Ubuntu下用Fortran实现机器学习的可行路径
在Ubuntu上，用Fortran实现机器学习可从三条主线入手：纯Fortran手写数值核心（如线性回归/逻辑回归/矩阵分解）、调用高性能数值库（如BLAS/LAPACK、NAG Fortran Numerical Library）、与Python协同（用f2py封装Fortran内核，上层用scikit-learn做数据处理与验证）。Fortran在数值计算与HPC场景中长期占优，适合实现性能敏感的底层算法与训练循环；而Python在数据管道、实验管理与深度学习生态上更便捷，二者结合能兼顾性能与效率。
环境与工具链
示例一 纯Fortran实现逻辑回归
下面示例演示用SGD训练二分类逻辑回归，数据按行存储（特征在前，标签在最后一列），使用BLAS的ddot与saxpy完成向量内积与更新（需链接**-lblas**）。
! logreg.f90
program logreg
implicit none
integer, parameter :: dp = kind(1.0d0)
integer :: n, d, i, j, nepoch, seed_size
real(dp), allocatable :: X(:,:), y(:), w(:), grad(:)
real(dp) :: eta, lambda, loss, pred, z
real(dp) :: t0, t1
integer :: seed(33)
! 1) 生成可复现数据：n=10000, d=20
n = 10000; d = 20
allocate(X(n,d), y(n), w(d), grad(d))
call random_seed(size=seed_size)
seed = 12345
call random_seed(put=seed)
call random_number(X)
X = X*4.0 - 2.0
! y = sign(x1 + x2 + noise)
y = tanh(X(:,1) + X(:,2) + 0.2*randn(n)) ! 近似二分类标签
y = merge(1.0_dp, -1.0_dp, y > 0.0_dp)
! 2) 初始化参数
w = 0.0_dp
eta = 0.01_dp
lambda = 0.01_dp
nepoch = 50
! 3) 训练
call cpu_time(t0)
!$omp parallel private(i,j,z,pred,grad) shared(X,y,w,eta,lambda,n,d)
!$omp do
do epoch = 1, nepoch
grad = 0.0_dp
!$omp do reduction(+:grad)
do i = 1, n
z = dot_product(X(i,:), w)
pred = 1.0_dp / (1.0_dp + exp(-z))
! 梯度：1/n * X_i * (pred - y_i) + lambda * w
grad = grad + (pred - y(i)) * X(i,:)
end do
!$omp end do
grad = grad / n + lambda * w
w = w - eta * grad
end do
!$omp end do
!$omp end parallel
call cpu_time(t1)
! 4) 评估损失
loss = 0.0_dp
do i = 1, n
z = dot_product(X(i,:), w)
pred = 1.0_dp / (1.0_dp + exp(-z))
loss = loss + log(1.0_dp + exp(-y(i)*z))
end do
loss = loss / n + 0.5_dp * lambda * sum(w*w)
print '(A,F12.6)', 'Training time(s): ', t1 - t0
print '(A,F12.6)', 'Final loss: ', loss
print '(A,*(F10.6))', 'Weights: ', w
deallocate(X, y, w, grad)
end program logreg
示例二 调用外部数值库与工程化
示例三 与Python协同 f2py封装与验证
# test_f2py.py
import numpy as np
from logregf import logreg # 由f2py生成
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import log_loss
# 生成与Fortran一致的数据
np.random.seed(0)
X = np.random.rand(10000, 20) * 4.0 - 2.0
y = np.tanh(X[:,0] + X[:,1] + 0.2*np.random.randn(10000)) > 0
y = y.astype(float) * 2.0 - 1.0 # 转为{-1,1}
# 标准化
scaler = StandardScaler().fit(X)
X = scaler.transform(X)
# 调用Fortran训练
w0 = np.zeros(X.shape[1])
eta, lam, nepoch = 0.01, 0.01, 50
w_f = logreg(X, y, w0, eta, lam, nepoch) # 需按f2py签名传参
# 用sklearn做基准
clf = LogisticRegression(fit_intercept=False, C=1.0/(lam*nepoch), solver='lbfgs', max_iter=200)
clf.fit(X, (y+1)//2) # 转为{0,1}
w_sk = clf.coef_.ravel()
print('Fortran weights (first 5):', w_f[:5])
print('sklearn weights (first 5):', w_sk[:5])
pred = 1.0 / (1.0 + np.exp(-X.dot(w_f)))
print('Fortran final loss:', log_loss((y+1)//2, pred))
性能与工程建议
