Rename package to keras_lmu

.gitignore

@@ -1,4 +1,4 @@
-lmu.egg-info
+keras_lmu.egg-info
 *.swo
 *.swp
 *.ipynb_checkpoints

.nengobones.yml

@@ -1,7 +1,7 @@
-project_name: NengoLMU
-pkg_name: lmu
-repo_name: nengo/lmu
-description: Legendre Memory Units
+project_name: KerasLMU
+pkg_name: keras_lmu
+repo_name: nengo/keras-lmu
+description: Keras implementation of Legendre Memory Units
 
 copyright_start: 2019
 
@@ -16,7 +16,7 @@ manifest_in: {}
 setup_py:
   python_requires: ">=3.6"
   install_req:
-    - scipy
+    - scipy>=1.0.0
     - tensorflow>=2.1.0
   tests_req:
     - pytest>=6.1.0
@@ -31,7 +31,6 @@ setup_py:
     - numpydoc>=0.6
   classifiers:
     - "Development Status :: 3 - Alpha"
-    - "Framework :: Nengo"
     - "Intended Audience :: Science/Research"
     - "License :: Free for non-commercial use"
     - "Operating System :: OS Independent"
@@ -51,10 +50,10 @@ docs_conf_py:
   html_redirects:
     getting_started.html: getting-started.html
   autoautosummary_change_modules:
-    lmu:
-      - lmu.layers.LMUCell
-      - lmu.layers.LMU
-      - lmu.layers.LMUFFT
+    keras_lmu:
+      - keras_lmu.layers.LMUCell
+      - keras_lmu.layers.LMU
+      - keras_lmu.layers.LMUFFT
   extensions:
     - nengo_sphinx_theme.ext.autoautosummary
   doctest_setup:

CHANGES.rst

@@ -19,9 +19,20 @@ Release history
    - Removed
    - Fixed
 
-0.2.1 (unreleased)
+0.3.0 (unreleased)
 ==================
 
+**Changed**
+
+- Renamed module from ``lmu`` to ``keras_lmu`` (so it will now be imported via
+  ``import keras_lmu``), renamed package from ``lmu`` to
+  ``keras-lmu`` (so it will now be installed via ``pip install keras-lmu``), and
+  changed any references to "NengoLMU" to "KerasLMU" (since this implementation is
+  based in the Keras framework rather than Nengo). In the future the ``lmu`` namespace
+  will be used as a meta-package to encapsulate LMU implementations in different
+  frameworks. (`#24`_)
+
+.. _#24: https://github.com/abr/lmu/pull/24
 
 0.2.0 (November 2, 2020)
 ========================
@@ -80,7 +91,7 @@ Release history
 0.1.0 (June 22, 2020)
 =====================
 
-Initial release of NengoLMU 0.1.0! Supports Python 3.5+.
+Initial release of KerasLMU 0.1.0! Supports Python 3.5+.
 
 The API is considered unstable; parts are likely to change in the future.
 

CONTRIBUTING.rst

@@ -1,19 +1,19 @@
 .. Automatically generated by nengo-bones, do not edit this file directly
 
 ************************
-Contributing to NengoLMU
+Contributing to KerasLMU
 ************************
 
 Issues and pull requests are always welcome!
-We appreciate help from the community to make NengoLMU better.
+We appreciate help from the community to make KerasLMU better.
 
 Filing issues
 =============
 
-If you find a bug in NengoLMU,
+If you find a bug in KerasLMU,
 or think that a certain feature is missing,
 please consider
-`filing an issue <https://github.com/nengo/lmu/issues>`_!
+`filing an issue <https://github.com/nengo/keras-lmu/issues>`_!
 Please search the currently open issues first
 to see if your bug or feature request already exists.
 If so, feel free to add a comment to the issue
@@ -22,7 +22,7 @@ so that we know that multiple people are affected.
 Making pull requests
 ====================
 
-If you want to fix a bug or add a feature to NengoLMU,
+If you want to fix a bug or add a feature to KerasLMU,
 we welcome pull requests.
 Ensure that you fill out all sections of the pull request template,
 deleting the comments as you go.

CONTRIBUTORS.rst

@@ -1,11 +1,11 @@
 .. Automatically generated by nengo-bones, do not edit this file directly
 
 *********************
-NengoLMU contributors
+KerasLMU contributors
 *********************
 
-See https://github.com/nengo/lmu/graphs/contributors
-for a list of the people who have committed to NengoLMU.
+See https://github.com/nengo/keras-lmu/graphs/contributors
+for a list of the people who have committed to KerasLMU.
 Thank you for your contributions!
 
 For the full list of the many contributors to the Nengo ecosystem,

LICENSE.rst

@@ -1,18 +1,18 @@
 .. Automatically generated by nengo-bones, do not edit this file directly
 
 ****************
-NengoLMU license
+KerasLMU license
 ****************
 
 Copyright (c) 2019-2020 Applied Brain Research
 
-NengoLMU is made available under a proprietary license
+KerasLMU is made available under a proprietary license
 that permits using, copying, sharing, and making derivative works from
-NengoLMU and its source code for any non-commercial purpose,
+KerasLMU and its source code for any non-commercial purpose,
 as long as the above copyright notice and this permission notice
 are included in all copies or substantial portions of the software.
 
-If you would like to use NengoLMU commercially,
+If you would like to use KerasLMU commercially,
 licenses can be purchased from Applied Brain Research.
 Please contact info@appliedbrainresearch.com for more information.
 

README.rst

@@ -1,9 +1,9 @@
-Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
-----------------------------------------------------------------------------------
+KerasLMU: Recurrent neural networks using Legendre Memory Units
+---------------------------------------------------------------
 
 `Paper <https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.pdf>`_
 
-This is a python software library containing various implementations of the
+This is a Keras-based implementation of the
 Legendre Memory Unit (LMU). The LMU is a novel memory cell for recurrent neural
 networks that dynamically maintains information across long windows of time using
 relatively few resources. It has been shown to perform as well as standard LSTM or

docs/api-reference.rst

@@ -9,7 +9,7 @@ API reference
 LMU Layers
 ==========
 
-.. automodule:: lmu.layers
+.. automodule:: keras_lmu.layers
 
-    .. autoautosummary:: lmu.layers
+    .. autoautosummary:: keras_lmu.layers
         :nosignatures:

docs/basic-usage.rst

@@ -5,15 +5,15 @@ Basic usage
 ***********
 
 The standard Legendre Memory Unit (LMU) layer
-implementation in NengoLMU is defined in the
-``lmu.LMU`` class. The following code creates
+implementation in KerasLMU is defined in the
+``keras_lmu.LMU`` class. The following code creates
 a new LMU layer:
 
 .. testcode::
 
-   import lmu
+   import keras_lmu
 
-   lmu_layer = lmu.LMU(
+   lmu_layer = keras_lmu.LMU(
        memory_d=1,
        order=256,
        theta=784,
@@ -30,7 +30,7 @@ and ``units`` represents the dimensionality of the hidden component.
 To learn more about these parameters, check out
 the :ref:`LMU class API reference <api-reference-lc>`.
 
-Creating NengoLMU layers
+Creating KerasLMU layers
 ------------------------
 
 The ``LMU`` class functions as a standard

docs/citation.rst

@@ -2,7 +2,7 @@
 Citation
 ********
 
-If you would like to cite NengoLMU in your research, please cite `this
+If you would like to cite KerasLMU in your research, please cite `this
 paper <http://compneuro.uwaterloo.ca/files/publications/voelker.2019.lmu.pdf>`_:
 
    Aaron R. Voelker, Ivana Kajić, and Chris Eliasmith. Legendre Memory Units:

docs/conf.py

@@ -4,7 +4,7 @@
 
 import os
 
-import lmu
+import keras_lmu
 
 extensions = [
     "sphinx.ext.autodoc",
@@ -31,7 +31,7 @@
 
 # -- sphinx.ext.doctest
 doctest_global_setup = """
-import lmu
+import keras_lmu
 import numpy as np
 import tensorflow as tf
 """
@@ -52,23 +52,23 @@
 
 # -- notfound.extension
 notfound_template = "404.html"
-notfound_urls_prefix = "/lmu/"
+notfound_urls_prefix = "/keras-lmu/"
 
 # -- numpydoc config
 numpydoc_show_class_members = False
 
 # -- nengo_sphinx_theme.ext.autoautosummary
 autoautosummary_change_modules = {
-    "lmu": [
-        "lmu.layers.LMUCell",
-        "lmu.layers.LMU",
-        "lmu.layers.LMUFFT",
+    "keras_lmu": [
+        "keras_lmu.layers.LMUCell",
+        "keras_lmu.layers.LMU",
+        "keras_lmu.layers.LMUFFT",
     ],
 }
 
 # -- nengo_sphinx_theme.ext.sourcelinks
-sourcelinks_module = "lmu"
-sourcelinks_url = "https://github.com/nengo/lmu"
+sourcelinks_module = "keras_lmu"
+sourcelinks_url = "https://github.com/nengo/keras-lmu"
 
 # -- sphinx
 nitpicky = True
@@ -84,20 +84,20 @@
 linkcheck_anchors = True
 default_role = "py:obj"
 pygments_style = "sphinx"
-user_agent = "lmu"
+user_agent = "keras_lmu"
 
-project = "NengoLMU"
+project = "KerasLMU"
 authors = "Applied Brain Research"
 copyright = "2019-2020 Applied Brain Research"
-version = ".".join(lmu.__version__.split(".")[:2])  # Short X.Y version
-release = lmu.__version__  # Full version, with tags
+version = ".".join(keras_lmu.__version__.split(".")[:2])  # Short X.Y version
+release = keras_lmu.__version__  # Full version, with tags
 
 # -- HTML output
 templates_path = ["_templates"]
 html_static_path = ["_static"]
 html_theme = "nengo_sphinx_theme"
-html_title = "NengoLMU {0} docs".format(release)
-htmlhelp_basename = "NengoLMU"
+html_title = "KerasLMU {0} docs".format(release)
+htmlhelp_basename = "KerasLMU"
 html_last_updated_fmt = ""  # Default output format (suppressed)
 html_show_sphinx = False
 html_favicon = os.path.join("_static", "favicon.ico")

docs/examples.rst

@@ -9,7 +9,7 @@ are Jupyter notebooks and if you would like to run them yourself, refer to
 the `Jupyter documentation <https://jupyter-notebook.readthedocs.io/en/latest/>`_ for
 instructions on how to install and run Jupyter.
 
-If you would like to see NengoLMU used to solve the Permuted Sequential MNIST task,
+If you would like to see KerasLMU used to solve the Permuted Sequential MNIST task,
 which is the problem used in the
 `original paper <https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.pdf>`_,
 you can look through the example below:

docs/examples/psMNIST.ipynb

@@ -27,7 +27,7 @@
     "to perform the task successfully, the network needs to process information across the\n",
     "whole length of the input sequence.\n",
     "\n",
-    "The following notebook uses a single NengoLMU layer inside a simple TensorFlow model to\n",
+    "The following notebook uses a single KerasLMU layer inside a simple TensorFlow model to\n",
     "showcase the accuracy and efficiency of performing the psMNIST task using these novel\n",
     "memory cells. Using the LMU for this task currently produces state-of-the-art results\n",
     "this task ([see\n",
@@ -47,7 +47,7 @@
     "from IPython.display import Image, display\n",
     "import tensorflow as tf\n",
     "\n",
-    "import lmu"
+    "import keras_lmu"
    ]
   },
   {
@@ -257,7 +257,7 @@
     "n_pixels = X_train.shape[1]\n",
     "\n",
     "lmu_layer = tf.keras.layers.RNN(\n",
-    "    lmu.LMUCell(\n",
+    "    keras_lmu.LMUCell(\n",
     "        memory_d=1,\n",
     "        order=256,\n",
     "        theta=n_pixels,\n",