用python实现多层神经网络的基本实验步骤

好的，以下是实现多层神经网络的基本实验步骤：

1. 导入必要的库

   import numpy as np
   import matplotlib.pyplot as plt
   from sklearn.datasets import make_classification
   from sklearn.model_selection import train_test_split

2. 生成数据集

   X, y = make_classification(n_samples=1000, n_features=10, n_informative=8, n_redundant=2, random_state=42)
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

3. 定义神经网络结构

   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.weights1 = np.random.randn(self.input_size, self.hidden_size)
           self.weights2 = np.random.randn(self.hidden_size, self.output_size)

       def forward(self, X):
           self.z = np.dot(X, self.weights1)
           self.z2 = self.sigmoid(self.z)
           self.z3 = np.dot(self.z2, self.weights2)
           o = self.sigmoid(self.z3)
           return o

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

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

       def backward(self, X, y, o):
           self.o_error = y - o
           self.o_delta = self.o_error * self.sigmoid_derivative(o)
           self.z2_error = self.o_delta.dot(self.weights2.T)
           self.z2_delta = self.z2_error * self.sigmoid_derivative(self.z2)
           self.weights1 += X.T.dot(self.z2_delta)
           self.weights2 += self.z2.T.dot(self.o_delta)

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

4. 训练神经网络

   nn = NeuralNetwork(10, 5, 1)
   for i in range(1000):
       nn.train(X_train, y_train)

   predictions = nn.forward(X_test)
   accuracy = np.mean((predictions > 0.5) == y_test)
   print("Accuracy:", accuracy)

以上就是用Python实现多层神经网络的基本实验步骤。