机器学习神经网络bp算法代码实现
时间: 2023-11-05 20:57:13 浏览: 38
好的,以下是一个简单的神经网络反向传播算法(BP)的代码实现:
```
import numpy as np
class NeuralNetwork:
def __init__(self, layers, learning_rate=0.1):
self.layers = layers
self.learning_rate = learning_rate
self.weights = [np.random.randn(layers[i], layers[i+1]) for i in range(len(layers)-1)]
self.biases = [np.zeros((1, layers[i+1])) for i in range(len(layers)-1)]
def sigmoid(self, z):
return 1 / (1 + np.exp(-z))
def sigmoid_prime(self, z):
return self.sigmoid(z) * (1 - self.sigmoid(z))
def feedforward(self, a):
for w, b in zip(self.weights, self.biases):
a = self.sigmoid(np.dot(a, w) + b)
return a
def backpropagation(self, x, y):
activations = [x]
zs = []
for w, b in zip(self.weights, self.biases):
z = np.dot(activations[-1], w) + b
zs.append(z)
activations.append(self.sigmoid(z))
delta = self.cost_derivative(activations[-1], y) * self.sigmoid_prime(zs[-1])
nabla_b = [delta]
nabla_w = [np.dot(activations[-2].T, delta)]
for i in range(2, len(self.layers)):
z = zs[-i]
sp = self.sigmoid_prime(z)
delta = np.dot(delta, self.weights[-i+1].T) * sp
nabla_b.append(delta)
nabla_w.append(np.dot(activations[-i-1].T, delta))
return nabla_b, nabla_w
def update_weights(self, mini_batch):
nabla_b = [np.zeros(b.shape) for b in self.biases]
nabla_w = [np.zeros(w.shape) for w in self.weights]
for x, y in mini_batch:
delta_nabla_b, delta_nabla_w = self.backpropagation(x, y)
nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
self.biases = [b-(self.learning_rate/len(mini_batch))*nb for b, nb in zip(self.biases, nabla_b)]
self.weights = [w-(self.learning_rate/len(mini_batch))*nw for w, nw in zip(self.weights, nabla_w)]
def train(self, training_data, epochs, batch_size):
for epoch in range(epochs):
np.random.shuffle(training_data)
mini_batches = [training_data[k:k+batch_size] for k in range(0, len(training_data), batch_size)]
for mini_batch in mini_batches:
self.update_weights(mini_batch)
def cost_derivative(self, output_activations, y):
return (output_activations - y)
```
该代码实现了一个简单的全连接神经网络,并使用了反向传播算法来训练网络。在初始化时,我们指定了神经网络的层数和学习率。在训练时,我们将训练数据分成多个小批次,每个小批次包含若干个训练样本,然后对每个小批次分别进行反向传播,并更新神经网络的权重和偏置项。最终,我们可以使用 `feedforward` 方法来得到神经网络的输出。