mmdNetLayerSizes = [25, 25] l2_penalty = 1e-2 #init = lambda shape, name:initializations.normal(shape, scale=.1e-4, name=name) #def my_init (shape): # return initializers.normal(stddev=.1e-4) #my_init = 'glorot_normal' ####################### ###### read data ###### ####################### # we load two CyTOF samples data = 'person1_3month' if data == 'person1_baseline': sourcePath = os.path.join(io.DeepLearningRoot(), 'Data/Person1Day1_baseline.csv') targetPath = os.path.join(io.DeepLearningRoot(), 'Data/Person1Day2_baseline.csv') if data == 'person2_baseline': sourcePath = os.path.join(io.DeepLearningRoot(), 'Data/Person2Day1_baseline.csv') targetPath = os.path.join(io.DeepLearningRoot(), 'Data/Person2Day2_baseline.csv') if data == 'person1_3month': sourcePath = os.path.join(io.DeepLearningRoot(), 'Data/Person1Day1_3month.csv') targetPath = os.path.join(io.DeepLearningRoot(), 'Data/Person1Day2_3month.csv') if data == 'person2_3month': sourcePath = os.path.join(io.DeepLearningRoot(),
from keras import initializations from keras.layers.normalization import BatchNormalization from keras.layers import Input, Dense, merge, Activation from keras.regularizers import l2 from keras.models import Model # configuration hyper parameters denoise = True # whether or not to train a denoising autoencoder to remover the zeros ###################### ###### get data ###### ###################### # we load two CyTOF samples p1d1Path = os.path.join(io.DeepLearningRoot(),'Data/Person1Day1_baseline.csv') p1d2Path = os.path.join(io.DeepLearningRoot(),'Data/Person1Day2_baseline.csv') p2d1Path = os.path.join(io.DeepLearningRoot(),'Data/Person2Day1_baseline.csv') p2d2Path = os.path.join(io.DeepLearningRoot(),'Data/Person2Day2_baseline.csv') p1Dae = load_model(os.path.join(io.DeepLearningRoot(),'savedModels/person1_baseline_DAE.h5')) p2Dae = load_model(os.path.join(io.DeepLearningRoot(),'savedModels/person2_baseline_DAE.h5')) p1d1 = genfromtxt(p1d1Path, delimiter=',', skip_header=0) p1d2 = genfromtxt(p1d2Path, delimiter=',', skip_header=0) p2d1 = genfromtxt(p2d1Path, delimiter=',', skip_header=0) p2d2 = genfromtxt(p2d2Path, delimiter=',', skip_header=0)
l2_penalty_ae = 1e-2 #MMD net configuration mmdNetLayerSizes = [25, 25] l2_penalty = 1e-2 #init = lambda shape, name:initializations.normal(shape, scale=.1e-4, name=name) #def my_init (shape): # return initializers.normal(stddev=.1e-4) #my_init = 'glorot_normal' ####################### ###### read data ###### ####################### # we load two CyTOF samples sourcePath = os.path.join(io.DeepLearningRoot(), 'Data/' + args.files[0]) targetPath = os.path.join(io.DeepLearningRoot(), 'Data/' + args.files[1]) ''' data = 'person2_baseline' if data =='person1_baseline': sourcePath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day1_baseline.csv') targetPath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day2_baseline.csv') if data =='person2_baseline': sourcePath = os.path.join(io.DeepLearningRoot(),'Data/Person2Day1_baseline.csv') targetPath = os.path.join(io.DeepLearningRoot(),'Data/Person2Day2_baseline.csv') if data =='person1_3month': sourcePath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day1_3month.csv') targetPath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day2_3month.csv') if data =='person2_3month': sourcePath = os.path.join(io.DeepLearningRoot(),'Data/Person2Day1_3month.csv')
from Calibration_Util import DataHandler as dh import argparse #detect display havedisplay = "DISPLAY" in os.environ # havedisplay = False #if we have a display use a plotting backend if havedisplay: matplotlib.use('TkAgg') else: matplotlib.use('Agg') parse = argparse.ArgumentParser( description='command line interface for mmd net') parse.add_argument('--source_path', type=str, default=os.path.join(io.DeepLearningRoot(), 'Data/Person1Day1_3month.csv'), help='Path to the source dataset') parse.add_argument('--target_path', type=str, default=os.path.join(io.DeepLearningRoot(), 'Data/Person1Day2_3month.csv'), help='Path to the source dataset') parse.add_argument('--epochs', type=int, default=500, help='Number of epochs to run for') parse.add_argument('--denoise', type=bool, default=False, help='Whether or not to denoise the datasets')
from keras.models import Model # configuration hyper parameters denoise = True # whether or not to use a denoising autoencoder to remove the zeros ###################### ###### get data ###### ###################### # we load two CyTOF samples data = 'person2_3month' if data =='person1_baseline': sourcePath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day1_baseline.csv') targetPath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day2_baseline.csv') sourceLabelPath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day1_baseline_label.csv') targetLabelPath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day2_baseline_label.csv') autoencoder = load_model(os.path.join(io.DeepLearningRoot(),'savedModels/person1_baseline_DAE.h5')) if data =='person2_baseline': sourcePath = os.path.join(io.DeepLearningRoot(),'Data/Person2Day1_baseline.csv') targetPath = os.path.join(io.DeepLearningRoot(),'Data/Person2Day2_baseline.csv') sourceLabelPath = os.path.join(io.DeepLearningRoot(),'Data/Person2Day1_baseline_label.csv') targetLabelPath = os.path.join(io.DeepLearningRoot(),'Data/Person2Day2_baseline_label.csv') autoencoder = load_model(os.path.join(io.DeepLearningRoot(),'savedModels/person2_baseline_DAE.h5')) if data =='person1_3month': sourcePath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day1_3month.csv') targetPath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day2_3month.csv') sourceLabelPath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day1_3month_label.csv') targetLabelPath = os.path.join(io.DeepLearningRoot(),'Data/Person1Day2_3month_label.csv')