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)
class sequence_classifier():
num_time_steps = 5
def __init__(self, ds):
num_inputs = ds.num_features * sequence_classifier.num_time_steps
self.alldata = SequenceClassificationDataSet(num_inputs,
target = 1,
nb_classes = ds.get_num_classes(),
class_labels = ds.get_classes() )
for Idx in range(len(ds.all_moves)):
if not (Idx + sequence_classifier.num_time_steps < len(ds.all_moves)):
continue
class_first = ds.all_moves[Idx].class_
features = []
for i in range(sequence_classifier.num_time_steps):
features = features + ds.all_moves[Idx + i].get_features()
class_last = ds.all_moves[Idx + sequence_classifier.num_time_steps].class_
if class_first == class_last:
self.alldata.appendLinked(features, [ds.get_classes().index(ds.all_moves[Idx].class_)])
self.alldata.newSequence()
self.tstdata, self.trndata = self.alldata.splitWithProportion(0.25)
self.trndata._convertToOneOfMany()
self.tstdata._convertToOneOfMany()
self.seq_rnn = None #buildNetwork(num_inputs, 2, self.trndata.outdim, hiddenclass=LSTMLayer,recurrent=True ,outclass=SoftmaxLayer)
self.create_network(num_inputs)
self.trainer = RPropMinusTrainer(module=self.seq_rnn, dataset=self.trndata)
def create_network(self, num_inputs):
self.seq_rnn = RecurrentNetwork()
in_layer = LinearLayer(num_inputs)
hidden_LSTM_ = LSTMLayer(24)
hidden_layer_0 = LinearLayer(12)
hidden_layer_1 = SigmoidLayer(12)
output_layer = LinearLayer(self.trndata.outdim)
self.seq_rnn.addInputModule(in_layer)
self.seq_rnn.addModule(hidden_layer_0)
self.seq_rnn.addModule(hidden_layer_1)
self.seq_rnn.addOutputModule(output_layer)
#Now add the connections:
in_to_LTSM = FullConnection(in_layer , hidden_LSTM)
LTSM_to_h0 = FullConnection(hidden_LSTM, hidden_layer_0)
in_to_h0 = FullConnection(in_layer , hidden_layer_0)
h0_to_h1 = FullConnection(hidden_layer_0, hidden_layer_1)
h1_to_out = FullConnection(hidden_layer_1, output_layer)
self.seq_rnn.addConnection(in_to_LSTM)
self.seq_rnn.addConnection(LSTM_to_h0)
self.seq_rnn.addConnection(in_to_h0)
self.seq_rnn.addConnection(h0_to_h1)
self.seq_rnn.addConnection(h1_to_out)
self.seq_rnn.sortModules()
def start_training(self):
f = open("./results/seq_rnn_perf.txt", "w");
for i in range(200):
print "training step: " , i
self.trainer.trainEpochs(1)
err = self.evaluate()
f.write(str(err) + ",")
f.flush()
f.close()
def evaluate(self):
print "epoch:" , self.trainer.totalepochs
correct = 0
wrong = 0
self.seq_rnn.sortModules()
for Idx in range (len(self.tstdata)):
out = self.seq_rnn.activate(self.tstdata['input'][Idx])
if argmax(out) == argmax(self.tstdata['target'][Idx]) :
correct += 1
else:
wrong += 1
correct_ratio = correct*1.0/(wrong + correct)
self.correct_perc.append(correct_ratio)
print "Wrong Predictions: " , wrong , "Ratio = ", wrong*100.0/(wrong+correct) , "%"
print "Correct Predictions: ", correct, "Ratio = ", correct*100.0/(wrong+correct) , "%"
if (self.max_ratio < correct_ratio):
print "Found new max, saving network"
self.write_out("best_perfrming_")
self.max_ratio = correct_ratio
return 1 - correct_ratio
def write_out(self, name=""):
NetworkWriter.writeToFile(self.seq_rnn, "./results/" + name + "req_rnn.xml")
