当输出的矩阵尺寸的大小与标签的大小不匹配时，如何计算神经网络中的误差？

Question

我尝试使用神经网络为图像分类编写代码，而不使用KERAS等库。

但是当我要计算误差时我遇到了困难，因为输出前馈的矩阵大小与一个热门标签[的矩阵标签不匹配。以下是一个热门

multiclass

标签的代码，在这里我使用20个类，每个类具有15种变化（300个输入，大小= 64 x 64像素）：

labels_train = np.array([0] * 15 + [1] * 15 + [2] * 15 + [3] * 15 + [4] * 15 + [5] * 15 + [6] * 15 + [7] 
                        * 15 + [8] * 15 + [9] * 15 + [10] * 15 + [11] * 15 + [12] * 15 + [13] * 15 + [14] 
                        * 15 + [15] * 15 + [16] * 15 + [17] * 15 + [18] * 15 + [19] * 15)

y_train = np.zeros((300,20))

for i in range(300):
    y_train[i, labels_train[i]] = 1

以下是用于前馈和错误值计算的代码：

for epoch in range(1):

    hasil_ih = np.dot(W_ih, x_train) + B_ih
    output_ih = sigmoid(hasil_ih)

    hasil_hh = np.dot(W_hh, output_ih) + B_hh
    output_hh = sigmoid(hasil_hh)

    hasil_ho = np.dot(W_ho, output_hh) + B_ho
    output_ho = sigmoid(hasil_ho)

    print(output_ho.shape)
    print(y_train.shape)
    error_o = labels_train - output_ho

这里是程序运行时的结果：

Traceback (most recent call last):
(300, 4096)
  File "D:/Pengenalan_Induk_Aksara/NN2.py", line 71, in <module>
(300, 20)
    error_o = labels_train - output_ho
ValueError: operands could not be broadcast together with shapes (300,) (300,4096) 

Process finished with exit code 1

请帮助我，我仍然是初学者，但尚未找到解决方案。以下是完整的代码：

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import os


dir = "data_train/LBP_R1/"
kategori = ["ka","ga","nga","pa","ba","ma","ta","da","na","ca","ja","nya","ya","a","la","ra","sa","wa","ha","gha"]
features = []; label = []

for y in kategori:
    path = os.path.join(dir, y)
    class_num = kategori.index(y)
    label.append(class_num)

    for img in os.listdir(path):
        im = Image.open(os.path.join(path,img))
        imgs = list(im.getdata())
        features.append(imgs)


x_train = np.vstack([features])
x_train = (x_train/255)


labels_train = np.array([0] * 15 + [1] * 15 + [2] * 15 + [3] * 15 + [4] * 15 + 
                        [5] * 15 + [6] * 15 + [7] * 15 + [8] * 15 + [9] * 15 + 
                        [10] * 15 + [11] * 15 + [12] * 15 + [13] * 15 + [14] * 
                        15 + [15] * 15 + [16] * 15 + [17] * 15 + [18] * 15 + [19] 
                        * 15)

y_train = np.zeros((300,20))

for i in range(300):
    y_train[i, labels_train[i]] = 1

#Training Phase
def sigmoid(x):
    return 1/(1+np.exp(-x)) 

def dsigmoid(y):
    return y*(1-y) 

attributes = x_train.shape[0] 
hidden_nodes = 4 
output_nodes = 300 

np.random.seed(42)
W_ih = np.random.rand(hidden_nodes,attributes) #bobot input layer 1
B_ih = np.random.rand(hidden_nodes,1) #Bias input layer 1

W_hh = np.random.rand(hidden_nodes, hidden_nodes) #weight hidden layer 2
B_hh = np.random.rand(hidden_nodes, 1) #bias hidden layer 2

W_ho = np.random.rand(output_nodes, hidden_nodes) #weight hidden output
B_ho = np.random.rand(output_nodes, 1) #bias hidden output

lr = 0.25; #Learning rate

for epoch in range(1):

    hasil_ih = np.dot(W_ih, x_train) + B_ih
    output_ih = sigmoid(hasil_ih)

    hasil_hh = np.dot(W_hh, output_ih) + B_hh
    output_hh = sigmoid(hasil_hh)

    hasil_ho = np.dot(W_ho, output_hh) + B_ho
    output_ho = sigmoid(hasil_ho)

    print(output_ho.shape)
    print(y_train.shape)

    error_o = labels_train - output_ho
    print(output_ho)
    print(y_train)
    print(error_o)

[我试图使用神经网络为图像分类编写代码，而不使用KERAS等库，但是由于矩阵的大小，我想计算误差时遇到了困难...

Answer 1