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Tavolo

tavolo aims to package together valuable modules and functionality written for TensorFlow high-level Keras API for ease of use.

You see, the deep learning world is moving fast, and new ideas keep on coming.

tavolo gathers implementations of these useful ideas from the community (by contribution, from Kaggle etc.) and makes them accessible in a single PyPI hosted package that compliments the tf.keras module.

Documentation

Tavolo documentation

Showcase

tavolo's API is straightforward and adopting its modules is as easy as it gets.

In tavolo, you'll find implementations for basic like PositionalEncoding and non-layer implementations that can ease development, like the LearningRateFinder.

For example, if we wanted to add head a Yang-style attention mechanism into our model and look for the optimal learning rate, it would look something like:

import tensorflow as tf
import tavolo as tvl

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_size, input_length=max_len),
    tvl.seq2vec.YangAttention(n_units=64),  # <--- Add Yang style attention
    tf.keras.layers.Dense(n_hidden_units, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')])

model.compile(optimizer=tf.keras.optimizers.SGD(), loss=tf.keras.losses.BinaryCrossentropy())

# Run learning rate range test
lr_finder = tvl.learning.LearningRateFinder(model=model)

learning_rates, losses = lr_finder.scan(train_data, train_labels, min_lr=0.0001, max_lr=1.0, batch_size=128)

### Plot the results to choose your learning rate

Contributing

Want to contribute? Please read our Contributing guide.