# Intricate
[](https://crates.io/crates/intricate)
[](https://cretes.io/crates/intricate)
A GPU accelerated library that creates/trains/runs neural networks in safe Rust code.
## Architechture overview
Intricate has a layout very similar to popular libraries out there such as Keras.
### Models
As said before, similar to Keras from Tensorflow, Intricate defines Models as basically
a list of `Layers` and the definition for "layer" is as follows.
### Layers
Every layer receives **inputs** and returns **outputs**,
they must also implement a `back_propagate` method that
will mutate the layer if needed and then return the derivatives
of the loss function with respected to the inputs,
written with **I** as the inputs of the layer,
**E** as the loss and **O** as the outputs of the layer:
```
dE/dI <- Model <- dE/dO
```
These layers can be anything you want and just propagates the previous inputs
to the next inputs for the next layer or for the outputs of the whole Model.
There are a few activations already implemented, but still many to be implemented.
## XoR using Intricate
If you look at the `examples/` in the repository
you will find XoR implemented using Intricate.
The following is basically just that example with some separate explanation.
### Setting up the training data
```rust
let training_inputs = vec![
vec![0.0, 0.0],
vec![0.0, 1.0],
vec![1.0, 0.0],
vec![1.0, 1.0],
];
let expected_outputs = vec![
vec![0.0],
vec![1.0],
vec![1.0],
vec![0.0],
];
```
### Setting up the layers
```rust
use intricate::layers::{
activations::TanH,
Dense
};
let mut layers: Vec<ModelLayer> = vec![
Dense::new(2, 3), // inputs amount, outputs amount
TanH::new (3),
Dense::new(3, 1),
TanH::new (1),
];
```
### Creating the model with the layers
```rust
use intricate::Model;
// Instantiate our model using the layers
let mut xor_model = Model::new(layers);
```
We make the model `mut` because we will call `fit` for training our model
which will tune each of the layers when necessary.
### Setting up OpenCL's state
Since Intricate does use OpenCL under the hood for doing calculations,
we do need to initialize a `OpenCLState` which is just a struct
containing some necessary OpenCL stuff:
```rust
use intricate::utils::{
setup_opencl,
DeviceType
}
// you can change this device type to GPU if you want
let opencl_state = setup_opencl(DeviceType::CPU).unwrap();
```
For our Model to be able actually do computations, we need to pass the OpenCL state into an `init`
function inside of the model as follows:
```rust
xor_model.init(&opencl_state).unwrap();
```
Beware that as v0.3.0 of Intricate, any method called before `init`
will panic because they do not have the necessary OpenCL state.
### Fitting our model
For training our Model we just need to call the `fit`
method and pass in some parameters as follows:
```rust
xor_model.fit(
&training_inputs,
&expected_outputs,
TrainingOptionsF64 {
learning_rate: 0.1,
loss_algorithm: MeanSquared::new(), // The Mean Squared loss function
should_print_information: true, // Should or not be verbose
epochs: 10000,
},
).unwrap(); // Will return an Option containing the last loss after training
```
As you can see it is extremely easy creating these models, and blazingly fast as well.
Although if you wish to do (just like in the actual XoR example) you
could write this using the F32 version of numbers which is 30% faster
overall and uses half the RAM but at the price of less precision.
## How to save and load models
Intricate implements a few functions for each layer that saves and loads the necessary
layer information to some file using the [savefile](https://github.com/avl/savefile) crate.
But a layer can save and load the data anyway it sees fit, as long as it does what the trait Layer requires.
### Saving the model
To load and save data, as an example, say for the XoR model
we trained above, we can just call the `save` function as such:
```rust
xor_model.sync_gpu_data_with_cpu().unwrap(); // sends the weights and biases from the GPU to the CPU
save_file("xor-model.bin", 0, &xor_model).unwrap();
```
Which will save all of the configuration of the XoR Model including what types of layers
it has inside and the trained parameters of each layer.
### Loading the model
As for loading our XoR model, we just need to call the counterpart of save_file: `load_file`.
```rust
let mut loaded_xor_model: Model = load_file("xor-model.bin", 0).unwrap();
```
Now of curse, **savefile** cannot load in the GPU state so if you want
to use the Model after loading it, you **must** call the `setup_opencl` again
and initialize the Model with the resulting OpenCLState.
## Things to be done still
- separate Intricate into more than one crate as to make development more lightweight with rust-analyzer
- implement convolutional layers and perhaps even solve some image classification problems in a example
- have some feature of Intricate, that should be optional, that would contain preloaded datasets, such as MNIST and others
- write many more unit tests to make code safer, like a test for the backprop of every activation layer
- perhaps write some kind of utility functions to help with writing repetitive tests for the backprop of activation functions
- improve documentation of Intricate overall, like adding at least a general description for every mod