LycorisNet's Python bindings are implemented based on Pybind11.
git clone "https://github.com/pybind/pybind11.git"
cd pybind11
mkdir build
cd build
cmake ..
make install
(If pybind11 and its header files are already installed, you can ignore the above steps.)
pip install LycorisNet
It can also be obtained via manual compilation:
cd Lycoris/python
cmake .
make
The APIs provided by Lycoris (from LycorisNet import Lycoris):
| Function | Description | Inputs | Returns |
|---|---|---|---|
| Lycoris(capacity, inputDim, outputDim, mode) | Constructor. The class Lycoris is the highest level abstraction of LycorisNet. |
capacity: Capacity of Lycoris. inputDim: Input dimension. outputDim: Output dimension. mode: Mode of Lycoris (classify or predict). |
An object of the class Lycoris. |
| preheat(nodes, connections, depths) | Preheating process of the neural network cluster. | nodes: The number of hidden nodes added for each neural network. connections: The number of connections added for each neural network. depths: Total layers of each neural network. |
|
| evolve(input, desire) | Evolve the neural network cluster. | input: Input data. desire: Expected output data. |
|
| fit(input, desire) | Fit all neural networks in the neural network cluster. | input: Input data. desire: Expected output data. |
|
| enrich() | Keep only the best one in the neural network cluster. | ||
| compute(input) | Forward Computing of the best individual. | input: Input data. | Returns the output data. |
| computeBatch(input) | Parallel forward Computing of the best individual. | input: Input data (two dimensions). | Returns the output data (two dimensions). |
| resize(capacity) | Resize the capacity of the neural network cluster. | As literally. | |
| openMemLimit(size) | Turn on memory-limit. | As literally. | |
| closeMemLimit() | Turn off memory-limit. | ||
| saveModel(path) | Export the current trained model. | path: File path of the current trained model. | |
| setMutateArgs(p) | Set p1 to p4 in the class Args. Parameters are passed in as List. |
p1: Probability of adding the new node between a connection. p2: Probability of deleting a node. p3: Probability of adding a new connection between two nodes. p4: Probability of deleting a connection. |
|
| setMutateOdds(odds) | Set the odds of mutating. | The param "odds" means one individual mutates odds times to form odds + 1 individuals. | |
| setCpuCores(num) | Set the number of worker threads to train the model. | As literally. | |
| setLR(lr) | Set the learning rate. | As literally. | |
| getSize() | Returns the size of the best individual. | ||
| getInputDim() | Returns the input dimension. | ||
| getOutputDim() | Returns the output dimension. | ||
| getCapacity() | Returns capacity of Lycoris. | ||
| getLoss() | Returns the loss. | ||
| getMode() | Returns mode of Lycoris (classify or predict). | ||
| getLayers() | Returns the number of nodes in each layer of the neural network. | ||
| getHiddenLayer(pos) | The parameter pos starts at index 0. | pos: The number of the layer needed. | Returns a vector of nodes in a specific layer of the best individual. |
@staticmethodversion() |
Returns version information and copyright information. |
The funtion used to import the pre-trained model (from LycorisNet import loadModel, loadViaString):
| Function | Description | Inputs | Returns |
|---|---|---|---|
| Lycoris loadModel(path, capacity) | Import the pre-trained model. | path: File path of the pre-trained model. capacity: Capacity of the neural network cluster. |
Returns an object of class Lycoris. |
| Lycoris loadViaString(model, capacity) | Import the pre-trained model via string. | model: The pre-trained model in the form of string. capacity: Capacity of the neural network cluster. |
Returns an object of class Lycoris. |
Information related to parameters and return values also appears within:
>>> help(Lycoris)
>>> help(loadModel)
- LycorisAD: an elegant outlier detection algorithm framework based on AutoEncoder.
- LycorisR: a lightweight recommendation algorithm framework based on LycorisNet.
- LycorisQ: a neat reinforcement learning framework based on LycorisNet.
- More examples will be released in the future.
Lycoris is released under the LGPL-3.0 license. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.