This programming assignment is to simulate the Backpropagation algorithm to solve the following non-linear separable problem.

(3-2). (5 points) (due: 5 pm, 2/18) This programming assignment is to simulate Backpropagation algorithm to solve the following non-linear separable problem. You can use your favorite computer language. Do not use Machine learning libraries for this question. The cube is given. Eight corners of this cube are classified into two groups. The four corners A, E, H, and D belong to one group, and the four corners B, C, G, and F belong to the other group Train your 3-layer neural network to classify this problem. You will need 3 input units in the input layer. One output unit is enough to solve this problem, but if you want to have more than one output unit for your exercise, that is fine. You can have any number of hidden units. G (0,1,1) H(1,1,1) E (0,0,1) F(1,0,1 C (0,1,0) D (1,1,0) A (0,0,0) B (1,0,0) The given problem can be displayed as below. A B D E F G H Input data Target attributel attribute2 attribute3 output 0 0 0 0 1 0 0 1 0 1 0 1 1 1 0 0 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 0 When your actual output of output unit is larger than 0.9, you can accept this output as 1. When your actual output of output unit is less than 0.1, you can accept this output as 0. You need to decide your learning coefficient. You will start with your small random initial weights for this problem. You do not need to print the process of the weights updating. After your training process is done, you need to print your final actual output (the result produced from Sigmoid function) for each input to verify your actual output is close enough to target output. (3-2). (5 points) (due: 5 pm, 2/18) This programming assignment is to simulate Backpropagation algorithm to solve the following non-linear separable problem. You can use your favorite computer language. Do not use Machine learning libraries for this question. The cube is given. Eight corners of this cube are classified into two groups. The four corners A, E, H, and D belong to one group, and the four corners B, C, G, and F belong to the other group Train your 3-layer neural network to classify this problem. You will need 3 input units in the input layer. One output unit is enough to solve this problem, but if you want to have more than one output unit for your exercise, that is fine. You can have any number of hidden units. G (0,1,1) H(1,1,1) E (0,0,1) F(1,0,1 C (0,1,0) D (1,1,0) A (0,0,0) B (1,0,0) The given problem can be displayed as below. A B D E F G H Input data Target attributel attribute2 attribute3 output 0 0 0 0 1 0 0 1 0 1 0 1 1 1 0 0 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 0 When your actual output of output unit is larger than 0.9, you can accept this output as 1. When your actual output of output unit is less than 0.1, you can accept this output as 0. You need to decide your learning coefficient. You will start with your small random initial weights for this problem. You do not need to print the process of the weights updating. After your training process is done, you need to print your final actual output (the result produced from Sigmoid function) for each input to verify your actual output is close enough to target output