Рекуррентный NN для предсказания не учится
Я пытаюсь построить рекуррентную нейронную сеть для прогнозирования. Я делаю это в PyBrain.
Я создал два простых сценария, чтобы проверить идеи и методы, прежде чем перейти к их реализации.
Я пытался следовать коду, который доказал свою работоспособность настолько, насколько я могу, то есть: на stackru и на github.
В первом примере я пытаюсь предсказать значения греха, учитывая временные рамки прошлых значений:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""An example of a simple RNN."""
import time
import math
import matplotlib.pyplot as plt
from normalizator import Normalizator
from pybrain.tools.shortcuts import buildNetwork
from pybrain.structure.modules import LSTMLayer
from pybrain.structure import LinearLayer, SigmoidLayer
from pybrain.supervised.trainers import BackpropTrainer
from pybrain.supervised import RPropMinusTrainer
from pybrain.datasets import SupervisedDataSet
from pybrain.datasets import SequentialDataSet
import pybrain.datasets.sequential
class Network(object):
"""Sieć neuronowa."""
def __init__(self, inputs, hidden, outputs):
"""Just a constructor."""
self.inputs = inputs
self.outputs = outputs
self.hidden = hidden
self.network = self.build_network(inputs, hidden, outputs)
self.norm = Normalizator()
def build_network(self, inputs, hidden, outputs):
"""Builds the network."""
network = buildNetwork(inputs, hidden, outputs,
hiddenclass=LSTMLayer,
#hiddenclass=SigmoidLayer,
outclass=SigmoidLayer,
bias = True,
outputbias=False, recurrent=True)
network.sortModules()
print "Constructed network:"
print network
return network
def train(self, learning_set, max_terations=100):
"""Trains the network."""
print "\nThe network is learning..."
time_s = time.time()
self.network.randomize()
#trainer = RPropMinusTrainer(self.network, dataset=learning_set,
# verbose=True)
learning_rate = 0.05
trainer = BackpropTrainer(self.network, learning_set, verbose=True,
momentum=0.8, learningrate=learning_rate)
errors = trainer.trainUntilConvergence(maxEpochs=max_terations)
#print "Last error in learning:", errors[-1]
time_d = time.time() - time_s
print "Learning took %d seconds." % time_d
return errors, learning_rate
def test(self, data):
"""Tests the network."""
print ("X\tCorrect\tOutput\t\tOutDenorm\tError")
mse = 0.0
outputs = []
#self.network.reset()
for item in data:
x_val = self.norm.denormalize("x", item[0])
sin_val = self.norm.denormalize("sin", item[1])
#get the output from the network
output = self.network.activate(item[0])[0]
out_denorm = self.norm.denormalize("sin", output)
outputs.append(out_denorm)
#compute the error
error = sin_val - out_denorm
mse += error**2
print "%f\t%f\t%f\t%f\t%f" % \
(round(x_val, 2), sin_val, output, out_denorm, error)
mse = mse / float(len(data))
print "MSE:", mse
return outputs, mse
def show_plot(self, correct, outputs, learn_x, test_x,
learning_targets, mse):
"""Plots some useful stuff :)"""
#print "learn_x:", learn_x
#print "test_x:", test_x
#print "output:", outputs
#print "correct:", correct
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(test_x, outputs, label="Prediction", color="red")
ax.plot(test_x, correct, ":", label="Original data")
ax.legend(loc='upper left')
plt.xlabel('X')
plt.ylabel('Sinus')
plt.title('Sinus... (mse=%f)' % mse)
#plot a portion of the learning data
learning_plt = fig.add_subplot(111)
learn_index = int(0.9 * len(learning_targets))
learning_plt.plot(learn_x[learn_index:], learning_targets[learn_index:],
label="Learning values", color="blue")
learning_plt.legend(loc='upper left')
plt.show()
def prepare_data(self):
"""Prepares the data."""
learn_inputs = [round(x, 2) for x in [y * 0.05 for y in range(0, 4001)]]
learn_targets = [math.sin(z) for z in learn_inputs]
test_inputs = [round(x, 2) for x in [y * 0.05 for y in range(4001, 4101)]]
test_targets = [math.sin(z) for z in test_inputs]
self.norm.add_feature("x", learn_inputs + test_inputs)
self.norm.add_feature("sin", learn_targets + test_targets)
#learning_set = pybrain.datasets.sequential.SupervisedDataSet(1, 1)
learning_set = SequentialDataSet(1, 1)
targ_close_to_zero = 0
for inp, targ in zip(learn_inputs, learn_targets):
if abs(targ) < 0.01:
targ_close_to_zero += 1
#if inp % 1 == 0.0:
if targ_close_to_zero == 2:
print "New sequence at", (inp, targ)
targ_close_to_zero = 0
learning_set.newSequence()
learning_set.appendLinked(self.norm.normalize("x", inp),
self.norm.normalize("sin", targ))
testing_set = []
for inp, targ in zip(test_inputs, test_targets):
testing_set.append([self.norm.normalize("x", inp),
self.norm.normalize("sin", targ), inp, targ])
return learning_set, testing_set, learn_inputs, test_inputs, learn_targets
if __name__ == '__main__':
nnetwork = Network(1, 20, 1)
learning_set, testing_set, learning_inputs, testing_inputs, learn_targets = \
nnetwork.prepare_data()
errors, rate = nnetwork.train(learning_set, 125)
outputs, mse = nnetwork.test(testing_set)
correct = [element[3] for element in testing_set]
nnetwork.show_plot(correct, outputs,
learning_inputs, testing_inputs, learn_targets, mse)
Результаты трагичны, если не сказать больше.
X Correct Output OutDenorm Error
200.050000 -0.847857 0.490775 -0.018445 -0.829411
200.100000 -0.820297 0.490774 -0.018448 -0.801849
200.150000 -0.790687 0.490773 -0.018450 -0.772237
200.200000 -0.759100 0.490772 -0.018452 -0.740648
200.250000 -0.725616 0.490770 -0.018454 -0.707162
Это безумие.
Второй аналог, основанный на данных солнечных пятен:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""An example of a simple RNN."""
import argparse
import sys
import operator
import time
from pybrain.tools.shortcuts import buildNetwork
from pybrain.structure import FullConnection
from pybrain.structure.modules import LSTMLayer
from pybrain.structure import LinearLayer, SigmoidLayer
from pybrain.supervised.trainers import BackpropTrainer
from pybrain.supervised import RPropMinusTrainer
from pybrain.datasets import SupervisedDataSet
import pybrain.datasets.sequential
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from normalizator import Normalizator
class Network(object):
"""Neural network."""
def __init__(self, inputs, hidden, outputs):
"""Constructor."""
self.inputs = inputs
self.outputs = outputs
self.hidden = hidden
self.network = self.build_network(inputs, hidden, outputs)
self.norm = Normalizator()
def build_network(self, inputs, hidden, outputs):
"""Builds the network."""
network = buildNetwork(inputs, hidden, outputs, bias=True,
hiddenclass=LSTMLayer,
#hiddenclass=SigmoidLayer,
outclass=SigmoidLayer,
outputbias=False, fast=False, recurrent=True)
#network.addRecurrentConnection(
# FullConnection(network['hidden0'], network['hidden0'], name='c3'))
network.sortModules()
network.randomize()
print "Constructed network:"
print network
return network
def train(self, learning_set, max_terations=100):
"""Trains the network."""
print "\nThe network is learning..."
time_s = time.time()
trainer = RPropMinusTrainer(self.network, dataset=learning_set,
verbose=True)
learning_rate = 0.001
#trainer = BackpropTrainer(self.network, learning_set, verbose=True,
# batchlearning=True, momentum=0.8, learningrate=learning_rate)
errors = trainer.trainUntilConvergence(maxEpochs=max_terations)
#print "Last error in learning:", errors[-1]
time_d = time.time() - time_s
print "Learning took %d seconds." % time_d
return errors, learning_rate
def test(self, data):
"""Tests the network."""
print ("Year\tMonth\tCount\tCount_norm\t" +
"Output\t\tOutDenorm\tError")
# do the testing
mse = 0.0
outputs = []
#print "Test data:", data
for item in data:
#month = self.norm.denormalize("month", item[1])
#year = self.norm.denormalize("year", item[2])
year, month = self.norm.denormalize("ym", item[5])
count = self.norm.denormalize("count", item[3])
#get the output from the network
output = self.network.activate((item[1], item[2]))
out_denorm = self.norm.denormalize("count", output[0])
outputs.append(out_denorm)
#compute the error
error = count - out_denorm
mse += error**2
print "%d\t%d\t%s\t%f\t%f\t%f\t%f" % \
(year, month, count, item[3],
output[0], out_denorm, error)
mse /= len(data)
print "MSE:", mse
#corrects = [self.norm.denormalize("count", item[3]) for item in data]
#print "corrects:", len(corrects)
return outputs, mse
def show_plot(self, correct, outputs, learn_x, test_x,
learning_targets, mse):
"""Rysuje wykres :)"""
#print "x_axis:", x_axis
#print "output:", output
#print "correct:", correct
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(test_x, outputs, label="Prediction", color="red")
ax.plot(test_x, correct, ":", label="Correct")
# int(201000.0 / 100)
ax.xaxis.set_major_formatter(FormatStrFormatter('%s'))
ax.legend(loc='upper left')
learn_index = int(0.8 * len(learn_x))
learn_part_x = learn_x[learn_index:]
learn_part_vals = learning_targets[learn_index:]
learning_plt = fig.add_subplot(111)
learning_plt.plot(learn_part_x, learn_part_vals,
label="Learning values", color="blue")
learning_plt.legend(loc='upper left')
plt.xlabel('Year-Month')
plt.ylabel('Values')
plt.title('... (mse=%f)' % mse)
plt.show()
def read_data(self, learnfile, testfile):
"""Wczytuje dane uczące oraz testowe."""
#read learning data
data_learn_tmp = []
for line in learnfile:
if line[1] == "#":
continue
row = line.split()
year = float(row[0][0:4])
month = float(row[0][4:6])
yearmonth = int(row[0])
count = float(row[2])
data_learn_tmp.append([month, year, count, yearmonth])
data_learn_tmp = sorted(data_learn_tmp, key=operator.itemgetter(1, 0))
# read test data
data_test_tmp = []
for line in testfile:
if line[0] == "#":
continue
row = line.split()
year = float(row[0][0:4])
month = float(row[0][4:6])
count = float(row[2])
year_month = int(row[0])
data_test_tmp.append([month, year, count, year_month])
data_test_tmp = sorted(data_test_tmp, key=operator.itemgetter(1, 0))
# prepare data for normalization
months = [item[0] for item in data_learn_tmp + data_test_tmp]
years = [item[1] for item in data_learn_tmp + data_test_tmp]
counts = [item[2] for item in data_learn_tmp + data_test_tmp]
self.norm.add_feature("month", months)
self.norm.add_feature("year", years)
ym = [(years[index], months[index]) for index in xrange(0, len(years))]
self.norm.add_feature("ym", ym, ranked=True)
self.norm.add_feature("count", counts)
#build learning data set
learning_set = pybrain.datasets.sequential.SequentialDataSet(2, 1)
#learning_set = pybrain.datasets.sequential.SupervisedDataSet(2, 1)
# add items to the learning dataset proper
last_year = -1
for item in data_learn_tmp:
if last_year != item[1]:
learning_set.newSequence()
last_year = item[1]
year_month = self.norm.normalize("ym", (item[1], item[0]))
count = self.norm.normalize("count", item[2])
learning_set.appendLinked((year_month), (count))
#build testing data set proper
words = ["N/A"] * len(data_test_tmp)
testing_set = []
for index in range(len(data_test_tmp)):
month = self.norm.normalize("month", data_test_tmp[index][0])
year = self.norm.normalize("year", data_test_tmp[index][3])
year_month = self.norm.normalize("ym",
(data_test_tmp[index][4], data_test_tmp[index][0]))
count = self.norm.normalize("count", data_test_tmp[index][5])
testing_set.append((words[index], month, year,
count, data_test_tmp[index][6], year_month))
#learning_set, testing_set, learn_inputs, test_inputs, learn_targets
learn_x = [element[3] for element in data_learn_tmp]
test_x = [element[3] for element in data_test_tmp]
learn_targets = [element[2] for element in data_learn_tmp]
test_targets = [element[2] for element in data_test_tmp]
return (learning_set, testing_set, learn_x, test_x,
learn_targets, test_targets)
def get_args():
"""Buduje parser cli."""
parser = argparse.ArgumentParser(
description='Trains a simple recurrent neural network.')
parser.add_argument('--inputs', type=int, default=2,
help='Number of input neurons.')
parser.add_argument('--hidden', type=int, default=5,
help='Number of hidden neurons.')
parser.add_argument('--outputs', type=int, default=1,
help='Number of output neurons.')
parser.add_argument('--iterations', type=int, default=100,
help='Maximum number of iteration epoch in training phase.')
parser.add_argument('trainfile', nargs='?', type=argparse.FileType('r'),
default=sys.stdin, help="File with learning dataset.")
parser.add_argument('testfile', nargs='?', type=argparse.FileType('r'),
default=sys.stdin, help="File with testing dataset.")
parser.add_argument('--version', action='version', version='%(prog)s 1.0')
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
nnetwork = Network(args.inputs, args.hidden, args.outputs)
learning_set, testing_set, learn_x, test_x, learn_targets, test_targets = \
nnetwork.read_data(args.trainfile, args.testfile)
errors, rate = nnetwork.train(learning_set, args.iterations)
outputs, mse = nnetwork.test(testing_set)
nnetwork.show_plot(test_targets, outputs,
learn_x, test_x, learn_targets, mse)
И здесь также я вижу только хаос, который я не могу показать вам на сюжете, так как мне не хватает очков репутации. Но в основном функция прогнозирования представляет собой циклическую кривую в форме зубьев, которая не сильно коррелирует с входными данными или прошлыми данными.
Year Month Count Count_norm Output OutDenorm Error
2009 9 4.3 0.016942 0.216687 54.995108 -50.695108
2009 10 4.8 0.018913 0.218810 55.534015 -50.734015
2009 11 4.1 0.016154 0.221876 56.312243 -52.212243
2009 12 10.8 0.042553 0.224774 57.047758 -46.247758
2010 1 13.2 0.052009 0.184361 46.790833 -33.590833
2010 2 18.8 0.074074 0.181018 45.942258 -27.142258
2010 3 15.4 0.060678 0.183226 46.502806 -31.102806
Я пробовал использовать два разных алгоритма обучения, множество комбинаций скрытых единиц, скоростей обучения, типов добавления элементов в набор данных обучения, но безрезультатно.
Я полностью потерян сейчас.
1 ответ
Если вы используете функцию логистической активации в выходном слое, выход будет ограничен диапазоном (0,1), Но ваша функция греха обеспечивает вывод с диапазоном (-1,1), Я думаю, именно поэтому ваш грех учиться трудно сходиться с небольшими ошибками. Вы даже не можете получить правильное предсказание функции греха в ваших тренировочных данных, не так ли? Возможно, вам придется масштабировать ваш набор ввода / вывода перед тренировкой и тестированием.