神经网络 误差逆传播算法实验

神经网络误差逆传播算法是一种常用的神经网络训练算法，用于解决分类和回归问题。下面是一个简单的神经网络误差逆传播算法实验的步骤：

1.导入必要的库和数据集

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

2.定义神经网络模型

class NeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.W1 = np.random.randn(self.input_size, self.hidden_size)
        self.b1 = np.zeros((1, self.hidden_size))
        self.W2 = np.random.randn(self.hidden_size, self.output_size)
        self.b2 = np.zeros((1, self.output_size))

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def sigmoid_derivative(self, x):
        return x * (1 - x)

    def forward(self, X):
        self.z1 = np.dot(X, self.W1) + self.b1
        self.a1 = self.sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.W2) + self.b2
        self.a2 = self.sigmoid(self.z2)
        return self.a2

    def backward(self, X, y, output):
        self.output_error = y - output
        self.output_delta = self.output_error * self.sigmoid_derivative(output)
        self.z2_error = self.output_delta.dot(self.W2.T)
        self.z2_delta = self.z2_error * self.sigmoid_derivative(self.a1)
        self.W1 += X.T.dot(self.z2_delta)
        self.b1 += np.sum(self.z2_delta, axis=0, keepdims=True)
        self.W2 += self.a1.T.dot(self.output_delta)
        self.b2 += np.sum(self.output_delta, axis=0)

    def train(self, X, y):
        output = self.forward(X)
        self.backward(X, y, output)

3.训练神经网络模型

nn = NeuralNetwork(4, 5, 3)
for i in range(1000):
    nn.train(X_train, y_train)

4.测试神经网络模型

y_pred = np.argmax(nn.forward(X_test), axis=1)
accuracy = np.mean(y_pred == y_test)
print("Accuracy:", accuracy)