# create a function that returns a model, taking as parameters things you
# want to verify using cross-valdiation and model selection
def create_model(optimizer='adagrad',
kernel_initializer='glorot_uniform',
dropout=0.2):
model = Sequential()
model.add(Dense(64,activation='relu',kernel_initializer=kernel_initializer))
model.add(Dropout(dropout))
model.add(Dense(1,activation='sigmoid',kernel_initializer=kernel_initializer))
model.compile(loss='binary_crossentropy',optimizer=optimizer, metrics=['accuracy'])
return model
# wrap the model using the function you created
clf = KerasRegressor(build_fn=create_model,verbose=0)
# just create the pipeline
pipeline = Pipeline([
('clf',clf)
])
pipeline.fit(X_train, y_train)