noisyTrainSpectra = distort.add_noise(trainSpectra, level=noiseLevel, seed=0)
noisyTestSpectra = distort.add_noise(testSpectra, level=noiseLevel, seed=numSpecsTotal)
for i in range(3):
noisyTrainSpectra = distort.add_distortions(noisyTrainSpectra, level=noiseLevel*2, seed=i * numSpecsTotal)
noisyTestSpectra = distort.add_ghost_peaks(noisyTestSpectra, level=noiseLevel*2, seed=2*i * numSpecsTotal)
noisyTestSpectra = distort.add_distortions(noisyTestSpectra, level=noiseLevel*2, seed=2*i * numSpecsTotal)
noisyTrainSpectra = distort.add_ghost_peaks(noisyTrainSpectra, level=noiseLevel*2, seed=i * numSpecsTotal)
print(f'Distorting spectra took {round(time.time()-t0, 2)} seconds')
trainSpectra = prepareSpecSet(trainSpectra, addDimension=False)
testSpectra = prepareSpecSet(testSpectra, addDimension=False)
noisyTrainSpectra = prepareSpecSet(noisyTrainSpectra, addDimension=False)
noisyTestSpectra = prepareSpecSet(noisyTestSpectra, addDimension=False)
rec: Reconstructor = getDenseReconstructor(dropout=0.0)
t0 = time.time()
history = rec.fit(noisyTrainSpectra, trainSpectra,
epochs=10,
validation_data=(noisyTestSpectra, testSpectra),
batch_size=32, shuffle=True)
print(f"Training took {round(time.time()-t0, 2)} seconds.")
t0 = time.time()
reconstructedSpecs = rec.call(noisyTestSpectra)
print(f'reconstruction took {round(time.time()-t0, 2)} seconds')
# histPLot = out.getHistPlot(history.history, annotate=True)
specPlot, boxPlot = out.getSpectraComparisons(testSpectra, noisyTestSpectra, reconstructedSpecs,
includeSavGol=False,
randomIndSeed=9,
noisyTrainSpectra = distort.add_noise(trainSpectra, level=noiseLevel, seed=0)
noisyTestSpectra = distort.add_noise(testSpectra, level=noiseLevel, seed=numSpecsTotal)
for i in range(3):
noisyTrainSpectra = distort.add_distortions(noisyTrainSpectra, level=noiseLevel*2, seed=i * numSpecsTotal)
noisyTestSpectra = distort.add_ghost_peaks(noisyTestSpectra, level=noiseLevel*2, seed=2*i * numSpecsTotal)
noisyTestSpectra = distort.add_distortions(noisyTestSpectra, level=noiseLevel*2, seed=2*i * numSpecsTotal)
noisyTrainSpectra = distort.add_ghost_peaks(noisyTrainSpectra, level=noiseLevel*2, seed=i * numSpecsTotal)
print(f'Distorting spectra took {round(time.time()-t0, 2)} seconds')
trainSpectra = prepareSpecSet(trainSpectra, addDimension=False)
testSpectra = prepareSpecSet(testSpectra, addDimension=False)
noisyTrainSpectra = prepareSpecSet(noisyTrainSpectra, addDimension=False)
noisyTestSpectra = prepareSpecSet(noisyTestSpectra, addDimension=False)
rec: Reconstructor = getDenseReconstructor(dropout=0.0 if randomShuffle else 0.00)
t0 = time.time()
history = rec.fit(noisyTrainSpectra, trainSpectra,
epochs=10,
validation_data=(noisyTestSpectra, testSpectra),
batch_size=32, shuffle=True)
print(f"Training took {round(time.time()-t0, 2)} seconds.")
t0 = time.time()
reconstructedSpecs = rec.call(noisyTestSpectra)
print(f'reconstruction took {round(time.time()-t0, 2)} seconds')
noisyTestSpectraEncoded = rec.encoder(noisyTestSpectra)
noisyTrainSpectraEncoded = rec.encoder(noisyTrainSpectra)
corrPlot = out.getCorrelationPCAPlot(noisyTestSpectraEncoded, reconstructedSpecs, testSpectra, noisyTrainSpectraEncoded)
np.random.seed(42)
for i in range(3):
noisyTrainSpectra = distort.add_noise(trainSpectra,
level=noiseLevel * (i + 1) * 0.5)
noisyTrainSpectra = distort.add_distortions(noisyTrainSpectra,
level=noiseLevel * (i + 1) * 2)
noisyTrainSpectra = distort.add_ghost_peaks(noisyTrainSpectra,
level=noiseLevel * (i + 1) * 2)
print(f'Distorting spectra took {round(time.time()-t0, 2)} seconds')
trainSpectra = prepareSpecSet(trainSpectra)
testSpectra = prepareSpecSet(cleanSpecs, transpose=False)
noisyTrainSpectra = prepareSpecSet(noisyTrainSpectra)
noisyTestSpectra = prepareSpecSet(noisySpecs, transpose=False)
rec = getDenseReconstructor(dropout=0.5)
history = rec.fit(noisyTrainSpectra,
trainSpectra,
epochs=10,
validation_data=(noisyTestSpectra, testSpectra),
batch_size=32,
shuffle=True)
reconstructedSpecs = rec.call(noisyTestSpectra)
specPlot, boxPlot = out.getSpectraComparisons(testSpectra,
noisyTestSpectra,
reconstructedSpecs,
includeSavGol=False,
wavenumbers=wavenums,
title=experimentTitle)