target = dh.preProcessSamplesCyTOFData(target)
'''
Train the de-noising auto encoder.
'''
print('Train the de-noising auto encoder.')
start = tm.time()
DAE = dae.trainDAE(target, dataPath, refSampleInd, trainIndex,
relevantMarkers, mode, keepProb, denoise,
loadModel, dataSet[choice])
denoiseTarget = dae.predictDAE(target, DAE, denoise)
'''
Train the feed-forward classifier on (de-noised) target.
'''
denoiseTarget, preprocessor = dh.standard_scale(denoiseTarget,
preprocessor = None)
if loadModel:
from keras.models import load_model
cellClassifier = load_model(os.path.join(io.DeepLearningRoot(),
'savemodels/' + dataSet[choice] +
'/cellClassifier.h5'))
else:
print('Train the classifier on de-noised Target')
cellClassifier = net.trainClassifier(denoiseTarget, mode, refSampleInd,
hiddenLayersSizes,
activation,
l2_penalty,
dataSet[choice])
end = tm.time()
print('Training time: ' + str(end - start))
75% of cells.
'''
print('Data set name: ', dataSet[choice])
for i in range(numSample[choice]):
# Load sample.
print('Load sample ', str(i + 1))
sample = dh.loadDeepCyTOFData(dataPath,
i + 1,
range(relevantMarkers[choice]),
'CSV',
skip_header=1)
# Pre-process sample.
print('Pre-process sample ', str(i + 1))
sample = dh.preProcessSamplesCyTOFData(sample)
sample, preprocessor = dh.standard_scale(sample, preprocessor=None)
# Split data into training and testing.
print('Split data into training and testing.')
trainSample, testSample = dh.splitData(sample, test_size=.75)
# Train a feed-forward neural net classifier on the training data.
print('Train a feed-forward neural net classifier on the training data.')
classifier = net.trainClassifier(trainSample,
dataSet[choice],
i,
hiddenLayersSizes,
activation=activation,
l2_penalty=l2_penalty)
# Run the classifier on the testing data.