## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(nnR) ## ----------------------------------------------------------------------------- layer_architecture = c(4,5,6,5) create_nn(layer_architecture) ## ----------------------------------------------------------------------------- nn = create_nn(c(9,4,5,6)) dep(nn) ## ----------------------------------------------------------------------------- nn = create_nn(c(9,4,5,6)) param(nn) ## ----------------------------------------------------------------------------- nn = create_nn(c(9,4,5,6)) inn(nn) ## ----------------------------------------------------------------------------- nn = create_nn(c(9,4,5,6)) out(nn) ## ----------------------------------------------------------------------------- nn = create_nn(c(9,4,5,6)) hid(nn) ## ----------------------------------------------------------------------------- nn = create_nn(c(9,4,5,6)) lay(nn) ## ----------------------------------------------------------------------------- create_nn(c(1, 3, 5, 6)) |> inst(ReLU, 8) ## ----------------------------------------------------------------------------- create_nn(c(3,4,5,1)) |> inst(ReLU,c(1,2,3)) ## ----------------------------------------------------------------------------- nn <- create_nn(c(2, 4, 1)) batch <- matrix(c(1, 2, 3, 4), nrow = 2) inst(nn, ReLU, batch) f <- realize_nn(nn, ReLU) f(batch) ## ----------------------------------------------------------------------------- c(1,5,6,3,3) |> create_nn() -> nu_1 c(3,4,6,3,2) |> create_nn() -> nu_2 nu_2 |> comp(nu_1) ## ----------------------------------------------------------------------------- c(1,3,4,8,1) |> create_nn() -> nn nn |> inst(ReLU,5) 2 |> slm(nn) |> inst(ReLU, 5) ## ----------------------------------------------------------------------------- c(1,3,4,8,1) |> create_nn() -> nn nn |> inst(ReLU, 5) nn |> srm(5) |> inst(ReLU,5) ## ----------------------------------------------------------------------------- c(3,4,6,3,7,1,3,4) |> create_nn() -> nn_1 c(2,6,4,5) |> create_nn() -> nn_2 (nn_1 |> stk(nn_2)) |> inst(ReLU, c(4,3,2,1,6)) print("Compare to:") nn_1 |> inst(ReLU, c(4,3,2)) nn_2 |> inst(ReLU, c(1,6)) ## ----------------------------------------------------------------------------- c(1,5,3,2,1) |> create_nn() -> nu c(1,5,4,9,1) |> create_nn() -> mu nu |> inst(ReLU,4) -> x_1 mu |> inst(ReLU,4) -> x_2 x_1 + x_2 -> result_1 print("The sum of the instantiated neural network is:") print(result_1) (nu |> nn_sum(mu)) |> inst(ReLU,4) -> result_2 print("The instantiation of their neural network sum") print(result_2) ## ----------------------------------------------------------------------------- Sqr(2.1,0.1) |> inst(ReLU,5) ## ----------------------------------------------------------------------------- Prd(2.1,0.1) |> inst(ReLU, c(2,3)) ## ----------------------------------------------------------------------------- Pwr(2.1, 0.1,3) |> inst(ReLU, 2) ## ----------------------------------------------------------------------------- polynomial <- Pnm(c(1, -2, 0.5), q = 3, eps = 0.2) inst(polynomial, ReLU, matrix(seq(-1, 1, length.out = 5), nrow = 1)) ## ----------------------------------------------------------------------------- Xpn(5,2.1,0.1) |> inst(ReLU, 2) print("Compare to:") exp(2) ## ----------------------------------------------------------------------------- Csn(3,2.1,0.1) |> inst(ReLU, 0.4) print("Compare to:") cos(0.4) ## ----------------------------------------------------------------------------- Sne(3,2.1,0.1) |> inst(ReLU, 0.4) print("Compare to:") sin(0.4) ## ----------------------------------------------------------------------------- h = 0.2 mesh = c(0,0+h) samples = sin(mesh) Trp(h) |> inst(ReLU, samples) Trp(h) |> inst(Sigmoid, samples) Trp(h) |> inst(Tanh, samples) ## ----------------------------------------------------------------------------- seq(0,pi, length.out = 1000) -> x sin(x) -> samples Etr(1000 - 1, pi / (1000 - 1)) |> inst(ReLU, samples) print("Compare with:") sin |> integrate(0,pi) ## ----------------------------------------------------------------------------- seq(0, 2 * pi, length.out = 500) -> x sin(x) -> y 1 -> L MC(x, y, L) |> inst(ReLU, 2.5) print("Compare to:") sin(2.5) ## ----------------------------------------------------------------------------- X <- matrix(c(0, 0, 1, 0, 0, 1, 1, 1), nrow = 2) y <- colSums(X) approximant <- MC(X, y, L = 1) inst(approximant, ReLU, X)