我之前开始了机器学习的探险（在我过去两年的学习中）。我读了很多书，写了很多有关机器学习算法的代码，但神经网络却超出了我的范围。这个话题非常吸引我，但我遇到了一个大问题：
请问您能否告诉我哪里可以找到多层感知（神经网络）的简单实现？我不需要理论知识，也不需要上下文嵌入的例子。我更喜欢一些脚本语言来节省时间和精力——我以前99%的工作都是用Python完成的。
以下是我以前阅读过的书籍列表（没有找到我想要的内容）：
这是一个使用Python类编写的易于阅读的实现。该实现以易懂性为代价来换取效率：
import math
import random
BIAS = -1
"""
To view the structure of the Neural Network, type
print network_name
"""
class Neuron:
def __init__(self, n_inputs ):
self.n_inputs = n_inputs
self.set_weights( [random.uniform(0,1) for x in range(0,n_inputs+1)] ) # +1 for bias weight
def sum(self, inputs ):
# Does not include the bias
return sum(val*self.weights[i] for i,val in enumerate(inputs))
def set_weights(self, weights ):
self.weights = weights
def __str__(self):
return 'Weights: %s, Bias: %s' % ( str(self.weights[:-1]),str(self.weights[-1]) )
class NeuronLayer:
def __init__(self, n_neurons, n_inputs):
self.n_neurons = n_neurons
self.neurons = [Neuron( n_inputs ) for _ in range(0,self.n_neurons)]
def __str__(self):
return 'Layer:\n\t'+'\n\t'.join([str(neuron) for neuron in self.neurons])+''
class NeuralNetwork:
def __init__(self, n_inputs, n_outputs, n_neurons_to_hl, n_hidden_layers):
self.n_inputs = n_inputs
self.n_outputs = n_outputs
self.n_hidden_layers = n_hidden_layers
self.n_neurons_to_hl = n_neurons_to_hl
# Do not touch
self._create_network()
self._n_weights = None
# end
def _create_network(self):
if self.n_hidden_layers>0:
# create the first layer
self.layers = [NeuronLayer( self.n_neurons_to_hl,self.n_inputs )]
# create hidden layers
self.layers += [NeuronLayer( self.n_neurons_to_hl,self.n_neurons_to_hl ) for _ in range(0,self.n_hidden_layers)]
# hidden-to-output layer
self.layers += [NeuronLayer( self.n_outputs,self.n_neurons_to_hl )]
else:
# If we don't require hidden layers
self.layers = [NeuronLayer( self.n_outputs,self.n_inputs )]
def get_weights(self):
weights = []
for layer in self.layers:
for neuron in layer.neurons:
weights += neuron.weights
return weights
@property
def n_weights(self):
if not self._n_weights:
self._n_weights = 0
for layer in self.layers:
for neuron in layer.neurons:
self._n_weights += neuron.n_inputs+1 # +1 for bias weight
return self._n_weights
def set_weights(self, weights ):
assert len(weights)==self.n_weights, "Incorrect amount of weights."
stop = 0
for layer in self.layers:
for neuron in layer.neurons:
start, stop = stop, stop+(neuron.n_inputs+1)
neuron.set_weights( weights[start:stop] )
return self
def update(self, inputs ):
assert len(inputs)==self.n_inputs, "Incorrect amount of inputs."
for layer in self.layers:
outputs = []
for neuron in layer.neurons:
tot = neuron.sum(inputs) + neuron.weights[-1]*BIAS
outputs.append( self.sigmoid(tot) )
inputs = outputs
return outputs
def sigmoid(self, activation,response=1 ):
# the activation function
try:
return 1/(1+math.e**(-activation/response))
except OverflowError:
return float("inf")
def __str__(self):
return '\n'.join([str(i+1)+' '+str(layer) for i,layer in enumerate(self.layers)])
如果你正在寻找一个带有学习（反向传播）的神经网络更高效的例子，请查看我的神经网络Github代码库。
input_values = [1, 0, 0, 1, 0.5] - jorgenkghttp://msdn.microsoft.com/en-us/magazine/jj658979.aspx
http://itee.uq.edu.au/~cogs2010/cmc/chapters/BackProp/
http://www.codeproject.com/Articles/19323/Image-Recognition-with-Neural-Networks
http://freedelta.free.fr/r/php-code-samples/artificial-intelligence-neural-network-backpropagation/
这里是一个使用numpy实现前馈神经网络的例子。首先导入numpy并指定输入和目标的维度。
import numpy as np
input_dim = 1000
target_dim = 10
dimensions = [input_dim+1, 500, 500, target_dim+1]
weight_matrices = []
for i in range(len(dimensions)-1):
weight_matrix = np.ones((dimensions[i], dimensions[i]))
weight_matrices.append(weight_matrix)
如果您的输入存储在一个2d numpy矩阵中，其中每行对应一个样本，每列对应于样本的属性，则可以按如下方式通过网络进行传播：（假设逻辑sigmoid函数作为激活函数）
def activate_network(inputs):
activations = [] # we store the activations for each layer here
a = np.ones((inputs.shape[0], inputs.shape[1]+1)) #add the bias to the inputs
a[:,:-1] = inputs
for w in weight_matrices:
x = a.dot(w) # sum of weighted inputs
a = 1. / (1. - np.exp(-x)) # apply logistic sigmoid activation
a[:,-1] = 1. # bias for the next layer.
activations.append(a)
return activations
activations中的最后一个元素将是您网络的输出，但要注意，您需要省略偏置项的额外列，因此您的输出将为activations[-1][:,:-1]。activations的最后一个元素循环到第一个元素。在每个反向传播步骤之前，请确保将误差信号中的偏置列设置为零。