Features

Getting started

importpytensorfrompytensorimporttensoraspt# Declare two symbolic floating-point scalarsa=pt.dscalar("a")b=pt.dscalar("b")# Create a simple example expressionc=a+b# Convert the expression into a callable object that takes `(a, b)`# values as input and computes the value of `c`.f_c=pytensor.function([a,b],c)assertf_c(1.5,2.5)==4.0# Compute the gradient of the example expression with respect to `a`dc=pytensor.grad(c,a)f_dc=pytensor.function([a,b],dc)assertf_dc(1.5,2.5)==1.0# Compiling functions with `pytensor.function` also optimizes# expression graphs by removing unnecessary operations and# replacing computations with more efficient ones.v=pt.vector("v")M=pt.matrix("M")d=a/a+ (M+a).dot(v)pytensor.dprint(d)#  Add [id A]#  ├─ ExpandDims{axis=0} [id B]#  │  └─ True_div [id C]#  │     ├─ a [id D]#  │     └─ a [id D]#  └─ dot [id E]#     ├─ Add [id F]#     │  ├─ M [id G]#     │  └─ ExpandDims{axes=[0, 1]} [id H]#     │     └─ a [id D]#     └─ v [id I]f_d=pytensor.function([a,v,M],d)# `a/a` -> `1` and the dot product is replaced with a BLAS function# (i.e. CGemv)pytensor.dprint(f_d)# Add [id A] 5#  ├─ [1.] [id B]#  └─ CGemv{inplace} [id C] 4#     ├─ AllocEmpty{dtype='float64'} [id D] 3#     │  └─ Shape_i{0} [id E] 2#     │     └─ M [id F]#     ├─ 1.0 [id G]#     ├─ Add [id H] 1#     │  ├─ M [id F]#     │  └─ ExpandDims{axes=[0, 1]} [id I] 0#     │     └─ a [id J]#     ├─ v [id K]#     └─ 0.0 [id L]

Installation

Background

Contributing