Machine learning tools to complement the AIronSuit package.
Project description
AIronTools
AIronTools (Beta) is a Python library that provides the user with deep learning tools built to work with tensorflow as a backend.
Key features:
- Model constructor that allows multiple models to be optimized in parallel across multiple GPUs.
- Block constructor to build customised blocks/models.
- Layer constructor to build customised layers.
- Preprocessing utils.
Installation
pip install airontools
Custom Keras subclass to build a variational autoencoder (VAE) with airontools and compatible with aironsuit
import numpy as np
from numpy.random import normal
from tensorflow.keras.optimizers import Adam
from airontools.constructors.models.unsupervised.vae import VAE
tabular_data = np.concatenate(
[
normal(loc=0.5, scale=1, size=(100, 10)),
normal(loc=-0.5, scale=1, size=(100, 10)),
]
)
model = VAE(
input_shape=tabular_data.shape[1:],
latent_dim=3,
)
model.compile(optimizer=Adam(learning_rate=0.001))
model.fit(
tabular_data,
epochs=10,
)
print("VAE evaluation:", float(model.evaluate(tabular_data)["loss"]))
More examples
see usage examples in aironsuit/examples
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