Model.py

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))