arogozhnikov/einops

Features

• Deep Learning Framework Abstractions - Acts as a universal abstraction layer for tensor operations across NumPy, PyTorch, TensorFlow, and JAX.
• Tensor Reshaping - Uses a declarative notation system to reshape multi-dimensional arrays, improving readability and reducing indexing errors.
• Einstein Summation Utilities - Provides utilities for complex tensor contractions and dot products using a flexible Einstein summation notation.
• Backend-Agnostic Engines - Provides a framework-agnostic interface that dispatches operations to NumPy, PyTorch, TensorFlow, or JAX at runtime.
• Tensor Libraries - Provides a comprehensive library for reshaping and rearranging multi-dimensional arrays using declarative notation.
• Tensor Reductions - Combines axis rearrangement with reductions like sum, mean, or max to condense multi-dimensional arrays.
• Tensor Packing - Combines tensors of varying shapes into a single array and tracks their shapes for later restoration.
• Tensor Unpacking - Splits a single tensor back into its original constituent parts using stored shape records.
• Semantic Dimension Mapping - Extracts lengths of tensor axes into dictionaries using named patterns to isolate relevant dimensions.
• Concatenation - Combines multiple arrays into a single tensor by adding dimensions or merging along existing ones.
• Multi-Dimensional Arrays - Implements a comprehensive toolkit for packing, unpacking, repeating, and reducing multi-dimensional arrays.
• Declarative Tensor Notations - Implements a declarative notation system that translates readable string patterns into backend-specific tensor operations.
• Composable Network Modules - Provides abstractions to integrate tensor transformations as modular, reusable components within neural network architectures.
• Custom Neural Network Layers - Implements pluggable layer objects that wrap tensor reshaping and reduction operations for sequential models.
• Data Preprocessing Utilities - Ships tools for packing, unpacking, and repeating tensors to prepare data for machine learning models.
• Deep Learning Architectures - Facilitates the design of deep learning architectures by integrating tensor transformations as pluggable layers.
• Dimensionality Reduction Utilities - Combines axis rearrangement with mathematical reductions like summing or averaging to condense tensor data.
• Neural Network Layers - Integrates tensor reshaping and reduction operations as pluggable layers compatible with sequential neural network definitions.
• Element Repetition - Duplicates elements across dimensions to perform tiling, broadcasting, and upsampling operations.
• Tensor Shape Inspection - Extracts dimension lengths from tensors into dictionaries using descriptive patterns for verification.
• Tensor Property Validators - Verifies that input tensor dimensions conform to expected string templates before executing operations.
• Einstein Summation Translations - Translates extended Einstein summation notation with multi-letter axis names into strings compatible with deep learning frameworks.
• Graph Compilation - Integrates tensor manipulations into compiled execution graphs to ensure high runtime performance.
• General Machine Learning - Deep learning operations for PyTorch, TensorFlow, and JAX.
• Machine Learning Frameworks - Utility for readable and reliable tensor manipulation across frameworks.
• Computation and Optimization - Flexible tensor operations for readable and reliable code.

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