2_refineDNN.py

# -*- coding: utf-8 -*-
from helpers import *
from helpers_cntk import *
locals().update(importlib.import_module("PARAMETERS").__dict__)


################################################
# MAIN
################################################
makeDirectory(workingDir)

# Load data
lutLabel2Id = loadFromPickle(lutLabel2IdPath)
imgFilenamesTest  = loadFromPickle(imgFilenamesTestPath)
imgFilenamesTrain = loadFromPickle(imgFilenamesTrainPath)

# Generate cntk test and train data, i.e. (image, label) pairs and write
# them to disk since in-memory passing is currently not supported by cntk
dataTest  = getImgLabelMap(imgFilenamesTest,  imgDir, lutLabel2Id)
dataTrain = getImgLabelMap(imgFilenamesTrain, imgDir, lutLabel2Id)
if rf_boBalanceTrainingSet:
    dataTrain = balanceDatasetUsingDuplicates(dataTrain)
writeTable(cntkTrainMapPath, dataTrain)
writeTable(cntkTestMapPath, dataTest)

# Train model
printDeviceType()
model = train_model(cntkPretrainedModelPath, cntkTrainMapPath, cntkTestMapPath, rf_inputResoluton,
                    rf_maxEpochs, rf_mbSize, rf_maxTrainImages, rf_lrPerMb, rf_momentumPerMb, rf_l2RegWeight,
                    rf_dropoutRate, rf_boFreezeWeights)
model.save(cntkRefinedModelPath)
print("Stored trained model at %s" % cntkRefinedModelPath)

print("DONE. Showing DNN accuracy vs training epoch plot.")
plt.show() # Accuracy vs training epochs plt