Cherry pick keras-team/keras 2.1.6 missing 4 commits

docs/templates/getting-started/faq.md

@@ -622,5 +622,5 @@ module via
 import h5py
 ```
 
-If it imports without error it is installed ortherwise you can find detaild
+If it imports without error it is installed otherwise you can find detailed
 installation instructions here: http://docs.h5py.org/en/latest/build.html

docs/templates/why-use-keras.md

@@ -30,13 +30,13 @@ Keras has also been adopted by researchers at large scientific organizations, in
 
 Your Keras models can be easily deployed across a greater range of platforms than any other deep learning framework:
 
-- On iOS, via [Apple’s CoreML](https://developer.apple.com/documentation/coreml) (Keras support officially provided by Apple)
-- On Android, via the TensorFlow Android runtime. Example: [Not Hotdog app](https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3)
-- In the browser, via GPU-accelerated JavaScript runtimes such as [Keras.js](https://transcranial.github.io/keras-js/#/) and [WebDNN](https://mil-tokyo.github.io/webdnn/)
-- On Google Cloud, via [TensorFlow-Serving](https://www.tensorflow.org/serving/)
-- [In a Python webapp backend (such as a Flask app)](https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html)
-- On the JVM, via [DL4J model import provided by SkyMind](https://deeplearning4j.org/model-import-keras)
-- On Raspberry Pi
+- On iOS, via [Apple’s CoreML](https://developer.apple.com/documentation/coreml) (Keras support officially provided by Apple). Here's [a tutorial](https://www.pyimagesearch.com/2018/04/23/running-keras-models-on-ios-with-coreml/).
+- On Android, via the TensorFlow Android runtime. Example: [Not Hotdog app](https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3).
+- In the browser, via GPU-accelerated JavaScript runtimes such as [Keras.js](https://transcranial.github.io/keras-js/#/) and [WebDNN](https://mil-tokyo.github.io/webdnn/).
+- On Google Cloud, via [TensorFlow-Serving](https://www.tensorflow.org/serving/).
+- [In a Python webapp backend (such as a Flask app)](https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html).
+- On the JVM, via [DL4J model import provided by SkyMind](https://deeplearning4j.org/model-import-keras).
+- On Raspberry Pi.
 
 ---
 

keras/__init__.py

@@ -23,4 +23,4 @@
 from .models import Model
 from .models import Sequential
 
-__version__ = '2.1.5'
+__version__ = '2.1.6'

keras/applications/imagenet_utils.py

@@ -152,13 +152,16 @@ def preprocess_input(x, data_format=None, mode='caffe'):
     # Arguments
         x: Input Numpy or symbolic tensor, 3D or 4D.
         data_format: Data format of the image tensor/array.
-        mode: One of "caffe", "tf".
+        mode: One of "caffe", "tf" or "torch".
             - caffe: will convert the images from RGB to BGR,
                 then will zero-center each color channel with
                 respect to the ImageNet dataset,
                 without scaling.
             - tf: will scale pixels between -1 and 1,
                 sample-wise.
+            - torch: will scale pixels between 0 and 1 and then
+                will normalize each channel with respect to the
+                ImageNet dataset.
 
     # Returns
         Preprocessed tensor or Numpy array.

keras/engine/topology.py

@@ -1895,7 +1895,7 @@ def updates(self):
         """Retrieves the model's updates.
 
         Will only include updates that are either
-        inconditional, or conditional on inputs to this model
+        unconditional, or conditional on inputs to this model
         (e.g. will not include updates that depend on tensors
         that aren't inputs to this model).
 
@@ -1924,7 +1924,7 @@ def losses(self):
         """Retrieves the model's losses.
 
         Will only include losses that are either
-        inconditional, or conditional on inputs to this model
+        unconditional, or conditional on inputs to this model
         (e.g. will not include losses that depend on tensors
         that aren't inputs to this model).
 

setup.py

@@ -3,12 +3,12 @@
 
 
 setup(name='Keras',
-      version='2.1.5',
+      version='2.1.6',
       description='Deep Learning for humans',
       author='Francois Chollet',
       author_email='francois.chollet@gmail.com',
       url='https://github.com/keras-team/keras',
-      download_url='https://github.com/keras-team/keras/tarball/2.1.5',
+      download_url='https://github.com/keras-team/keras/tarball/2.1.6',
       license='MIT',
       install_requires=['numpy>=1.9.1',
                         'scipy>=0.14',

tests/test_model_saving.py

@@ -529,7 +529,7 @@ def test_saving_model_with_long_layer_names():
 
     os.remove(fname)
 
-    # The chunking of layer names array should have happend.
+    # The chunking of layer names array should have happened.
     assert n_layer_names_arrays > 0
 
     out2 = model.predict(x)
@@ -574,7 +574,7 @@ def test_saving_model_with_long_weights_names():
 
     os.remove(fname)
 
-    # The chunking of layer names array should have happend.
+    # The chunking of layer names array should have happened.
     assert n_weight_names_arrays > 0
 
     out2 = model.predict(x)