remove keras_iris from build and update keras version

build/Makefile

@@ -15,7 +15,6 @@ ALL_LIST=\
 	seldonio_examples_ml10m \
 	seldonio_examples_reuters_data \
 	seldonio_reuters_example \
-	seldonio_iris_keras \
 	seldonio_mysql \
 	seldonio_grafana \
 	seldonio_iago \

docker/examples/iris/keras/Dockerfile.in

@@ -1,6 +1,9 @@
 FROM seldonio/pyseldon:%SELDON_PYTHON_PACKAGE_VERSION%
 
+RUN pip install theano
+
 ENV SELDON_HOME=/home/seldon
+ENV KERAS_BACKEND=theano
 COPY keras_pipeline.py $SELDON_HOME/keras_pipeline.py
 COPY create-json.py $SELDON_HOME/create-json.py
 

docker/pyseldon/Dockerfile

@@ -43,7 +43,7 @@ RUN pip install kazoo filechunkio Flask gunicorn pylibmc gensim annoy smart_open
 
 RUN cd /usr/local/src && mkdir keras && cd keras && \
        git clone https://github.com/fchollet/keras.git && \
-       cd keras && git checkout tags/0.2.0 && python setup.py install
+       cd keras && git checkout tags/2.0.0 && python setup.py install
 
 # bayes_opt
 

python/seldon/__init__.py

@@ -2,7 +2,7 @@
 
 from .util import RecommenderWrapper,Recommender,Extension,ExtensionWrapper,Recommender_wrapper,Extension_wrapper
 
-__version__ = '2.2.5'
+__version__ = '2.2.6'
 
 class NullHandler(logging.Handler):
     def emit(self, record):

python/seldon/keras.py

@@ -34,7 +34,7 @@ def default_classification_model(input_width,num_classes):
 
 
 class KerasClassifier(BasePandasEstimator,BaseEstimator,ClassifierMixin):
-    def __init__(self,model_create=default_classification_model,tmp_model="/tmp/model",target=None, target_readable=None,included=None,excluded=None,id_map={},optimizer='adam', loss='categorical_crossentropy', train_batch_size=128, test_batch_size=128, nb_epoch=100, shuffle=True, show_accuracy=False, validation_split=0, validation_data=None, callbacks=None,verbose=0):
+    def __init__(self,model_create=default_classification_model,tmp_model="/tmp/model",target=None, target_readable=None,included=None,excluded=None,id_map={},optimizer='adam', loss='categorical_crossentropy', train_batch_size=128, test_batch_size=128, nb_epoch=100, shuffle=True, validation_split=0, validation_data=None, callbacks=None,verbose=0):
         """
         Wrapper for keras with pandas support
         Derived from https://github.com/fchollet/keras/blob/master/keras/wrappers/scikit_learn.py
@@ -64,8 +64,6 @@ def __init__(self,model_create=default_classification_model,tmp_model="/tmp/mode
            Number of training epochs.
         shuffle : boolean, optional
            Wheter to shuffle the samples at each epoch.
-        show_accuracy : boolean, optional
-           Whether to display class accuracy in the logs at each epoch.
         validation_split : float [0, 1], optional
            Fraction of the data to use as held-out validation data.
         validation_data : tuple (X, y), optional
@@ -94,7 +92,6 @@ def __init__(self,model_create=default_classification_model,tmp_model="/tmp/mode
         self.test_batch_size=test_batch_size 
         self.nb_epoch=nb_epoch
         self.shuffle=shuffle
-        self.show_accuracy=show_accuracy
         self.validation_split=validation_split
         self.validation_data=validation_data
         self.callbacks = [] if callbacks is None else callbacks
@@ -165,7 +162,7 @@ class labels
             self.compiled_model_.compile(optimizer=self.optimizer, loss=self.loss)
         history = self.compiled_model_.fit(
             X, y, batch_size=self.train_batch_size, nb_epoch=self.nb_epoch, verbose=self.verbose,
-            shuffle=self.shuffle, show_accuracy=self.show_accuracy,
+            shuffle=self.shuffle,
             validation_split=self.validation_split, validation_data=self.validation_data,
             callbacks=self.callbacks)