输出基于python的使用bp神经网络,含有双隐层的代码
时间: 2023-06-03 15:06:21 浏览: 129
代码 基于双隐含层BP神经网络的预测
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当然可以,以下是一个基于Python实现的双隐层BP神经网络的代码示例:
```python
import numpy as np
class NeuralNetwork:
def __init__(self, input_layer_size, hidden_layer_size1, hidden_layer_size2, output_layer_size):
self.input_layer_size = input_layer_size
self.hidden_layer_size1 = hidden_layer_size1
self.hidden_layer_size2 = hidden_layer_size2
self.output_layer_size = output_layer_size
self.weights1 = np.random.normal(0.0, pow(self.hidden_layer_size1, -0.5), (self.hidden_layer_size1, self.input_layer_size))
self.biases1 = np.zeros((self.hidden_layer_size1, 1))
self.weights2 = np.random.normal(0.0, pow(self.hidden_layer_size2, -0.5), (self.hidden_layer_size2, self.hidden_layer_size1))
self.biases2 = np.zeros((self.hidden_layer_size2, 1))
self.weights3 = np.random.normal(0.0, pow(self.output_layer_size, -0.5), (self.output_layer_size, self.hidden_layer_size2))
self.biases3 = np.zeros((self.output_layer_size, 1))
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def softmax(self, x):
return np.exp(x) / np.sum(np.exp(x), axis=0)
def feedforward(self, inputs):
inputs = inputs[:,np.newaxis]
hidden_outputs1 = self.sigmoid(np.dot(self.weights1, inputs) + self.biases1)
hidden_outputs2 = self.sigmoid(np.dot(self.weights2, hidden_outputs1) + self.biases2)
final_outputs = self.softmax(np.dot(self.weights3, hidden_outputs2) + self.biases3)
return final_outputs
def train(self, inputs_array, targets_array, learning_rate):
inputs_array = inputs_array.T
targets_array = targets_array[:,np.newaxis].T
hidden_outputs1 = self.sigmoid(np.dot(self.weights1, inputs_array) + self.biases1)
hidden_outputs2 = self.sigmoid(np.dot(self.weights2, hidden_outputs1) + self.biases2)
final_outputs = self.softmax(np.dot(self.weights3, hidden_outputs2) + self.biases3)
output_errors = targets_array - final_outputs
hidden_errors2 = np.dot(self.weights3.T, output_errors) * hidden_outputs2 * (1 - hidden_outputs2)
hidden_errors1 = np.dot(self.weights2.T, hidden_errors2) * hidden_outputs1 * (1 - hidden_outputs1)
self.weights3 += learning_rate * np.dot((output_errors * final_outputs * (1 - final_outputs)), hidden_outputs2.T)
self.biases3 += learning_rate * output_errors * final_outputs * (1 - final_outputs)
self.weights2 += learning_rate * np.dot(hidden_errors2, hidden_outputs1.T)
self.biases2 += learning_rate * hidden_errors2
self.weights1 += learning_rate * np.dot(hidden_errors1, inputs_array.T)
self.biases1 += learning_rate * hidden_errors1
```
备注:以上代码仅供参考,可能有改进的空间,需要根据具体任务进行调整。
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