神经网络示例源代码（最好是 Python）
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Neural Network Example Source-code (preferably Python)
提问by Fifth-Edition
I wonder if anyone has some example code of a Neural network in python. If someone know of some sort of tutorial with a complete walkthrough that would be awesome, but just example source would be great as well!
我想知道是否有人在 python 中有一些神经网络的示例代码。如果有人知道某种带有完整演练的教程，那会很棒，但仅示例源也很棒！
Thanks
谢谢
采纳答案by jfs
Take a look at Learning from Clicksfrom the book Programming Collective Intelligence.
回答by bayer
Here is a simple example by Armin Rigo: http://codespeak.net/pypy/dist/demo/bpnn.py. If you want to use more sophisticated stuff, there is also http://pybrain.org.
这是 Armin Rigo 的一个简单示例：http: //codespeak.net/pypy/dist/demo/bpnn.py。如果你想使用更复杂的东西，还有http://pybrain.org。
Edit:Link is broken. Anyway, the current way to go with neural nets in python is probably Theano.
编辑：链接已损坏。无论如何，目前在 python 中使用神经网络的方法可能是Theano。
回答by maazza
Found this interresting discusion on ubuntu forums http://ubuntuforums.org/showthread.php?t=320257
在 ubuntu 论坛上发现了这个有趣的讨论 http://ubuntuforums.org/showthread.php?t=320257
import time
import random
# Learning rate:
# Lower = slower
# Higher = less precise
rate=.2
# Create random weights
inWeight=[random.uniform(0, 1), random.uniform(0, 1)]
# Start neuron with no stimuli
inNeuron=[0.0, 0.0]
# Learning table (or gate)
test =[[0.0, 0.0, 0.0]]
test+=[[0.0, 1.0, 1.0]]
test+=[[1.0, 0.0, 1.0]]
test+=[[1.0, 1.0, 1.0]]
# Calculate response from neural input
def outNeuron(midThresh):
global inNeuron, inWeight
s=inNeuron[0]*inWeight[0] + inNeuron[1]*inWeight[1]
if s>midThresh:
return 1.0
else:
return 0.0
# Display results of test
def display(out, real):
if out == real:
print str(out)+" should be "+str(real)+" ***"
else:
print str(out)+" should be "+str(real)
while 1:
# Loop through each lesson in the learning table
for i in range(len(test)):
# Stimulate neurons with test input
inNeuron[0]=test[i][0]
inNeuron[1]=test[i][1]
# Adjust weight of neuron #1
# based on feedback, then display
out = outNeuron(2)
inWeight[0]+=rate*(test[i][2]-out)
display(out, test[i][2])
# Adjust weight of neuron #2
# based on feedback, then display
out = outNeuron(2)
inWeight[1]+=rate*(test[i][2]-out)
display(out, test[i][2])
# Delay
time.sleep(1)
EDIT: there is also a framework named chainer https://pypi.python.org/pypi/chainer/1.0.0
编辑：还有一个名为 chainer https://pypi.python.org/pypi/chainer/1.0.0的框架
回答by Akavall
Here is a probabilistic neural network tutorial :http://www.youtube.com/watch?v=uAKu4g7lBxU
这是一个概率神经网络教程：http: //www.youtube.com/watch?v=uAKu4g7lBxU
And my Python Implementation:
还有我的 Python 实现：
import math
data = {'o' : [(0.2, 0.5), (0.5, 0.7)],
'x' : [(0.8, 0.8), (0.4, 0.5)],
'i' : [(0.8, 0.5), (0.6, 0.3), (0.3, 0.2)]}
class Prob_Neural_Network(object):
def __init__(self, data):
self.data = data
def predict(self, new_point, sigma):
res_dict = {}
np = new_point
for k, v in self.data.iteritems():
res_dict[k] = sum(self.gaussian_func(np[0], np[1], p[0], p[1], sigma) for p in v)
return max(res_dict.iteritems(), key=lambda k : k[1])
def gaussian_func(self, x, y, x_0, y_0, sigma):
return math.e ** (-1 *((x - x_0) ** 2 + (y - y_0) ** 2) / ((2 * (sigma ** 2))))
prob_nn = Prob_Neural_Network(data)
res = prob_nn.predict((0.2, 0.6), 0.1)
Result:
结果：
>>> res
('o', 0.6132686067117191)
回答by Jukka Matilainen
You might want to take a look at Monte:
你可能想看看Monte：
Monte (python) is a Python framework for building gradient based learning machines, like neural networks, conditional random fields, logistic regression, etc. Monte contains modules (that hold parameters, a cost-function and a gradient-function) and trainers (that can adapt a module's parameters by minimizing its cost-function on training data).
Modules are usually composed of other modules, which can in turn contain other modules, etc. Gradients of decomposable systems like these can be computed with back-propagation.
Monte (python) 是一个 Python 框架，用于构建基于梯度的学习机器，如神经网络、条件随机场、逻辑回归等。 Monte 包含模块（保存参数、成本函数和梯度函数）和训练器（用于可以通过最小化其在训练数据上的成本函数来调整模块的参数）。
模块通常由其他模块组成，这些模块又可以包含其他模块等。像这样的可分解系统的梯度可以通过反向传播来计算。
