有没有关于如何在 python 中使用 NARX 模型训练和预测/推理数据的端到端示例？ 有库 PyNeurgen NARX PyNeurgen library 但 PyNeurgen 的文档不是很完整。
这个OP似乎已经编写了一个Keras实现，但是代码缺少推理/预测的实现。 NARX implementation using keras
最佳答案
我认为您正在寻找 model.fit() 的等效项& model.predict() ，分别是net.learn()和net.test()在 pyneurgen 。基于此tutorial还有这个example ，我编写了这个演示(在 python 2.7 中)，让您了解它是如何完成的。我希望它有帮助。
import math
import numpy as np
import matplotlib.pylab as plt
from pyneurgen.neuralnet import NeuralNet
from pyneurgen.recurrent import NARXRecurrent
def plot_results(x, y, y_true):
plt.subplot(3, 1, 1)
plt.plot([i[1] for i in population])
plt.title("Population")
plt.grid(True)
plt.subplot(3, 1, 2)
plt.plot(x, y, 'bo', label='targets')
plt.plot(x, y_true, 'ro', label='actuals')
plt.grid(True)
plt.legend(loc='lower left', numpoints=1)
plt.title("Test Target Points vs Actual Points")
plt.subplot(3, 1, 3)
plt.plot(range(1, len(net.accum_mse) + 1, 1), net.accum_mse)
plt.xlabel('epochs')
plt.ylabel('mean squared error')
plt.grid(True)
plt.title("Mean Squared Error by Epoch")
plt.show()
def generate_data():
# all samples are drawn from this population
pop_len = 200
factor = 1.0 / float(pop_len)
population = [[i, math.sin(float(i) * factor * 10.0)] for i in range(pop_len)]
population_shuffle = population[:]
all_inputs = []
all_targets = []
np.random.shuffle(population_shuffle)
for position, target in population_shuffle:
all_inputs.append([position * factor])
all_targets.append([target])
# print(all_inputs[-1], all_targets[-1])
return population, all_inputs, all_targets
# generate data
population, all_inputs, all_targets = generate_data()
# NARXRecurrent
input_nodes, hidden_nodes, output_nodes = 1, 10, 1
output_order, incoming_weight_from_output = 3, .6
input_order, incoming_weight_from_input = 2, .4
# init neural network
net = NeuralNet()
net.init_layers(input_nodes, [hidden_nodes], output_nodes,
NARXRecurrent(output_order, incoming_weight_from_output,
input_order, incoming_weight_from_input))
net.randomize_network()
net.set_halt_on_extremes(True)
# set constrains and rates
net.set_random_constraint(.5)
net.set_learnrate(.1)
# set inputs and outputs
net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)
# set lengths
length = len(all_inputs)
learn_end_point = int(length * .8)
# set ranges
net.set_learn_range(0, learn_end_point)
net.set_test_range(learn_end_point + 1, length - 1)
# add activation to layer 1
net.layers[1].set_activation_type('tanh')
# fit data to model
net.learn(epochs=150, show_epoch_results=True, random_testing=False)
# define mean squared error
mse = net.test()
print "Testing mse = ", mse
# define data
x = [item[0][0] * 200.0 for item in net.get_test_data()]
y = [item[0][0] for item in net.test_targets_activations]
y_true = [item[1][0] for item in net.test_targets_activations]
# plot results
plot_results(x, y, y_true)
这会产生以下图:
有关更多示例，请查看以下链接:
关于python - python 中的 NARX 示例 - 训练和预测，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/59277119/