finanacial_distress_model.py

model = models.Sequential([
    layers.Dense(32, activation = 'relu', input_shape = Xtrain[0].shape),
    layers.Dense(16, activation = 'relu'),
    layers.Dense(8, activation = 'relu'),
    layers.Dense(1, activation = 'sigmoid')
])

cb = [callbacks.EarlyStopping(patience = 5, restore_best_weights = True)]

model.compile(optimizer = optimizers.Adam(0.001), loss = losses.BinaryCrossentropy(), metrics = ['accuracy'])
history = model.fit(Xtrain, ytrain, validation_data = (Xval, yval), epochs = 256, callbacks = cb)

model.compile(optimizer = optimizers.Adam(0.0001), loss = losses.BinaryCrossentropy(), metrics = ['accuracy'])
history1 = model.fit(Xtrain, ytrain, validation_data = (Xval, yval), epochs = 256, callbacks = cb)