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auto_adaptative_block_learning #18

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24 changes: 12 additions & 12 deletions src/CustomLossFunction.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -8,10 +8,9 @@ function _sigmoid(ŷ::Matrix{T}, y::T) where {T<:AbstractFloat}
end;

function _leaky_relu(ŷ::Matrix{T}, y::T) where {T<:AbstractFloat}
return min.(0.001 .* (y .- ŷ) .+ 1., leakyrelu.((y .- ŷ) .* 10, 0.001))
return min.(0.001 .* (y .- ŷ) .+ 1.0, leakyrelu.((y .- ŷ) .* 10, 0.001))
end;


"""
ψₘ(y, m)

Expand Down Expand Up @@ -105,7 +104,7 @@ function adaptative_block_learning(nn_model, data, hparams)
@showprogress for epoch in 1:(hparams.epochs)
loss, grads = Flux.withgradient(nn_model) do nn
aₖ = zeros(hparams.K + 1)
for i in 1:hparams.samples
for i in 1:(hparams.samples)
x = rand(hparams.transform, hparams.K)
yₖ = nn(x')
aₖ += generate_aₖ(yₖ, data.data[i])
Expand Down Expand Up @@ -149,22 +148,23 @@ function convergence_to_uniform(aₖ::Vector{T}) where {T<:Int}
end;

function get_better_K(nn_model, data, min_K, hparams)
K = hparams.max_k
for k in min_K:hparams.max_k
if !convergence_to_uniform(get_window_of_Aₖ(hparams.transform, nn_model, data, k))
K = k
break
end
range = min_K:1:(hparams.max_k)
index = findfirst(
k -> !convergence_to_uniform(get_window_of_Aₖ(hparams.transform, nn_model, data, k)),
range
)
if index === nothing
return hparams.max_k
end
return K
return range[index]
end;

"""
auto_adaptative_block_learning(model, data, hparams)

Custom loss function for the model.
"""
function auto_adaptative_block_learning(nn_model, data, hparams)
function auto_adaptative_block_learning(nn_model, data, hparams::AutoAdaptativeHyperParams)
@assert length(data) == hparams.samples

K = 2
Expand All @@ -179,7 +179,7 @@ function auto_adaptative_block_learning(nn_model, data, hparams)
end
loss, grads = Flux.withgradient(nn_model) do nn
aₖ = zeros(K + 1)
for i in 1:hparams.samples
for i in 1:(hparams.samples)
x = rand(hparams.transform, K)
yₖ = nn(x')
aₖ += generate_aₖ(yₖ, data.data[i])
Expand Down