我正在尝试构建一个用于预测的递归神经网络。我是在PyBrain中完成的。
我已经创建了两个简单的脚本来测试这些想法和技术，然后再将它们实现为更复杂的东西。
我已经尝试遵循已经证明可以尽可能多地工作的代码，即： 在stackoverflow和github上。
在第一个例子中，我试图在给定过去值的时间范围的情况下预测罪的值：
#!/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)
至少可以说，结果是悲惨的。
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
这太疯狂了。
第二个类似，基于sun spots数据：
#!/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)
在这里，我只看到混乱，我无法在剧情中向你展示，因为我没有足够的声望点。但基本上，预测函数是一个周期性的牙形曲线，与输入或过去的数据不相关。
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
我尝试过两种不同的学习算法，隐藏单元的多种组合，学习率，在学习数据集中添加元素的类型，但无济于事。
我现在完全失去了。
答案 0 :(得分：5)
如果在输出图层中使用逻辑激活功能，则输出将限制在(0,1)范围内。但是你的sin函数提供的输出范围为(-1,1)。我认为这就是为什么你的学习难以收敛到一个小错误的原因。你甚至无法在训练数据中得到正确的sin函数预测，对吗？也许您可能需要在训练和测试之前扩展输入/输出集。