import keras from keras.layers import Dense, BatchNormalization from keras import regularizers from keras.optimizers import Adam from keras.callbacks import ModelCheckpoint, EarlyStopping import pandas as pd import numpy as np # Model parameters: activation = 'relu' final_activation = 'sigmoid' loss = 'binary_crossentropy' batchsize = 200 epochs = 100 lr = 0.00005 # Model architecture: model = keras.Sequential() model.add( Dense(units=300, input_dim=x_train.shape[1], activation=activation, kernel_regularizer=regularizers.L1(0.001))) model.add(BatchNormalization()) model.add(Dense(units=102, activation=activation, kernel_regularizer=regularizers.L1(0.001))) model.add(BatchNormalization()) model.add(Dense(units=12, activation=activation, kernel_regularizer=regularizers.L1(0.001))) model.add(BatchNormalization()) model.add(Dense(units=6, activation=activation, kernel_regularizer=regularizers.L1(0.001))) model.add(BatchNormalization()) model.add(Dense(units=1, activation=final_activation)) model.compile(optimizer=Adam(learning_rate=lr), loss=loss, metrics=['accuracy', 'AUC']) model.summary() # Model checkpoints: saveModel = ModelCheckpoint('best_model.hdf5', save_best_only=True, monitor='val_loss', mode='min') # Model training: history = model.fit( x_train, y_train, batch_size=batchsize, callbacks=[EarlyStopping(verbose=True, patience=10, monitor='val_loss'), saveModel], epochs=epochs, validation_data=( x_val, y_val))