tensor-shape-generic

Shape-generic tensor functions: a 维度 shape param binds implicitly at call sites.

Syntax: 函数 name<T, 维度 Figura>(张量<T, Figura> …) → 张量<T, Figura>

Category#

type

Related#

• 张量
• magnitudo
• 函数
• vector

Examples#

radix/corpus/tensor/generic-shape.fab (canonical · concept)#

Shape-generic tensor functions: a 维度 shape param binds implicitly at call sites.

# =============================================================================
# tensor-shape-generic — shape-generic 张量 functions (维度 Figura)
# =============================================================================
#
# What this teaches:
#   • A `维度 Figura` shape param stands for the whole 张量 shape; the
#     shape is rank-agnostic, so one signature covers `[4]`, `[2, 2]`, `[1, 2]`,
#     and `[]` witnesses.
#   • The typechecker binds the shape param implicitly at call sites from the
#     argument shapes or the expected type — no explicit figura arguments.
#   • Fully static bodies compose elementwise ops (`reple`, `multiplica`,
#     `subtrahe`) whose results keep the same shape symbolically.
#
# Common mistakes:
#   • Spelling the shape as a rank-1 tuple `[Figura]` — that only matches
#     rank-1 witnesses; use the bare `Figura` for rank-agnostic shapes.
#   • Expecting a shape param bound by neither arguments nor the expected type
#     to guess: it errors (SEM014) instead.
#
# See also: 张量, 维度, 函数, 向量
# =============================================================================

# One generic `sgd_step` replaces the concrete [4]/[2,2]/[1,2]/[] overloads:
# the param/grad pair shares `Figura`, so every elementwise step proves the
# same-shape result.

函数 sgd_step<维度 Figura>(张量<f32, Figura> param, 张量<f32, Figura> grad, f32 lr) → 张量<f32, Figura> {
    常量 张量<f32, Figura> lr_fill ← param.填充(lr)
    常量 张量<f32, Figura> scaled ← grad.乘以(lr_fill)
    返回 param.减去(scaled)
}

显示 "shape-generic sgd_step parata"

Expected output:

shape-generic sgd_step parata