下面是我写的BP神经网络代码，问问如何将训练好的神经网络模型保存并可以在其他py文件中直接调用？请老师给出详细的代码，谢谢
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
from pandas.plotting import radviz
'''
构建一个具有1个隐藏层的神经网络，隐层的大小为10
输入层为4个特征，输出层为3个分类
(1,0,0)为第一类，(0,1,0)为第二类，(0,0,1)为第三类
'''
# 1.初始化参数
def initialize_parameters(n_x, n_h, n_y):
np.random.seed(2)
# 权重和偏置矩阵
w1 = np.random.randn(n_h, n_x) * 0.01
b1 = np.zeros(shape=(n_h, 1))
w2 = np.random.randn(n_y, n_h) * 0.01
b2 = np.zeros(shape=(n_y, 1))
# 通过字典存储参数
parameters = {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}
return parameters
# 2.前向传播
def forward_propagation(X, parameters):
w1 = parameters['w1']
b1 = parameters['b1']
w2 = parameters['w2']
b2 = parameters['b2']
# 通过前向传播来计算a2
z1 = np.dot(w1, X) + b1 # 这个地方需注意矩阵加法：虽然(w1*X)和b1的维度不同，但可以相加
a1 = np.tanh(z1) # 使用tanh作为第一层的激活函数
z2 = np.dot(w2, a1) + b2
a2 = 1 / (1 + np.exp(-z2)) # 使用sigmoid作为第二层的激活函数
# 通过字典存储参数
cache = {'z1': z1, 'a1': a1, 'z2': z2, 'a2': a2}
return a2, cache
# 3.计算代价函数
def compute_cost(a2, Y, parameters):
m = Y.shape[1] # Y的列数即为总的样本数
# 采用交叉熵（cross-entropy）作为代价函数
logprobs = np.multiply(np.log(a2), Y) + np.multiply((1 - Y), np.log(1 - a2))
cost = - np.sum(logprobs) / m
return cost
# 4.反向传播（计算代价函数的导数）
def backward_propagation(parameters, cache, X, Y):
m = Y.shape[1]
w2 = parameters['w2']
a1 = cache['a1']
a2 = cache['a2']
# 反向传播，计算dw1、db1、dw2、db2
dz2 = a2 - Y
dw2 = (1 / m) * np.dot(dz2, a1.T)
db2 = (1 / m) * np.sum(dz2, axis=1, keepdims=True)
dz1 = np.multiply(np.dot(w2.T, dz2), 1 - np.power(a1, 2))
dw1 = (1 / m) * np.dot(dz1, X.T)
db1 = (1 / m) * np.sum(dz1, axis=1, keepdims=True)
grads = {'dw1': dw1, 'db1': db1, 'dw2': dw2, 'db2': db2}
return grads
# 5.更新参数
def update_parameters(parameters, grads, learning_rate=0.06):
w1 = parameters['w1']
b1 = parameters['b1']
w2 = parameters['w2']
b2 = parameters['b2']
dw1 = grads['dw1']
db1 = grads['db1']
dw2 = grads['dw2']
db2 = grads['db2']
# 更新参数
w1 = w1 - dw1 * learning_rate
b1 = b1 - db1 * learning_rate
w2 = w2 - dw2 * learning_rate
b2 = b2 - db2 * learning_rate
parameters = {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}
return parameters
# 6.模型评估
def predict(parameters, x_test, y_test):
w1 = parameters['w1']
b1 = parameters['b1']
w2 = parameters['w2']
b2 = parameters['b2']
z1 = np.dot(w1, x_test) + b1
a1 = np.tanh(z1)
z2 = np.dot(w2, a1) + b2
a2 = 1 / (1 + np.exp(-z2))
# 结果的维度
n_rows = y_test.shape[0]
n_cols = y_test.shape[1]
# 预测值结果存储
output = np.empty(shape=(n_rows, n_cols), dtype=int)
# for i in range(n_rows):
# for j in range(n_cols):
# if a2[i][j] > 0.5:
# output[i][j] = 1
# else:
# output[i][j] = 0
for i in range(n_cols):
# 将每条测试数据的预测结果（概率）存为一个行向量
temp = np.zeros(shape=n_rows)
for j in range(n_rows):
temp[j] = a2[j][i]
# 将每条结果（概率）从小到大排序，并获得相应下标
sorted_dist = np.argsort(temp)
length = len(sorted_dist)
# 将概率最大的置为1，其它置为0
for k in range(length):
if k == sorted_dist[length - 1]:
output[k][i] = 1
else:
output[k][i] = 0
print('预测结果：')
print(output)
print('真实结果：')
print(y_test)
count = 0
for k in range(0, n_cols):
if output[0][k] == y_test[0][k] and output[1][k] == y_test[1][k] and output[2][k] == y_test[2][k]:
count = count + 1
else:
print(k)
acc = count / int(y_test.shape[1]) * 100
print('准确率：%.2f%%' % acc)
return output
# 建立神经网络
def nn_model(X, Y, n_h, n_input, n_output, num_iterations=10000, print_cost=False):
np.random.seed(3)
n_x = n_input # 输入层节点数
n_y = n_output # 输出层节点数
# 1.初始化参数
parameters = initialize_parameters(n_x, n_h, n_y)
# 梯度下降循环
for i in range(0, num_iterations):
# 2.前向传播
a2, cache = forward_propagation(X, parameters)
# 3.计算代价函数
cost = compute_cost(a2, Y, parameters)
# 4.反向传播
grads = backward_propagation(parameters, cache, X, Y)
# 5.更新参数
parameters = update_parameters(parameters, grads)
# 每1000次迭代，输出一次代价函数
if print_cost and i % 1000 == 0:
print('迭代第%i次，代价函数为：%f' % (i, cost))
return parameters
# 结果可视化
# 特征有4个维度，类别有1个维度，一共5个维度，故采用了RadViz图
# def result_visualization(x_test, y_test, result):
# cols = y_test.shape[1]
# y = []
# pre = []
#
# # 反转换类别的独热编码
# for i in range(cols):
# if y_test[0][i] == 0 and y_test[1][i] == 0 and y_test[2][i] == 1:
# y.append('setosa')
# elif y_test[0][i] == 0 and y_test[1][i] == 1 and y_test[2][i] == 0:
# y.append('versicolor')
# elif y_test[0][i] == 1 and y_test[1][i] == 0 and y_test[2][i] == 0:
# y.append('virginica')
#
# for j in range(cols):
# if result[0][j] == 0 and result[1][j] == 0 and result[2][j] == 1:
# pre.append('setosa')
# elif result[0][j] == 0 and result[1][j] == 1 and result[2][j] == 0:
# pre.append('versicolor')
# elif result[0][j] == 1 and result[1][j] == 0 and result[2][j] == 0:
# pre.append('virginica')
# else:
# pre.append('unknown')
#
# # 将特征和类别矩阵拼接起来
# real = np.column_stack((x_test.T, y))
# prediction = np.column_stack((x_test.T, pre))
#
# # 转换成DataFrame类型，并添加columns
# df_real = pd.DataFrame(real, index=None,
# columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width', 'Species'])
# df_prediction = pd.DataFrame(prediction, index=None,
# columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width', 'Species'])
#
# # 将特征列转换为float类型，否则radviz会报错
# df_real[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']] = df_real[
# ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']].astype(float)
# df_prediction[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']] = df_prediction[
# ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']].astype(float)
#
# # 绘图
# plt.figure('真实分类')
# radviz(df_real, 'Species', color=['blue', 'green', 'red', 'yellow'])
# plt.figure('预测分类')
# radviz(df_prediction, 'Species', color=['blue', 'green', 'red', 'yellow'])
# plt.show()
if __name__ == "__main__":
# 读取数据
data_set = pd.read_csv('C:/Users/29291/Desktop/sjwl.csv', header=None)
# # 第1种取数据方法：
# X = data_set.iloc[0:671, 0:269].values # 前四列是特征，T表示转置
# Y = data_set.iloc[672:674, 0:269].values # 后三列是标签
# 第2种取数据方法：
# X = data_set.ix[0:671, 0:269].values
# Y = data_set.ix[672:674, 0:269].values
# 第3种取数据方法：
X = data_set.loc[0:671, 0:269].values
x_mean = np.mean(X, axis=0)
x_std = np.std(X, axis=0)
X = (X - x_mean) / x_std
Y = data_set.loc[672:674, 0:269].values
# X = data_set.loc[1:670, 1:270].values
# Y = data_set.loc[672:674, 0:269].values
# 第4种取数据方法：
# X = data_set[data_set.columns[0:671, 0:269].values.T
# Y = data_set[data_set.columns[672:674, 0:269]].values.T
Y = Y.astype('uint8')
# 开始训练
start_time = datetime.datetime.now()
# 输入4个节点，隐层10个节点，输出3个节点，迭代10000次
parameters = nn_model(X, Y, n_h=30, n_input=672, n_output=3, num_iterations=10000, print_cost=True)
end_time = datetime.datetime.now()
print("用时：" + str((end_time - start_time).seconds) + 's' + str(
round((end_time - start_time).microseconds / 1000)) + 'ms')
# 对模型进行测试
data_test = pd.read_csv('C:/Users/29291/Desktop/sjwl.csv', header=None)
# x_test = data_test.iloc[0:671, 270:299].values
# y_test = data_test.iloc[672:674, 270:299].values
x_test =data_set.loc[0:671, 270:299].values
x_mean = np.mean(x_test , axis=0)
x_std = np.std(x_test , axis=0)
x_test = (x_test - x_mean) / x_std
y_test =data_set.loc[672:674, 270:299].values
y_test = y_test.astype('uint8')
result = predict(parameters, x_test, y_test)
