A GPU accelerated library that creates/trains/runs neural networks in safe Rust code.
Intricate has a layout very similar to popular libraries out there such as Keras.
It consists at the surface of a Model, which consists then of Layers which can be adjusted using a Loss Function that is also helped by a Optimizer.
As said before, similar to Keras, Intricate defines Models as basically a list of 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.
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_parameterscompute_gradientsapply_gradientscompute_loss_to_input_derivativesMostly the optimize_parameters will rely on an Optimizer 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 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'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.
Intricate currently only does have one optimizer since it is still on heavy development and changing architecture constantly so writing many implementations would be really annoying to change later.
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.
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.
```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], ]; ```
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),
];
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.
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();
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
xormodel
.fit(
&traininginputs,
&expectedoutputs,
&mut TrainingOptions {
lossfn: &mut loss, // the type of loss function that should be used for Intricate
// to determine how bad the Model is
verbosity: TrainingVerbosity {
showcurrentepoch: true, // show a message for each epoch like epoch #5
showepochprogress: false, // show a progress bar of the training steps in a
// epoch
showepochelapsed: true, // show elapsed time in calculations for one epoch
printaccuracy: true, // should print the accuracy after each epoch
printloss: true, // should print the loss after each epoch
haltingconditionwarning: true,
},
// a condition for stopping the training if a min loss is reached
haltingcondition: Some(HaltingCondition::MinAccuracyReached(0.95)),
computeaccuracy: false, // if Intricate should compute the accuracy after each
// training step
computeloss: true, // if Intricate should compute the loss after each training
// step
optimizer: &mut optimizer,
batchsize: 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.
For saving and loading models Intricate uses the savefile crate which makes it very simple and fast to save models.
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();
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).
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