import sys,os
import unittest
from keras import applications
from keras.layers import *
from keras.models import Model
from nyoka import KerasToPmml
from nyoka import PMML44 as ny
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import pandas as pd
from keras.layers import Input
from keras.models import Sequential
import base64
class TestMethods(unittest.TestCase):
@classmethod
def setUpClass(self):
print("******* Unit Test for Keras *******")
model = applications.MobileNet(weights='imagenet', include_top=False,input_shape = (224, 224,3))
activType='sigmoid'
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation=activType)(x)
self.model_final = Model(inputs =model.input, outputs = predictions,name='predictions')
def test_keras_01(self):
cnn_pmml = KerasToPmml(self.model_final,model_name="MobileNet",description="Demo",\
copyright="Internal User",dataSet='image',predictedClasses=['cats','dogs'])
cnn_pmml.export(open('2classMBNet.pmml', "w"), 0)
reconPmmlObj=ny.parse('2classMBNet.pmml',True)
self.assertEqual(os.path.isfile("2classMBNet.pmml"),True)
self.assertEqual(len(self.model_final.layers), len(reconPmmlObj.DeepNetwork[0].NetworkLayer))
def test_keras_02(self):
boston = load_boston()
data = pd.DataFrame(boston.data)
features = list(boston.feature_names)
target = 'PRICE'
data.columns = features
data['PRICE'] = boston.target
x_train, x_test, y_train, y_test = train_test_split(data[features], data[target], test_size=0.20, random_state=42)
model = Sequential()
model.add(Dense(13, input_dim=13, kernel_initializer='normal', activation='relu'))
model.add(Dense(23))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(x_train, y_train, epochs=1000, verbose=0)
pmmlObj=KerasToPmml(model)
pmmlObj.export(open('sequentialModel.pmml','w'),0)
reconPmmlObj=ny.parse('sequentialModel.pmml',True)
self.assertEqual(os.path.isfile("sequentialModel.pmml"),True)
self.assertEqual(len(model.layers), len(reconPmmlObj.DeepNetwork[0].NetworkLayer)-1)
def test_encoded_script(self):
script_content = open("nyoka/tests/preprocess.py",'r').read()
pmml_obj=KerasToPmml(self.model_final,
dataSet='image',
predictedClasses=['cat','dog'],
script_args = {
"content" : script_content,
"def_name" : "getBase64EncodedString",
"return_type" : "string",
"encode":True
}
)
pmml_obj.export(open("script_with_keras_encoded.pmml",'w'),0)
self.assertEqual(os.path.isfile("script_with_keras_encoded.pmml"),True)
reconPmmlObj = ny.parse("script_with_keras_encoded.pmml",True)
content=reconPmmlObj.TransformationDictionary.DefineFunction[0].Apply.Extension[0].anytypeobjs_[0]
content = base64.b64decode(content).decode()
self.assertEqual(script_content, content)
self.assertEqual(len(self.model_final.layers), len(reconPmmlObj.DeepNetwork[0].NetworkLayer))
def test_plain_text_script(self):
script_content = open("nyoka/tests/preprocess.py",'r').read()
pmml_obj=KerasToPmml(self.model_final,
dataSet='image',
predictedClasses=['cat','dog'],
script_args = {
"content" : script_content,
"def_name" : "getBase64EncodedString",
"return_type" : "string",
"encode":False
}
)
pmml_obj.export(open("script_with_keras_plain.pmml",'w'),0)
self.assertEqual(os.path.isfile("script_with_keras_plain.pmml"),True)
reconPmmlObj = ny.parse("script_with_keras_plain.pmml",True)
content=reconPmmlObj.TransformationDictionary.DefineFunction[0].Apply.Extension[0].anytypeobjs_
content[0] = content[0].replace("\t","")
content="\n".join(content)
self.assertEqual(script_content, content)
self.assertEqual(len(self.model_final.layers), len(reconPmmlObj.DeepNetwork[0].NetworkLayer))
if __name__=='__main__':
unittest.main(warnings='ignore')
