81 lines
1.8 KiB
Markdown
81 lines
1.8 KiB
Markdown
# Python AutoEncoder from scratch using Numpy
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<figure>
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<p align="center">
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<img
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src="https://raw.githubusercontent.com/lenoctambule/autoencoder/refs/heads/main/media/latent-space.png"
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alt="Latent-space of the MNIST dataset"
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width=70%>
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<figcaption>
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<p align="center">
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Latent-space representation of the MNIST dataset using Variational Autoencoder
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</p>
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</figcaption>
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</p>
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</figure>
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## Usage
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1. To install from source :
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```sh
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$ git clone git@github.com:lenoctambule/autoencoder.git
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$ pip install -e autoencoder/
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```
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Or install from PyPI :
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```sh
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$ pip install easyvae
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```
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2. Optionally, run mnist_test.py to see it in action on the MNIST dataset.
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```sh
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$ cd examples
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$ py mnist_test.py
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```
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## Training
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Instatiate an `ClassicalAutoencoder` or `VariationalAutoencoder` object :
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```py
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from easyvae.autoencoder import ClassicalAutoencoder, VariationalAutoencoder
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from easyvae.activations import LeakyReLU
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autoencoder = ClassicalAutoencoder(
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[768, 64, 16],
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[16, 64, 768],
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0.01,
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LeakyReLU()
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)
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# or
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autoencoder = VariationalAutoencoder(
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[768, 64, 16],
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[16, 64, 768],
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0.01,
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LeakyReLU()
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)
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```
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And then via the `train_dataset` method to train over a dataset :
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```py
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autoencoder.train_dataset(data)
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```
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Or via the `train` method to input each data points iteratively :
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```py
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autoencoder.train(v)
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```
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After training, you can save your model via the `save` method and load that model using `load` method :
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```
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autoencoder.save("mymodel.npy)
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autoencoder.load("mymodel.npy")
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```
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## Inference
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Use your `Autoencoder` object with the `encode`, `decode`, `forward` methods like so :
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```py
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example = ...
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code = autoencoder.encode(example)
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output = autoencoder.decode(code)
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output, code = autoencoder.forward(example)
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```
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