Python 神经网络调教程序，python神经网络调教,import rando

Python 神经网络调教程序，python神经网络调教,import rando

import randomimport mathfrom pyneurgen.neuralnet import NeuralNetfrom pyneurgen.nodes import BiasNode, Connectionpop_len = 360factor = 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 inputsfor 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 = .20net.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 matplotlibfrom pylab import plot, legend, subplot, gridfrom pylab import xlabel, ylabel, show, titletest_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 resultssubplot(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()#该片段来自于http://byrx.net