不麻木的基本神经网络->了解迭代步骤

问题描述

我试图可视化以下神经网络代码正在执行的步骤。这是绝对的基础，甚至不使用Numpy也能直观地看到并意识到代码中正在发生的事情：

这是到目前为止的注释代码：

weights = ([0.1,0.1,-0.3],#first matrix weight´s
           [0.1,0.2,0.0],[0.0,1.3,0.1])

info1 = [8.5,9.5,9.9,9.0]     #second matrix info´s
info2 = [0.65,0.8,0.9]
info3 = [1.2,0.5,1.0]

input = [info1[0],info2[0],info3[0]]

def w_sum(a,b):              #(weighted) sum function
    assert(len(a) == len(b))
    output = 0
    for i in range(len(a)):
        output += (a[i] * b[i])
    return output

def vect_mat_mul(vect,matrix):        #dot product function
    assert(len(vect) == len(matrix))
    output = [0,0]
    
    for i in range (len(vect)):
        output[i] = w_sum(vect,matrix[i])  # updates the output = [0,0] list with the 
                                            # result of w_sum(a,b) on position [i]
                                            # every iteration step i
    return output

def neural_network(input,weights):    #inputting the start values into the network
    pred = vect_mat_mul(input,weights)
    return pred

pred = neural_network(input,weights)  #calling the network --> the variable
                                       # 'output = [0,0] is Now updated with the
                                       # values from the dot product
                                       # and 'transported outwards' by assigning the return of the network to "pred"

print(pred)

在这里，我试图可视化正在发生的事情：

代码正在执行的第一个“迭代”步骤