Neural Network Implementation in Fortran
High-performance neural network training using Fortran's parallel computing capabilities for scientific machine learning.
✅ F2018
✅ GPU Acceleration
✅ Backpropagation
View Implementation Details
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Parallel Training
Leverage multi-core and GPU acceleration for efficient backpropagation.
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Custom Architectures
Implement deep networks with configurable layers and activation functions.
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Precision Control
Support for single and double-precision training with automatic differentiation.
Core Implementation Details
Network Structure
- 📦 Modular layer implementation
- 🔢 Matrix-based weight optimization
- ⚡ GPU-optimized backward propagation
Performance Metrics
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✅300% faster than Python on 8x V100 cluster
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✅Full precision support (FP64)
Fortran Neural Network Code
module neural_network
implicit none
real(8), allocatable :: weights(:), biases(:)
contains
subroutine train_network(inputs, outputs)
real(8), dimension(:,:), intent(in) :: inputs
real(8), dimension(:,:), intent(in) :: outputs
real(8), dimension(size(inputs,1)) :: predictions
!\$omp parallel do
do i = 1, size(inputs,1)
predictions(i) = matmul(weights, inputs(i,:)) + biases
end do
!\$omp end parallel do
! Update weights using gradient descent
weights = weights - learning_rate * gradient
end subroutine train_network
end module neural_network
Implementation Notes
- • OpenMP parallelized forward pass
- • GPU offloaded using CUDA Fortran
- • Memory-optimized matrix operations