Develop a python program with the keras and tensorflow packages to implement a neural network to classify the data points in a 2D region (0

1. In particular, the training samples are below:

The coordinates of the red dots: (1, 5); (2, 4); (7, 7); (4,6); (6,4) (2)

The coordinate of the blue dots: (6, 9); (4, 2); (8, 6); (5, 5); (3, 8)

2. Your neural network must have more than 2 hidden layers.

3. Your program needs to display the history of the training loss on the screen when the training is over.

4. After training, your program uses the trained model to predict the class of all data points within the region and plot the decision boundary picture similar to the picture above.

5. Hint: Since this is a classification project, you may need to set your deep learning model as shown below:

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

10 6 8 7 9 5 4 3 N 0 0 2 4 9 8 10 10 6 8 7 9 5 4 3 N 0 0 2 4 9 8 10