![]() We can freeze a specific parameter of a specific layer which already entered a Params object ps, by simply deleting it from ps: ps = Flux.params(m)ĭelete!(ps, m.bias) Custom multiple input or output layer Sometimes, a more fine-tuned control is needed. It would look something like this: Flux.params(m, m) A simple demonstration would be if we wanted to omit optimising the second Dense layer in the previous example. The Zygote.Params object ps now holds a reference to only the parameters of the layers passed to it.ĭuring training, the gradients will only be computed for (and applied to) the last Dense layer, therefore only that would have its parameters changed.įlux.params also takes multiple inputs to make it easy to collect parameters from heterogenous models with a single call. We can obtain this using the slicing features Chain provides: m = Chain( When upgrading for Flux 0.15, it should be replaced by freeze! and thaw!.Ĭonsider a simple multi-layer perceptron model where we want to avoid optimising the first two Dense layers. The mechanism described here is for Flux's old "implicit" training style. transfer learning), we can simply not pass in those layers into our call to params. When it is desired to not include all the model parameters (for e.g. This requires the struct to have a corresponding constructor that accepts only W as an argument, and the ignored fields will not be seen by functions like gpu (which is usually undesired). ![]() It is also possible to further restrict what fields are seen by writing Affine (W,). (For example, activation functions, or Boolean flags.) These are always ignored by params and by training: julia> Flux.params(Affine(true, )) Note that there is no need to overload trainable to hide fields which do not contain trainable parameters. But all fields wil be seen by gpu and similar functions, for example: julia> a |> f16Īffine(Float16, Float16) Only the fields returned by trainable will be collected as trainable parameters of the layer when calling Flux.params, and only these fields will be seen by tup and Flux.update! for training. Julia> ainable(a::Affine) = ( a.W) # returns a NamedTuple using the field's name Julia> a = Affine(Float32, Float32)Īffine(Float32, Float32) With Flux, the way to mark some fields of our layer as trainable is through overloading the trainable function: julia> Affine Taking reference from our example Affine layer from the basics.īy default all the fields in the Affine type are collected as its parameters, however, in some cases it may be desired to hold other metadata in our "layers" that may not be needed for training, and are hence supposed to be ignored while the parameters are collected. Customising Parameter Collection for a Model You can then use the model like: chain = Chain(Dense(10, 10))įor an intro to Flux and automatic differentiation, see this tutorial. # Zygote does not allow some operations, like mutating arrays. # Arbitrary code can go here, but note that everything will be differentiated. It simply adds the input to the result from the neural network. Here is a basic example of a custom model. Here we will try and describe usage of some more advanced features that Flux provides to give more control over model building. Edit on GitHub Defining Customised Layers
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