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  • Getting Started
  • User Guide
  • API
  • Examples
  • Contributing
  • GitHub
  • Twitter
  • YouTube
  • Discourse

Section Navigation

  • compile – Transforming Expression Graphs to Functions
    • shared - defines pytensor.shared
    • function - defines pytensor.function
    • io - defines pytensor.function [TODO]
    • ops – Some Common Ops and extra Ops stuff
    • mode – controlling compilation
    • debugmode
    • nanguardmode
  • config – PyTensor Configuration
  • d3viz – d3viz: Interactive visualization of PyTensor compute graphs
  • graph – PyTensor Graph Internals
    • graph – Interface for the PyTensor graph
    • fg – Graph Container [doc TODO]
    • replace – High level graph transformations
    • features – [doc TODO]
    • op – Objects that define operations
    • type – Interface for types of variables
    • utils – Utilities functions operating on the graph
  • misc.pkl_utils - Tools for serialization.
  • printing – Graph Printing and Symbolic Print Statement
  • scalar – Symbolic Scalar Types, Ops [doc TODO]
  • scan – Looping in PyTensor
  • sparse – Symbolic Sparse Matrices
  • sparse.sandbox – Sparse Op Sandbox
  • tensor – Tensor operations in PyTensor
    • Basic Tensor Functionality
    • random – Random number functionality
    • tensor.utils – Tensor Utils
    • tensor.elemwise – Tensor Elemwise
    • tensor.extra_ops – Tensor Extra Ops
    • tensor.io – Tensor IO Ops
    • tensor.slinalg – Linear Algebra Ops Using Scipy
    • tensor.nlinalg – Linear Algebra Ops Using Numpy
    • tensor.fft – Fast Fourier Transforms
    • tensor.conv – Tensor Convolutions
    • tensor.rewriting.math – Tensor Rewrites for Math Operations
    • tensor.rewriting.basic – Tensor Rewrites
    • vectorize()
  • typed_list – Typed List
  • API Documentation

tensor – Tensor operations in PyTensor#

PyTensor’s strength is in expressing symbolic calculations involving tensors.

PyTensor tries to emulate the numpy interface as much as possible in the tensor module. This means that once TensorVariables are created, it should be possibly to define symbolic expressions using calls that look just like numpy calls, such as pt.exp(x).transpose(0, 1)[:, None]

  • Basic Tensor Functionality
  • random – Random number functionality
  • tensor.utils – Tensor Utils
  • tensor.elemwise – Tensor Elemwise
  • tensor.extra_ops – Tensor Extra Ops
  • tensor.io – Tensor IO Ops
  • tensor.slinalg – Linear Algebra Ops Using Scipy
  • tensor.nlinalg – Linear Algebra Ops Using Numpy
  • tensor.fft – Fast Fourier Transforms
  • tensor.conv – Tensor Convolutions
  • tensor.rewriting.math – Tensor Rewrites for Math Operations
  • tensor.rewriting.basic – Tensor Rewrites
  • vectorize()

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sparse.sandbox – Sparse Op Sandbox

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Basic Tensor Functionality

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