https://github.com/lucianbuzzo/gradnite
A simple Autograd engine written in Crystal
https://github.com/lucianbuzzo/gradnite
autograd crystal neural-network
Last synced: 12 months ago
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A simple Autograd engine written in Crystal
- Host: GitHub
- URL: https://github.com/lucianbuzzo/gradnite
- Owner: LucianBuzzo
- Created: 2023-07-21T11:49:13.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-08-05T14:55:12.000Z (over 2 years ago)
- Last Synced: 2024-10-18T10:30:56.437Z (over 1 year ago)
- Topics: autograd, crystal, neural-network
- Language: Crystal
- Homepage:
- Size: 5.69 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
A simple Autograd engine written in Crystal.
## Usage
Add Gradnite to your `shard.yml` and run `shards install`:
```yaml
dependencies:
gradnite:
github: lucianbuzzo/gradnite
version: 0.1.0
```
You can now require the module and use it's classes:
```crystal
# require the gradnite module
require "gradnite"
# create a new Node
node = Gradnite::Node.new(1.0)
# add two nodes together
node = Gradnite::Node.new(1.0) + Gradnite::Node.new(2.0)
# multiply two nodes together
node = Gradnite::Node.new(1.0) * Gradnite::Node.new(2.0)
# divide two nodes together
node = Gradnite::Node.new(1.0) / Gradnite::Node.new(2.0)
# create a Neuron with 4 inputes
neuron = Gradnite::Neuron.new(4)
# create an MLP with 2 inputs, 2 hidden layers of 3 neurons each and 1 output
mlp = Gradnite::MLP.new(2, [3, 3, 1])
# run a forward pass on the mlp
mlp.forward([1.0, 2.0])
# run back propagation on the mlp
mlp.backward
# update the weights of the mlp
mlp.parameters.each { |p|
p.value += -0.1 * p.grad
}
```
Gradnite also includes a utility for visualizing the computation graph of a neural network using `graphviz`. To use it, make sure you have `graphviz` installed locally and call the `draw_dot` function with a node:
```crystal
a = Gradnite::Node.new value: 1.0, label: "a"
b = Gradnite::Node.new value: 2.0, label: "b"
c = a * b
c.label = "c"
Gradnite::draw_dot(c, "examples/multiply.dot")
```
You can now generate a png image of the computation graph using the `dot` command:
```bash
dot -Tpng examples/multiply.dot -o examples/multiply.png
```
For more examples of usage see the [examples](examples) directory.
## Development
Install prerequisites:
```bash
brew install crystal watchexec graphviz
```
Run in watch mode:
```bash
./watch.sh examples/binary_classifier.cr
```