Python使用numpy实现BP神经网络

本文完全利用numpy实现一个简单的BP神经网络，由于是做regression而不是classification，因此在

本文完全利用numpy实现一个简单的BP神经网络，由于是做regression而不是classification，因此在这里输出层选取的激励函数就是f(x)=x。BP神经网络的具体原理此处不再介绍。

import numpy as np 
 
class NeuralNetwork(object): 
 def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate): 
 # Set number of nodes in input, hidden and output layers.设定输入层、隐藏层和输出层的node数目 
 self.input_nodes = input_nodes 
 self.hidden_nodes = hidden_nodes 
 self.output_nodes = output_nodes 
 
 # Initialize weights，初始化权重和学习速率 
 self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5, 
 ( self.hidden_nodes, self.input_nodes)) 
 
 self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5, 
 (self.output_nodes, self.hidden_nodes)) 
 self.lr = learning_rate 
 
 # 隐藏层的激励函数为sigmoid函数，Activation function is the sigmoid function 
 self.activation_function = (lambda x: 1/(1 + np.exp(-x))) 
 
 def train(self, inputs_list, targets_list): 
 # Convert inputs list to 2d array 
 inputs = np.array(inputs_list, ndmin=2).T # 输入向量的shape为 [feature_diemension, 1] 
 targets = np.array(targets_list, ndmin=2).T 
 
 # 向前传播，Forward pass 
 # TODO: Hidden layer 
 hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer 
 hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer 
 
 
 # 输出层，输出层的激励函数就是 y = x 
 final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # signals into final output layer 
 final_outputs = final_inputs # signals from final output layer 
 
 ### 反向传播 Backward pass，使用梯度下降对权重进行更新 ### 
 
 # 输出误差 
 # Output layer error is the difference between desired target and actual output. 
 output_errors = (targets_list-final_outputs) 
 
 # 反向传播误差 Backpropagated error 
 # errors propagated to the hidden layer 
 hidden_errors = np.dot(output_errors, self.weights_hidden_to_output)*(hidden_outputs*(1-hidden_outputs)).T 
 
 # 更新权重 Update the weights 
 # 更新隐藏层与输出层之间的权重 update hidden-to-output weights with gradient descent step 
 self.weights_hidden_to_output += output_errors * hidden_outputs.T * self.lr 
 # 更新输入层与隐藏层之间的权重 update input-to-hidden weights with gradient descent step 
 self.weights_input_to_hidden += (inputs * hidden_errors * self.lr).T 
 
 # 进行预测 
 def run(self, inputs_list): 
 # Run a forward pass through the network 
 inputs = np.array(inputs_list, ndmin=2).T 
 
 #### 实现向前传播 Implement the forward pass here #### 
 # 隐藏层 Hidden layer 
 hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer 
 hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer 
 
 # 输出层 Output layer 
 final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # signals into final output layer 
 final_outputs = final_inputs # signals from final output layer 
 
 return final_outputs 

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。