Version update - 0.5.0

Version update - 0.5.0

Author: eliorc

.circleci/config.yml

@@ -56,6 +56,10 @@ jobs:
             source venv/bin/activate
             pytest -s --cov=tavolo tests/
             codecov
+  test-3.8:
+    <<: *test-template
+    docker:
+      - image: circleci/python:3.6
   test-3.6:
     <<: *test-template
     docker:

README.rst

@@ -6,10 +6,10 @@
 
 ------------
 
-.. image:: https://img.shields.io/badge/python-3.5%20%7C%203.6%20%7C%203.7-blue.svg
+.. image:: https://img.shields.io/badge/python-3.5%20%7C%203.6%20%7C%203.7%20%7C%203.8-blue.svg
     :alt: Supported Python versions
 
-.. image:: https://img.shields.io/badge/tensorflow-2.0.0--rc0-orange.svg
+.. image:: https://img.shields.io/badge/tensorflow-2.0-orange.svg
     :alt: Supported TensorFlow versions
 
 .. image:: https://codecov.io/gh/eliorc/tavolo/branch/master/graph/badge.svg
@@ -27,8 +27,7 @@ Tavolo
 | 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.
-|
-| *Notice: tavolo is developed for TensorFlow 2.0 (right now on pre-release), most modules will work with earlier versions but some won't (like LayerNormalization)*
+
 
 Documentation
 -------------
@@ -41,8 +40,8 @@ Showcase
 --------
 
 | tavolo's API is straightforward and adopting its modules is as easy as it gets.
-| In tavolo, you'll find implementations for basic layers like `LayerNormalization`_ to complex modules like the Transformer's
-  `MultiHeadedSelfAttention`_. You'll also find non-layer implementations that can ease development, like the `LearningRateFinder`_.
+| In tavolo, you'll find implementations for basic layers like `PositionalEncoding`_ to complex modules like the Transformer's
+  `MultiHeadedAttention`_. You'll also find non-layer implementations that can ease development, like the `LearningRateFinder`_.
 | For example, if we wanted to add head a multi-headed attention mechanism into our model and look for the optimal learning rate, it would look something like:
 
 .. code-block:: python3
@@ -52,7 +51,7 @@ Showcase
 
     model = tf.keras.Sequential([
         tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_size, input_length=max_len),
-        tvl.seq2seq.MultiHeadedSelfAttention(n_heads=8),  # <--- Add self attention
+        tvl.seq2seq.MultiHeadedAttention(n_heads=8),  # <--- Add self attention
         tf.keras.layers.LSTM(n_lstm_units, return_sequences=True),
         tf.keras.layers.Dense(n_hidden_units, activation='relu'),
         tf.keras.layers.Dense(1, activation='sigmoid')])
@@ -70,8 +69,8 @@ Showcase
 .. _`TensorFlow`: https://www.tensorflow.org/
 .. _`Kaggle`: https://www.kaggle.com
 .. _`tf.keras`: https://www.tensorflow.org/guide/keras
-.. _`LayerNormalization`: https://tavolo.readthedocs.io/en/latest/normalization.html#layer-normalization
-.. _`MultiHeadedSelfAttention`: https://tavolo.readthedocs.io/en/latest/seq2seq.html#multi-headed-self-attention
+.. _`PositionalEncoding`: https://tavolo.readthedocs.io/en/latest/embeddings.html#module-embeddings.PositionalEncoding
+.. _`MultiHeadedAttention`: https://tavolo.readthedocs.io/en/latest/seq2seq.html#multi-headed-self-attention
 .. _`LearningRateFinder`: https://tavolo.readthedocs.io/en/latest/learning.html#learning-rate-finder
 
 

docs/source/index.rst

@@ -9,16 +9,13 @@ Welcome to tavolo's documentation!
 | 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.
 
-.. warning::
-
-    tavolo is developed for TensorFlow 2.0 (right now on pre-release), most modules will work with earlier versions but some won't (like LayerNormalization)
 
 Showcase
 --------
 
 | tavolo's API is straightforward and adopting its modules is as easy as it gets.
-| In tavolo, you'll find implementations for basic layers like :ref:`layer_normalization` to complex modules like the Transformer's
-  :ref:`multi_headed_self_attention`. You'll also find non-layer implementations that can ease development, like the :ref:`learning_rate_finder`.
+| In tavolo, you'll find implementations for basic layers like :ref:`positional_encoding` to complex modules like the Transformer's
+  :ref:`multi_headed_attention`. You'll also find non-layer implementations that can ease development, like the :ref:`learning_rate_finder`.
 | For example, if we wanted to add head a multi-headed attention mechanism into our model and look for the optimal learning rate, it would look something like:
 
 .. code-block:: python3
@@ -28,7 +25,7 @@ Showcase
 
     model = tf.keras.Sequential([
         tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_size, input_length=max_len),
-        tvl.seq2seq.MultiHeadedSelfAttention(n_heads=8),  # <--- Add self attention
+        tvl.seq2seq.MultiHeadedAttention(n_heads=8),  # <--- Add self attention
         tf.keras.layers.LSTM(n_lstm_units, return_sequences=True),
         tf.keras.layers.Dense(n_hidden_units, activation='relu'),
         tf.keras.layers.Dense(1, activation='sigmoid')])
@@ -59,7 +56,6 @@ Showcase
 
    embeddings
    learning
-   normalization
    seq2seq
    seq2vec
 

docs/source/normalization.rst

@@ -10,9 +10,9 @@ Layers mapping sequences to sequences
 
 -------
 
-.. _`multi_headed_self_attention`:
+.. _`multi_headed_attention`:
 
-``MultiHeadedSelfAttention``
+``MultiHeadedAttention``
 ++++++++++++++++++++++++++++
 
-.. automodule:: seq2seq.MultiHeadedSelfAttention
+.. automodule:: seq2seq.MultiHeadedAttention

docs/source/seq2seq.rst

@@ -1,6 +1,6 @@
 from setuptools import setup
 
-VERSION = '0.4.1'
+VERSION = '0.5.0'
 
 setup(name='tavolo',
       version=VERSION,

setup.py

@@ -1,8 +1,7 @@
 __name__ = 'tavolo'
-__version__ = '0.4.1'
+__version__ = '0.5.0'
 
 from . import embeddings
-from . import normalization
 from . import seq2vec
 from . import seq2seq
 from . import learning