51 lines
1.7 KiB
Markdown
51 lines
1.7 KiB
Markdown
# Training
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To actually train a model we need three things:
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* A *model loss function*, that evaluates how well a model is doing given some input data.
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* A collection of data points that will be provided to the loss function.
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* An [optimiser](optimisers.md) that will update the model parameters appropriately.
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With these we can call `Flux.train!`:
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```julia
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Flux.train!(modelLoss, data, opt)
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```
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There are plenty of examples in the [model zoo](https://github.com/FluxML/model-zoo).
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## Loss Functions
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The `loss` that we defined in [basics](../models/basics.md) is completely valid for training. We can also define a loss in terms of some model:
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```julia
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m = Chain(
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Dense(784, 32, σ),
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Dense(32, 10), softmax)
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# Model loss function
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loss(x, y) = Flux.mse(m(x), y)
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```
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The loss will almost always be defined in terms of some *cost function* that measures the distance of the prediction `m(x)` from the target `y`. Flux has several of these built in, like `mse` for mean squared error or `logloss` for cross entropy loss, but you can calculate it however you want.
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## Callbacks
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`train!` takes an additional argument, `cb`, that's used for callbacks so that you can observe the training process. For example:
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```julia
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train!(loss, data, opt, cb = () -> println("training"))
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```
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Callbacks are called for every batch of training data. You can slow this down using `Flux.throttle(f, timeout)` which prevents `f` from being called more than once every `timeout` seconds.
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A more typical callback might look like this:
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```julia
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test_x, test_y = # ... create single batch of test data ...
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evalcb() = @show(loss(test_x, test_y))
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Flux.train!(loss, data, opt,
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cb = throttle(evalcb, 5))
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```
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