Move to larq organisation (#140)

* Move to larq organisation * Update azure badges
larq · Jul 12, 2019 · 7997a11 · 7997a11
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diff --git a/README.md b/README.md
@@ -1,8 +1,8 @@
 # Larq
 
-[![Azure DevOps builds](https://img.shields.io/azure-devops/build/plumerai/larq/5.svg?logo=azure-devops)](https://plumerai.visualstudio.com/larq/_build/latest?definitionId=5&branchName=master) [![Azure DevOps coverage](https://img.shields.io/azure-devops/coverage/plumerai/larq/5.svg?logo=azure-devops)](https://plumerai.visualstudio.com/larq/_build/latest?definitionId=5&branchName=master) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/larq.svg)](https://pypi.org/project/larq/) [![PyPI](https://img.shields.io/pypi/v/larq.svg)](https://pypi.org/project/larq/) [![PyPI - License](https://img.shields.io/pypi/l/larq.svg)](https://github.com/plumerai/larq/blob/master/LICENSE) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
+[![Azure DevOps builds](https://img.shields.io/azure-devops/build/plumerai/larq/14.svg?logo=azure-devops)](https://plumerai.visualstudio.com/larq/_build/latest?definitionId=14&branchName=master) [![Azure DevOps coverage](https://img.shields.io/azure-devops/coverage/plumerai/larq/14.svg?logo=azure-devops)](https://plumerai.visualstudio.com/larq/_build/latest?definitionId=14&branchName=master) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/larq.svg)](https://pypi.org/project/larq/) [![PyPI](https://img.shields.io/pypi/v/larq.svg)](https://pypi.org/project/larq/) [![PyPI - License](https://img.shields.io/pypi/l/larq.svg)](https://github.com/larq/larq/blob/master/LICENSE) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
 
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/plumerai/larq/master?filepath=docs%2Fexamples) [![Join the community on Spectrum](https://withspectrum.github.io/badge/badge.svg)](https://spectrum.chat/larq)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/larq/larq/master?filepath=docs%2Fexamples) [![Join the community on Spectrum](https://withspectrum.github.io/badge/badge.svg)](https://spectrum.chat/larq)
 
 Larq is an open-source deep learning library for training neural networks with extremely low precision weights and activations, such as Binarized Neural Networks (BNNs).
 

diff --git a/docs/examples/binarynet_advanced_cifar10.ipynb b/docs/examples/binarynet_advanced_cifar10.ipynb
@@ -5,7 +5,7 @@
       "source": [
         "# BinaryNet on CIFAR10 (Advanced)\n",
         "\n",
-        "<a href=\"https://mybinder.org/v2/gh/plumerai/larq/master?filepath=docs%2Fexamples%2Fbinarynet_advanced_cifar10.ipynb\"><button data-md-color-primary=\"blue\">Run on Binder</button></a> <a href=\"https://github.com/plumerai/larq/blob/master/docs/examples/binarynet_advanced_cifar10.ipynb\"><button data-md-color-primary=\"blue\">View on GitHub</button></a>\n",
+        "<a href=\"https://mybinder.org/v2/gh/larq/larq/master?filepath=docs%2Fexamples%2Fbinarynet_advanced_cifar10.ipynb\"><button data-md-color-primary=\"blue\">Run on Binder</button></a> <a href=\"https://github.com/larq/larq/blob/master/docs/examples/binarynet_advanced_cifar10.ipynb\"><button data-md-color-primary=\"blue\">View on GitHub</button></a>\n",
         "\n",
         "In this example we demonstrate how to use Larq to build and train BinaryNet on the CIFAR10 dataset to achieve a validation accuracy of around 90% using a heavy lifting GPU like a Nvidia V100.\n",
         "On a Nvidia V100 it takes approximately 250 minutes to train. Compared to the original papers, [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://arxiv.org/abs/1511.00363), and [Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1](http://arxiv.org/abs/1602.02830), we do not implement image whitening, but we use image augmentation, and a stepped learning rate scheduler."
@@ -293,4 +293,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}
diff --git a/docs/examples/binarynet_cifar10.ipynb b/docs/examples/binarynet_cifar10.ipynb
@@ -5,7 +5,7 @@
       "source": [
         "# BinaryNet on CIFAR10\n",
         "\n",
-        "<a href=\"https://mybinder.org/v2/gh/plumerai/larq/master?filepath=docs%2Fexamples%2Fbinarynet_cifar10.ipynb\"><button data-md-color-primary=\"blue\">Run on Binder</button></a> <a href=\"https://github.com/plumerai/larq/blob/master/docs/examples/binarynet_cifar10.ipynb\"><button data-md-color-primary=\"blue\">View on GitHub</button></a>\n",
+        "<a href=\"https://mybinder.org/v2/gh/larq/larq/master?filepath=docs%2Fexamples%2Fbinarynet_cifar10.ipynb\"><button data-md-color-primary=\"blue\">Run on Binder</button></a> <a href=\"https://github.com/larq/larq/blob/master/docs/examples/binarynet_cifar10.ipynb\"><button data-md-color-primary=\"blue\">View on GitHub</button></a>\n",
         "\n",
         "In this example we demonstrate how to use Larq to build and train BinaryNet on the CIFAR10 dataset to achieve a validation accuracy approximately 83% on laptop hardware.\n",
         "On a Nvidia GTX 1050 Ti Max-Q it takes approximately 200 minutes to train. For simplicity, compared to the original papers [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://arxiv.org/abs/1511.00363), and [Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1](http://arxiv.org/abs/1602.02830), we do not impliment learning rate scaling, or image whitening."
@@ -542,4 +542,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}
diff --git a/docs/examples/mnist.ipynb b/docs/examples/mnist.ipynb
@@ -5,7 +5,7 @@
       "source": [
         "# Introduction to BNNs with Larq\n",
         "\n",
-        "<a href=\"https://mybinder.org/v2/gh/plumerai/larq/master?filepath=docs%2Fexamples%2Fmnist.ipynb\"><button data-md-color-primary=\"blue\">Run on Binder</button></a> <a href=\"https://github.com/plumerai/larq/blob/master/docs/examples/mnist.ipynb\"><button data-md-color-primary=\"blue\">View on GitHub</button></a>\n",
+        "<a href=\"https://mybinder.org/v2/gh/larq/larq/master?filepath=docs%2Fexamples%2Fmnist.ipynb\"><button data-md-color-primary=\"blue\">Run on Binder</button></a> <a href=\"https://github.com/larq/larq/blob/master/docs/examples/mnist.ipynb\"><button data-md-color-primary=\"blue\">View on GitHub</button></a>\n",
         "\n",
         "This tutorial demonstrates how to train a simple binarized Convolutional Neural Network (CNN) to classify MNIST digits. This simple network will achieve approximately 98% accuracy on the MNIST test set. This tutorial uses Larq and the [Keras Sequential API](https://www.tensorflow.org/guide/keras), so creating and training our model will require only a few lines of code."
       ],
@@ -320,4 +320,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}
diff --git a/docs/papers.md b/docs/papers.md
@@ -2,7 +2,7 @@
 
 One of the main focuses of Larq is to accelerate research on neural networks with extremely low precision weights and activations.
 
-If you puplish a paper using using Larq please let us know and [add it to the list below](https://github.com/plumerai/larq/edit/master/docs/papers.md). Feel free to also add the author names, abstract and links to the paper and source code.
+If you puplish a paper using using Larq please let us know and [add it to the list below](https://github.com/larq/larq/edit/master/docs/papers.md). Feel free to also add the author names, abstract and links to the paper and source code.
 
 <h2><a class="headerlink" style="float:right; opacity: 1;" href="https://github.com/plumerai/rethinking-bnn-optimization" title="Source code"><i class="md-icon">code</i></a> <a class="headerlink" style="float:right; opacity: 1;" href="https://arxiv.org/abs/1906.02107" title="arXiv paper"><i class="md-icon">library_books</i></a> Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization</h2>
 

diff --git a/generate_api_docs.py b/generate_api_docs.py
@@ -15,7 +15,7 @@ def callable_to_source_link(obj, scope):
     path = scope.__file__.lstrip(".")
     source = inspect.getsourcelines(obj)
     line = source[-1] + 1 if source[0][0].startswith("@") else source[-1]
-    link = f"https://github.com/plumerai/larq/blob/master{path}#L{line}"
+    link = f"https://github.com/larq/larq/blob/master{path}#L{line}"
     return f'<a class="headerlink code-link" style="float:right;" href="{link}" title="Source code"></a>'
 
 

diff --git a/mkdocs.yml b/mkdocs.yml
@@ -31,8 +31,8 @@ nav:
       - About: about.md
       - FAQ: faq.md
 
-repo_url: https://github.com/plumerai/larq
-repo_name: plumerai/larq
+repo_url: https://github.com/larq/larq
+repo_name: larq/larq
 edit_uri: ""
 theme:
   name: material