ph_model.py

model = models.Sequential([
    layers.Dense(128, activation = 'relu', input_shape = Xtrain[0].shape),
    layers.Dense(64, activation = 'relu'),
    #layers.Dense(16, activation = 'relu'),
    layers.Dense(8, activation = 'relu'),
    layers.Dense(1)
])

cb = callbacks.EarlyStopping(patience = 10, restore_best_weights = True)

model.compile(optimizer = optimizers.Adam(0.001), loss = losses.MeanSquaredError(), metrics = ['mae'])
history = model.fit(Xtrain, ytrain, validation_data = (Xval, yval), epochs = 1024, callbacks = cb)