小波神经网络的python代码
时间: 2023-07-27 11:06:06 浏览: 42
以下是使用Python实现小波神经网络的基本代码:
```python
import numpy as np
import pywt
# 定义小波神经网络类
class WaveletNeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
# 初始化网络参数
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# 随机初始化权重和偏置
self.weights_ih = np.random.randn(hidden_size, input_size)
self.bias_ih = np.random.randn(hidden_size, 1)
self.weights_ho = np.random.randn(output_size, hidden_size)
self.bias_ho = np.random.randn(output_size, 1)
# 定义激活函数
self.activation_function = lambda x: 1 / (1 + np.exp(-x))
# 前向传播
def forward(self, inputs):
# 小波变换
coeffs = pywt.dwt(inputs, 'haar')
inputs = coeffs[0]
# 输入层到隐层
hidden = np.dot(self.weights_ih, inputs) + self.bias_ih
hidden = self.activation_function(hidden)
# 隐层到输出层
outputs = np.dot(self.weights_ho, hidden) + self.bias_ho
return outputs
# 训练网络
def train(self, inputs, targets, learning_rate):
# 前向传播
outputs = self.forward(inputs)
# 反向传播
error = targets - outputs
# 输出层到隐层的权重和偏置调整
gradient = error * learning_rate
hidden_T = np.transpose(hidden)
weights_ho_deltas = np.dot(gradient, hidden_T)
self.weights_ho += weights_ho_deltas
self.bias_ho += gradient
# 隐层到输入层的权重和偏置调整
who_T = np.transpose(self.weights_ho)
hidden_errors = np.dot(who_T, error)
hidden_gradient = hidden * (1 - hidden) * hidden_errors
inputs_T = np.transpose(inputs)
weights_ih_deltas = np.dot(hidden_gradient, inputs_T)
self.weights_ih += weights_ih_deltas
self.bias_ih += hidden_gradient
# 预测
def predict(self, inputs):
# 前向传播
outputs = self.forward(inputs)
# 取最大值作为预测结果
prediction = np.argmax(outputs)
return prediction
```
这是一个简单的小波神经网络实现,可以根据实际需要进行修改和扩展。
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