Flux.jl/release-0.3/training/optimisers.html
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<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Optimisers · Flux</title><script>(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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b = param(rand(2))
predict(x) = W*x .+ b
loss(x, y) = sum((predict(x) .- y).^2)
x, y = rand(5), rand(2) # Dummy data
l = loss(x, y) # ~ 3
back!(l)</code></pre><p>We want to update each parameter, using the gradient, in order to improve (reduce) the loss. Here&#39;s one way to do that:</p><pre><code class="language-julia">using Flux.Tracker: data, grad
function update()
η = 0.1 # Learning Rate
for p in (W, b)
x, Δ = data(p), grad(p)
x .-= η .* Δ # Apply the update
Δ .= 0 # Clear the gradient
end
end</code></pre><p>If we call <code>update</code>, the parameters <code>W</code> and <code>b</code> will change and our loss should go down.</p><p>There are two pieces here: one is that we need a list of trainable parameters for the model (<code>[W, b]</code> in this case), and the other is the update step. In this case the update is simply gradient descent (<code>x .-= η .* Δ</code>), but we might choose to do something more advanced, like adding momentum.</p><p>In this case, getting the variables is trivial, but you can imagine it&#39;d be more of a pain with some complex stack of layers.</p><pre><code class="language-julia">m = Chain(
Dense(10, 5, σ),
Dense(5, 2), softmax)</code></pre><p>Instead of having to write <code>[m[1].W, m[1].b, ...]</code>, Flux provides a params function <code>params(m)</code> that returns a list of all parameters in the model for you.</p><p>For the update step, there&#39;s nothing whatsoever wrong with writing the loop above it&#39;ll work just fine but Flux provides various <em>optimisers</em> that make it more convenient.</p><pre><code class="language-julia">opt = SGD([W, b], 0.1) # Gradient descent with learning rate 0.1
opt()</code></pre><p>An optimiser takes a parameter list and returns a function that does the same thing as <code>update</code> above. We can pass either <code>opt</code> or <code>update</code> to our <a href="training.html">training loop</a>, which will then run the optimiser after every mini-batch of data.</p><footer><hr/><a class="previous" href="../models/layers.html"><span class="direction">Previous</span><span class="title">Layer Reference</span></a><a class="next" href="training.html"><span class="direction">Next</span><span class="title">Training</span></a></footer></article></body></html>