def getPmml(self, architecture):
fName = 'classification'
lenOfArch = len(architecture)
mName = 'Keras Model'
netWorkInfo = []
scriptVal = []
extensionInfoForData = [pml.Extension(value=[], anytypeobjs_=[''])]
dataVal = {}
for counta, j in enumerate(architecture):
if counta == 0:
someConectionId = 'na'
else:
someConectionId = tempConId
if j['itemType'] in ['CODE']:
# print ('##################',j)
scriptFile = open(j['filePath'], 'r')
scriptCode = scriptFile.read()
scriptCode = scriptCode.replace('<', '<')
scriptInfo = {}
scrptVal = []
# dataVal['scriptUrl']=j['url']
useFor = j['useFor']
extensionInfoForScript = [
pml.Extension(value=scrptVal, anytypeobjs_=[''])
]
scrp = pml.script(content=scriptCode,
Extension=extensionInfoForScript,
for_=useFor)
scriptVal.append(scrp)
tempConId = None
elif j['itemType'] in ['DATA']:
try:
dataVal['dataUrl'] = j['filePath']
extensionInfoForData = [
pml.Extension(value=dataVal, anytypeobjs_=[''])
]
except:
pass
tempConId = None
elif j['itemType'] == 'FOLDING':
# print (j)
for k in j['children']:
tempdata7 = self.convertToStandardJson(k)
tempdata7['connectionLayerId'] = someConectionId
tempConId = tempdata7['layerId']
pp = addLayer(tempdata7)
netWorkInfo.append(pp)
someConectionId = tempConId
else:
# print ('Start of tamasha$$$$$$$$$$',j)
tempdata7 = self.convertToStandardJson(j)
tempdata7['connectionLayerId'] = someConectionId
tempConId = tempdata7['layerId']
# print ('pakda', tempdata7)
pp = self.addLayer(tempdata7)
netWorkInfo.append(pp)
kk = pml.DeepNetwork(modelName=mName,
functionName=fName,
NetworkLayer=netWorkInfo,
numberOfLayers=lenOfArch)
tt = pml.Timestamp(datetime.now())
hd = pml.Header(copyright="Copyright (c) 2018 Software AG",
Extension=extensionInfoForData,
description="Neural Network Model",
Timestamp=pml.Timestamp(datetime.now()))
jj = pml.PMML(version="4.3Ext",
script=scriptVal,
Header=hd,
DeepNetwork=[kk])
return jj
def addLayer(self, tempData):
# print ('addLayerPMML 00000111>>>> ',tempData)
tempLayerBiasobject = pml.LayerBias(content=[])
tempBia = tempLayerBiasobject.__dict__
tempBia['weightsFlattenAxis'] = "0"
tempLayerWeightobject = pml.LayerWeights(content=[])
tempWei = tempLayerWeightobject.__dict__
tempWei['weightsFlattenAxis'] = "0"
if tempData['layerType'] in ['Input']:
tempData['properties']['outputDimension'] = self.outputForInput(
tempData)
if tempData['layerType'] in ['Dropout']:
if tempData['properties']['dropoutRate'] == '':
tempData['properties']['dropoutRate'] = '0'
tempData['properties']['outputDimension'] = self.outputForInput(
tempData)
elif tempData['layerType'] in ['BatchNormalization']:
tempData['properties']['outputDimension'] = self.outputForInput(
tempData)
tempWei['weightsShape'] = self.weightBatchNormalization(tempData)
elif tempData['layerType'] in ['Activation']:
tempData['properties']['outputDimension'] = tempData['properties'][
'inputDimension']
elif tempData['layerType'] in ['Conv2D', 'DepthwiseConv2D']:
tempData['properties']['outputDimension'] = self.outputForFlatten(
tempData)
tempWei['weightsShape'] = self.weightConvo(tempData)
elif tempData['layerType'] in ['Flatten', 'GlobalAveragePooling2D']:
tempData['properties']['outputDimension'] = self.outputForFlatten(
tempData)
elif tempData['layerType'] in ['MaxPooling2D', 'AveragePooling2D']:
tempData['properties']['outputDimension'] = self.outputForFlatten(
tempData)
elif tempData['layerType'] in ['MaxPooling1D', 'AveragePooling1D']:
tempData['properties']['outputDimension'] = self.outputForFlatten(
tempData)
tempData['properties']['poolSize'] = str(
tempData['properties']['poolSize'])
tempData['properties']['stride'] = str(
tempData['properties']['stride'])
elif tempData['layerType'] in ['Reshape']:
tempData['properties']['outputDimension'] = self.outputForFlatten(
tempData)
tempData['properties']['reshapeTarget'] = str(
tuple(tempData['properties']['reshapeTarget']))
elif tempData['layerType'] in ['ZeroPadding2D', 'ZeroPadding1D']:
tempData['properties']['outputDimension'] = self.outputForFlatten(
tempData)
if len(tempData['properties']['paddingDims']) == 4:
top_pad, bottom_pad, left_pad, right_pad = tempData[
'properties']['paddingDims']
tempData['properties']['paddingDims'] = ((top_pad, bottom_pad),
(left_pad, right_pad))
elif len(tempData['properties']['paddingDims']) == 1:
pad = tempData['properties']['paddingDims'][0]
tempData['properties']['paddingDims'] = (pad, pad)
tempData['properties']['paddingDims'] = str(
tuple(tempData['properties']['paddingDims']))
elif tempData['layerType'] in ['Dense']:
if tempData['properties']['activationFunction'] == "relu":
tempData['properties']['activationFunction'] = "rectifier"
elif tempData['properties']['activationFunction'] == "tanh":
tempData['properties']['activationFunction'] = "tanch"
tempData['properties']['outputDimension'] = self.outputForDense(
tempData)
tempWei['weightsShape'] = self.weightBiasDense(tempData)[0]
tempBia['weightsShape'] = self.weightBiasDense(tempData)[1]
# print ('$$$$$$$$$$$$$$$tempData',tempData)
tempLayerobject = pml.LayerParameters()
tempvals = tempLayerobject.__dict__
for j in list(tempvals.keys()):
try:
tempvals[j] = tempData['properties'][j]
except:
pass
if tempData['sectionId'] == None:
if tempData['layerType'] == 'Input':
input_filed_name = "base64String"
kk = pml.NetworkLayer(
inputFieldName=input_filed_name,
layerType=tempData['layerType'],
layerId=tempData['layerId'],
connectionLayerId=tempData['connectionLayerId'],
LayerParameters=tempLayerobject)
elif tempData['layerType'] == 'Dense':
kk=pml.NetworkLayer(layerType=tempData['layerType'],layerId=tempData['layerId'],\
connectionLayerId=tempData['connectionLayerId'],\
LayerParameters=tempLayerobject,LayerWeights=tempLayerWeightobject,LayerBias=tempLayerBiasobject)
elif tempData['layerType'] in [
'Conv2D', 'DepthwiseConv2D', 'BatchNormalization'
]:
kk=pml.NetworkLayer(layerType=tempData['layerType'],layerId=tempData['layerId'],\
connectionLayerId=tempData['connectionLayerId'],\
LayerParameters=tempLayerobject,LayerWeights=tempLayerWeightobject)
else:
kk=pml.NetworkLayer(layerType=tempData['layerType'],layerId=tempData['layerId'],\
connectionLayerId=tempData['connectionLayerId'],\
LayerParameters=tempLayerobject)
else:
extensionInfo = [
pml.Extension(value={'sectionId': tempData['sectionId']},
anytypeobjs_=[''])
]
if tempData['layerType'] == 'Input':
input_filed_name = "base64String"
kk=pml.NetworkLayer(inputFieldName=input_filed_name,Extension=[extensionInfo], layerType=tempData['layerType'],\
layerId=tempData['layerId'],connectionLayerId=tempData['connectionLayerId'],\
LayerParameters=tempLayerobject)
elif tempData['layerType'] == 'Dense':
kk=pml.NetworkLayer(layerType=tempData['layerType'],layerId=tempData['layerId'],Extension=extensionInfo,\
connectionLayerId=tempData['connectionLayerId'],\
LayerParameters=tempLayerobject,LayerWeights=tempLayerWeightobject,LayerBias=tempLayerBiasobject)
elif tempData['layerType'] in [
'Conv2D', 'DepthwiseConv2D', 'BatchNormalization'
]:
kk=pml.NetworkLayer(layerType=tempData['layerType'],layerId=tempData['layerId'],Extension=extensionInfo,\
connectionLayerId=tempData['connectionLayerId'],\
LayerParameters=tempLayerobject,LayerWeights=tempLayerWeightobject)
else:
kk=pml.NetworkLayer(layerType=tempData['layerType'],layerId=tempData['layerId'],Extension=extensionInfo,\
connectionLayerId=tempData['connectionLayerId'],\
LayerParameters=tempLayerobject)
return kk