import numpy as np import os l1 = 2 l2 = 2 modelpath = './your_models_SSAE/' if not os.path.exists(modelpath): os.makedirs(modelpath) feature = 'LPS' #feature = 'LogMFE' print('Feature used is {}'.format(feature)) print('Loading data...') data = my_functions.load_data(l1, l2, feature) inp_train, inp_dev, inp_test, op_reg_train, op_reg_dev, op_reg_test, feat_dim_X, feat_dim_Y = data print('Data loaded') ############################################### # Configurations #pDrop = 0.2; BN = 0 # Fraction of the input units to drop. #pDrop = 0; BN = 'a' pDrop = 0 BN = 'b' #pDrop = 0; BN = 0 #HL = [512,256,10,256,512] HL = [1024, 512, 10, 512, 1024] activations = ['tanh', 'tanh', 'tanh', 'tanh', 'tanh', 'linear'] act = 'tttttl'
n_phi = 100 # 100 (b) in the manuscript dropout = 0.0 # let's apply dropout in the second layer # Training Parameters learning_rate = 0.05 batch_size = 64 seed = 10003 early_stopping = True n_epochs = 100 display = 5 #100 # ----- prepare data k = 1 # work on 1st k fold for testing purpose train_split, test_split = create_k_splits() X_train, y_train = load_data('train', train_split[k], test_split[k], SummaryMeasure) X_test, y_test = load_data('test', train_split[k], test_split[k], SummaryMeasure) # prepare mask and masker mask = np.load("brain_mask", allow_pickle=True) masker = prepare_masker(X_train) # ------- # flatten data for mlp X_train = X_train.reshape(len(X_train), -1) #109350 X_test = X_test.reshape(len(X_test), -1) X_train = X_train[:, mask.reshape(-1)] #28542 X_test = X_test[:, mask.reshape(-1)] # Construct MLP with Feature Grouping