Package: nnR 0.1.0
nnR: Neural Networks Made Algebraic
Do algebraic operations on neural networks. We seek here to implement in R, operations on neural networks and their resulting approximations. Our operations derive their descriptions mainly from Rafi S., Padgett, J.L., and Nakarmi, U. (2024), "Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials", <doi:10.48550/arXiv.2402.01058>, Grohs P., Hornung, F., Jentzen, A. et al. (2023), "Space-time error estimates for deep neural network approximations for differential equations", <doi:10.1007/s10444-022-09970-2>, Jentzen A., Kuckuck B., von Wurstemberger, P. (2023), "Mathematical Introduction to Deep Learning Methods, Implementations, and Theory" <doi:10.48550/arXiv.2310.20360>. Our implementation is meant mainly as a pedagogical tool, and proof of concept. Faster implementations with deeper vectorizations may be made in future versions.
Authors:
nnR_0.1.0.tar.gz
nnR_0.1.0.zip(r-4.5)nnR_0.1.0.zip(r-4.4)nnR_0.1.0.zip(r-4.3)
nnR_0.1.0.tgz(r-4.4-any)nnR_0.1.0.tgz(r-4.3-any)
nnR_0.1.0.tar.gz(r-4.5-noble)nnR_0.1.0.tar.gz(r-4.4-noble)
nnR_0.1.0.tgz(r-4.4-emscripten)nnR_0.1.0.tgz(r-4.3-emscripten)
nnR.pdf |nnR.html✨
nnR/json (API)
| # Install 'nnR' in R: |
| install.packages('nnR', repos = c('https://2shakilrafi.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/2shakilrafi/nnr/issues
Last updated 7 months agofrom:4bd5cde841. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 17 2024 |
| R-4.5-win | NOTE | Sep 17 2024 |
| R-4.5-linux | NOTE | Sep 17 2024 |
| R-4.4-win | NOTE | Sep 17 2024 |
| R-4.4-mac | NOTE | Sep 17 2024 |
| R-4.3-win | NOTE | Sep 17 2024 |
| R-4.3-mac | NOTE | Sep 17 2024 |
Exports:AA_kAffBC_kckcompCpycreate_block_diagonalcreate_nnCsndepEtrgenerate_random_matrixhidiIdinninstis_nnlayMCMxmnn_sumNrmoutparamPhiPhi_kPrdPwrReLUSigmoidslmSneSqrsrmstkSumTanhTayTrpTunview_nnXpn
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| This is an intermediate variable. See the reference | A |
| A_k: The function that returns the matrix A_k | A_k |
| Aff | Aff |
| This is an intermediate variable, see reference. | B |
| C_k: The function that returns the C_k matrix | C_k |
| The ck function | ck |
| comp | %comp% comp |
| Cpy | Cpy |
| Function for creating a block diagonal given two matrices. | create_block_diagonal |
| create_nn | create_nn |
| Csn | Csn |
| dep | dep |
| Etr | Etr |
| Function to generate a random matrix with specified dimensions. | generate_random_matrix |
| hid | hid |
| i | i |
| : Id | Id |
| inn | inn |
| inst | inst |
| is_nn | is_nn |
| lay | lay |
| The MC neural network | MC |
| Mxm | Mxm |
| nn_sum | %nn_sum% nn_sum |
| Nrm | Nrm |
| out | out |
| param | param |
| The Phi function | Phi |
| The Phi_k function | Phi_k |
| Prd | Prd |
| Pwr | Pwr |
| : ReLU | ReLU |
| : Sigmoid | Sigmoid |
| slm | %slm% slm |
| Sne | Sne |
| Sqr | Sqr |
| srm | %srm% srm |
| stk | %stk% stk |
| Sum | Sum |
| Tanh | Tanh |
| The Tay function | Tay |
| Trp | Trp |
| Tun: The function that returns tunneling neural networks | Tun |
| view_nn | view_nn |
| The Xpn function | Xpn |
