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.
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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 rateContributing
Want to contribute? Please read our Contributing guide.
