Merge pull request #193 from awslabs/keras-mxnet-2.2.4

Keras-mxnet 2.2.4 release

.coveragerc

@@ -15,6 +15,7 @@ fail_under = 85
 show_missing = True
 omit =
     keras/applications/*
+    keras/preprocessing/*
     keras/datasets/*
     keras/layers/cudnn_recurrent.py
     keras/legacy/*

.github/ISSUE_TEMPLATE/a--tensorflow-backend-users.md

@@ -0,0 +1,17 @@
+---
+name: a) TensorFlow backend users
+about: Select this is you're using Keras with the TensorFlow backend (default).
+
+---
+
+Please make sure that the boxes below are checked before you submit your issue.
+If your issue is an **implementation question**, please ask your question on [StackOverflow](http://stackoverflow.com/questions/tagged/keras) or [on the Keras Slack channel](https://keras-slack-autojoin.herokuapp.com/) instead of opening a GitHub 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/keras-team/keras.git --upgrade --no-deps`
+
+- [ ] Check that your version of TensorFlow is up-to-date. The installation instructions can be found [here](https://www.tensorflow.org/get_started/os_setup).
+
+- [ ] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).

.github/ISSUE_TEMPLATE/b--theano-backend-users.md

@@ -0,0 +1,18 @@
+---
+name: b) Theano backend users
+about: Select this if you're using Keras with the Theano backend.
+
+---
+
+Please make sure that the boxes below are checked before you submit your issue.
+If your issue is an **implementation question**, please ask your question on [StackOverflow](http://stackoverflow.com/questions/tagged/keras) or [on the Keras Slack channel](https://keras-slack-autojoin.herokuapp.com/) instead of opening a GitHub 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/keras-team/keras.git --upgrade --no-deps`
+
+- [ ] Check that you are up-to-date with the master branch of Theano. You can update with:
+`pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps`
+
+- [ ] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).

.github/ISSUE_TEMPLATE/c--cntk-backend-users.md

@@ -0,0 +1,17 @@
+---
+name: c) CNTK backend users
+about: Select this if you're using Keras with the CNTK backend.
+
+---
+
+Please make sure that the boxes below are checked before you submit your issue.
+If your issue is an **implementation question**, please ask your question on [StackOverflow](http://stackoverflow.com/questions/tagged/keras) or [on the Keras Slack channel](https://keras-slack-autojoin.herokuapp.com/) instead of opening a GitHub 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/keras-team/keras.git --upgrade --no-deps`
+
+- [ ] Check that your version of CNTK is up-to-date.
+
+- [ ] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).

.travis.yml

@@ -1,14 +1,15 @@
 sudo: required
 dist: trusty
 language: python
+cache:
+  directories:
+    - $HOME/.theano
 matrix:
     include:
-        - python: 2.7
-          env: KERAS_BACKEND=tensorflow TEST_MODE=PEP8
         - python: 2.7
           env: KERAS_BACKEND=tensorflow TEST_MODE=INTEGRATION_TESTS
         - python: 3.6
-          env: KERAS_BACKEND=tensorflow TEST_MODE=DOC
+          env: KERAS_BACKEND=tensorflow TEST_MODE=PEP8_DOC
         - python: 2.7
           env: KERAS_BACKEND=tensorflow
         - python: 3.6
@@ -21,10 +22,6 @@ matrix:
           env: KERAS_BACKEND=cntk PYTHONWARNINGS=ignore
         - python: 3.6
           env: KERAS_BACKEND=cntk PYTHONWARNINGS=ignore
-        - python: 2.7
-          env: KERAS_BACKEND=mxnet PYTHONWARNINGS=ignore
-        - python: 3.6
-          env: KERAS_BACKEND=mxnet PYTHONWARNINGS=ignore
 install:
   # code below is taken from http://conda.pydata.org/docs/travis.html
   # We do this conditionally because it saves us some downloading if the
@@ -42,40 +39,33 @@ install:
   # Useful for debugging any issues with conda
   - conda info -a
 
-  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION nose scipy matplotlib pandas pytest h5py
+  - travis_retry conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION
   - source activate test-environment
-  - pip install --only-binary=numpy,scipy numpy nose scipy matplotlib h5py theano
+  - travis_retry pip install --only-binary=numpy,scipy,pandas numpy nose scipy matplotlib h5py theano pytest pytest-pep8 pandas
   - pip install keras_applications keras_preprocessing
-  - conda install mkl mkl-service
 
   # set library path
   - export LD_LIBRARY_PATH=$HOME/miniconda/envs/test-environment/lib/:$LD_LIBRARY_PATH
 
-  # install PIL for preprocessing tests
-  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
-      conda install pil;
-    else
-      conda install Pillow;
+  # install PIL for preprocessing tests (they are integration tests).
+  - if [[ "$TEST_MODE" == "INTEGRATION_TESTS" ]] || [[ "$TEST_MODE" == "PEP8_DOC" ]]; then
+      if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
+        export PIL=Pil;
+      else
+        export PIL=Pillow;
+      fi
     fi
 
+    # install pydot for visualization tests
+  - travis_retry conda install mkl mkl-service pydot graphviz $PIL
+
   - pip install -e .[tests]
 
   # install TensorFlow (CPU version).
-  - pip install tensorflow==1.7
-
-    # install Apache MXNet (CPU version).
-  - pip install mxnet
-  - pip install --upgrade numpy
-
-  # install cntk
-  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
-      pip install https://cntk.ai/PythonWheel/CPU-Only/cntk-2.5.1-cp27-cp27mu-linux_x86_64.whl;
-    elif [[ "$TRAVIS_PYTHON_VERSION" == "3.6" ]]; then
-      pip install https://cntk.ai/PythonWheel/CPU-Only/cntk-2.5.1-cp36-cp36m-linux_x86_64.whl;
-    fi
+  - pip install tensorflow==1.9
 
-  # install pydot for visualization tests
-  - conda install pydot graphviz
+  # install cntk
+  - pip install cntk
 
   # exclude different backends to measure a coverage for the designated backend only
   - if [[ "$KERAS_BACKEND" != "tensorflow" ]]; then
@@ -87,9 +77,6 @@ install:
   - if [[ "$KERAS_BACKEND" != "cntk" ]]; then
       echo '    keras/backend/cntk_backend.py' >> .coveragerc;
     fi
-  - if [[ "$KERAS_BACKEND" != "mxnet" ]]; then
-      echo '    keras/backend/mxnet_backend.py' >> .coveragerc;
-    fi
 
   # detect whether core files are changed or not
   - export CORE_CHANGED=False;
@@ -118,10 +105,8 @@ script:
   - echo -e "Running tests with the following config:\n$(cat ~/.keras/keras.json)"
   - if [[ "$TEST_MODE" == "INTEGRATION_TESTS" ]]; then
       PYTHONPATH=$PWD:$PYTHONPATH py.test tests/integration_tests;
-    elif [[ "$TEST_MODE" == "PEP8" ]]; then
-      PYTHONPATH=$PWD:$PYTHONPATH py.test --pep8 -m pep8 -n0;
-    elif [[ "$TEST_MODE" == "DOC" ]]; then
-      PYTHONPATH=$PWD:$PYTHONPATH py.test tests/test_documentation.py;
+    elif [[ "$TEST_MODE" == "PEP8_DOC" ]]; then
+      PYTHONPATH=$PWD:$PYTHONPATH py.test --pep8 -m pep8 -n0 && py.test tests/test_documentation.py;
     else
       PYTHONPATH=$PWD:$PYTHONPATH py.test tests/ --ignore=tests/integration_tests --ignore=tests/test_documentation.py --ignore=tests/keras/legacy/layers_test.py --cov-config .coveragerc --cov=keras tests/;
     fi

docker/Dockerfile

@@ -41,8 +41,8 @@ RUN conda install -y python=${python_version} && \
     pip install --upgrade pip && \
     pip install \
       sklearn_pandas \
-      tensorflow-gpu && \
-    pip install https://cntk.ai/PythonWheel/GPU/cntk-2.1-cp36-cp36m-linux_x86_64.whl && \
+      tensorflow-gpu \
+      cntk-gpu && \
     conda install \
       bcolz \
       h5py \
@@ -52,6 +52,7 @@ RUN conda install -y python=${python_version} && \
       notebook \
       Pillow \
       pandas \
+      pydot \
       pygpu \
       pyyaml \
       scikit-learn \

docs/autogen.py

@@ -91,6 +91,7 @@
 
 EXCLUDE = {
     'Optimizer',
+    'TFOptimizer',
     'Wrapper',
     'get_session',
     'set_session',
@@ -173,6 +174,7 @@
             layers.SeparableConv2D,
             layers.Conv2DTranspose,
             layers.Conv3D,
+            layers.Conv3DTranspose,
             layers.Cropping1D,
             layers.Cropping2D,
             layers.Cropping3D,

docs/templates/applications.md

@@ -172,17 +172,19 @@ model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=T
 
 | Model | Size | Top-1 Accuracy | Top-5 Accuracy | Parameters | Depth |
 | ----- | ----: | --------------: | --------------: | ----------: | -----: |
-| [Xception](#xception) | 88 MB | 0.790 | 0.945| 22,910,480 | 126 |
-| [VGG16](#vgg16) | 528 MB| 0.715 | 0.901 | 138,357,544 | 23
-| [VGG19](#vgg19) | 549 MB | 0.727 | 0.910 | 143,667,240 | 26
-| [ResNet50](#resnet50) | 99 MB | 0.759 | 0.929 | 25,636,712 | 168
-| [InceptionV3](#inceptionv3) | 92 MB | 0.788 | 0.944 | 23,851,784 | 159 |
-| [InceptionResNetV2](#inceptionresnetv2) | 215 MB | 0.804 | 0.953 | 55,873,736 | 572 |
-| [MobileNet](#mobilenet) | 17 MB | 0.665 | 0.871 | 4,253,864 | 88
-| [DenseNet121](#densenet) | 33 MB | 0.745 | 0.918 | 8,062,504 | 121
-| [DenseNet169](#densenet) | 57 MB | 0.759 | 0.928 | 14,307,880 | 169
-| [DenseNet201](#densenet) | 80 MB | 0.770 | 0.933 | 20,242,984 | 201
-
+| [Xception](#xception) | 88 MB | 0.790 | 0.945 | 22,910,480 | 126 |
+| [VGG16](#vgg16) | 528 MB | 0.713 | 0.901 | 138,357,544 | 23 |
+| [VGG19](#vgg19) | 549 MB | 0.713 | 0.900 | 143,667,240 | 26 |
+| [ResNet50](#resnet50) | 99 MB | 0.749 | 0.921 | 25,636,712 | 168 |
+| [InceptionV3](#inceptionv3) | 92 MB | 0.779 | 0.937 | 23,851,784 | 159 |
+| [InceptionResNetV2](#inceptionresnetv2) | 215 MB | 0.803 | 0.953 | 55,873,736 | 572 |
+| [MobileNet](#mobilenet) | 16 MB | 0.704 | 0.895 | 4,253,864 | 88 |
+| [MobileNetV2](#mobilenetv2) | 14 MB | 0.713 | 0.901 | 3,538,984 | 88 |
+| [DenseNet121](#densenet) | 33 MB | 0.750 | 0.923 | 8,062,504 | 121 |
+| [DenseNet169](#densenet) | 57 MB | 0.762 | 0.932 | 14,307,880 | 169 |
+| [DenseNet201](#densenet) | 80 MB | 0.773 | 0.936 | 20,242,984 | 201 |
+| [NASNetMobile](#nasnet) | 23 MB | 0.744 | 0.919 | 5,326,716 | - |
+| [NASNetLarge](#nasnet) | 343 MB | 0.825 | 0.960 | 88,949,818 | - |
 
 The top-1 and top-5 accuracy refers to the model's performance on the ImageNet validation dataset.
 
@@ -269,7 +271,7 @@ The default input size for this model is 224x224.
     has to be `(224, 224, 3)` (with `'channels_last'` data format)
     or `(3, 224, 224)` (with `'channels_first'` data format).
     It should have exactly 3 inputs channels,
-    and width and height should be no smaller than 48.
+    and width and height should be no smaller than 32.
     E.g. `(200, 200, 3)` would be one valid value.
 - pooling: Optional pooling mode for feature extraction
     when `include_top` is `False`.
@@ -324,7 +326,7 @@ The default input size for this model is 224x224.
     has to be `(224, 224, 3)` (with `'channels_last'` data format)
     or `(3, 224, 224)` (with `'channels_first'` data format).
     It should have exactly 3 inputs channels,
-    and width and height should be no smaller than 48.
+    and width and height should be no smaller than 32.
     E.g. `(200, 200, 3)` would be one valid value.
 - pooling: Optional pooling mode for feature extraction
     when `include_top` is `False`.
@@ -381,7 +383,7 @@ The default input size for this model is 224x224.
     has to be `(224, 224, 3)` (with `'channels_last'` data format)
     or `(3, 224, 224)` (with `'channels_first'` data format).
     It should have exactly 3 inputs channels,
-    and width and height should be no smaller than 197.
+    and width and height should be no smaller than 32.
     E.g. `(200, 200, 3)` would be one valid value.
 - pooling: Optional pooling mode for feature extraction
     when `include_top` is `False`.
@@ -436,7 +438,7 @@ The default input size for this model is 299x299.
     has to be `(299, 299, 3)` (with `'channels_last'` data format)
     or `(3, 299, 299)` (with `'channels_first'` data format).
     It should have exactly 3 inputs channels,
-    and width and height should be no smaller than 139.
+    and width and height should be no smaller than 75.
     E.g. `(150, 150, 3)` would be one valid value.
 - pooling: Optional pooling mode for feature extraction
     when `include_top` is `False`.
@@ -491,7 +493,7 @@ The default input size for this model is 299x299.
     has to be `(299, 299, 3)` (with `'channels_last'` data format)
     or `(3, 299, 299)` (with `'channels_first'` data format).
     It should have exactly 3 inputs channels,
-    and width and height should be no smaller than 139.
+    and width and height should be no smaller than 75.
     E.g. `(150, 150, 3)` would be one valid value.
 - pooling: Optional pooling mode for feature extraction
     when `include_top` is `False`.
@@ -618,9 +620,11 @@ The default input size for this model is 224x224.
     to use as image input for the model.
 - input_shape: optional shape tuple, only to be specified
     if `include_top` is False (otherwise the input shape
-    has to be `(224, 224, 3)` (with `channels_last` data format)
-    or `(3, 224, 224)` (with `channels_first` data format).
-    It should have exactly 3 inputs channels.
+    has to be `(224, 224, 3)` (with `'channels_last'` data format)
+    or `(3, 224, 224)` (with `'channels_first'` data format).
+    It should have exactly 3 inputs channels,
+    and width and height should be no smaller than 32.
+    E.g. `(200, 200, 3)` would be one valid value.
 - pooling: optional pooling mode for feature extraction
     when `include_top` is `False`.
     - `None` means that the output of the model will be

docs/templates/datasets.md

@@ -14,7 +14,7 @@ from keras.datasets import cifar10
 
 - __Returns:__
     - 2 tuples:
-        - __x_train, x_test__: uint8 array of RGB image data with shape (num_samples, 3, 32, 32).
+        - __x_train, x_test__: uint8 array of RGB image data with shape (num_samples, 3, 32, 32) or (num_samples, 32, 32, 3) based on the `image_data_format` backend setting of either `channels_first` or `channels_last` respectively.
         - __y_train, y_test__: uint8 array of category labels (integers in range 0-9) with shape (num_samples,).
 
 
@@ -34,7 +34,7 @@ from keras.datasets import cifar100
 
 - __Returns:__
     - 2 tuples:
-        - __x_train, x_test__: uint8 array of RGB image data with shape (num_samples, 3, 32, 32).
+        - __x_train, x_test__: uint8 array of RGB image data with shape (num_samples, 3, 32, 32) or (num_samples, 32, 32, 3) based on the `image_data_format` backend setting of either `channels_first` or `channels_last` respectively.
         - __y_train, y_test__: uint8 array of category labels with shape (num_samples,).
 
 - __Arguments:__
@@ -206,4 +206,4 @@ from keras.datasets import boston_housing
     - __test_split__: fraction of the data to reserve as test set.
 
 - __Returns:__
-    Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
+    Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

docs/templates/getting-started/faq.md

@@ -19,7 +19,7 @@
 - [How can I use HDF5 inputs with Keras?](#how-can-i-use-hdf5-inputs-with-keras)
 - [Where is the Keras configuration file stored?](#where-is-the-keras-configuration-file-stored)
 - [How can I obtain reproducible results using Keras during development?](#how-can-i-obtain-reproducible-results-using-keras-during-development)
-- [How can I install HDF5 or h5py to save my models in Keras?](#how-can-i-install-HDF5-or-h5py-to-save-my-models-in-Keras)
+- [How can I install HDF5 or h5py to save my models in Keras?](#how-can-i-install-hdf5-or-h5py-to-save-my-models-in-keras)
 
 ---
 
@@ -149,7 +149,7 @@ You can then use `keras.models.load_model(filepath)` to reinstantiate your model
 `load_model` will also take care of compiling the model using the saved training configuration
 (unless the model was never compiled in the first place).
 
-Please also see [How can I install HDF5 or h5py to save my models in Keras?](#how-can-i-install-HDF5-or-h5py-to-save-my-models-in-Keras) for instructions on how to install `h5py`.
+Please also see [How can I install HDF5 or h5py to save my models in Keras?](#how-can-i-install-hdf5-or-h5py-to-save-my-models-in-keras) for instructions on how to install `h5py`.
 
 Example:
 
@@ -210,7 +210,7 @@ If you need to load weights into a *different* architecture (with some layers in
 model.load_weights('my_model_weights.h5', by_name=True)
 ```
 
-Please also see [How can I install HDF5 or h5py to save my models in Keras?](#how-can-i-install-HDF5-or-h5py-to-save-my-models-in-Keras) for instructions on how to install `h5py`.
+Please also see [How can I install HDF5 or h5py to save my models in Keras?](#how-can-i-install-hdf5-or-h5py-to-save-my-models-in-keras) for instructions on how to install `h5py`.
 
 For example:
 
@@ -517,7 +517,7 @@ with h5py.File('input/file.hdf5', 'r') as f:
     model.predict(x_data)
 ```
 
-Please also see [How can I install HDF5 or h5py to save my models in Keras?](#how-can-i-install-HDF5-or-h5py-to-save-my-models-in-Keras) for instructions on how to install `h5py`.
+Please also see [How can I install HDF5 or h5py to save my models in Keras?](#how-can-i-install-hdf5-or-h5py-to-save-my-models-in-keras) for instructions on how to install `h5py`.
 
 ---