# Intricate
[](https://crates.io/crates/intricate)
[](https://crates.io/crates/intricate)
A GPU accelerated library that creates/trains/runs neural networks in safe Rust code.
---
### Table of contents
* [Architechture overview](#architechture-overview)
* [Models](#models)
* [Layers](#layers)
* [Optimizers](#optimizers)
* [Loss Functions](#loss-functions)
* [XoR using Intricate](#xor-using-intricate)
* [Setting up the training data](#setting-up-the-training-data)
* [Setting up the layers](#setting-up-the-layers)
* [Setting up OpenCL](#setting-up-opencls-state)
* [Fitting our Model](#fitting-our-model)
* [How to save and load models](#how-to-save-and-load-models)
* [Saving the Model](#saving-the-model)
* [Loading the Model](#loading-the-model)
* [Things to be done still](#things-to-be-done-still)
---
## Architechture overview
Intricate has a layout very similar to popular libraries out there such as Keras.
It consists at the surface of a [Model](#models), which consists then
of [Layers](#layers) which can be adjusted using a [Loss Function](#loss-functions)
that is also helped by a [Optimizer](#optimizers).
### Models
As said before, similar to Keras, Intricate defines Models as basically
a list of [Layers](#layers).
A model does not have much logic in it, mostly it delegates most of the work to the layers,
all that it does is orchestrate how the layers should work together and how the data goes from
a layer to another.
### Layers
Every layer receives **inputs** and returns **outputs** following some rule that they must define.
They must also implement four methods that together constitute backpropagation:
- `optimize_parameters`
- `compute_gradients`
- `apply_gradients`
- `compute_loss_to_input_derivatives`
Mostly the optimize_parameters will rely on an [Optimizer](#optimizers) that will try to improve
the parameters that the Layer allows it to optimize.
These methods together will be called sequentially to do backpropagation in the Model and
using the results from the `compute_loss_to_input_derivatives` we will then do the same for
the last layer and so on.
These layers can be really any type of transformation on the inputs and outputs.
An example of this is the activation functions in Intricate which are actual
layers instead of being one with other layers
which does simplify calculations tremendously and works like a charm.
### Optimizers
Optimizers the do just what you might think, they optimize.
Specifically they optimize both the parameters a Layer allows them to optimize, as well
as the [Layer](#layers)'s gradients so that the Layer can use them to apply the optimized gradients on itself.
This is useful because anyone using Intricate can develop and perhaps debug a Optimizer to see how well it does
for certain use cases which is very good for where I want Intricate to go. All you have to do is create some struct
that implements the `Optimizer` trait.
### Loss Functions
Loss Functions are just basically some implementations of a certain trait that are used
to determine how bad a Model is.
Loss Functions are **NOT** used in a layer, they are used
for the Model itself. Even though a Layer will use derivatives with respect
to the loss they don't really communicate with the Loss Function directly.
---
## 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 to actually do computations, we need to pass the OpenCL state
into the `init` method inside of the Model as follows:
```rust
xor_model.init(&opencl_state).unwrap();
```
### Fitting our model
For training our Model we just need to call the `fit`
method and pass in some parameters as follows:
```rust
use intricate::{
loss_functions::MeanSquared,
optimizers::BasicOptimizer,
types::{TrainingOptions, TrainingVerbosity},
};
let mut loss = MeanSquared::new();
let mut optimizer = BasicOptimizer::new(0.1);
// Fit the model however many times we want
xor_model
.fit(
&training_inputs,
&expected_outputs,
&mut TrainingOptions {
loss_fn: &mut loss, // the type of loss function that should be used for Intricate
// to determine how bad the Model is
verbosity: TrainingVerbosity {
show_current_epoch: true, // show a message for each epoch like `epoch #5`
show_epoch_progress: false, // show a progress bar of the training steps in a
// epoch
show_epoch_elapsed: true, // show elapsed time in calculations for one epoch
print_accuracy: true, // should print the accuracy after each epoch
print_loss: true, // should print the loss after each epoch
halting_condition_warning: true,
},
// a condition for stopping the training if a min accuracy is reached
halting_condition: Some(HaltingCondition::MinAccuracyReached(0.95)),
compute_accuracy: false, // if Intricate should compute the accuracy after each
// training step
compute_loss: true, // if Intricate should compute the loss after each training
// step
optimizer: &mut optimizer,
batch_size: 4, // the size of the mini-batch being used in Intricate's Mini-batch
// Gradient Descent
epochs: 10000,
},
)
.unwrap();
```
As you can see it is extremely easy creating these models, and blazingly fast as well.
---
## How to save and load models
For saving and loading models Intricate uses the [savefile](https://github.com/avl/savefile) crate which makes it very simple and fast to save models.
### Saving the model
As an example let's try saving and loading our XoR model.
For doing that we will first need to sync all of the relevant layer information
of the Model with OpenCL's `host`, (or just with the CPU), and then we will need
to call the `save_file` method as follows:
```rust
xor_model.sync_data_from_buffers_to_host().unwrap(); // sends the weights and biases from
// OpenCL buffers to Rust Vec's
save_file("xor-model.bin", 0, &xor_model).unwrap();
```
### Loading the model
As for loading our XoR model, we just need to call the
counterpart of the save_file method: `load_file`.
```rust
let mut loaded_xor_model: Model = load_file("xor-model.bin", 0).unwrap();
```
Now of curse, the savefile crate cannot load in the data to the GPU, so if you want
to use the Model after loading it, you **must** call the `init` method in the `loaded_xor_model`
(done in examples/xor.rs).
## 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, should be optional, that would contain preloaded datasets, such as MNIST and others
- add a way to send into the training process a callback closure that would be called everytime a epoch finished or even a step too with some cool info
- make an example after doing the thing above ^, that uses that same function to plot the loss realtime using a crate like `textplots`
- add embedding layers for text such as bag of words with an expected vocabulary size
- add optimizers to make Intricate actually be able to solve some problems