AIronTools
AIronTools (Beta) is a Python library that provides the user with higher level state-of-the-art deep learning tools built to work with tensorflow as a backend. The main goal of this repository is to enable fast model design for both POCs and production.
Key features:
- Out-of-the-box models ready to be used.
- Block constructor to build customised blocks/models.
- Layer constructor to build customised layers such as sequential, convolutional, self-attention or dense, and combinations of them.
- Preprocessing tools.
- On the fly non-topological hyper-parameter optimization. For now only the dropout regularization is compatible with this feature, in the future others such as l1 and l2 regularization will be compatible too.
- Latent representations for visualization purposes.
Installation
pip install airontools
Custom Keras subclass to build a variational autoencoder (VAE) with airontools and compatible with aironsuit
import numpy as np
import tensorflow as tf
from airontools.constructors.models.unsupervised.vae import VAE
from numpy.random import normal
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=tf.keras.optimizers.Adam(learning_rate=0.001))
model.fit(
tabular_data,
epochs=10,
)
print(f"VAE evaluation: {model.evaluate(tabular_data)['loss']:.4f}")
More examples
see usage examples in aironsuit/examples
1 maintainer
Metadata
1 maintainer
Release files for airontools 0.1.73
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