Merge branch 'main' into improve-directory-setup

Author: benedict-96

Project.toml

@@ -11,7 +11,7 @@ RuntimeGeneratedFunctions = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
-AbstractNeuralNetworks = "0.3, 0.4"
+AbstractNeuralNetworks = "0.3, 0.4, 0.5"
 Documenter = "1.8.0"
 ForwardDiff = "0.10.38"
 GeometricMachineLearning = "0.3.7"

docs/src/hamiltonian_neural_network.md

@@ -5,7 +5,7 @@ Here we build a Hamiltonian neural network as a symbolic neural network.
 ```julia hnn
 using SymbolicNeuralNetworks
 using GeometricMachineLearning
-using AbstractNeuralNetworks: Dense, initialparameters, UnknownArchitecture, Model
+using AbstractNeuralNetworks: Dense, UnknownArchitecture, Model
 using LinearAlgebra: norm
 using ChainRulesCore
 using KernelAbstractions
@@ -45,7 +45,7 @@ nothing # hide
 We can now train the network:
 
 ```julia hnn
-ps = NeuralNetworkParameters(initialparameters(c, T))
+ps = NeuralNetwork(c, T).params
 dl = DataLoader(z_data, hvf_analytic(z_data))
 o = Optimizer(AdamOptimizer(.01), ps)
 batch = Batch(200)

docs/src/symbolic_neural_networks.md

@@ -6,7 +6,7 @@ We first call the symbolic neural network that only consists of one layer:
 
 ```@example snn
 using SymbolicNeuralNetworks
-using AbstractNeuralNetworks: Chain, Dense, initialparameters, params
+using AbstractNeuralNetworks: Chain, Dense, params
 
 input_dim = 2
 output_dim = 1

src/build_function/build_function_arrays.jl

@@ -83,7 +83,7 @@ Return an executable function for each entry in `eqs`. This still has to be proc
 
 ```jldoctest
 using SymbolicNeuralNetworks: function_valued_parameters, SymbolicNeuralNetwork
-using AbstractNeuralNetworks: Chain, Dense, initialparameters, NeuralNetworkParameters, params
+using AbstractNeuralNetworks: Chain, Dense,fffff, NeuralNetworkParameters, params
 import Random
 Random.seed!(123)
 

src/derivatives/jacobian.jl

@@ -45,7 +45,7 @@ We can use `Jacobian` together with [`build_nn_function`](@ref):
 ```jldoctest
 using SymbolicNeuralNetworks
 using SymbolicNeuralNetworks: Jacobian, derivative
-using AbstractNeuralNetworks: Dense, Chain, initialparameters
+using AbstractNeuralNetworks: Dense, Chain, NeuralNetwork
 using Symbolics
 import Random
 
@@ -59,7 +59,7 @@ nn = SymbolicNeuralNetwork(c)
 □ = SymbolicNeuralNetworks.Jacobian(nn)
 # here we need to access the derivative and convert it into a function
 jacobian1 = build_nn_function(derivative(□), nn)
-ps = initialparameters(c, Float64)
+ps = NeuralNetwork(c, Float64).params
 input = rand(input_dim)
 #derivative
 Dtanh(x::Real) = 4 * exp(2 * x) / (1 + exp(2x)) ^ 2

src/derivatives/pullback.jl

@@ -8,15 +8,17 @@
 ```jldoctest
 using SymbolicNeuralNetworks
 using AbstractNeuralNetworks
+using AbstractNeuralNetworks: params
 import Random
 Random.seed!(123)
 
 c = Chain(Dense(2, 1, tanh))
-nn = SymbolicNeuralNetwork(c)
+nn = NeuralNetwork(c)
+snn = SymbolicNeuralNetwork(nn)
 loss = FeedForwardLoss()
-pb = SymbolicPullback(nn, loss)
-ps = initialparameters(c) |> NeuralNetworkParameters
-pb_values = pb(ps, nn.model, (rand(2), rand(1)))[2](1) |> typeof
+pb = SymbolicPullback(snn, loss)
+ps = params(nn)
+typeof(pb(ps, nn.model, (rand(2), rand(1)))[2](1))
 
 # output
 

test/derivatives/jacobian.jl

@@ -1,6 +1,6 @@
 using Test, SymbolicNeuralNetworks
 using SymbolicNeuralNetworks: Jacobian, derivative
-using AbstractNeuralNetworks: Chain, Dense, initialparameters, NeuralNetworkParameters
+using AbstractNeuralNetworks: Chain, Dense, NeuralNetwork
 using LinearAlgebra: norm
 import Symbolics, Random, ForwardDiff
 
@@ -26,7 +26,7 @@ function test_jacobian(n::Integer, T = Float32)
     nn = SymbolicNeuralNetwork(c)
     g = Jacobian(nn)
 
-    params = initialparameters(c, T) |> NeuralNetworkParameters
+    params = NeuralNetwork(c, T).params
     input = rand(T, n)
     @test build_nn_function(g.output, nn)(input, params) ≈ c(input, params)
     @test build_nn_function(derivative(g), nn)(input, params) ≈ ForwardDiff.jacobian(input -> c(input, params), input)