python – 用于预测的经常性NN不学习

我正在尝试构建一个用于预测的递归神经网络.我在PyBrain这样做. 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：stackoverflow和github. 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行值： #!/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 疯了吧. 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) 而且在这里,我只看到混乱,我无法在剧情中向您展示,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.