test_nn_matrix

import nn_matrix as mx
from utility import print_histograms

import math
import numpy as np
import random

xs = np.array(([3,5], [5,1], [10,2]), dtype=float)
ys = np.array(([75], [82], [93]), dtype=float)

xs = xs / np.amax(xs, axis=0)
ys = ys / 100 #Max test score is 100

nn_mx = mx.Neural_Network(2, 3, 1)

y_pred = nn_mx.forward(xs)

print_histograms(ys, y_pred)

delta = 0.0000001
learning_rate = 3
max_iterations = 1000000
prev_cost = nn_mx.cost(xs, ys)
cost = np.finfo(float).max
iteration = 0  # счетчик итераций

while iteration < max_iterations and abs(cost - prev_cost) > delta:

    dJdW1, dJdW2, dJB1, dJB2 = nn_mx.backward(xs, ys)

    nn_mx.W1 -= learning_rate * dJdW1
    nn_mx.W2 -= learning_rate * dJdW2
    nn_mx.B1 -= learning_rate * dJB1
    nn_mx.B2 -= learning_rate * dJB2
    
    prev_cost = cost
    cost = nn_mx.cost(xs, ys)

    iteration += 1

print("it : ",  iteration, ' / ', max_iterations, ' -> ', cost)

x_new = [3 / 10, 5 / 5]

pred = nn_mx.forward(x_new)

print('pred : ', x_new, ' : ', pred)

y_pred = nn_mx.forward(xs)
 
print_histograms(ys, y_pred)