bp 神经网络 分类 python

首先，你需要准备好数据集，然后导入必要的库：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

接着，加载数据集并对其进行预处理：

df = pd.read_csv('data.csv')
X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

然后，定义神经网络模型：

class NeuralNetwork:
    def __init__(self, layers, alpha=0.1):
        self.W = []
        self.layers = layers
        self.alpha = alpha
        for i in range(0, len(layers) - 2):
            w = np.random.randn(layers[i] + 1, layers[i + 1] + 1)
            self.W.append(w / np.sqrt(layers[i]))
        w = np.random.randn(layers[-2] + 1, layers[-1])
        self.W.append(w / np.sqrt(layers[-2]))

    def __sigmoid(self, x):
        return 1.0 / (1 + np.exp(-x))

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

    def fit(self, X, y, epochs=1000, displayUpdate=100):
        X = np.c_[X, np.ones((X.shape[0]))]
        for epoch in range(0, epochs):
            for (x, target) in zip(X, y):
                self.fit_partial(x, target)
            if epoch == 0 or (epoch + 1) % displayUpdate == 0:
                loss = self.calculate_loss(X, y)
                print("[INFO] epoch={}, loss={:.7f}".format(epoch + 1, loss))

    def fit_partial(self, x, y):
        A = [np.atleast_2d(x)]
        for layer in range(0, len(self.W)):
            net = A[layer].dot(self.W[layer])
            out = self.__sigmoid(net)
            A.append(out)
        error = A[-1] - y
        D = [error * self.__sigmoid_derivative(A[-1])]
        for layer in range(len(A) - 2, 0, -1):
            delta = D[-1].dot(self.W[layer].T)
            delta = delta * self.__sigmoid_derivative(A[layer])
            D.append(delta)
        D = D[::-1]
        for layer in range(0, len(self.W)):
            self.W[layer] += -self.alpha * A[layer].T.dot(D[layer])

    def predict(self, X, addBias=True):
        p = np.atleast_2d(X)
        if addBias:
            p = np.c_[p, np.ones((p.shape[0]))]
        for layer in range(0, len(self.W)):
            p = self.__sigmoid(np.dot(p, self.W[layer]))
        return p

    def calculate_loss(self, X, targets):
        targets = np.atleast_2d(targets)
        predictions = self.predict(X, addBias=False)
        loss = 0.5 * np.sum((predictions - targets) ** 2)
        return loss

最后，训练并测试模型：

nn = NeuralNetwork([X_train.shape[1], 16, 8, 1])
nn.fit(X_train, y_train, epochs=1000)

predictions = nn.predict(X_test)
predictions[predictions <= 0.5] = 0
predictions[predictions > 0.5] = 1

accuracy = np.mean(predictions == y_test)
print("[INFO] accuracy: {:.2f}%".format(accuracy * 100))

这样，你就可以使用 BP 神经网络进行分类了。