python 神经网络调教程序

最新推荐文章于 2022-08-11 12:20:04 发布

原创最新推荐文章于 2022-08-11 12:20:04 发布 · 934 阅读

0 ·

CC 4.0 BY-SA版权

文章标签：

#Python #.net #360 #HTML

Python 专栏收录该内容

79 篇文章

订阅专栏

本文介绍了一个使用PyNeurGen库训练神经网络来预测正弦波的例子。通过生成正弦波数据集并利用随机生成的输入进行训练，展示了如何设置网络结构、调整学习率以及评估模型性能。此外，还提供了使用Matplotlib绘制预测结果和误差的图表。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

[size=large]
#from http://pyneurgen.sourceforge.net/tutorial_nn.html

import random
import math

from pyneurgen.neuralnet import NeuralNet
from pyneurgen.nodes import BiasNode, Connection

pop_len = 360
factor = 1.0 / float(pop_len)
population = [
(i, math.sin(float(i) * factor )) for i in range(pop_len)
]

all_inputs = []
all_targets = []

def population_gen(population):
pop_sort = [item for item in population]
random.shuffle(pop_sort)
for item in pop_sort:
yield item

# Build the inputs
for position, target in population_gen(population):
pos = float(position)
all_inputs.append([random.random(), pos * factor])
all_targets.append([target])

net = NeuralNet()
net.init_layers(2, [10], 1)
net.randomize_network()
net.learnrate = .20

net.randomize_network()
net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)
length = len(all_inputs)

learn_end_point = int(length * .8)
net.set_learn_range(0, learn_end_point)
net.set_test_range(learn_end_point + 1, length - 1)
net.layers[1].set_activation_type('tanh')
net.learn(epochs=125, show_epoch_results=True,random_testing=False)
mse = net.test()

import matplotlib
from pylab import plot, legend, subplot, grid
from pylab import xlabel, ylabel, show, title

test_positions = [item[0][1] * 1000.0 for item in net.get_test_data()]

all_targets1 = [item[0][0] for item in net.test_actuals_targets]
allactuals = [item[1][0] for item in net.test_actuals_targets]

# This is quick and dirty, but it will show the results
subplot(3, 1, 1)
plot([i[1] for i in population])
title("Population")
grid(True)

subplot(3, 1, 2)
plot(test_positions, all_targets1, 'bo', label='targets')
plot(test_positions, allactuals, 'ro', label='actuals')
grid(True)
legend(loc='lower left', numpoints=1)
title("Test Target Points vs Actual Points")

subplot(3, 1, 3)
plot(range(1, len(net.accum_mse) + 1, 1), net.accum_mse)
xlabel('epochs')
ylabel('mean squared error')
grid(True)
title("Mean Squared Error by Epoch")

show()

[/size]