updated links to point to the new new githup repo (keras-team#8790)

CONTRIBUTING.md

@@ -36,7 +36,7 @@ You can also use Github issues to request features you would like to see in Kera
 
 ## Requests for Contributions
 
-[This is the board](https://github.com/fchollet/keras/projects/1) where we list current outstanding issues and features to be added. If you want to start contributing to Keras, this is the place to start.
+[This is the board](https://github.com/keras-team/keras/projects/1) where we list current outstanding issues and features to be added. If you want to start contributing to Keras, this is the place to start.
 
 
 ---
@@ -45,8 +45,8 @@ You can also use Github issues to request features you would like to see in Kera
 
 **Where should I submit my pull request?**
 
-1. **Keras improvements and bugfixes** go to the [Keras `master` branch](https://github.com/fchollet/keras/tree/master).
-2. **Experimental new features** such as layers and datasets go to [keras-contrib](https://github.com/farizrahman4u/keras-contrib). Unless it is a new feature listed in [Requests for Contributions](https://github.com/fchollet/keras/projects/1), in which case it belongs in core Keras. If you think your feature belongs in core Keras, you can submit a design doc to explain your feature and argue for it (see explanations below).
+1. **Keras improvements and bugfixes** go to the [Keras `master` branch](https://github.com/keras-team/keras/tree/master).
+2. **Experimental new features** such as layers and datasets go to [keras-contrib](https://github.com/farizrahman4u/keras-contrib). Unless it is a new feature listed in [Requests for Contributions](https://github.com/keras-team/keras/projects/1), in which case it belongs in core Keras. If you think your feature belongs in core Keras, you can submit a design doc to explain your feature and argue for it (see explanations below).
 
 Please note that PRs that are primarily about **code style** (as opposed to fixing bugs, improving docs, or adding new functionality) will likely be rejected.
 
@@ -90,4 +90,4 @@ Here's a quick guide to submitting your improvements:
 
 ## Adding new examples
 
-Even if you don't contribute to the Keras source code, if you have an application of Keras that is concise and powerful, please consider adding it to our collection of examples. [Existing examples](https://github.com/fchollet/keras/tree/master/examples) show idiomatic Keras code: make sure to keep your own script in the same spirit.
+Even if you don't contribute to the Keras source code, if you have an application of Keras that is concise and powerful, please consider adding it to our collection of examples. [Existing examples](https://github.com/keras-team/keras/tree/master/examples) show idiomatic Keras code: make sure to keep your own script in the same spirit.

ISSUE_TEMPLATE.md

@@ -3,7 +3,7 @@ Please make sure that the boxes below are checked before you submit your issue.
 Thank you!
 
 - [ ] Check that you are up-to-date with the master branch of Keras. You can update with:
-pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps
+pip install git+git://github.com/keras-team/keras.git --upgrade --no-deps
 
 - [ ] If running on TensorFlow, check that you are up-to-date with the latest version. The installation instructions can be found [here](https://www.tensorflow.org/get_started/os_setup).
 

README.md

@@ -2,8 +2,8 @@
 
 ![Keras logo](https://s3.amazonaws.com/keras.io/img/keras-logo-2018-large-1200.png)
 
-[![Build Status](https://travis-ci.org/fchollet/keras.svg?branch=master)](https://travis-ci.org/fchollet/keras)
-[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/fchollet/keras/blob/master/LICENSE)
+[![Build Status](https://travis-ci.org/keras-team/keras.svg?branch=master)](https://travis-ci.org/keras-team/keras)
+[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/keras-team/keras/blob/master/LICENSE)
 
 ## You have just found Keras.
 
@@ -104,7 +104,7 @@ For a more in-depth tutorial about Keras, you can check out:
 - [Getting started with the Sequential model](https://keras.io/getting-started/sequential-model-guide)
 - [Getting started with the functional API](https://keras.io/getting-started/functional-api-guide)
 
-In the [examples folder](https://github.com/fchollet/keras/tree/master/examples) of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.
+In the [examples folder](https://github.com/keras-team/keras/tree/master/examples) of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.
 
 
 ------------------
@@ -143,7 +143,7 @@ pip install keras
 First, clone Keras using `git`:
 
 ```sh
-git clone https://github.com/fchollet/keras.git
+git clone https://github.com/keras-team/keras.git
 ```
 
  Then, `cd` to the Keras folder and run the install command:
@@ -169,7 +169,7 @@ You can ask questions and join the development discussion:
 - On the [Keras Google group](https://groups.google.com/forum/#!forum/keras-users).
 - On the [Keras Slack channel](https://kerasteam.slack.com). Use [this link](https://keras-slack-autojoin.herokuapp.com/) to request an invitation to the channel.
 
-You can also post **bug reports and feature requests** (only) in [Github issues](https://github.com/fchollet/keras/issues). Make sure to read [our guidelines](https://github.com/fchollet/keras/blob/master/CONTRIBUTING.md) first.
+You can also post **bug reports and feature requests** (only) in [Github issues](https://github.com/keras-team/keras/issues). Make sure to read [our guidelines](https://github.com/keras-team/keras/blob/master/CONTRIBUTING.md) first.
 
 
 ------------------

docker/Dockerfile

@@ -35,8 +35,8 @@ RUN conda install -y python=${python_version} && \
     conda install Pillow scikit-learn notebook pandas matplotlib mkl nose pyyaml six h5py && \
     conda install theano pygpu bcolz && \
     pip install sklearn_pandas && \
-    git clone git://github.com/fchollet/keras.git /src && pip install -e /src[tests] && \
-    pip install git+git://github.com/fchollet/keras.git && \
+    git clone git://github.com/keras-team/keras.git /src && pip install -e /src[tests] && \
+    pip install git+git://github.com/keras-team/keras.git && \
     conda clean -yt
 
 ADD theanorc /home/keras/.theanorc

docs/mkdocs.yml

@@ -2,7 +2,7 @@ site_name: Keras Documentation
 theme: readthedocs
 # theme_dir: theme
 docs_dir: sources
-repo_url: http://github.com/fchollet/keras
+repo_url: http://github.com/keras-team/keras
 site_url: http://keras.io/
 site_description: 'Documentation for Keras, the Python Deep Learning library.'
 

docs/templates/getting-started/faq.md

@@ -32,7 +32,7 @@ Please cite Keras in your publications if it helps your research. Here is an exa
   author={Chollet, Fran\c{c}ois and others},
   year={2015},
   publisher={GitHub},
-  howpublished={\url{https://github.com/fchollet/keras}},
+  howpublished={\url{https://github.com/keras-team/keras}},
 }
 ```
 
@@ -318,7 +318,7 @@ You can do batch training using `model.train_on_batch(x, y)` and `model.test_on_
 
 Alternatively, you can write a generator that yields batches of training data and use the method `model.fit_generator(data_generator, steps_per_epoch, epochs)`.
 
-You can see batch training in action in our [CIFAR10 example](https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py).
+You can see batch training in action in our [CIFAR10 example](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py).
 
 ---
 
@@ -497,9 +497,9 @@ For a detailed example of how to use such a pre-trained model for feature extrac
 
 The VGG16 model is also the basis for several Keras example scripts:
 
-- [Style transfer](https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py)
-- [Feature visualization](https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py)
-- [Deep dream](https://github.com/fchollet/keras/blob/master/examples/deep_dream.py)
+- [Style transfer](https://github.com/keras-team/keras/blob/master/examples/neural_style_transfer.py)
+- [Feature visualization](https://github.com/keras-team/keras/blob/master/examples/conv_filter_visualization.py)
+- [Deep dream](https://github.com/keras-team/keras/blob/master/examples/deep_dream.py)
 
 ---
 
@@ -564,7 +564,7 @@ import random as rn
 # have reproducible behavior for certain hash-based operations.
 # See these references for further details:
 # https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSEED
-# https://github.com/fchollet/keras/issues/2280#issuecomment-306959926
+# https://github.com/keras-team/keras/issues/2280#issuecomment-306959926
 
 import os
 os.environ['PYTHONHASHSEED'] = '0'

docs/templates/getting-started/sequential-model-guide.md

@@ -134,7 +134,7 @@ model.fit(data, one_hot_labels, epochs=10, batch_size=32)
 
 Here are a few examples to get you started!
 
-In the [examples folder](https://github.com/fchollet/keras/tree/master/examples), you will also find example models for real datasets:
+In the [examples folder](https://github.com/keras-team/keras/tree/master/examples), you will also find example models for real datasets:
 
 - CIFAR10 small images classification: Convolutional Neural Network (CNN) with realtime data augmentation
 - IMDB movie review sentiment classification: LSTM over sequences of words

docs/templates/losses.md

@@ -20,7 +20,7 @@ You can either pass the name of an existing loss function, or pass a TensorFlow/
 
 The actual optimized objective is the mean of the output array across all datapoints.
 
-For a few examples of such functions, check out the [losses source](https://github.com/fchollet/keras/blob/master/keras/losses.py).
+For a few examples of such functions, check out the [losses source](https://github.com/keras-team/keras/blob/master/keras/losses.py).
 
 ## Available loss functions
 

docs/templates/regularizers.md

@@ -43,4 +43,4 @@ model.add(Dense(64, input_dim=64,
 ```
 
 Alternatively, you can write your regularizers in an object-oriented way;
-see the [keras/regularizers.py](https://github.com/fchollet/keras/blob/master/keras/regularizers.py) module for examples.
+see the [keras/regularizers.py](https://github.com/keras-team/keras/blob/master/keras/regularizers.py) module for examples.

examples/neural_doodle.py

@@ -35,7 +35,7 @@
 - [Torch code for fast-neural-doodle](https://github.com/DmitryUlyanov/fast-neural-doodle)
 - [Torch code for online-neural-doodle](https://github.com/DmitryUlyanov/online-neural-doodle)
 - [Paper Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](http://arxiv.org/abs/1603.03417)
-- [Discussion on parameter tuning](https://github.com/fchollet/keras/issues/3705)
+- [Discussion on parameter tuning](https://github.com/keras-team/keras/issues/3705)
 
 # Resources
 

setup.py

@@ -7,8 +7,8 @@
       description='Deep Learning for Python',
       author='Francois Chollet',
       author_email='[email protected]',
-      url='https://github.com/fchollet/keras',
-      download_url='https://github.com/fchollet/keras/tarball/2.1.2',
+      url='https://github.com/keras-team/keras',
+      download_url='https://github.com/keras-team/keras/tarball/2.1.2',
       license='MIT',
       install_requires=['numpy>=1.9.1',
                         'scipy>=0.14',

tests/keras/layers/recurrent_test.py

@@ -232,7 +232,7 @@ def test_trainability(layer_class):
 @keras_test
 def test_masking_layer():
     ''' This test based on a previously failing issue here:
-    https://github.com/fchollet/keras/issues/1567
+    https://github.com/keras-team/keras/issues/1567
     '''
     inputs = np.random.random((6, 3, 4))
     targets = np.abs(np.random.random((6, 3, 5)))
@@ -355,7 +355,7 @@ def test_initial_states_as_other_inputs(layer_class):
 @rnn_test
 def test_specify_state_with_masking(layer_class):
     ''' This test based on a previously failing issue here:
-    https://github.com/fchollet/keras/issues/1567
+    https://github.com/keras-team/keras/issues/1567
     '''
     num_states = 2 if layer_class is recurrent.LSTM else 1