Assignment-2/training.py at main · Brain-Inspired-Computing/Assignment-2 · GitHub

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from and_net import and_net
import matplotlib.pyplot as plt

net = and_net([], [])
net.create_simple_net()
# X at [0], Y at [1], Teacher at [2]
# AND: .6, .4
# OR: .5, .5
# XOR: .6, .4
net.connections[0].weight = .6
net.connections[1].weight = .4
net.connections[2].weight = 1
# data[0] holds X, data[1] holds Y, output calculated by data[0][i] && data[1][i]
data = []
i = 0
j = 0
k = 0


def raster_plot(net, length):
    # plt.title('Raster plot for network activity')
    plt.figure('Raster plot for network activity')

    plt.subplot(3, 1, 1)
    plt.xlabel('Time')
    plt.ylabel('Input Neuron 1')
    # length = len(net.neurons[0].output)
    plt.xlim(0, 100)
    for i in range(length):
        if net.neurons[0].output[i] == 1:
            plt.axvline(i)

    plt.subplot(3, 1, 2)
    plt.xlabel('Time')
    plt.ylabel('Input Neuron 2')
    plt.xlim(0, 100)
    # length = len(net.neurons[1].output)
    for j in range(length):
        if net.neurons[1].output[j] == 1:
            plt.axvline(j)

    plt.subplot(3, 1, 3)
    plt.xlabel('Time')
    plt.ylabel('Output Neuron')
    plt.xlim(0, 100)
    # length = len(net.neurons[3].output)
    for k in range(length):
        if net.neurons[3].output[k] == 1:
            plt.axvline(k)

    plt.show()


# lines = open(input("Enter Filepath: "), "r").readlines()
lines = open("data.txt", "r").readlines()
runs = len(lines)
for i in range(runs):
    temp = []
    for j in range(2):
        if lines[i][j] == '0':
            temp.append(0)
        else:
            temp.append(1)
    data.append(temp)

# Trains the network
for r in range(runs):
    net.run_net(data[r])
    time = len(net.neurons[0].output)
    w_max = 1
    for i in range(int(time / 100)):
        # Finds rate for each neuron in a time step of 100
        v_x = sum(net.neurons[0].output[100 * i:100 * (i + 1)])
        v_y = sum(net.neurons[1].output[100 * i:100 * (i + 1)])
        v_o = sum(net.neurons[3].output[100 * i:100 * (i + 1)])
        # Sets a_corr value
        a_corr_x = w_max - net.connections[0].weight
        a_corr_y = w_max - net.connections[1].weight
        # If the rate is below 105, set value to 0
        if v_x <= 10:
            v_x = 0
        if v_y <= 10:
            v_y = 0
        if v_o <= 10:
            v_o = 0
        # Hebb with postsynaptic LTP/LTD threshold
        net.connections[0].weight += a_corr_x * v_x * (v_o - .01)
        net.connections[1].weight += a_corr_y * v_y * (v_o - .01)
        if net.connections[0].weight < 0:
            net.connections[0].weight = 0
        elif net.connections[0].weight > w_max:
            net.connections[0].weight = w_max
        if net.connections[1].weight < 0:
            net.connections[1].weight = 0
        elif net.connections[1].weight > w_max:
            net.connections[1].weight = w_max
    x_spikes = sum(net.neurons[0].output)
    y_spikes = sum(net.neurons[1].output)
    o_spikes = sum(net.neurons[3].output)
    # Change operator to and, or, ^
    #print((data[r][0] and data[r][1]), "||", net.spikes_to_binary(net.neurons[3]), "\nX =",
    #      net.connections[0].weight, x_spikes, "\nY =", net.connections[1].weight, y_spikes, "\n", o_spikes, "\n")

    raster_plot(net, time)
    for i in range(4):
        net.neurons[i].clear()

# net.run_net(data[r])
# time = len(net.neurons[0].output)
#raster_plot(net, time)