[BPnet识别MNIST07]神经网络的实现以及调优

Posted 2021-08-31 AIplusX

写在前面

经过周末的休息，脑子得到了足够的休息，所以今天调程序的时候就特别顺利，完成了标准BP神经网络的程序搭建，并且开始了参数的调整，那么今天这篇博客主要分析程序实现以及模型调优的尝试过程。

正文

主要内容在我的古月居博客：
[BPnet识别MNIST07]神经网络的实现以及调优

神经网络

最终我确定下来的神经网络长这样：

def calcuEntImagedist(x):
    x_out = np.zeros(((1, 2)), dtype=float)
    gray_temp = np.zeros((1, 256), dtype=float)

    for i in range(0, 784):
        temp = int(x[i])
        gray_temp[0][temp] = gray_temp[0][temp] + 1

    sum_tmp = float(0)

    for i in range(0, 256):
        gray_temp[0][i] = gray_temp[0][i] / 784

    result = float(0)
    for i in range(0, 256):
        if(gray_temp[0][i] > 0):
            result = result - gray_temp[0][i] * np.log2(gray_temp[0][i])

    # print(result)

    for i in range(0, 784):
        sum_tmp = sum_tmp + x[i]

    sum_tmp = sum_tmp / 784

    temp_s = float(0)
    for i in range(0 ,784):
        temp_s = temp_s + pow((x[i] - sum_tmp), 3)
    temp_s = pow((temp_s/784),(1/3))
    # print(temp_s)

    x_out[0][0] = result
    x_out[0][1] = temp_s
    # print("x_out:", x_out)
    return x_out

公式推导

 m = np.dot(input_x, w) - theta_1
 # print(m)
 for i in range(0, 4):
     m[0][i] = sigmoid(m[0][i])
 n = np.dot(m, v) - theta_2
 for i in range(0, 4):
     n[0][i] = sigmoid(n[0][i])
 y_out = np.dot(n, gamma) - theta_3
 y_out = sigmoid(y_out)

# update dw,dv,dgama,dtheta_1,dtheta_2,dtheta_3
dtheta_3 = -1 * (y_out - y) * (y_out * (1 - y_out))
for i in range(0, 4):
    dtheta_2[0][i] = -1 * (y_out - y) * (y_out * (1 - y_out)) * gamma[i] * \\
                     (n[0][i] * (1 - n[0][i]))
    dgamma[i] = (y_out - y) * (y_out * (1 - y_out)) * n[0][i]
for i in range(0, 4):
    for j in range(0, 4):
        dv[i][j] =  (y_out - y) * (y_out * (1 - y_out)) * (n[0][j] * (1 - n[0][j])) * \\
                  gamma[j] * m[0][i]
        dtheta_1[0][i] =  -1 * (y_out - y) * (y_out * (1 - y_out)) * gamma[j] * \\
                        (n[0][j] * (1 - n[0][j])) * v[i][j] * (m[0][i] * (1 - m[0][i])) + dtheta_1[0][i]
for i in range(0, 2):
    for j in range(0, 4):
        for k in range(0, 4):
            dw[i][j] = dw[i][j] + (y_out - y) * (y_out * (1 - y_out)) * gamma[k] * \\
                      (n[0][i] * (1 - n[0][i])) * v[j][k] * (m[0][i] * (1 - m[0][i])) * input_x[0][i]

# update w,v,gama,theta_1,theta_2,theta_3
for i in range(0, 4):
    for j in range(0, 2):
        w[j][i] = w[j][i] - study_step * dw[j][i]
# print(w)
for i in range(0, 4):
    theta_2[0][i] = theta_2[0][i] + study_step * dtheta_2[0][i]
    theta_1[0][i] = theta_1[0][i] + study_step * dtheta_1[0][i]
    gamma[i] = gamma[i] - study_step * dgamma[i]
for i in range(0, 4):
    for j in range(0, 4):
        v[i][j] = v[i][j] - study_step * dv[i][j]
theta_3 = theta_3 + study_step * dtheta_3

程序结果以及参数调优

total_n = 60000
# train_aside_n = 48000 #set aside ,0.8-0.2
train_aside_n = 1
study_step = 0.8
epoch = 200
start_rand_max = 0.4

w = np.zeros(((2,4)), dtype=float)#(()) is used to confirm line and row
gamma = np.zeros((4,1), dtype=float)
v = np.zeros((4,4), dtype=float)
theta_1 = np.zeros(((1,4)), dtype=float)
theta_2 = np.zeros(((1,4)), dtype=float)
theta_3 = random.uniform(0,start_rand_max)
m = np.ones(((1,4)), dtype=float)
n = np.ones(((1,4)), dtype=float)

input_x = np.zeros(((1, 2)), dtype=float)
y_out = float(0)

# init para
for i in range(0, 4):  # [1,4]
    gamma[i] = random.uniform(0,start_rand_max)
    theta_1[0][i] = random.uniform(0,start_rand_max)
    theta_2[0][i] = random.uniform(0,start_rand_max)
    for j in range(0, 4):
        v[i][j] = random.uniform(0,start_rand_max)
    for k in range(0, 2):
        w[k][i] = random.uniform(0, start_rand_max)

正文

主要内容在我的古月居博客：
[BPnet识别MNIST07]神经网络的实现以及调优