dims))agg=mean,</code></pre><p><code>weight</code> can be <code>nothing</code>, a number or an array. <code>weight=nothing</code> acts like <code>weight=1</code> but is faster.</p><p>See also: <a href="#Flux.logitcrossentropy"><code>Flux.logitcrossentropy</code></a>, <a href="#Flux.binarycrossentropy"><code>Flux.binarycrossentropy</code></a>, <a href="#Flux.logitbinarycrossentropy"><code>Flux.logitbinarycrossentropy</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/bf9fe18c47e89df1f0f09df06be3b7f2c7925a3e/src/layers/losses.jl#L54-L66">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.logitcrossentropy" href="#Flux.logitcrossentropy"><code>Flux.logitcrossentropy</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">logitcrossentropy(ŷ, y; weight=nothing, agg=mean, dims=1)</code></pre><p>Return the crossentropy computed after a <a href="../nnlib/#NNlib.logsoftmax"><code>Flux.logsoftmax</code></a> operation; calculated as</p><pre><code class="language-none">agg(.-sum(weight .* y .* logsoftmax(ŷ; dims=dims); dims=dims))</code></pre><p><code>logitcrossentropy(ŷ, y)</code> is mathematically equivalent to <a href="#Flux.crossentropy"><code>Flux.crossentropy(softmax(log.(ŷ)), y)</code></a> but it is more numerically stable.</p><p>See also: <a href="#Flux.crossentropy"><code>Flux.crossentropy</code></a>, <a href="#Flux.binarycrossentropy"><code>Flux.binarycrossentropy</code></a>, <a href="#Flux.logitbinarycrossentropy"><code>Flux.logitbinarycrossentropy</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/bf9fe18c47e89df1f0f09df06be3b7f2c7925a3e/src/layers/losses.jl#L71-L83">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.binarycrossentropy" href="#Flux.binarycrossentropy"><code>Flux.binarycrossentropy</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">binarycrossentropy(ŷ, y; ϵ=eps(ŷ))</code></pre><p>Return <span>$-y*\log(ŷ + ϵ) - (1-y)*\log(1-ŷ + ϵ)$</span>. The <code>ϵ</code> term provides numerical stability.</p><p>Typically, the prediction <code>ŷ</code> is given by the output of a <a href="../nnlib/#NNlib.sigmoid"><code>sigmoid</code></a> activation.</p><p>See also: <a href="#Flux.crossentropy"><code>Flux.crossentropy</code></a>, <a href="#Flux.logitcrossentropy"><code>Flux.logitcrossentropy</code></a>, <a href="#Flux.logitbinarycrossentropy"><code>Flux.logitbinarycrossentropy</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/bf9fe18c47e89df1f0f09df06be3b7f2c7925a3e/src/layers/losses.jl#L88-L96">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.logitbinarycrossentropy" href="#Flux.logitbinarycrossentropy"><code>Flux.logitbinarycrossentropy</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">logitbinarycrossentropy(ŷ, y; agg=mean)</code></pre><p><code>logitbinarycrossentropy(ŷ, y)</code> is mathematically equivale
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			`Huber loss = \|`
			\| δ * (\|ŷ - y\| - 0.5 * δ), otherwise</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/bf9fe18c47e89df1f0f09df06be3b7f2c7925a3e/src/layers/losses.jl#L33-L42">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.crossentropy" href="#Flux.crossentropy"><code>Flux.crossentropy</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">crossentropy(ŷ, y; weight=nothing, dims=1, ϵ=eps(eltype(ŷ)), agg=mean)</code></pre><p>Return the cross entropy between the given probability distributions; calculated as</p><pre><code class="language-none">agg(.-sum(weight .* y .* log.(ŷ .+ ϵ); dims=dims))agg=mean,</code></pre><p><code>weight</code> can be <code>nothing</code>, a number or an array. <code>weight=nothing</code> acts like <code>weight=1</code> but is faster.</p><p>See also: <a href="#Flux.logitcrossentropy"><code>Flux.logitcrossentropy</code></a>, <a href="#Flux.binarycrossentropy"><code>Flux.binarycrossentropy</code></a>, <a href="#Flux.logitbinarycrossentropy"><code>Flux.logitbinarycrossentropy</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/bf9fe18c47e89df1f0f09df06be3b7f2c7925a3e/src/layers/losses.jl#L54-L66">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.logitcrossentropy" href="#Flux.logitcrossentropy"><code>Flux.logitcrossentropy</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">logitcrossentropy(ŷ, y; weight=nothing, agg=mean, dims=1)</code></pre><p>Return the crossentropy computed after a <a href="../nnlib/#NNlib.logsoftmax"><code>Flux.logsoftmax</code></a> operation; calculated as</p><pre><code class="language-none">agg(.-sum(weight .* y .* logsoftmax(ŷ; dims=dims); dims=dims))</code></pre><p><code>logitcrossentropy(ŷ, y)</code> is mathematically equivalent to <a href="#Flux.crossentropy"><code>Flux.crossentropy(softmax(log.(ŷ)), y)</code></a> but it is more numerically stable.</p><p>See also: <a href="#Flux.crossentropy"><code>Flux.crossentropy</code></a>, <a href="#Flux.binarycrossentropy"><code>Flux.binarycrossentropy</code></a>, <a href="#Flux.logitbinarycrossentropy"><code>Flux.logitbinarycrossentropy</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/bf9fe18c47e89df1f0f09df06be3b7f2c7925a3e/src/layers/losses.jl#L71-L83">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.binarycrossentropy" href="#Flux.binarycrossentropy"><code>Flux.binarycrossentropy</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">binarycrossentropy(ŷ, y; ϵ=eps(ŷ))</code></pre><p>Return <span>$-y\log(ŷ + ϵ) - (1-y)\log(1-ŷ + ϵ)$</span>. The <code>ϵ</code> term provides numerical stability.</p><p>Typically, the prediction <code>ŷ</code> is given by the output of a <a href="../nnlib/#NNlib.sigmoid"><code>sigmoid</code></a> activation.</p><p>See also: <a href="#Flux.crossentropy"><code>Flux.crossentropy</code></a>, <a href="#Flux.logitcrossentropy"><code>Flux.logitcrossentropy</code></a>, <a href="#Flux.logitbinarycrossentropy"><code>Flux.logitbinarycrossentropy</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/bf9fe18c47e89df1f0f09df06be3b7f2c7925a3e/src/layers/losses.jl#L88-L96">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.logitbinarycrossentropy" href="#Flux.logitbinarycrossentropy"><code>Flux.logitbinarycrossentropy</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">logitbinarycrossentropy(ŷ, y; agg=mean)</code></pre><p><code>logitbinarycrossentropy(ŷ, y)</code> is mathematically equivale