def calc(I):
model = dp.trainmodel(Data_compact,
TrainList,
EvalList,
retrain=1,
epochs=100,
batch_size=100,
ModelType=I)
dp.testmodel(model, Data_compact, TestList, ModelType=I)
import DeePore as dp
# Retrain and test the model:
# If you want to try you own architecture of neural network or retrain the present one
# 1. check or download the compact data
Data_compact = 'Data\DeePore_Compact_Data.h5'
# Data_compact='..\..\..\BigData\DeePore\DeePore_Compact_Data.h5'
dp.check_get(
'https://zenodo.org/record/4297035/files/DeePore_Compact_Data.h5?download=1',
Data_compact)
# 2. prepare the dataset by removing outliers and creating list of training, evaluation and test samples
List = dp.prep(Data_compact)
TrainList, EvalList, TestList = dp.splitdata(List)
# 3. retrain the model
model = dp.trainmodel(Data_compact,
TrainList,
EvalList,
retrain=0,
epochs=50,
batch_size=100,
ModelType=3)
# 4. test the model
dp.testmodel(model, Data_compact, TestList)