model = Sequential()
model.add(Dense(300, 
                activation = 'relu', 
                input_shape = x_train.shape[1:]))
model.add(Dense(100, 
                activation = 'relu'))
model.add(Dense(1))
model.compile(optimizer = 'adam', 
              loss = 'mse', 
              metrics = ['mae'])
model.fit(x_train, y_train, 
          epochs = 30, 
          batch_size = 32, 
          validation_split = 0.1)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)