tensortrax 0.0.1

Math on (Hyper-Dual) Tensors with Trailing Axes

tensortrax

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Math on (Hyper-Dual) Tensors with Trailing Axes.

Features

• Designed to operate on input arrays with trailing axes
• Essential vector/tensor Hyper-Dual number math, including limited support for einsum (restricted to max. two operands)
• Forward Mode Automatic Differentiation (AD) using Hyper-Dual Tensors, up to second order derivatives
• Create functions in terms of Hyper-Dual Tensors
• Evaluate the function, the gradient (jacobian) and the hessian on given input arrays
• Straight-forward definition of custom functions in variational-calculus notation

Not Features

• Not imitating NumPy (like Autograd)
• No arbitrary-order gradients

Usage

Let's define a scalar-valued function which operates on a tensor.

import tensortrax as tr
import tensortrax.math as tm

def fun(F):
    C = F.T() @ F
    I1 = tm.trace(C)
    J = tm.det(F)
    return J ** (-2 / 3) * I1 - 3

The hessian of the scalar-valued function w.r.t. the function argument is evaluated by variational calculus (Forward Mode AD implemented as Hyper-Dual Tensors). The function is called once for each component of the hessian (symmetry is taken care of). The function and the gradient are evaluated with no additional computational cost.

import numpy as np

# some random input data
np.random.seed(125161)
F = np.random.rand(3, 3, 8, 50) / 10
for a in range(3):
    F[a, a] += 1

# W = tr.function(fun, ntrax=2)(F)
# dWdF, W = tr.gradient(fun, ntrax=2)(F)
d2WdF2, dWdF, W = tr.hessian(fun, ntrax=2)(F)