def LayerWeights_init(self,
weightsShape=None,
weightsFlattenAxis=None,
content=None,
floatType="float32",
floatsPerLine=12,
src=None,
Extension=None,
mixedclass_=None):
self.original_tagname_ = None
self.weightsShape = supermod._cast(None, weightsShape)
self.weightsFlattenAxis = supermod._cast(None, weightsFlattenAxis)
self.src = supermod._cast(None, src)
if Extension is None:
self.Extension = []
else:
self.Extension = Extension
if mixedclass_ is None:
self.mixedclass_ = supermod.MixedContainer
else:
self.mixedclass_ = mixedclass_
validFloatTypes = [
"float6", "float7", "float8", "float16", "float32", "float64"
]
if floatType not in validFloatTypes:
floatType = "float32"
from nyoka.Base64 import FloatBase64
base64string = "\t\t\t\t" + "data:" + floatType + ";base64," + FloatBase64.from_floatArray(
content, floatsPerLine)
base64string = base64string.replace("\n", "\n\t\t\t\t")
self.content_ = [supermod.MixedContainer(1, 2, "", base64string)]
self.valueOf_ = base64string
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)
import nyoka
if self.src is not None:
raw_content = open(self.src, "r").read()
elif self.content_ is not None and self.content_[0].value is not None:
raw_content = self.content_[0].value
raw_content = raw_content.replace(' ', '')
raw_content = raw_content.replace('\t', '')
raw_content = raw_content.replace('\n', '')
if raw_content.startswith("data:float32;base64,") or raw_content.startswith("data:float64;base64,") or raw_content.startswith("data:float16;base64,"):
raw_content = raw_content[20:] + "=="
elif raw_content.startswith("data:float;base64,"):
raw_content = raw_content[18:] + "=="
else:
return None
from nyoka.Base64 import FloatBase64
if raw_content.find("+") > 0:
return FloatBase64.to_floatArray_urlsafe(raw_content)
else:
return FloatBase64.to_floatArray(raw_content)