def main(): generated_data = [0 for i in range(10000)] rate, data = get_data_from_wav("../../data/natabhairavi_violin.wav") data = data[1000:190000] print("Got wav") ds = SequentialDataSet(1, 1) for sample, next_sample in zip(data, cycle(data[1:])): ds.addSample(sample, next_sample) net = buildNetwork(1, 5, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 10 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i+1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS), end="") stdout.flush() # predict new values old_sample = [100] for i in xrange(500000): new_sample = net.activate(old_sample) old_sample = new_sample generated_data[i] = new_sample[0] print(new_sample) wavfile.write("../../output/test.wav", rate, np.array(generated_data))
def train(self, params): self.net.reset() ds = SequentialDataSet(self.nDimInput, self.nDimOutput) trainer = RPropMinusTrainer(self.net, dataset=ds, verbose=False) history = self.window(self.history, params) resets = self.window(self.resets, params) for i in xrange(params['prediction_nstep'], len(history)): if not resets[i-1]: ds.addSample(self.inputEncoder.encode(history[i-params['prediction_nstep']]), self.outputEncoder.encode(history[i][0])) if resets[i]: ds.newSequence() # print ds.getSample(0) # print ds.getSample(1) # print ds.getSample(1000) # print " training data size", ds.getLength(), " len(history) ", len(history), " self.history ", len(self.history) # print ds if len(history) > 1: trainer.trainEpochs(params['num_epochs']) self.net.reset() for i in xrange(len(history) - params['prediction_nstep']): symbol = history[i] output = self.net.activate(ds.getSample(i)[0]) if resets[i]: self.net.reset()
def train(self, params): """ Train LSTM network on buffered dataset history After training, run LSTM on history[:-1] to get the state correct :param params: :return: """ if params['reset_every_training']: n = params['encoding_num'] self.net = buildNetwork(n, params['num_cells'], n, hiddenclass=LSTMLayer, bias=True, outputbias=params['output_bias'], recurrent=True) self.net.reset() # prepare training dataset ds = SequentialDataSet(params['encoding_num'], params['encoding_num']) history = self.window(self.history, params) resets = self.window(self.resets, params) for i in xrange(1, len(history)): if not resets[i - 1]: ds.addSample(self.encoder.encode(history[i - 1]), self.encoder.encode(history[i])) if resets[i]: ds.newSequence() if params['num_epochs'] > 1: trainer = RPropMinusTrainer(self.net, dataset=ds, verbose=params['verbosity'] > 0) if len(history) > 1: trainer.trainEpochs(params['num_epochs']) # run network on buffered dataset after training to get the state right self.net.reset() for i in xrange(len(history) - 1): symbol = history[i] output = self.net.activate(self.encoder.encode(symbol)) self.encoder.classify(output, num=params['num_predictions']) if resets[i]: self.net.reset() else: self.trainer.setData(ds) self.trainer.train() # run network on buffered dataset after training to get the state right self.net.reset() for i in xrange(len(history) - 1): symbol = history[i] output = self.net.activate(self.encoder.encode(symbol)) self.encoder.classify(output, num=params['num_predictions']) if resets[i]: self.net.reset()
def train(self, params): n = params['encoding_num'] net = buildNetwork(n, params['num_cells'], n, hiddenclass=LSTMLayer, bias=True, outputbias=params['output_bias'], recurrent=True) net.reset() ds = SequentialDataSet(n, n) trainer = RPropMinusTrainer(net, dataset=ds) history = self.window(self.history, params) resets = self.window(self.resets, params) for i in xrange(1, len(history)): if not resets[i-1]: ds.addSample(self.encoder.encode(history[i-1]), self.encoder.encode(history[i])) if resets[i]: ds.newSequence() if len(history) > 1: trainer.trainEpochs(params['num_epochs']) net.reset() for i in xrange(len(history) - 1): symbol = history[i] output = self.net.activate(self.encoder.encode(symbol)) predictions = self.encoder.classify(output, num=params['num_predictions']) if resets[i]: net.reset() return net
def train(self, params): """ Train LSTM network on buffered dataset history After training, run LSTM on history[:-1] to get the state correct :param params: :return: """ if params['reset_every_training']: n = params['encoding_num'] self.net = buildNetwork(n, params['num_cells'], n, hiddenclass=LSTMLayer, bias=True, outputbias=params['output_bias'], recurrent=True) self.net.reset() # prepare training dataset ds = SequentialDataSet(params['encoding_num'], params['encoding_num']) history = self.window(self.history, params) resets = self.window(self.resets, params) for i in xrange(1, len(history)): if not resets[i - 1]: ds.addSample(self.encoder.encode(history[i - 1]), self.encoder.encode(history[i])) if resets[i]: ds.newSequence() print "Train LSTM network on buffered dataset of length ", len(history) if params['num_epochs'] > 1: trainer = RPropMinusTrainer(self.net, dataset=ds, verbose=params['verbosity'] > 0) if len(history) > 1: trainer.trainEpochs(params['num_epochs']) # run network on buffered dataset after training to get the state right self.net.reset() for i in xrange(len(history) - 1): symbol = history[i] output = self.net.activate(self.encoder.encode(symbol)) self.encoder.classify(output, num=params['num_predictions']) if resets[i]: self.net.reset() else: self.trainer.setData(ds) self.trainer.train() # run network on buffered dataset after training to get the state right self.net.reset() for i in xrange(len(history) - 1): symbol = history[i] output = self.net.activate(self.encoder.encode(symbol)) self.encoder.classify(output, num=params['num_predictions']) if resets[i]: self.net.reset()
def trainLSTMnet(net, numTrainSequence, seedSeq=1): np.random.seed(seedSeq) for _ in xrange(numTrainSequence): (ds, in_seq, out_seq) = getReberDS(maxLength) print("train seq", _, sequenceToWord(in_seq)) trainer = RPropMinusTrainer(net, dataset=ds) trainer.trainEpochs(rptPerSeq) return net
def trainLSTMnet(net, numTrainSequence, seedSeq=1): np.random.seed(seedSeq) for _ in xrange(numTrainSequence): (ds, in_seq, out_seq) = getReberDS(maxLength) print("train seq", _, sequenceToWord(in_seq)) trainer = RPropMinusTrainer(net, dataset=ds) trainer.trainEpochs(rptPerSeq) return net
def train(self, ds, epochs_per_cycle, cycles): trainer = RPropMinusTrainer(self.n, dataset=ds) train_errors = [] for i in xrange(cycles): trainer.trainEpochs(epochs_per_cycle) train_errors.append(trainer.testOnData()) epoch = (i + 1) * epochs_per_cycle print("\r epoch {}/{}".format(epoch, epochs_per_cycle * cycles)) sys.stdout.flush() print("Final Error: " + str(train_errors[-1])) return train_errors[-1]
def ltsmXY(tin, tout, title='ltsm.png'): #datain = zip(tin[:-3], tin[1:-2], tin[2:-1]) #datain = zip(tin[:-8], tin[1:-7], tin[2:-6], tin[3:-5], tin[4:-4], tin[5:-3],tin[6:-2], tin[7:-1]) #datain = zip(tin[:-12], tin[1:-11], tin[2:-10], tin[3:-9], tin[4:-8], tin[5:-7],tin[6:-6], tin[7:-5], tin[8:-4], tin[9:-3], tin[10:-2], tin[11:-1]) datain = zip(tin[:-16], tin[1:-15], tin[2:-14], tin[3:-13], tin[4:-12], tin[5:-11],tin[6:-10], tin[7:-9], tin[8:-8], tin[9:-7], tin[10:-6], tin[11:-5], tin[12:-4], tin[13:-3], tin[14:-2], tin[15:-1]) #dataout = tout[3:] #dataout = tout[8:] #dataout = tout[12:] dataout = tout[16:] #ds = SequentialDataSet(3, 1) #ds = SequentialDataSet(8, 1) #ds = SequentialDataSet(12, 1) ds = SequentialDataSet(16, 1) for x, y in zip(datain[:len(datain)/2], dataout[:len(datain)/2]): ds.addSample(x, y) # add layers until overfitting the training data #net = buildNetwork(3,5,1,hiddenclass=LSTMLayer, outputbias=False, recurrent=True) #net = buildNetwork(8, 8, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) #net = buildNetwork(12, 20, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) net = buildNetwork(16, 20, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] EPOCHS_PER_CYCLE = 5 CYCLES = 100 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i+1) * EPOCHS_PER_CYCLE #print "\r epoch {}/{}".format(epoch, EPOCHS) stdout.flush() print "final error =", train_errors[-1] pred_out = [] for i in range(len(datain)): pred_out.append(net.activate(datain[i])) fig = plt.figure() #tout[16:].plot(ax=ax, title='Occupancy') plt.plot(tout[16:].index, tout[16:], 'y', linewidth=1.5) plt.plot(tout[16:].index, pred_out, 'b+') plt.legend(['Occupancy', 'LTSM']) fig.tight_layout() plt.savefig(title,inches='tight')
def train(self, params, verbose=False): if params['reset_every_training']: if verbose: print 'create lstm network' random.seed(6) if params['output_encoding'] == None: self.net = buildNetwork(self.nDimInput, params['num_cells'], self.nDimOutput, hiddenclass=LSTMLayer, bias=True, outputbias=True, recurrent=True) elif params['output_encoding'] == 'likelihood': self.net = buildNetwork(self.nDimInput, params['num_cells'], self.nDimOutput, hiddenclass=LSTMLayer, bias=True, outclass=SigmoidLayer, recurrent=True) self.net.reset() ds = SequentialDataSet(self.nDimInput, self.nDimOutput) networkInput = self.window(self.networkInput, params) targetPrediction = self.window(self.targetPrediction, params) # prepare a training data-set using the history for i in xrange(len(networkInput)): ds.addSample(self.inputEncoder.encode(networkInput[i]), self.outputEncoder.encode(targetPrediction[i])) if params['num_epochs'] > 1: trainer = RPropMinusTrainer(self.net, dataset=ds, verbose=verbose) if verbose: print " train LSTM on ", len( ds), " records for ", params['num_epochs'], " epochs " if len(networkInput) > 1: trainer.trainEpochs(params['num_epochs']) else: self.trainer.setData(ds) self.trainer.train() # run through the training dataset to get the lstm network state right self.net.reset() for i in xrange(len(networkInput)): self.net.activate(ds.getSample(i)[0])
def ltsm(data): from pybrain.datasets import SequentialDataSet from itertools import cycle datain = zip(data[:-6], data[1:-5], data[2:-4], data[3:-3], data[4:-2], data[5:-1]) dataout = data[6:] ds = SequentialDataSet(6, 1) for x, y in zip(datain, dataout): ds.addSample(x, y) from pybrain.tools.shortcuts import buildNetwork from pybrain.structure.modules import LSTMLayer net = buildNetwork(6, 7, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) from pybrain.supervised import RPropMinusTrainer from sys import stdout trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] EPOCHS_PER_CYCLE = 5 CYCLES = 100 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i+1) * EPOCHS_PER_CYCLE #print "\r epoch {}/{}".format(epoch, EPOCHS) stdout.flush() print "final error =", train_errors[-1] ''' plt.figure() plt.plot(range(0, EPOCHS, EPOCHS_PER_CYCLE), train_errors) plt.xlabel('epoch') plt.ylabel('error') plt.show() ''' test_error = 0. cnt = 0 for sample, target in ds.getSequenceIterator(0): #print "sample = ", sample #print "predicted next sample = %4.1f" % net.activate(sample) #print "actual next sample = %4.1f" % target test_error += abs(net.activate(sample) - target) cnt += 1 test_error /= cnt print "test (train) error =", test_error
def handle(self, *args, **options): ticker = args[0] print("****** STARTING PREDICTOR " + ticker + " ******* ") prices = Price.objects.filter( symbol=ticker).order_by('-created_on').values_list('price', flat=True) data = normalization(list(prices[0:NUM_MINUTES_BACK].reverse())) data = [int(x * MULT_FACTOR) for x in data] print(data) ds = SupervisedDataSet(5, 1) try: for i, val in enumerate(data): DS.addSample((data[i], data[i + 1], data[i + 2], data[i + 3], data[i + 4]), (data[i + 5], )) except Exception: pass net = buildNetwork(5, 40, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 100 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i + 1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS), end="") stdout.flush() print() print("final error =", train_errors[-1]) for sample, target in ds.getSequenceIterator(0): show_pred_sample = net.activate(sample) / MULT_FACTOR show_sample = sample / MULT_FACTOR show_target = target / MULT_FACTOR show_diff = show_pred_sample - show_target show_diff_pct = 100 * show_diff / show_pred_sample print("{} => {}, act {}. ({}%)".format( show_sample[0], round(show_pred_sample[0], 3), show_target[0], int(round(show_diff_pct[0], 0))))
def train(d, cycles=100, epochs_per_cycle=7): ds = SequentialDataSet(1, 1) net = buildNetwork(1, 5, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=False) for sample, next_sample in zip(d, cycle(d[1:])): ds.addSample(sample, next_sample) trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later for i in xrange(cycles): trainer.trainEpochs(epochs_per_cycle) train_errors.append(trainer.testOnData()) stdout.flush() return net, train_errors
def train(context, trainX, trainY): ds = SequentialDataSet(4, 1) for dataX, dataY in zip(trainX, trainY): ds.addSample(dataX, dataY) net = buildNetwork(4, 1, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) trainer = RPropMinusTrainer(net, dataset=ds) EPOCHS_PER_CYCLE = 5 CYCLES = 5 for i in range(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) return net, trainer.testOnData()
def train (ds, net): # Train the network trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 100 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) error = trainer.testOnData() train_errors.append(error) epoch = (i+1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS)) stdout.flush() # print("final error =", train_errors[-1]) return train_errors, EPOCHS, EPOCHS_PER_CYCLE
def train(ds, net): # Train the network trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 100 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) error = trainer.testOnData() train_errors.append(error) epoch = (i + 1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS)) stdout.flush() # print("final error =", train_errors[-1]) return train_errors, EPOCHS, EPOCHS_PER_CYCLE
def train(self, params, verbose=False): if params['reset_every_training']: if verbose: print 'create lstm network' random.seed(6) if params['output_encoding'] == None: self.net = buildNetwork(self.nDimInput, params['num_cells'], self.nDimOutput, hiddenclass=LSTMLayer, bias=True, outputbias=True, recurrent=True) elif params['output_encoding'] == 'likelihood': self.net = buildNetwork(self.nDimInput, params['num_cells'], self.nDimOutput, hiddenclass=LSTMLayer, bias=True, outclass=SigmoidLayer, recurrent=True) self.net.reset() ds = SequentialDataSet(self.nDimInput, self.nDimOutput) networkInput = self.window(self.networkInput, params) targetPrediction = self.window(self.targetPrediction, params) # prepare a training data-set using the history for i in xrange(len(networkInput)): ds.addSample(self.inputEncoder.encode(networkInput[i]), self.outputEncoder.encode(targetPrediction[i])) if params['num_epochs'] > 1: trainer = RPropMinusTrainer(self.net, dataset=ds, verbose=verbose) if verbose: print " train LSTM on ", len(ds), " records for ", params['num_epochs'], " epochs " if len(networkInput) > 1: trainer.trainEpochs(params['num_epochs']) else: self.trainer.setData(ds) self.trainer.train() # run through the training dataset to get the lstm network state right self.net.reset() for i in xrange(len(networkInput)): self.net.activate(ds.getSample(i)[0])
def handle(self, *args, **options): ticker = args[0] print("****** STARTING PREDICTOR " + ticker + " ******* ") prices = Price.objects.filter(symbol=ticker).order_by('-created_on').values_list('price',flat=True) data = normalization(list(prices[0:NUM_MINUTES_BACK].reverse())) data = [ int(x * MULT_FACTOR) for x in data] print(data) ds = SupervisedDataSet(5, 1) try: for i,val in enumerate(data): DS.addSample((data[i], data[i+1], data[i+2], data[i+3], data[i+4]), (data[i+5],)) except Exception: pass; net = buildNetwork(5, 40, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 100 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i+1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS), end="") stdout.flush() print() print("final error =", train_errors[-1]) for sample, target in ds.getSequenceIterator(0): show_pred_sample = net.activate(sample) / MULT_FACTOR show_sample = sample / MULT_FACTOR show_target = target / MULT_FACTOR show_diff = show_pred_sample - show_target show_diff_pct = 100 * show_diff / show_pred_sample print("{} => {}, act {}. ({}%)".format(show_sample[0],round(show_pred_sample[0],3),show_target[0],int(round(show_diff_pct[0],0))))
def train(self, params): n = params['encoding_num'] net = buildNetwork(n, params['num_cells'], n, hiddenclass=LSTMLayer, bias=True, outputbias=params['output_bias'], recurrent=True) net.reset() ds = SequentialDataSet(n, n) trainer = RPropMinusTrainer(net, dataset=ds) history = self.window(self.history, params) resets = self.window(self.resets, params) for i in xrange(1, len(history)): if not resets[i - 1]: ds.addSample(self.encoder.encode(history[i - 1]), self.encoder.encode(history[i])) if resets[i]: ds.newSequence() if len(history) > 1: trainer.trainEpochs(params['num_epochs']) net.reset() for i in xrange(len(history) - 1): symbol = history[i] output = net.activate(self.encoder.encode(symbol)) predictions = self.encoder.classify(output, num=params['num_predictions']) if resets[i]: net.reset() return net
def main(): generated_data = [0 for i in range(10000)] rate, data = get_data_from_wav("../../data/natabhairavi_violin.wav") data = data[1000:190000] print("Got wav") ds = SequentialDataSet(1, 1) for sample, next_sample in zip(data, cycle(data[1:])): ds.addSample(sample, next_sample) net = buildNetwork(1, 5, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 10 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i + 1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS), end="") stdout.flush() # predict new values old_sample = [100] for i in xrange(500000): new_sample = net.activate(old_sample) old_sample = new_sample generated_data[i] = new_sample[0] print(new_sample) wavfile.write("../../output/test.wav", rate, np.array(generated_data))
def say_hello_text(username = "******",text="You are good"): object_data_new = pd.read_csv('/Users/ruiyun_zhou/Documents/cmpe-274/data/data.csv') data_area_new = object_data_new[object_data_new.Area==username] data_area_new_1=data_area_new[data_area_new.Disease== text] data_list_new = data_area_new_1['Count'].values.tolist() print data_list_new.__len__() data_list=data_list_new ds = SequentialDataSet(1,1) isZero=0; for sample,next_sample in zip(data_list,cycle(data_list[1:])): ds.addSample(sample, next_sample) if sample: isZero=1 if(isZero==0): return '[0, 0]' net = buildNetwork(1,5,1,hiddenclass=LSTMLayer,outputbias=False,recurrent=True) trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 10 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): print "Doing epoch %d" %i trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i+1) * EPOCHS_PER_CYCLE # return '<p>%d</p>\n' % (data_list_new.__len__()) # print("final error =", train_errors[-1]) # print "Value for last week is %4.1d" % abs(data_list[-1]) # print "Value for next week is %4.1d" % abs(net.activate(data_list[-1])) # result = (abs(data_list[-1])) result = (abs(net.activate(data_list[-1]))) result_1 = (abs(net.activate(result))) return '[%d, %d]' % (result,result_1)
def Train(self, dataset, error_observer, logger, dump_file): gradientCheck(self.m_net) net_dataset = SequenceClassificationDataSet(4, 2) for record in dataset: net_dataset.newSequence() gl_raises = record.GetGlRises() gl_min = record.GetNocturnalMinimum() if DayFeatureExpert.IsHypoglycemia(record): out_class = [1, 0] else: out_class = [0, 1] for gl_raise in gl_raises: net_dataset.addSample([gl_raise[0][0].total_seconds() / (24*3600), gl_raise[0][1] / 300, gl_raise[1][0].total_seconds() / (24*3600), gl_raise[1][1] / 300] , out_class) train_dataset, test_dataset = net_dataset.splitWithProportion(0.8) trainer = RPropMinusTrainer(self.m_net, dataset=train_dataset, momentum=0.8, learningrate=0.3, lrdecay=0.9, weightdecay=0.01, verbose=True) validator = ModuleValidator() train_error = [] test_error = [] for i in range(0, 80): trainer.trainEpochs(1) train_error.append(validator.MSE(self.m_net, train_dataset)) # here is validate func, think it may be parametrised by custom core function test_error.append(validator.MSE(self.m_net, test_dataset)) print train_error print test_error error_observer(train_error, test_error) gradientCheck(self.m_net) dump_file = open(dump_file, 'wb') pickle.dump(self.m_net, dump_file)
def train(data,name): ds = SequentialDataSet(1, 1) for sample, next_sample in zip(data, cycle(data[1:])): ds.addSample(sample, next_sample) net = buildNetwork(1, 200, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 20 EPOCHS = EPOCHS_PER_CYCLE * CYCLES store=[] for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i+1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS)) print tm.time()-atm stdout.flush() for sample, target in ds.getSequenceIterator(0): store.append(net.activate(sample)) abcd=pd.DataFrame(store) abcd.to_csv(pwd+"lstmdata/"+name+".csv",encoding='utf-8') print "result printed to file"
#rnn.addOutputModule(SoftmaxLayer(1, name='out')) # #rnn.addConnection(FullConnection(rnn['in'], rnn['hidden'], name='c1')) #rnn.addConnection(FullConnection(rnn['hidden'], rnn['out'], name='c2')) # #rnn.addRecurrentConnection(FullConnection(rnn['hidden'], rnn['hidden'], name='c3')) #rnn.sortModules() # define a training method trainer = RPropMinusTrainer(rnn, dataset=trndata, verbose=True ) # instead, you may also try ##trainer = BackpropTrainer( rnn, dataset=trndata, verbose=True, momentum=0.9, learningrate=0.00001 ) # carry out the training for i in range(100): trainer.trainEpochs( 2 ) trnresult = 100. * (1.0-testOnSequenceData(rnn, trndata)) tstresult = 100. * (1.0-testOnSequenceData(rnn, tstdata)) print("train error: %5.2f%%" % trnresult, ", test error: %5.2f%%" % tstresult) # just for reference, plot the first 5 timeseries plot(trndata['input'][0:250,:],'-o') hold(True) plot(trndata['target'][0:250,0]) show()
net.addRecurrentConnection(FullConnection(h, h, inSliceTo = dim, outSliceTo = 4*dim, name = 'r1')) net.addRecurrentConnection(IdentityConnection(h, h, inSliceFrom = dim, outSliceFrom = 4*dim, name = 'rstate')) net.addConnection(FullConnection(h, o, inSliceTo = dim, name = 'f3')) net.sortModules() print net ds = SequentialDataSet(15, 1) ds.newSequence() input = open(sys.argv[1], 'r') for line in input.readlines(): row = np.array(line.split(',')) ds.addSample([float(x) for x in row[:15]], float(row[16])) print ds if len(sys.argv) > 2: test = SequentialDataSet(15, 1) test.newSequence() input = open(sys.argv[2], 'r') for line in input.readlines(): row = np.array(line.split(',')) test.addSample([float(x) for x in row[:15]], float(row[16])) else: test = ds print test net.reset() trainer = RPropMinusTrainer( net, dataset=ds, verbose=True) trainer.trainEpochs(1000) evalRnnOnSeqDataset(net, test, verbose = True)
1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) # Initialize trainer trainer = RPropMinusTrainer(rnn, dataset=ds) # Predefine iterations: epochs & cycles EPOCHS_PER_CYCLE = 5 CYCLES = 100 EPOCHS = EPOCHS_PER_CYCLE * CYCLES # Training loop for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) error = trainer.testOnData() epoch = (i + 1) * EPOCHS_PER_CYCLE print("\r Epoch: {}/{} Error: {}".format(epoch, EPOCHS, error), end="") stdout.flush() # Save model NetworkWriter.writeToFile(rnn, 'rnn3.xml') # Ad hoc test for test in test_data: for i in xrange(0, len(test) - 6, 5): # Get 5 obs, 6th we wish to predict obs, nxt = test[i:i + 5], test[i + 6] # Predict all
net = buildNetwork(1, 12, 1, hiddenclass=LSTMLayer, peepholes = False, outputbias=False, recurrent=True) # net = buildNetwork(1, 1, 1, hiddenclass=LSTMLayer, peepholes = True, outputbias=False, recurrent=True) # rnn = buildNetwork( trndata.indim, 5, trndata.outdim, hiddenclass=LSTMLayer, outclass=SoftmaxLayer, outputbias=False, recurrent=True) from pybrain.supervised import RPropMinusTrainer from sys import stdout trainer = RPropMinusTrainer(net, dataset=ds, verbose = True) #trainer.trainUntilConvergence() train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 100 # increasing the epochs to 20, decreases accuracy drastically, decreasing epochs is desiredepoch # 5 err = 0.04 CYCLES = 10 # vary the epochs adn the cycles and the LSTM cells to get more accurate results. EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) # train on the given data set for given number of epochs train_errors.append(trainer.testOnData()) epoch = (i+1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS), end="") stdout.flush() print() print("final error =", train_errors[-1]) ## Plot the data and the training import matplotlib.pyplot as plt plt.plot(range(0, EPOCHS, EPOCHS_PER_CYCLE), train_errors) plt.xlabel('epoch') plt.ylabel('error') plt.show()
ds = SequentialDataSet(nDim, nDim) trainer = RPropMinusTrainer(net) trainer.setData(ds) for _ in xrange(1000): # Batch training mode # print "generate a dataset of sequences" import random random.shuffle(sequences) concat_sequences = [] for sequence in sequences: concat_sequences += sequence concat_sequences.append(random.randrange(100, 1000000)) for j in xrange(len(concat_sequences) - 1): ds.addSample(num2vec(concat_sequences[j], nDim), num2vec(concat_sequences[j+1], nDim)) trainer.trainEpochs(rptNum) print print "test LSTM, repeats =", rptNum # test LSTM correct = [] for i in xrange(len(sequences)): net.reset() sequence = sequences[i] sequence = sequence + [random.randrange(100, 1000000)] print sequence predictedInput = [] for j in xrange(len(sequence)): sample = num2vec(sequence[j], nDim) netActivation = net.activate(sample) if j+1 < len(sequence) - 1:
ds.newSequence() for j in range(length): ds.addSample(x[j], target[j]) return ds if __name__ == '__main__': # Choose network parameters (see pygfnn.tools.shortcuts for more) oscParams = gfnn.OSC_CRITICAL freqDist = { 'fspac': 'log', 'min': 0.5, 'max': 8 } gfnnLearnParams = None gfnnDim = 50 lstmDim = 5 # Build network n = buildGFNNLSTM(gfnnDim, lstmDim, oscParams = oscParams, freqDist = freqDist, learnParams = gfnnLearnParams) # Create a dataset - 10, 40s pulses at various tempos ds = buildDS(n, 10, 40) # Train (hopfully you'll see errors go down!) tr = RPropMinusTrainer(n, dataset=ds, verbose=True) timer = timeit.default_timer start = timer() err = tr.trainEpochs(5) end = timer() print('Elapsed time is %f seconds' % (end - start))
trndata._convertToOneOfMany(bounds=[0., 1.]) tstdata = generateNoisySines(50, 20) tstdata._convertToOneOfMany(bounds=[0., 1.]) # construct LSTM network - note the missing output bias rnn = buildNetwork(trndata.indim, 5, trndata.outdim, hiddenclass=LSTMLayer, outclass=SoftmaxLayer, outputbias=False, recurrent=True) # define a training method trainer = RPropMinusTrainer(rnn, dataset=trndata, verbose=True) # instead, you may also try ##trainer = BackpropTrainer( rnn, dataset=trndata, verbose=True, momentum=0.9, learningrate=0.00001 ) # carry out the training for i in xrange(100): trainer.trainEpochs(2) trnresult = 100. * (1.0 - testOnSequenceData(rnn, trndata)) tstresult = 100. * (1.0 - testOnSequenceData(rnn, tstdata)) print "train error: %5.2f%%" % trnresult, ", test error: %5.2f%%" % tstresult # just for reference, plot the first 5 timeseries plot(trndata['input'][0:250, :], '-o') hold(True) plot(trndata['target'][0:250, 0]) show()
trainer = RPropMinusTrainer(net, dataset=ds) epochcount = 0 while True: startingnote = random.choice(range(1, 17)) startingnote2 = random.choice(range(1, 17)) startingduration = random.choice(range(1, 17)) startingduration2 = random.choice(range(1, 17)) song = [[ startingnote, startingduration, 1, 1, 0, startingnote2, startingduration2, 1, 1, 0 ]] length = 50 while len(song) < length: song.append(net.activate(song[-1]).tolist()) newsong = [] for x in song: newx = [] newy = [] for i in x: if len(newx) < 5: newx.append(int(i)) else: newy.append(int(i)) newsong.append(newx) newsong.append(newy) print newsong print "The above song is after " + str(epochcount) + " epochs." trainer.trainEpochs(epochs=1) epochcount += 1
train_errors = [] train_errors_2 = [] train_errors_3 = [] train_errors_4 = [] train_errors_5 = [] train_errors_6 = [] train_errors_8 = [] train_errors_9 = [] train_errors_10 = [] # Training EPOCHS_per_CYCLE = 6 NUM_CYCLES = 15 EPOCHS = EPOCHS_per_CYCLE * NUM_CYCLES for i in xrange(NUM_CYCLES): trainer.trainEpochs(EPOCHS_per_CYCLE) train_errors.append(trainer.testOnData()) trainer_2.trainEpochs(EPOCHS_per_CYCLE) train_errors_2.append(trainer_2.testOnData()) trainer_3.trainEpochs(EPOCHS_per_CYCLE) train_errors_3.append(trainer_3.testOnData()) trainer_4.trainEpochs(EPOCHS_per_CYCLE) train_errors_4.append(trainer_4.testOnData()) trainer_5.trainEpochs(EPOCHS_per_CYCLE) train_errors_5.append(trainer_5.testOnData()) trainer_6.trainEpochs(EPOCHS_per_CYCLE) train_errors_6.append(trainer_6.testOnData()) trainer_8.trainEpochs(EPOCHS_per_CYCLE) train_errors_8.append(trainer_8.testOnData()) trainer_9.trainEpochs(EPOCHS_per_CYCLE) train_errors_9.append(trainer_9.testOnData())
def rnn(): # load dataframe from csv file df = pi.load_data_frame('../../data/NABIL.csv') # column name to match with indicator calculating modules # TODO: resolve issue with column name df.columns = [ 'Transactions', 'Traded_Shares', 'Traded_Amount', 'High', 'Low', 'Close' ] data = df.Close.values # TODO: write min_max normalization # normalization # cp = dataframe.pop(' Close Price') # x = cp.values temp = np.array(data).reshape(len(data), 1) min_max_scaler = preprocessing.MinMaxScaler() data = min_max_scaler.fit_transform(temp) # dataframe[' Close Price'] = x_scaled # prepate sequential dataset for pyBrain rnn network ds = SequentialDataSet(1, 1) for sample, next_sample in zip(data, cycle(data[1:])): ds.addSample(sample, next_sample) # build rnn network with LSTM layer # if saved network is available if (os.path.isfile('random.xml')): net = NetworkReader.readFrom('network.xml') else: net = buildNetwork(1, 20, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) # build trainer trainer = RPropMinusTrainer(net, dataset=ds, verbose=True) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 5 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in range(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i + 1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS), end="") sys.stdout.flush() # save the network NetworkWriter.writeToFile(net, 'network.xml') print() print("final error =", train_errors[-1]) predicted = [] for dat in data: predicted.append(net.activate(dat)[0]) # data = min_max_scaler.inverse_transform(data) # predicted = min_max_scaler.inverse_transform(predicted) predicted_array = min_max_scaler.inverse_transform( np.array(predicted).reshape(-1, 1)) print(predicted_array[-1]) plt.figure() legend_actual, = plt.plot(range(0, len(data)), temp, label='actual', linestyle='--', linewidth=2, c='blue') legend_predicted, = plt.plot(range(0, len(data)), predicted_array, label='predicted', linewidth=1.5, c='red') plt.legend(handles=[legend_actual, legend_predicted]) plt.savefig('error.png') plt.show()
def rnn(): # load dataframe from csv file df = pi.load_data_frame('../../data/NABIL.csv') # column name to match with indicator calculating modules # TODO: resolve issue with column name df.columns = [ 'Transactions', 'Traded_Shares', 'Traded_Amount', 'High', 'Low', 'Close'] data = df.Close.values # TODO: write min_max normalization # normalization # cp = dataframe.pop(' Close Price') # x = cp.values temp = np.array(data).reshape(len(data),1) min_max_scaler = preprocessing.MinMaxScaler() data = min_max_scaler.fit_transform(temp) # dataframe[' Close Price'] = x_scaled # prepate sequential dataset for pyBrain rnn network ds = SequentialDataSet(1, 1) for sample, next_sample in zip(data, cycle(data[1:])): ds.addSample(sample, next_sample) # build rnn network with LSTM layer # if saved network is available if(os.path.isfile('random.xml')): net = NetworkReader.readFrom('network.xml') else: net = buildNetwork(1, 20, 1, hiddenclass=LSTMLayer, outputbias=False, recurrent=True) # build trainer trainer = RPropMinusTrainer(net, dataset=ds, verbose = True) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 5 CYCLES = 5 EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in range(CYCLES): trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i+1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS), end="") sys.stdout.flush() # save the network NetworkWriter.writeToFile(net,'network.xml') print() print("final error =", train_errors[-1]) predicted = [] for dat in data: predicted.append(net.activate(dat)[0]) # data = min_max_scaler.inverse_transform(data) # predicted = min_max_scaler.inverse_transform(predicted) predicted_array = min_max_scaler.inverse_transform(np.array(predicted).reshape(-1,1)) print(predicted_array[-1]) plt.figure() legend_actual, = plt.plot(range(0, len(data)),temp, label = 'actual', linestyle = '--', linewidth = 2, c = 'blue') legend_predicted, = plt.plot(range(0, len(data)), predicted_array, label = 'predicted', linewidth = 1.5, c='red') plt.legend(handles=[legend_actual, legend_predicted]) plt.savefig('error.png') plt.show()
net.addConnection(FullConnection(net["input"], net["hidden1"], name="c1")) net.addConnection(FullConnection(net["hidden1"], net["hidden2"], name="c3")) net.addConnection(FullConnection(net["bias"], net["hidden2"], name="c4")) net.addConnection(FullConnection(net["hidden2"], net["output"], name="c5")) net.addRecurrentConnection(FullConnection(net["hidden1"], net["hidden1"], name="c6")) net.sortModules() # net = buildNetwork(n_input, 256, n_output, hiddenclass=LSTMLayer, outclass=TanhLayer, outputbias=False, recurrent=True) # net = NetworkReader.readFrom('signal_weight.xml') # train network trainer = RPropMinusTrainer(net, dataset=training_dataset, verbose=True, weightdecay=0.01) # trainer = BackpropTrainer(net, dataset=training_dataset, learningrate = 0.04, momentum = 0.96, weightdecay = 0.02, verbose = True) for i in range(100): # train the network for 1 epoch trainer.trainEpochs(5) # evaluate the result on the training and test data trnresult = percentError(trainer.testOnClassData(), training_dataset['class']) tstresult = percentError(trainer.testOnClassData(dataset=testing_dataset), testing_dataset['class']) # print the result print("epoch: %4d" % trainer.totalepochs, \ " train error: %5.2f%%" % trnresult, \ " test error: %5.2f%%" % tstresult) if tstresult <= 0.5 : print('Bingo !!!!!!!!!!!!!!!!!!!!!!') break # export network NetworkWriter.writeToFile(net, 'signal_weight.xml')
# net = buildNetwork(1, 1, 1, hiddenclass=LSTMLayer, peepholes = True, outputbias=False, recurrent=True) # rnn = buildNetwork( trndata.indim, 5, trndata.outdim, hiddenclass=LSTMLayer, outclass=SoftmaxLayer, outputbias=False, recurrent=True) from pybrain.supervised import RPropMinusTrainer from sys import stdout trainer = RPropMinusTrainer(net, dataset=ds, verbose=True) #trainer.trainUntilConvergence() train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 100 # increasing the epochs to 20, decreases accuracy drastically, decreasing epochs is desiredepoch # 5 err = 0.04 CYCLES = 10 # vary the epochs adn the cycles and the LSTM cells to get more accurate results. EPOCHS = EPOCHS_PER_CYCLE * CYCLES for i in xrange(CYCLES): trainer.trainEpochs( EPOCHS_PER_CYCLE ) # train on the given data set for given number of epochs train_errors.append(trainer.testOnData()) epoch = (i + 1) * EPOCHS_PER_CYCLE print("\r epoch {}/{}".format(epoch, EPOCHS), end="") stdout.flush() print() print("final error =", train_errors[-1]) ## Plot the data and the training import matplotlib.pyplot as plt plt.plot(range(0, EPOCHS, EPOCHS_PER_CYCLE), train_errors) plt.xlabel('epoch') plt.ylabel('error')
net.addConnection(FullConnection(net['hidden' + str(layerCount - 1)], net['out'], name='cOut')) net.sortModules() from pybrain.supervised import RPropMinusTrainer trainer = RPropMinusTrainer(net, dataset=ds) epochcount = 0 while True: startingnote = random.choice(range(1, 17)) startingnote2 = random.choice(range(1, 17)) startingduration = random.choice(range(1,17)) startingduration2 = random.choice(range(1, 17)) song = [[startingnote, startingduration, 1, 1, 0, startingnote2, startingduration2, 1, 1, 0]] length = 50 while len(song) < length: song.append(net.activate(song[-1]).tolist()) newsong = [] for x in song: newx = [] newy = [] for i in x: if len(newx) < 5: newx.append(int(i)) else: newy.append(int(i)) newsong.append(newx) newsong.append(newy) print newsong print "The above song is after " + str(epochcount) + " epochs." trainer.trainEpochs(epochs=1) epochcount += 1
from pybrain.supervised import RPropMinusTrainer from sys import stdout print 'Starting to train neural network. . .' trainer = RPropMinusTrainer(net, dataset=ds) train_errors = [] # save errors for plotting later EPOCHS_PER_CYCLE = 2 #CYCLES = 200 CYCLES = 100 EPOCHS = EPOCHS_PER_CYCLE * CYCLES print 'Entering loop. . .' for i in xrange(CYCLES): # Does the training trainer.trainEpochs(EPOCHS_PER_CYCLE) train_errors.append(trainer.testOnData()) epoch = (i + 1) * EPOCHS_PER_CYCLE print 'i: ', i print ('\r epoch {}/{}'.format(epoch, EPOCHS)) stdout.flush() print 'Exit loop' print '' print 'final error =', train_errors[-1] # Plot the errors (note that in this simple toy example, # we are testing and training on the same dataset, which # is of course not what you'd do for a real project!):
trainer = RPropMinusTrainer(net) trainer.setData(ds) for _ in xrange(1000): # Batch training mode # print "generate a dataset of sequences" import random random.shuffle(sequences) concat_sequences = [] for sequence in sequences: concat_sequences += sequence concat_sequences.append(random.randrange(100, 1000000)) for j in xrange(len(concat_sequences) - 1): ds.addSample(num2vec(concat_sequences[j], nDim), num2vec(concat_sequences[j + 1], nDim)) trainer.trainEpochs(rptNum) print print "test LSTM, repeats =", rptNum # test LSTM correct = [] for i in xrange(len(sequences)): net.reset() sequence = sequences[i] sequence = sequence + [random.randrange(100, 1000000)] print sequence predictedInput = [] for j in xrange(len(sequence)): sample = num2vec(sequence[j], nDim) netActivation = net.activate(sample) if j + 1 < len(sequence) - 1: