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How to train a small neural network in pure Go with Tensai

Run the bundled XOR example to see forward passes and backprop work with no dependencies.

Short answer: Install Go 1.26 or newer; then run git clone https://github.com/mattn/tensai; run cd tensai/_example/xor. All steps.

Who it is forA Go developer who wants to learn neural networks and has no Python setup or GPU available.

The problemMost tutorials assume Python and heavy frameworks, which makes it hard to see how training works in plain code.

The outcomeA working XOR classifier trained by Tensai, plus a readable Go program to modify for other small problems.

What you need first

  • Go toolchain (Go 1.26 or 1.27 is needed only for the GOEXPERIMENT=simd build)
  • git to clone the repository
  • Disk space for model checkpoints when using the image, audio, or embedding commands

Steps

  1. Install Go 1.26 or newer. (inferred, check the README)
  2. Run git clone https://github.com/mattn/tensai (inferred, check the README)
  3. Run cd tensai/_example/xor (inferred, check the README)
  4. Run go run . (inferred, check the README)
  5. Check the printed results, if any, against the XOR targets of 0, 1, 1, 0.

Prompt for your agent

Clone the mattn/tensai repository and run the program in _example/xor with go run. Report the trained predictions for the four XOR inputs. Verify that the outputs are close to 0, 1, 1, 0.

Caveat. The README lists the XOR example but the exact run command and printed output are not shown, so the steps above are inferred from the repository layout.

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