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Replace fit by build #42

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merged 1 commit into from Jun 9, 2020
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Replace fit by build #42

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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -192,7 +192,7 @@ mutable struct MyNetwork <: MLJFlux.Builder
n2 :: Int
end

function MLJFlux.fit(nn::MyNetwork, n_in, n_out)
function MLJFlux.build(nn::MyNetwork, n_in, n_out)
return Chain(Dense(n_in, nn.n1), Dense(nn.n1, nn.n2), Dense(nn.n2, n_out))
end
```
Expand All @@ -205,8 +205,8 @@ More generally, defining a new builder means defining a new struct
and defining a new `MLJFlux.fit` method with one of these signatures:

```julia
MLJFlux.fit(builder::MyNetwork, n_in, n_out)
MLJFlux.fit(builder::MyNetwork, n_in, n_out, n_channels) # for use with `ImageClassifier`
MLJFlux.build(builder::MyNetwork, n_in, n_out)
MLJFlux.build(builder::MyNetwork, n_in, n_out, n_channels) # for use with `ImageClassifier`
```

This method must return a `Flux.Chain` instance, `chain`, subject to the
Expand Down
4 changes: 2 additions & 2 deletions src/classifier.jl
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Expand Up @@ -40,7 +40,7 @@ function MLJModelInterface.fit(model::NeuralNetworkClassifier,
n_input = Tables.schema(X).names |> length
levels = MLJModelInterface.classes(y[1])
n_output = length(levels)
chain = Flux.Chain(fit(model.builder, n_input, n_output),
chain = Flux.Chain(build(model.builder, n_input, n_output),
model.finaliser)

data = collate(model, X, y)
Expand Down Expand Up @@ -86,7 +86,7 @@ function MLJModelInterface.update(model::NeuralNetworkClassifier,
chain = old_chain
epochs = model.epochs - old_model.epochs
else
chain = Flux.Chain(fit(model.builder, n_input, n_output),
chain = Flux.Chain(build(model.builder, n_input, n_output),
model.finaliser)
data = collate(model, X, y)
epochs = model.epochs
Expand Down
4 changes: 2 additions & 2 deletions src/core.jl
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Expand Up @@ -135,7 +135,7 @@ mutable struct Linear <: Builder
σ
end
Linear(; σ=Flux.relu) = Linear(σ)
fit(builder::Linear, n::Integer, m::Integer) =
build(builder::Linear, n::Integer, m::Integer) =
Flux.Chain(Flux.Dense(n, m, builder.σ))

# baby example 2:
Expand All @@ -145,7 +145,7 @@ mutable struct Short <: Builder
σ
end
Short(; n_hidden=0, dropout=0.5, σ=Flux.sigmoid) = Short(n_hidden, dropout, σ)
function fit(builder::Short, n, m)
function build(builder::Short, n, m)
n_hidden =
builder.n_hidden == 0 ? round(Int, sqrt(n*m)) : builder.n_hidden
return Flux.Chain(Flux.Dense(n, n_hidden, builder.σ),
Expand Down
4 changes: 2 additions & 2 deletions src/image.jl
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Expand Up @@ -45,7 +45,7 @@ function MLJModelInterface.fit(model::ImageClassifier, verbosity::Int, X_, y_)
n_channels = 3 # 3-D color image
end

chain = Flux.Chain(fit(model.builder, n_input, n_output, n_channels), model.finaliser)
chain = Flux.Chain(build(model.builder, n_input, n_output, n_channels), model.finaliser)

optimiser = deepcopy(model.optimiser)

Expand Down Expand Up @@ -93,7 +93,7 @@ function MLJModelInterface.update(model::ImageClassifier,
else
n_channels = 3 # 3-D color image
end
chain = Flux.Chain(fit(model.builder, n_input, n_output, n_channels),
chain = Flux.Chain(build(model.builder, n_input, n_output, n_channels),
model.finaliser)
data = collate(model, X, y)
epochs = model.epochs
Expand Down
4 changes: 2 additions & 2 deletions src/regressor.jl
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Expand Up @@ -74,7 +74,7 @@ function MLJModelInterface.fit(model::Regressor, verbosity::Int, X, y)
end

n_output = length(target_column_names)
chain = fit(model.builder, n_input, n_output)
chain = build(model.builder, n_input, n_output)

optimiser = deepcopy(model.optimiser)

Expand Down Expand Up @@ -110,7 +110,7 @@ function MLJModelInterface.update(model::Regressor,
chain = old_chain
epochs = model.epochs - old_model.epochs
else
chain = fit(model.builder, n_input, n_output)
chain = build(model.builder, n_input, n_output)
data = collate(model, X, y)
epochs = model.epochs
end
Expand Down
2 changes: 1 addition & 1 deletion test/core.jl
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Expand Up @@ -111,7 +111,7 @@ end

@testset "dropout" begin
model = MLJFlux.Short()
chain = MLJFlux.fit(model, 5, 1)
chain = MLJFlux.build(model, 5, 1)

input = rand(5,1)
# At the moment, Dropout is active:
Expand Down
4 changes: 2 additions & 2 deletions test/image.jl
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Expand Up @@ -10,7 +10,7 @@ mutable struct mnistclassifier <: MLJFlux.Builder
filters2
end

MLJFlux.fit(model::mynn, ip, op, n_channels) =
MLJFlux.build(model::mynn, ip, op, n_channels) =
Flux.Chain(Flux.Conv(model.kernel1, n_channels=>2),
Flux.Conv(model.kernel2, 2=>1),
x->reshape(x, :, size(x)[end]),
Expand Down Expand Up @@ -60,7 +60,7 @@ end
return reshape(x, :, size(x)[end])
end

function MLJFlux.fit(model::mnistclassifier, ip, op, n_channels)
function MLJFlux.build(model::mnistclassifier, ip, op, n_channels)
cnn_output_size = [3,3,32]

return Chain(
Expand Down