def test_04_plain_text_script(self):
model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3))
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation='sigmoid')(x)
model_final = Model(inputs=model.input,
outputs=predictions,
name='predictions')
script_content = open("nyoka/tests/preprocess.py", 'r').read()
pmml_obj = KerasToPmml(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.pmml", 'w'), 0)
self.assertEqual(os.path.isfile("script_with_keras.pmml"), True)
reconPmmlObj = pml.parse("script_with_keras.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(model_final.layers),
len(reconPmmlObj.DeepNetwork[0].NetworkLayer))
def test_01_image_classifier_with_image_as_input(self):
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)
model_final = Model(inputs=model.input,
outputs=predictions,
name='predictions')
cnn_pmml = KerasToPmml(model_final,model_name="MobileNetImage",description="Demo",\
copyright="Internal User",dataSet='image',predictedClasses=['dogs','cats'])
cnn_pmml.export(open('2classMBNet.pmml', "w"), 0)
img = image.load_img('nyoka/tests/resizedCat.png')
img = img_to_array(img)
img = preprocess_input(img)
imgtf = np.expand_dims(img, axis=0)
model_pred = model_final.predict(imgtf)
model_preds = {'dogs': model_pred[0][0], 'cats': model_pred[0][1]}
model_name = self.adapa_utility.upload_to_zserver('2classMBNet.pmml')
predictions, probabilities = self.adapa_utility.score_in_zserver(
model_name, 'nyoka/tests/resizedCat.png', 'DN')
self.assertEqual(
abs(probabilities['cats'] - model_preds['cats']) < 0.00001, True)
self.assertEqual(
abs(probabilities['dogs'] - model_preds['dogs']) < 0.00001, True)
def test_keras_03(self):
input_tensor = x = Input(shape=(2, 4, 1))
x = Conv2D(activation="relu",
filters=32,
kernel_size=(2, 3),
padding='same',
strides=(1, 1))(x)
x = BatchNormalization(center=True, scale=False)(x)
x = ZeroPadding2D(padding=((3, 4), (1, 2)))(x)
x = Conv2D(activation="relu",
filters=64,
kernel_size=(2, 2),
padding='valid',
strides=(1, 1))(x)
x = MaxPooling2D(padding="same", strides=(2, 2), pool_size=(2, 2))(x)
x = Dropout(rate=0.25)(x)
x = BatchNormalization(center=False, scale=True)(x)
x = Conv2D(activation="relu",
filters=32,
kernel_size=(2, 2),
padding='valid',
strides=(1, 1))(x)
x = MaxPooling2D(padding="same", strides=(2, 2), pool_size=(2, 2))(x)
x = Dropout(rate=0.25)(x)
x = BatchNormalization(center=False, scale=False)(x)
x = Flatten()(x)
x = Dense(units=2, activation="softmax")(x)
model = Model(input=input_tensor, output=x)
pmml_obj = KerasToPmml(keras_model=model,
predictedClasses=["yes", "no"])
pmml_obj.export(open("custom_model.pmml", 'w'), 0)
self.assertEqual(os.path.isfile("custom_model.pmml"), True)
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_validate_keras_densenet(self):
input_tensor = Input(shape=(224, 224, 3))
model = DenseNet121(weights="imagenet", input_tensor=input_tensor)
file_name = "keras" + model.name + ".pmml"
pmml_obj = KerasToPmml(model,
dataSet="image",
predictedClasses=[str(i) for i in range(1000)])
pmml_obj.export(open(file_name, 'w'), 0)
self.assertEqual(self.schema.is_valid(file_name), True)
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_01(self):
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)
model_final = Model(inputs =model.input, outputs = predictions,name='predictions')
cnn_pmml = KerasToPmml(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(model_final.layers), len(reconPmmlObj.DeepNetwork[0].NetworkLayer))
def test_keras_01(self):
model = applications.MobileNet(weights='imagenet', include_top=False,input_shape = (224, 224,3)) #last layer not included
activType='sigmoid'
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation=activType)(x)
model_final = Model(inputs =model.input, outputs = predictions,name='predictions')
cnn_pmml = KerasToPmml(model_final,predictedClasses=['cats','dogs'])
cnn_pmml.export(open('2classMBNet.pmml', "w"), 0)
self.assertEqual(os.path.isfile("2classMBNet.pmml"),True)
def test_02_image_classifier_with_base64string_as_input(self):
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)
model_final = Model(inputs=model.input,
outputs=predictions,
name='predictions')
cnn_pmml = KerasToPmml(model_final,model_name="MobileNetBase64",description="Demo",\
copyright="Internal User",dataSet='imageBase64',predictedClasses=['dogs','cats'])
cnn_pmml.export(open('2classMBNetBase64.pmml', "w"), 0)
img = image.load_img('nyoka/tests/resizedCat.png')
img = img_to_array(img)
img = preprocess_input(img)
imgtf = np.expand_dims(img, axis=0)
base64string = "data:float32;base64," + FloatBase64.from_floatArray(
img.flatten(), 12)
base64string = base64string.replace("\n", "")
csvContent = "imageBase64\n\"" + base64string + "\""
text_file = open("input.csv", "w")
text_file.write(csvContent)
text_file.close()
model_pred = model_final.predict(imgtf)
model_preds = {'dogs': model_pred[0][0], 'cats': model_pred[0][1]}
model_name = self.adapa_utility.upload_to_zserver(
'2classMBNetBase64.pmml')
predictions, probabilities = self.adapa_utility.score_in_zserver(
model_name, 'input.csv', 'DN')
self.assertEqual(
abs(probabilities['cats'] - model_preds['cats']) < 0.00001, True)
self.assertEqual(
abs(probabilities['dogs'] - model_preds['dogs']) < 0.00001, True)
def test_03_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.pmml", 'w'), 0)
self.assertEqual(os.path.isfile("script_with_keras.pmml"), True)
reconPmmlObj = pml.parse("script_with_keras.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_01_image_classifier_with_image_as_input(self):
cnn_pmml = KerasToPmml(self.model_final,model_name="MobileNetImage",description="Demo",\
copyright="Internal User",dataSet='image',predictedClasses=['dogs','cats'])
cnn_pmml.export(open('2classMBNet.pmml', "w"), 0)
img = image.load_img('nyoka/tests/resizedCat.png')
img = img_to_array(img)
img = preprocess_input(img)
imgtf = np.expand_dims(img, axis=0)
model_pred = self.model_final.predict(imgtf)
model_preds = {'dogs': model_pred[0][0], 'cats': model_pred[0][1]}
model_name = self.adapa_utility.upload_to_zserver('2classMBNet.pmml')
predictions, probabilities = self.adapa_utility.score_in_zserver(
model_name, 'nyoka/tests/resizedCat.png', 'DN')
self.assertEqual(
abs(probabilities['cats'] - model_preds['cats']) < 0.00001, True)
self.assertEqual(
abs(probabilities['dogs'] - model_preds['dogs']) < 0.00001, True)
