def Save(self):
print("Saving network...")
now = datetime.datetime.now()
NetworkWriter.writeToFile(self.net, "saved_networks/network_" + str(now.day) + "_" + str(now.month) + "_" + str(now.year))
print("Neural network saved.")
def Treinar(rede,base):
treinamento = BackpropTrainer(rede, dataset = base, learningrate = 0.01,
momentum = 0.06)
for i in range(1, 10000):
erro = treinamento.train()
NetworkWriter.writeToFile(rede, 'filename.xml')
def mutationEvol(itVar ,rna, rank, verbose, path, inn): aux4 = 0 z = 0 it = 0 gen = itVar rna = copy.copy(rna) ind = 0 cnt = 0 p1 = [] for i in range(len(rna.params)): p1.append(0.0) for i in range((len(rank)*2)): #DUAS VEZES RANKING #if(ind<2): rna1 = NetworkReader.readFrom(path+'/gen'+str(gen)+'/rna'+str(rank[cnt])+'.xml') for k in range(len(rna1.params)): if(ind == 0): rna.params[k] = rna1.params[k] p1[k] = rna1.params[k] for j in range(len(rna.params)): aux1 = randint(1, 9) aux2 = randint(10, (15+(15*cnt))) aux = randint(aux1, aux2) if(ind!=0): if(aux%2==0): rna.params[j] = p1[j] if(j>(inn-1)): #rna.params[j] *= float(randint(10000, 18000)/10000.0) rna.params[j] += float(randint(1000, 8000)/10000.0) else: rna.params[j] = p1[j] if(j>(inn-1)): #rna.params[j] *= float(randint(2000, 10000)/10000.0) rna.params[j] -= float(randint(1000, 8000)/10000.0) cnt+=1 if(cnt==(len(rank))): cnt=0 ind+=1 if(verbose==True): print "rna.params:" print rna.params print "\n" NetworkWriter.writeToFile(rna, path+'/gen'+str(gen+1)+'/rna'+str((i+1))+'.xml')
def fstGenCreate(rna, path, rnaNmbr): """ #Criacao da primeira geracao de um sistema genetico ##Parametros -rna-> rede neural base para do sistema criada a partir da funcao (buildNetwork()) - NeuralNetwork -path-> endereco para a pasta que ira alocar as geracoes - String -rnaNmbr-> numero de redes neurais de cada geracao - Inteiro ##Retorno none """ newpath = path + '/gen1' if not os.path.exists(newpath): os.makedirs(newpath) for i in range(rnaNmbr): a=i+1 rna1 = copy.copy(rna) for k in range(len(rna1.params)): rna1.params[k] = float(randint(-2000, 2000))/1000.0 NetworkWriter.writeToFile(rna1, path+'/gen1/rna'+str(a)+'.xml')
def move_function(board):
global net
best_max_move = None
max_value = -1000
best_min_move = None
min_value = 1000
#value is the chance of black winning
for m in board.get_moves():
nextboard = board.peek_move(m)
value = net.activate(board_to_input(nextboard))
if value > max_value:
max_value = value
best_max_move = m
if value < min_value:
min_value = value
best_min_move = m
ds = SupervisedDataSet(97, 1)
best_move = None
#active player
if board.active == BLACK:
ds.addSample(board_to_input(board), max_value)
best_move = best_max_move
elif board.active == WHITE:
ds.addSample(board_to_input(board), min_value)
best_move = best_min_move
trainer = BackpropTrainer(net, ds)
trainer.train()
NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_random_black_mini_140.xml')
NetworkWriter.writeToFile(net, 'SynapsemonPie/synapsemon_random_black_mini_140_copy.xml')
return best_move
def keyPressEvent(self, e):
global All_Cells
if e.key() == Qt.Key_Space:
self.stop = not (self.stop)
elif e.key() == Qt.Key_W:
today = '%i%02i%02i%02i%02i%02i' % (
datetime.datetime.today().year,
datetime.datetime.today().month, datetime.datetime.today().day,
datetime.datetime.today().hour,
datetime.datetime.today().minute,
datetime.datetime.today().second)
os.mkdir('../data/%s' % today)
for i in range(len(All_Cells)):
NetworkWriter.writeToFile(All_Cells[i].brain,
'../data/%s/%02i.xml' % (today, i))
elif not (self.timer_set):
self.timer_set = True
@pyqtSlot()
def step():
self.evolution_step()
self.timer = QTimer(self)
self.timer.timeout.connect(step)
self.timer.start(10)
def train(self): print "Enter the number of times to train, -1 means train until convergence:" t = int(raw_input()) print "Training the Neural Net" print "self.net.indim = "+str(self.net.indim) print "self.train_data.indim = "+str(self.train_data.indim) trainer = BackpropTrainer(self.net, dataset=self.train_data, momentum=0.1, verbose=True, weightdecay=0.01) if t == -1: trainer.trainUntilConvergence() else: for i in range(t): trainer.trainEpochs(1) trnresult = percentError( trainer.testOnClassData(), self.train_data['class']) # print self.test_data tstresult = percentError( trainer.testOnClassData(dataset=self.test_data), self.test_data['class'] ) print "epoch: %4d" % trainer.totalepochs, \ " train error: %5.2f%%" % trnresult, \ " test error: %5.2f%%" % tstresult if i % 10 == 0 and i > 1: print "Saving Progress... Writing to a file" NetworkWriter.writeToFile(self.net, self.path) print "Done training... Writing to a file" NetworkWriter.writeToFile(self.net, self.path) return trainer
def pickleWeights():
global neuralNetwork
save = True if input("Save to file? (y/n) ").lower() == "y" else False
if save:
filename = input("File name? (Please do not indicate the extension)\n") + ".xml"
print("Saving to", filename)
NetworkWriter.writeToFile(neuralNetwork, filename)
def save(self, filename, desc=None):
NetworkWriter.writeToFile(self.net, filename + '.xml')
params = {'labels': self.labels,
'mean': self.mean.tolist(),
'std': self.std.tolist()}
with open(filename + '.yaml', 'w') as f:
f.write(yaml.dump(params, default_flow_style=False))
def trainData(data, filename):
net = buildNetwork(data.indim, 40, data.outdim, hiddenclass=TanhLayer, outclass=SigmoidLayer)
trainer = BackpropTrainer( net, dataset=data, verbose=True, momentum=0.1, weightdecay=0.01)
_ , valid_errors = trainer.trainUntilConvergence(continueEpochs=2)
NetworkWriter.writeToFile(net, filename)
print "Valid error: ", min(valid_errors)
return net
def main():
train_file = 'data/train.csv'
# validation_file = 'data/validation.csv'
output_model_file = 'model.xml'
# hidden_size = 4
epochs = 500
# load data
# def loadData():
train = np.loadtxt(train_file, delimiter=' ')
Input = train[0:,0:3]
Output = train[0:,3:5]
# validation = np.loadtxt(validation_file, delimiter=',')
# train = np.vstack((train, validation))
# x_train = train[:, 0:-1]
# y_train = train[:, -1]
# y_train = y_train.reshape(-1, 1)
# input_size = x_train.shape[1]
# target_size = y_train.shape[1]
# prepare dataset
# def prepare dataset(input_size, target_size):
ds = SDS(Input,Output)
# ds.addSample(input_size)
# ds.setField('input', x_train)
# ds.setField('target', y_train)
# init and train
# def initTrain(input_size, hidden_size, input, output):
# net = buildNetwork(input_size, hidden_size, target_size, bias=True)
net = buildNetwork(3, # input layer
4, # hidden0
2, # output
hiddenclass=SigmoidLayer,
outclass=SigmoidLayer,
bias=True
)
net = NetworkReader.readFrom('model.xml')
for i,o in zip(Input,Output):
ds.addSample(i,o)
print i, o
trainer = BackpropTrainer(net, ds)
print "training for {} epochs...".format(epochs)
for i in range(epochs):
mse = trainer.train()
rmse = sqrt(mse)
print "training RMSE, epoch {}: {}".format(i + 1, rmse)
if os.path.isfile("../stopfile.txt") == True:
break
NetworkWriter.writeToFile(net, output_model_file)
def SaveNetwork(self):
"""
Creating dump of network.
"""
FCLogger.debug('Saving network to PyBrain xml-formatted file...')
NetworkWriter.writeToFile(self.network, self.networkFile)
FCLogger.info('Network saved to file: {}'.format(os.path.abspath(self.networkFile)))
def writetrainedinfo(self, neuralnetwork):
"""
# Using the Python pickle
fileObject = open('traininfo', 'w')
pickle.dump(neuralnetwork, fileObject)
fileObject.close()
"""
# Writing file using the NetworkWriter
NetworkWriter.writeToFile(neuralnetwork, 'trainedinfo.xml')
def save(self, filename, desc=None):
NetworkWriter.writeToFile(self.net, filename + '.xml')
params = {
'labels': self.labels,
'mean': self.mean.tolist(),
'std': self.std.tolist()
}
with open(filename + '.yaml', 'w') as f:
f.write(yaml.dump(params, default_flow_style=False))
def writetrainedinfo(self, neuralnetwork):
"""
# Using the Python pickle
fileObject = open('traininfo', 'w')
pickle.dump(neuralnetwork, fileObject)
fileObject.close()
"""
# Writing file using the NetworkWriter
NetworkWriter.writeToFile(neuralnetwork, 'xtrainedinfo.xml')
def saveNetwork(net, name):
"""Экспорт нейронной сети в файл
Аргументы:
net - нейронная сеть, PyBrain network
name -- имя файла, строка
"""
NetworkWriter.writeToFile(net, name)
def SaveNetwork(self):
"""
Creating dump of network.
"""
FCLogger.debug('Saving network to PyBrain xml-formatted file...')
NetworkWriter.writeToFile(self.network, self.networkFile)
FCLogger.info('Network saved to file: {}'.format(
os.path.abspath(self.networkFile)))
def save_values():
global best, additional_left
NetworkWriter.writeToFile(net, NETWORK_TEMP_FILE_NAME)
if 'avg_error' in globals() and avg_error < best:
best = avg_error
NetworkWriter.writeToFile(net, NETWORK_FILE_NAME)
print "Updated best network and saved to file"
additional_left = ADDITIONAL_EPOCH
with open(NETWORK_VAL_FILE_NAME, "wb") as f:
dump((epoch, additional_left, best), f)
print "Network values saved"
def save( history, net ):
"""
This function gets called after each training/testing block or when the
script gets closed. It saves the neural net and RL history of the agent so
that it can be restored or reused in another model.
"""
base = os.path.splitext( sys.argv[2] )[0]
print 'Saving network to: ' + base + '.xml'
NetworkWriter.writeToFile( net, base + '.xml' )
fileObject = open( base + '.history', 'w' )
pickle.dump( history, fileObject )
fileObject.close()
def SaveNetwork(self):
"""
Creating dump of network.
"""
FCLogger.debug(
'Autosaving - enabled. Trying to save network as PyBrain xml-formatted file...'
)
NetworkWriter.writeToFile(self.network, self.networkFile)
FCLogger.info('Current network saved to file: {}'.format(
os.path.abspath(self.networkFile)))
def save_network(network, network_file):
if bucket:
k = Key(bucket)
k.key = network_file
tmp_file = NamedTemporaryFile(delete=False)
NetworkWriter.writeToFile(network, tmp_file.name)
k.set_contents_from_filename(tmp_file.name)
os.unlink(tmp_file.name)
print "network saved to s3"
else:
NetworkWriter.writeToFile(network, network_storage_path + network_file)
print "network saved to local filesystem"
def perceptron(hidden_neurons=20, weightdecay=0.01, momentum=0.1):
INPUT_FEATURES = 1000
CLASSES = 9
HIDDEN_NEURONS = hidden_neurons
WEIGHTDECAY = weightdecay
MOMENTUM = momentum
g = generate_data()
alldata = g['d']
testdata = generate_Testdata(g['index'])['d']
#tstdata, trndata = alldata.splitWithProportion(0.25)
#print type(tstdata)
trndata = _convert_supervised_to_classification(alldata, CLASSES)
tstdata = _convert_supervised_to_classification(testdata, CLASSES)
trndata._convertToOneOfMany()
tstdata._convertToOneOfMany()
#fnn = NetworkReader.readFrom('ncibig(500+83.85).xml')
fnn = buildNetwork(trndata.indim,
HIDDEN_NEURONS,
trndata.outdim,
outclass=SoftmaxLayer)
trainer = BackpropTrainer(fnn,
dataset=trndata,
momentum=MOMENTUM,
verbose=True,
weightdecay=WEIGHTDECAY,
learningrate=0.01)
result = 0
ssss = 0
for i in range(200):
trainer.trainEpochs(1)
trnresult = percentError(trainer.testOnClassData(), trndata['class'])
tstresult = percentError(trainer.testOnClassData(dataset=tstdata),
tstdata['class'])
out = fnn.activateOnDataset(tstdata)
ssss = out
out = out.argmax(axis=1)
result = out
df = pd.DataFrame(ssss)
df.to_excel("ncibigout.xls")
df = pd.DataFrame(result)
df.insert(1, '1', tstdata['class'])
df.to_excel("ncibig.xls")
error = 0
for i in range(len(tstdata['class'])):
if tstdata['class'][i] != result[i]:
error = error + 1
#print (len(tstdata['class'])-error)*1.0/len(tstdata['class'])*100
print AAC(result, tstdata['class'])
print AUC(np.transpose(tstdata['class'])[0], result.transpose())
print Fscore(np.transpose(tstdata['class'])[0], result.transpose())
NetworkWriter.writeToFile(fnn, 'ncibig.xml')
def save_network_to_file(self, filename):
"""Save Network to File
Saves the neural network including all connection weights into a
NetworkWriter format xml file for future loading.
Arguments:
filename: The filename into which the network should be saved.
"""
NetworkWriter.writeToFile(self._network, filename)
return
def trainNetwork():
print "[Training] Network has Started..."
inputSize = 0
with open('file1.txt', 'r') as f: #automatically closes file at the end of the block
#first_line = f.readline()
#inputSize = len(first_line)
dataset = SupervisedDataSet(4, 1) #specify size of data and target
f.seek(0) #Move back to beginnning of file
#iterate through the file. 1 picture per line
for line in f:
mylist = json.loads(line) #list object
target = mylist[-1] #retrieve and then delete the target classification
del mylist[-2:]
#print target
dataset.addSample(tuple(mylist), (target,))
#print json.loads(line)
if os.path.isfile('annModel.xml'):
skynet = NetworkReader.readFrom('annModel.xml')#for use if individual sample files used
else:
skynet = buildNetwork(dataset.indim, 8, dataset.outdim, bias=True, hiddenclass=TanhLayer) #input,hidden,output
#SoftmaxLayer, SigmoidLayer, LinearLayer, GaussianLayer
#Note hidden neuron number is arbitrary, can try 1 or 4 or 3 or 5 if this methods doesnt work out
trainer = BackpropTrainer(skynet, dataset,learningrate = 0.3, weightdecay = 0.01,momentum = 0.9)
#trainer.trainUntilConvergence()
for i in xrange(1000):
trainer.train()
#trainer.trainEpochs(1000)
#Save the now trained neural network
NetworkWriter.writeToFile(skynet,'annModel.xml')
print "[Network] has been Written"
################## SVM Method #######################
#Change append method in write method for target persistence
dataX = []
datay = []
with open(writeFile, 'r') as f:
for line in f:
mylist = json.loads(line)
target2 = mylist[-1]
dataX.append(mylist[:-2])
datay.append(target2)
#datay = [target2] * len(dataX) #Targets, size is n_samples, for use with indiviual sample files with same target
print [target2]
print dataX
print datay
clf = svm.LinearSVC()
clf.fit(dataX,datay)
#Persist the trained model
joblib.dump(clf,'svmModel.pkl')
def Treinar():
treinamento = BackpropTrainer(rede, dataset = base, learningrate = 0.01,
momentum = 0.06)
for i in range(1, 10000):
erro = treinamento.train()
NetworkWriter.writeToFile(rede, 'filename.xml')
rede = buildNetwork(3, 4, 1)
base = SupervisedDataSet(3, 1)
PrimeiraCarga()
Treinar()
#rede = NetworkReader.readFrom('filename.xml')
print(rede.activate([0.8, 0.3, 0.3]))
def Treinar():
print 'Inicializando o treinamento da Rede......Aguarde'
ds = SupervisedDataSet(50,1)
with open('trainning.txt') as f:
for line in f:
if line[0] != '#':
line = line.replace('\n','')
line = line.split(',')
exemplo = []
for x in line: exemplo.append(x)
ds.addSample(exemplo[1:],exemplo[:1]) # o 1: pega o primeiro valor que e targer.
## Dataset
#trainer = BackpropTrainer(net, learningrate = 0.04, momentum = 0.07, verbose = False)
trainer = BackpropTrainer(net, learningrate = 0.04, momentum = 0.07, verbose = False)
trainer.trainOnDataset(ds,10000)
NetworkWriter.writeToFile(net, 'filename.xml')
print 'Treinado e Pronto'
def train():
global neuralNetwork, inputDataSet, outputData
if len(inputDataSet) == 1 and inputDataSet[0][2] < 0.78:
outputData = 0.3 #Encourage exploration
else:
outputData = float(input("Money Earned? "))
if inputDataSet[len(inputDataSet)-1][2] < 0.3 and outputData < 0:
outputData = 0.3 #Encourage exploration if risk is low
print("Training with", outputData)
trainingSets = SupervisedDataSet(3, 1)
for i in range(len(inputDataSet)):
print(inputDataSet[i])
trainingSets.addSample(inputDataSet[i], outputData)
trainer = BackpropTrainer(neuralNetwork, trainingSets, learningrate = 0.7, momentum = 0.3)
trainer.trainEpochs()
del inputDataSet[:]
NetworkWriter.writeToFile(neuralNetwork, "network.xml")
print("Updating neural network")
def perceptron(hidden_neurons=20, weightdecay=0.01, momentum=0.1):
INPUT_FEATURES = 200
CLASSES = 9
HIDDEN_NEURONS = hidden_neurons
WEIGHTDECAY = weightdecay
MOMENTUM = momentum
g = generate_data()
alldata = g['d']
testdata = generate_Testdata(g['index'])['d']
#tstdata, trndata = alldata.splitWithProportion(0.25)
#print type(tstdata)
trndata = _convert_supervised_to_classification(alldata,CLASSES)
tstdata = _convert_supervised_to_classification(testdata,CLASSES)
trndata._convertToOneOfMany()
tstdata._convertToOneOfMany()
#fnn = NetworkReader.readFrom('ncibig(500+83.85).xml')
fnn = buildNetwork(trndata.indim, HIDDEN_NEURONS, trndata.outdim,outclass=SoftmaxLayer)
trainer = BackpropTrainer(fnn, dataset=trndata, momentum=MOMENTUM,verbose=True, weightdecay=WEIGHTDECAY,learningrate=0.01)
result = 0;
ssss = 0;
for i in range(200):
trainer.trainEpochs(1)
trnresult = percentError(trainer.testOnClassData(),trndata['class'])
tstresult = percentError(trainer.testOnClassData(dataset=tstdata), tstdata['class'])
out = fnn.activateOnDataset(tstdata)
ssss = out
out = out.argmax(axis=1)
result = out
df = pd.DataFrame(ssss)
df.to_excel("ncibigout.xls")
df = pd.DataFrame(result)
df.insert(1,'1',tstdata['class'])
df.to_excel("ncibig.xls")
error = 0;
for i in range(len(tstdata['class'])):
if tstdata['class'][i] != result[i]:
error = error+1
#print (len(tstdata['class'])-error)*1.0/len(tstdata['class'])*100
print AAC(result,tstdata['class'])
print AUC(np.transpose(tstdata['class'])[0],result.transpose())
print Fscore(np.transpose(tstdata['class'])[0],result.transpose())
NetworkWriter.writeToFile(fnn, 'ncibig.xml')
def main(data_source=default_src):
train_data, train_labels, valid_data, valid_labels, test_data, test_labels = generate_datasets(
data_source)
net = nn.nn_setup()
timestamp = datetime.now().strftime("%Y%m%d_%H:%M:%S")
netpath = net_file_base + timestamp + ".p"
trained_net = nn.train_network_until_convergence(net,
train_data,
train_labels,
valid_data,
valid_labels,
file_path=result_path)
NetworkWriter.writeToFile(trained_net, netpath)
testset_file = open(testset_path, "wb+")
test_set = {'test_data': test_data, 'test_labels': test_labels}
pickle.dump(test_set, testset_file)
def Treinar(rede, base):
try:
print("Iniciando treinamento")
erro = 10
erro_old = 0
tentativas = 0
tentativa_controle = 0
'''xml = "D:\SmartCane"
dir = os.listdir(xml)
for file in dir:
if file == "baseIA.xml":
os.remove(file)'''
treinamento = BackpropTrainer(rede,
dataset=base,
learningrate=0.01,
momentum=0.06)
#for i in range(1, 100):
while (tentativas < 10000) and (round(erro, 3) > 0.001):
print("Preparando treinamento")
erro = treinamento.train()
if (erro < 0.001):
print("Erro baixo")
else:
print(erro)
if (tentativa_controle > 1000) and (erro_old > 0) and (erro_old <=
erro):
break
if tentativa_controle > 1000:
erro_old = erro
tentativa_controle = 0
tentativas += 1
tentativa_controle += 1
NetworkWriter.writeToFile(rede, 'baseIA.xml')
print("Base gravada com sucesso")
except:
global threadON
threadON = False
def init_brain(learn_data, epochs, hidden_count, TrainerClass=BackpropTrainer):
global data_dir
print("\t Epochs: ", epochs)
if learn_data is None:
return None
print("Building network")
net = buildNetwork(7 * 7, hidden_count, 4, hiddenclass=SigmoidLayer)
# net = buildNetwork(64 * 64, 32 * 32, 8 * 8, 5)
# net = buildNetwork(64 * 64, 5, hiddenclass=LinearLayer)
# fill dataset with learn data
trans = {'0': 0, '1': 1, '2': 2, '3': 3}
ds = ClassificationDataSet(7 * 7,
nb_classes=4,
class_labels=['0', '1', '2', '3'])
for inp, out in learn_data:
ds.appendLinked(inp, [trans[out]])
ds.calculateStatistics()
print("\tNumber of classes in dataset = {0}".format(ds.nClasses))
print("\tOutput in dataset is ", ds.getField('target').transpose())
ds._convertToOneOfMany(bounds=[0, 1])
print("\tBut after convert output in dataset is \n", ds.getField('target'))
trainer = TrainerClass(net, learningrate=0.1, verbose=True)
trainer.setData(ds)
print(
"\tEverything is ready for learning.\nPlease wait, training in progress..."
)
start = time.time()
trainer.trainEpochs(epochs=epochs)
end = time.time()
f = open(data_dir + "/values.txt", "w")
f.write("Training time: %.2f \n" % (end - start))
f.write("Total epochs: %s \n" % (trainer.totalepochs))
# f.write("Error: %.22f" % (trainer.trainingErrors[len(trainer.trainingErrors) - 1]))
f.close()
print("Percent of error: ",
percentError(trainer.testOnClassData(), ds['class']))
print("\tOk. We have trained our network.")
NetworkWriter.writeToFile(net, data_dir + "/net.xml")
return net
def genTrain(rnaNmbr, gen, epochs, genPath, dataset):
"""
#Treinamento de uma geracao (backpropagation)
##Parametros
-rnaNmbr-> numero de redes neurais de cada geracao - Inteiro
-gen-> numero da geracao para o treinamento - Inteiro
-epochs-> numero de iteracoes de treino para cada rna - Inteiro
-genPath-> endereco para a pasta que aloca as geracoes de um sistema genetico - String
-dataset-> array contendo os dados do dataset - Array de Float
##Retorno
none
"""
k = 0
inNmbr = len(dataset[0])
outNmbr = len(dataset[1])
ds = SupervisedDataSet(inNmbr, outNmbr)
for i in range(len(dataset)/2):
ds.addSample(dataset[k], dataset[k+1])
k = k+2
for l in range(rnaNmbr):
rna = NetworkReader.readFrom(genPath+'/gen'+str(gen)+'/rna'+str(l+1)+'.xml')
t = BackpropTrainer(rna , learningrate=0.01, momentum=0.1, verbose=True)
t.trainOnDataset(ds, epochs)
NetworkWriter.writeToFile(rna, genPath+'/gen'+str(gen)+'/rna'+str(l+1)+'.xml')
print "Geracao: {:}".format(l+1)
def end_function(board, lose):
global net
ds = SupervisedDataSet(97, 1)
if lose:
if board.active == BLACK:
ds.addSample(board_to_input(board), 0)
whiteboard = board_to_input(board)
whiteboard[96] = 0
ds.addSample(whiteboard, 1)
elif board.active == WHITE:
ds.addSample(board_to_input(board), 1)
blackboard = board_to_input(board)
blackboard[96] = 1
ds.addSample(blackboard, 0)
else:
#black loses
if board.active == BLACK:
ds.addSample(board_to_input(board), 0)
whiteboard = board_to_input(board)
whiteboard[96] = 0
ds.addSample(whiteboard, 0)
#black wins
elif board.active == WHITE:
ds.addSample(board_to_input(board), 1)
blackboard = board_to_input(board)
blackboard[96] = 1
ds.addSample(blackboard, 1)
trainer = BackpropTrainer(net, ds)
trainer.train()
NetworkWriter.writeToFile(
net, 'CheckersMini/synapsemon_random_white_mini_50.xml')
NetworkWriter.writeToFile(
net, 'CheckersMini/synapsemon_random_white_mini_50_copy.xml')
def move_function(board):
global net
#active player
#if board.active == BLACK:
# ds.addSample(board_to_input(board), max_value)
# best_move = best_max_move
#elif board.active == WHITE:
# ds.addSample(board_to_input(board), min_value)
# best_move = best_min_move
boardString = board_to_input(board)
black_material = 0
white_material = 0
for i in range(32):
isKing = boardString[i + 64] == 1
if boardString[i] == 1:
if isKing:
black_material = black_material + 2
else:
black_material = black_material + 1
if boardString[i + 32] == 1:
if isKing:
white_material = white_material + 2
else:
white_material = white_material + 1
board_val = black_material / (black_material + white_material)
#create a new dataset. Add a sample with board as input, value as output
ds = SupervisedDataSet(97, 1)
ds.addSample(boardString, board_val)
trainer = BackpropTrainer(net, ds)
trainer.train()
NetworkWriter.writeToFile(net, 'SynapsemonPie/synapsemon_primer110.xml')
NetworkWriter.writeToFile(net,
'SynapsemonPie/synapsemon_primer110_copy.xml')
return random.choice(board.get_moves())
def end_function(board, lose):
global net
ds = SupervisedDataSet(97, 1)
if lose:
if board.active == BLACK:
ds.addSample(board_to_input(board), 0)
whiteboard = board_to_input(board)
whiteboard[96] = 0
ds.addSample(whiteboard, 1)
elif board.active == WHITE:
ds.addSample(board_to_input(board), 1)
blackboard = board_to_input(board)
blackboard[96] = 1
ds.addSample(blackboard, 0)
else:
#black loses
if board.active == BLACK:
ds.addSample(board_to_input(board), 0)
whiteboard = board_to_input(board)
whiteboard[96] = 0
ds.addSample(whiteboard, 0)
#black wins
elif board.active == WHITE:
ds.addSample(board_to_input(board), 1)
blackboard = board_to_input(board)
blackboard[96] = 1
ds.addSample(blackboard, 1)
trainer = BackpropTrainer(net, ds)
trainer.train()
NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_random_black_mini_140.xml')
NetworkWriter.writeToFile(net, 'SynapsemonPie/synapsemon_random_black_mini_140_copy.xml')
def move_function(board):
global net
#active player
#if board.active == BLACK:
# ds.addSample(board_to_input(board), max_value)
# best_move = best_max_move
#elif board.active == WHITE:
# ds.addSample(board_to_input(board), min_value)
# best_move = best_min_move
boardString=board_to_input(board)
black_material=0
white_material=0
for i in range(32):
isKing=boardString[i+64]=='1'
if boardString[i]=='1':
if isKing:
black_material=black_material+2
else:
black_material=black_material+1
if boardString[i+32]=='1':
if isKing:
white_material=white_material+2
else:
white_material=white_material+1
board_val = black_material/(black_material+white_material)
#create a new dataset. Add a sample with board as input, value as output
ds = SupervisedDataSet(97, 1)
ds.addSample(boardString, board_val)
trainer = BackpropTrainer(net, ds)
trainer.train()
NetworkWriter.writeToFile(net, 'SynapsemonPie/synapsemon_primer1.xml')
NetworkWriter.writeToFile(net, 'SynapsemonPie/synapsemon_primer1_copy.xml')
return random.choice(board.get_moves())
forecasts = []
for i in range(30):
fc = bestnet.activate(unknown)
forecasts.append(fc)
unknown = np.append(unknown[1:], fc)
plt.plot(validds['target'])
plt.plot(bestnet.activateOnDataset(validds))
plt.title('Validation')
plt.figure()
pred = bestnet.activateOnDataset(testds)
plt.plot(testds['target'])
plt.plot(pred)
plt.title('Testing')
# plt.figure()
# plt.plot(testds['target'][:30])
# plt.plot(forecasts)
# plt.title('Forecasts')
plt.show()
#
NetworkWriter.writeToFile(
bestnet, '../data/cpu_rnn_networks/' + machine + ".xml")
with open('../data/cpu_rnn_networks/hyperparams.csv', mode='a') as f:
print([
machine, besterr, besthparams[1], besthparams[3],
besthparams[4], besthparams[2]
],
sep=',',
end='\n',
file=f)
t0 = cpu_time()
df_freq['target'] = classification_encoder.fit_transform(df.target)
binary_classes = binary_categorizer.fit_transform(df_freq.target.values.reshape(len(df_freq), 1))
binary_target_labels = list(df_freq.columns[-9:])
for i, label in enumerate(class_labels):
df_freq[label] = binary_classes[:, i]
print("Transforming labels from text took {} sec of the CPU's time.".format(cpu_time() - t0))
ds = util.dataset_from_dataframe(df_freq, normalize=False, delays=[0], inputs=feature_labels, outputs=class_labels,
verbosity=1)
nn = util.ann_from_ds(ds, N_hidden=[27, 18], hidden_layer_type=['Linear', 'Sigmoid'], output_layer_type='Linear',
verbosity=1)
trainer = util.build_trainer(nn, ds=ds, verbosity=1)
trainer.trainUntilConvergence(maxEpochs=500, verbose=True)
NetworkWriter.writeToFile(trainer.module, __file__ + '.xml')
# this only works for the linear NN where the output is a float 0..8
# df['target_i'] = df_freq.target
# df['predicted_i'] = np.clip(np.round(trainer.module.activateOnDataset(ds)), 0, 8)
# df['predicted'] = [class_labels[i] for i in df['predicted_i']]
# df.to_csv('training_set_with_predictions')
# columns = feature_labels + target_labels + ['Predicted--{}'.format(outp) for outp in target_labels]
predicted_prob = pd.DataFrame((pd.np.array(trainer.module.activate(i)) for i in trainer.ds['input']),
columns=class_labels)
def llfun(act, pred):
small_value = 1e-15
allScore += creature.score
allScore /= len(creatures)
creatures.sort(key=CreaturesScoresComparator, reverse=True)
# Finding the best and save it in case of beating record
bestCreature = creatures[0]
print("Best score:", bestCreature.score)
graphFile = open(graphFileAdress, 'a')
graphFile.write(str(iteration))
graphFile.write(';')
graphFile.write(str(bestCreature.score))
graphFile.write('\n')
graphFile.close()
if scoreRecord < bestCreature.score:
scoreRecord = bestCreature.score
NetworkWriter.writeToFile(bestCreature.Network, bestBotFileAdress)
# Graphing average
print("Average score:", allScore)
graphFile = open(averageFileAdress, 'a')
graphFile.write(str(iteration))
graphFile.write(';')
graphFile.write(str(allScore))
graphFile.write('\n')
graphFile.close()
# Opening graph program
if iteration == 0: _thread.start_new_thread(grapherThread, ())
# Getting half-final
halfFinal = []
if(oldtstError==0):
oldtstError = tstError
if(oldtstError<tstError):
tstErrorCount = tstErrorCount+1
print 'No Improvement, count=%d' % tstErrorCount
print ' Old Validation Error:', oldtstError
print 'Current Validation Error:', tstError
if(oldtstError>tstError):
print 'Improvement made!'
print ' Old Validation Error:', oldtstError
print 'Current Validation Error:', tstError
tstErrorCount=0
oldtstError = tstError
NetworkWriter.writeToFile(TDNNClassificationNet, networkPath)
plotLearningCurve()
trainingTime = time.time()-time_start
trainingTime=np.reshape(trainingTime, (1))
np.savetxt("25sigmoid/Trainingtime.txt", trainingTime)
####################
# Manual OFFLINE Test
####################
# TDNNClassificationNet = NetworkReader.readFrom('25sigmoid/TrainUntilConv.xml')
# print 'Loaded Trained Network!'
#
# print TDNNClassificationNet.paramdim
) # train- set error mode
trainer.trainUntilConvergence(maxEpochs=MAX_EPOCHS, validationProportion=VALIDATION_PROPORTION) # train
predictedVals = trainer.testOnClassData(dataset=alldata) # set
trainerror = percentError(predictedVals, alldata["class"]) # validation
# prediction
answerlist = predictOnData(test_X)
####################################################################
END_TIME = "-".join(str(datetime.datetime.now()).split(":"))
t1 = time.time()
print("[Total Time]")
print(t1 - t0)
# report
NetworkWriter.writeToFile(n, "NN_model(%s).xml" % START_TIME)
report = NN_Report()
print(report)
with open("NN_result(%s).txt" % START_TIME, "w+") as f:
f.writelines("[predicted y]")
f.write(str([y + 1 for y in answerlist])) # because of Y=Y-1 before
f.write("\n#############################\n")
f.writelines(report)
####################################################
# def NN_Report():
# print("[Time]")
# print(time_info)
# print("---------------------------------------")
# print("#1 Data Description")
if(oldtstError==0):
oldtstError = tstError
if(oldtstError<tstError):
tstErrorCount = tstErrorCount+1
print 'No Improvement, count=%d' % tstErrorCount
print ' Old Validation Error:', oldtstError
print 'Current Validation Error:', tstError
if(oldtstError>tstError):
print 'Improvement made!'
print ' Old Validation Error:', oldtstError
print 'Current Validation Error:', tstError
tstErrorCount=0
oldtstError = tstError
NetworkWriter.writeToFile(LSTMClassificationNet, networkPath)
plotLearningCurve()
trainingTime = time.time()-time_start
trainingTime=np.reshape(trainingTime, (1))
np.savetxt("20LSTMCell/Trainingtime.txt", trainingTime)
####################
# Manual OFFLINE Test
####################
# Test accuraccy on the TEST data
# testingdata = SequenceClassificationDataSet(10,1, nb_classes=8)
#
# for i in range(16000,20000):
# if i%200==0:
return pca
def logistic_regression_classifier(train_x, train_y):
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(penalty='l2')
model.fit(train_x, train_y)
return model
if __name__ == '__main__':
data_file = "mnist.pkl.gz"
train_x, train_y, test_x, test_y = read_data(data_file)
print "create pca...."
pca = pca_process(train_x, 335)
train_z = pca.transform(train_x)
test_z = pca.transform(test_x) #Xtest也需要转化为Ztest
print train_z.shape[1]
print "create model....."
#model = logistic_regression_classifier(train_z, train_y)
#trainpredict = model.predict(train_z)
#testpredict = model.predict(test_z)
#trainaccuracy = metrics.accuracy_score(train_y, trainpredict)
#print 'trainaccuracy: %.2f%%' % (100 * trainaccuracy)
#testaccuracy = metrics.accuracy_score(test_y, testpredict)
#print 'testaccuracy: %.2f%%' % (100 * testaccuracy)
start_time = time.time()
net = detection1.create_network(train_z, train_y, test_z, test_y)
print 'training took %fs!' % (time.time() - start_time)
NetworkWriter.writeToFile(net, 'mynetwork2.xml')
print "Network Player 4 wins over Tactical Player"
count += 1
wincnt += 1
semicount += 1
semiwincnt += 1
else:
outcome = 0
print "Tie Game"
ds = smartPlayer.gameOver(outcome)
if (ds != None):
trainer = BackpropTrainer(nn, dataset=ds)
trainer.trainUntilConvergence(maxEpochs=50)
NetworkWriter.writeToFile(nn, "othelloNetwork4.xml")
#print "Terminate now to safely save"
#time.sleep(3)
perc = float(wincnt) / float(count)
perc *= float(100)
print perc
if semicount == 20:
semiperc = float(semiwincnt) / float(semicount)
semiperc *= float(100)
print "Last ", semicount, " games: ", semiperc, "%"
semicount = 0
semiwincnt = 0
def goClassifer(self, iteration, learningrate, momentum, toFile):
self.TrainingSetEventList[:] = []
print "Iteration Count: " + str(iteration)
#Set up Classicication Data, 4 input, output is a one dim. and 2 possible outcome or two possible classes
trndata = ClassificationDataSet(14, nb_classes=7)
tstdata = ClassificationDataSet(14, nb_classes=7)
SAMPLE_SIZE = 100
AmountPerSpecies = 100
SingleBatIDToAdd = [1, 2, 3, 5, 6] # for single
MultiBatIDToAdd = [10, 11, 12, 14]# for multi
AddBatIDToAdd = [1, 2, 3, 5, 6]
AddSingleMulti = [1, 2, 3, 5, 6,10, 11, 12, 14]
TraningDataAmount = 5000
print "Adding Bat Single Species Events"
minFreq, maxFreq, Durantion, fl1, fl2, fl3, fl4, fl5, fl6, fl7, fl8, fl9, fl10, pixelAverage, target = self.getTrainingSpeciesDistributedData(SingleBatIDToAdd, AmountPerSpecies)
SAMPLE_SIZE = len(minFreq)
for i in range (0, SAMPLE_SIZE):
#trndata.addSample([ minFreq[i], maxFreq[i], Durantion[i], fl1[i], fl2[i], fl3[i], fl4[i], fl5[i], fl6[i], fl7[i], fl8[i], fl9[i], fl10[i], pixelAverage[i] ], [1]) #self.convertID(target[i])
trndata.addSample([ minFreq[i], maxFreq[i], Durantion[i], fl1[i], fl2[i], fl3[i], fl4[i], fl5[i], fl6[i], fl7[i], fl8[i], fl9[i], fl10[i], pixelAverage[i] ], [self.convertIDSingle(target[i])]) #self.convertID(target[i])
#print "Adding Bat Multi Species Events"
#minFreq, maxFreq, Durantion, fl1, fl2, fl3, fl4, fl5, fl6, fl7, fl8, fl9, fl10, pixelAverage, target = self.getTrainingSpeciesDistributedData(MultiBatIDToAdd, AmountPerSpecies)
#SAMPLE_SIZE = len(minFreq)
#for i in range (0, SAMPLE_SIZE):
# trndata.addSample([ minFreq[i], maxFreq[i], Durantion[i], fl1[i], fl2[i], fl3[i], fl4[i], fl5[i], fl6[i], fl7[i], fl8[i], fl9[i], fl10[i], pixelAverage[i] ], [2])
print "Adding noise events"
NoiseID = 8
minFreq, maxFreq, Durantion, fl1, fl2, fl3, fl4, fl5, fl6, fl7, fl8, fl9, fl10, pixelAverage = self.getDistributedData(AmountPerSpecies, NoiseID)
SAMPLE_SIZE = len(minFreq)
for i in range (0, SAMPLE_SIZE):
trndata.addSample([ minFreq[i], maxFreq[i], Durantion[i], fl1[i], fl2[i], fl3[i], fl4[i], fl5[i], fl6[i], fl7[i], fl8[i], fl9[i], fl10[i], pixelAverage[i] ], [self.convertIDSingle(NoiseID)]) #self.convertID(NoiseID)
print "Adding something else events"
SomethingElseID = 9
SEAmount = 20
minFreq, maxFreq, Durantion, fl1, fl2, fl3, fl4, fl5, fl6, fl7, fl8, fl9, fl10, pixelAverage = self.getDistributedData(SEAmount, SomethingElseID)
SAMPLE_SIZE = len(minFreq)
for i in range (0, SAMPLE_SIZE):
trndata.addSample([ minFreq[i], maxFreq[i], Durantion[i], fl1[i], fl2[i], fl3[i], fl4[i], fl5[i], fl6[i], fl7[i], fl8[i], fl9[i], fl10[i], pixelAverage[i] ], [self.convertIDSingle(SomethingElseID)])
# Try to put all multievent in the something else event
print "Adding something else events"
SomethingElseID = 9
BatIDToAdd2 = [10, 11, 12, 14]
minFreq, maxFreq, Durantion, fl1, fl2, fl3, fl4, fl5, fl6, fl7, fl8, fl9, fl10, pixelAverage, target = self.getTrainingSpeciesDistributedData(BatIDToAdd2, SEAmount)
SAMPLE_SIZE = len(minFreq)
for i in range (0, SAMPLE_SIZE):
trndata.addSample([ minFreq[i], maxFreq[i], Durantion[i], fl1[i], fl2[i], fl3[i], fl4[i], fl5[i], fl6[i], fl7[i], fl8[i], fl9[i], fl10[i], pixelAverage[i] ], [self.convertIDSingle(SomethingElseID)])
print "Adding test data"
minFreq, maxFreq, Durantion, fl1, fl2, fl3, fl4, fl5, fl6, fl7, fl8, fl9, fl10, pixelAverage, target = self.getDistrubedTestData(TraningDataAmount, SingleBatIDToAdd)
maxSize = len(minFreq)
for i in range (0, maxSize):
tstdata.addSample([minFreq[i], maxFreq[i], Durantion[i], fl1[i], fl2[i], fl3[i], fl4[i], fl5[i], fl6[i], fl7[i], fl8[i], fl9[i], fl10[i], pixelAverage[i]], [ self.convertIDSingle (target[i]) ])
trndata._convertToOneOfMany( )
tstdata._convertToOneOfMany( )
print "Number of training patterns: ", len(trndata)
print "Input and output dimensions: ", trndata.indim, trndata.outdim
print "Learning Rate: " + str(learningrate)
print "Momentum: " + str(momentum)
#print "First sample (input, target, class):"
#print trndata['input'][0], trndata['target'][0], trndata['class'][0]
#print "200th sample (input, target, class):"
#print trndata['input'][100], trndata['target'][100], trndata['class'][100]
#set up the Feed Forward Network
HiddenNeurons = 10
#learningrate = 0.01
#momentum = 0.1
weightdecay = 0
#from datainterface import ModuleWrapper, ClassificationModuleWrapper
#from sgd import SGD
net = buildNetwork(trndata.indim, HiddenNeurons, trndata.outdim, bias=True, outclass=SoftmaxLayer)
#p0 = net.params.copy()
#provider = ClassificationModuleWrapper(trndata, net, shuffling=False)
#algo = SGD(provider, net.params.copy(), callback=self.printy, learning_rate=learningrate, momentum=momentum)
#print '\n' * 2
#print 'SGD-CE'
#algo.run(1000)
trainer = BackpropTrainer(net, dataset=trndata, momentum=momentum, learningrate=learningrate, verbose=False, weightdecay=weightdecay)
#raw_input("Press Enter to continue...")
print "Training data"
if toFile:
#filename = "InputN" + str(trndata.indim) + "HiddenN" + str(HiddenNeurons) + "OutputN" + str(trndata.outdim) + "Momentum"+ str(momentum) + "LearningRate" + str(learningrate) + "Weightdecay" + str(weightdecay)
root = "/home/anoch/Dropbox/SDU/10 Semester/MSc Project/Data Results/Master/BinarySpeciesTestMSE/"
filename = "ClassifierSpeciesTest_" + str(iteration) +"_MSE_LR_"+str(learningrate) + "_M_"+str(momentum)
folderName = root + "ClassifierSpeciesTest_MSE_LR_"+str(learningrate) + "_M_"+str(momentum)
if not os.path.exists(folderName):
os.makedirs(folderName)
f = open(folderName + "/"+ filename + ".txt", 'w')
value = "Added Bat Species: " + str(AddBatIDToAdd) + "\n"
f.write(value)
value = "Number of bat patterns: " + str(len(trndata)) + "\n"
f.write(value)
value = "Number of noise patterns: " + str(AmountPerSpecies) + "\n"
f.write(value)
value = "Number of patterns per species: " + str(AmountPerSpecies) + "\n"
f.write(value)
value = "Number of test data: " + str(TraningDataAmount) + "\n"
f.write(value)
value = "Input, Hidden and output dimensions: " + str(trndata.indim) + ", " + str(HiddenNeurons) + ", " + str(trndata.outdim) + "\n"
f.write(value)
value = "Momentum: " + str(momentum) + "\n"
f.write(value)
value = "Learning Rate: " + str(learningrate) + "\n"
f.write(value)
value = "Weight Decay: " + str(weightdecay) + "\n"
f.write(value)
f.write("Input Activation function: Linear function\n")
f.write("Hidden Activation function: Sigmoid function\n")
f.write("Output Activation function: Softmax function\n")
maxEpoch = 100
for i in range(0,maxEpoch):
# Train one epoch
trainer.trainEpochs(10)
averageError = trainer.testOnData(dataset=tstdata, verbose=False)
#averageCEE = self.CrossEntropyErrorAveraged(net, tstdata)
#print "Average Cross Entropy Error: " + str(averageCEE)
#print "Mean Square Error: " + str(averageError)
#"""procentError(out, true) return percentage of mismatch between out and target values (lists and arrays accepted) error= ((out - true)/true)*100"""
trnresult = percentError(trainer.testOnClassData(), trndata['class'])
tstresult = percentError(trainer.testOnClassData(dataset=tstdata), tstdata['class'])
print("epoch: %4d" % trainer.totalepochs," train error: %5.2f%%" % trnresult," test error: %5.2f%%" % tstresult)
if tstresult < 27.0:
raw_input("Press Enter to continue...")
break
if toFile:
dataString = str(trainer.totalepochs) + ", " + str(averageError) + ", " + str(trnresult) + ", " + str(tstresult) + "\n"
f.write(dataString)
NetworkWriter.writeToFile(net, "ThirdStageClassifier.xml")
if toFile:
import numpy as np
f.close()
ConfusionMatrix, BatTarget = self.CorrectRatio(trainer.testOnClassData(dataset=tstdata), tstdata['class'])
filename = filename+ "_CR"
result_file = open(folderName + "/"+ filename + ".txt", 'w')
result_file.write("[Species]")
result_file.write(str(BatTarget))
result_file.write(str(ConfusionMatrix))
np.savetxt(folderName + "/"+ filename+".csv", ConfusionMatrix, delimiter=",")
result_file.close()
self.CorrectRatio(trainer.testOnClassData(dataset=tstdata), tstdata['class'])
print "Done training"
def persistData(self, data, name):
with open(self.relPathFromFilename(name), "wb") as f:
pickle.dump(data, f)
if name == NEURAL_NET_DUMP_NAME:
NetworkWriter.writeToFile(data.net, self.relPathFromFilename(name + DATA_DUMP_NN_EXT))
out = rnn.activate(X_train[0])
out = out.argmax(axis=0)
index=0
# evaluate the net in test data
result = []
for x in X_test:
result.append(rnn.activate(x).argmax())
# filling the metrics values
mresult = confusion_matrix(y_test,result)
precision.append((precision_score(y_test,result)))
recall.append((recall_score(y_test,result)))
f1.append((f1_score(y_test,result)))
accuracy.append((accuracy_score(y_test,result)))
# saving the params
NetworkWriter.writeToFile(rnn, 'params.xml')
# printing the results
print ("precision %4.2f,%4.2f"%(np.mean(precision),np.std(precision)))
print ("recall %4.2f,%4.2f"%(np.mean(recall),np.std(recall)))
print ("f1 %4.2f,%4.2f"%(np.mean(f1),np.std(f1)))
print ("accuracy %4.2f,%4.2f"%(np.mean(accuracy),np.std(accuracy)))
df_freq = (df_freq - df_freq.mean()) / df_freq.std()
print("Computing the TFIDF took {} sec of the CPU's time.".format(cpu_time() - t0))
df_binarized = otto.binarize_text_categories(df, class_labels=class_labels, target_column='target')
for c in df_binarized.columns:
df_freq[c] = df_binarized[c]
ds = ann.dataset_from_dataframe(df_freq, normalize=False, delays=[0], inputs=feature_labels, outputs=class_labels,
verbosity=1)
nn = ann.ann_from_ds(ds, N_hidden=[64, 32, 16], hidden_layer_type=['SteepSigmoid', 'SteepSigmoid', 'SteepSigmoid'],
output_layer_type='SteepSigmoid', verbosity=1)
trainer = ann.build_trainer(nn, ds=ds, verbosity=1)
trainer.trainUntilConvergence(maxEpochs=80, validationProportion=0.1, verbose=True)
NetworkWriter.writeToFile(trainer.module, nlp.make_timetag() + '.xml')
# columns = feature_labels + target_labels + ['Predicted--{}'.format(outp) for outp in target_labels]
predicted_prob = pd.np.clip(pd.DataFrame((pd.np.array(trainer.module.activate(i))
for i in df_freq[feature_labels].values),
columns=class_labels), 0, 1)
log_losses = [round(otto.log_loss(ds['target'], otto.normalize_dataframe(predicted_prob).values, method=m).sum(), 3)
for m in 'ksfohe']
print('The log losses for the training set were {}'.format(log_losses))
# df = pd.DataFrame(table, columns=columns, index=df.index[max(delays):])
# ################################################################################
# ########## Predict labels for Validation/Test Set for Kaggle submission
# #
if __name__ == "__main__":
trainingSetsA = linspace(0, 2, num=15)
validationSetsA = linspace(0, 2, num=5)
trainingSetsB = linspace(0, 1, num=15)
validationSetsB = linspace(0, 1, num=5)
a = lambda x: exp(multiply(-1, sqrt(x)))
b = lambda x: arctan(x)
network1 = buildNetwork(
1, 3,
1) if not isfile("./Q2A.xml") else NetworkReader.readFrom("./Q2A.xml")
network2 = buildNetwork(
1, 3,
1) if not isfile("./Q2B.xml") else NetworkReader.readFrom("./Q2B.xml")
if not isfile("./Q2A.xml"):
NetworkWriter.writeToFile(network1, "./Q2A.xml")
if not isfile("./Q2B.xml"):
NetworkWriter.writeToFile(network2, "./Q2B.xml")
trainingSets1 = SupervisedDataSet(1, 1)
trainingSets2 = SupervisedDataSet(1, 1)
[
trainingSets1.addSample(trainingSetsA[i], a(trainingSetsA[i]))
for i in range(len(trainingSetsA))
]
[
trainingSets2.addSample(trainingSetsB[i], b(trainingSetsB[i]))
for i in range(len(trainingSetsB))
]
trainer1 = BackpropTrainer(network1, trainingSets1, learningrate=0.1)
trainer2 = BackpropTrainer(network2, trainingSets2, learningrate=0.1)
trainer1.trainUntilConvergence()
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()
#Model settings
DIM_LAYER = [alldata.indim] + N_NEURAL + [alldata.outdim]
NETWORK_SETTINGS = {"outclass": SoftmaxLayer, "bias":True}
N_HIDDEN_LAYER = len(N_NEURAL)
LEARNING_RATE = 0.005
MOMENTUM = 0.5
WEIGHTDECAY = 0.01
MAX_EPOCHS = 1000
VALIDATION_PROPORTION = 0.1
#####################################
n = buildNetwork(*DIM_LAYER, **NETWORK_SETTINGS)# set Neural Network
trainer = BackpropTrainer(n, dataset=alldata, learningrate=LEARNING_RATE, momentum=MOMENTUM, verbose=True, weightdecay=WEIGHTDECAY)# train- set error mode
trainer.trainUntilConvergence(maxEpochs=MAX_EPOCHS, validationProportion=VALIDATION_PROPORTION)# train
predictedVals = trainer.testOnClassData(dataset=alldata)# set
trainerror = percentError(predictedVals ,alldata['class'])# validation
# prediction
answerlist = predictOnData(test_X)
predictedVals2Raw = [y+1 for y in answerlist]
####################################################################
END_TIME = "-".join(str(datetime.datetime.now()).split(":"))
t1 = time.time()
# report
filename = "NN%sn%s" % (str(NROWS), "x".join([str(nn) for nn in N_NEURAL]))
NetworkWriter.writeToFile(n, filename+".xml")
NN_Report()
def train(self, X, y):
if self._generate:
self._model = train_network(X, y)
NetworkWriter.writeToFile(self._model, self._filename)
else:
self._model = NetworkReader.readFrom(self._filename)
#fnn = buildNetwork( trndata.indim, 100 , trndata.outdim, outclass=SoftmaxLayer )
fnn = buildNetwork( trndata.indim, 100 , trndata.outdim, outclass=SoftmaxLayer )
trainer = BackpropTrainer( fnn, dataset=trndata, momentum=0.1, learningrate=0.01 , verbose=True, weightdecay=0.01)
print 'neural net made.'
print 'training start.'
trainer.trainEpochs (25) # the more recipes there are, the longer each epoch will take
print 'Percent Error on Test dataset: ' , percentError( trainer.testOnClassData (
dataset=tstdata )
, tstdata['class'] )
NetworkWriter.writeToFile(fnn, 'Desktop/food.xml')
print 'time taken: ', time.clock()
# TRAINING RESULTS
#
# dataset size epochs trained percent error(%)
# 500 25 0.8
# 1000 50 0.4 ... it might be a 40% error rate...
# 1000 100 0.4
# 1000 150 0.4 ..still 0.4% lol. though 99.6% accuracy rate sure is pretty high
# 2000 50 0.2 pretty sure there are more decimals but
# 2000 100 0.2 seems like percentError() truncated it
# 3000 50 0.13333333333 ...nvm
# 4000 50 0.1
#
# using all 39774 recipes will probably take days to train
print("Number of test patterns:", len(test_set))
print("Input and output dimensions:", training_set.indim, training_set.outdim)
print("Number of hidden units:", hidden_units)
print()
print("First sample (input, target, class):")
print(training_set['input'][0], training_set['target'][0], training_set['class'][0])
print()
network = buildNetwork(training_set.indim, hidden_units, training_set.outdim, outclass=SoftmaxLayer)
trainer = BackpropTrainer(network, dataset = training_set)
for i in range(1000):
error = trainer.train()
training_result = percentError(trainer.testOnClassData(dataset = training_set), training_set['class']) / 100
test_result = percentError(trainer.testOnClassData(dataset = test_set), test_set['class']) / 100
print("epoch: {0:4d} trnerr: {1:10.8f} tsterr: {2:10.8f} err: {3:10.8f}"
.format(trainer.totalepochs, training_result, test_result, error))
# save network after every 10 epochs
if (i+1) % 10 == 0:
print()
for c in range(test_set.nClasses):
(class_, _) = test_set.splitByClass(c)
sensitivity = (100 - percentError(trainer.testOnClassData(dataset = class_), class_['class']) / 100) if len(class_) > 0 else 0
print("Class {0:2d}: {1:4d} items. Correctly classified: {2:10.8f}".format(c, len(class_), sensitivity))
NetworkWriter.writeToFile(network, xmldir + networkname)
print("Network saved as", networkname)
print()
tanhTrainedNetworks = ["1tanh.xml", "2tanh.xml", "3tanh.xml"]
if readFile:
for f in regTrainedNetworks + tanhTrainedNetworks:
if os.path.isfile(f):
print "reading network",
print f
net = NetworkReader.readFrom(f)
testNetwork(net)
else:
print "file",file,"does not exist"
else:
file = "newNetwork.xml"
train_ds = SupervisedDataSet(28 * 28, 1)
train_images, train_labels = mnist.load_mnist(dataset='training')
for image, label in zip(train_images, train_labels):
train_ds.addSample(ravel(image), label)
if tanh:
net = buildNetwork(train_ds.indim, 98, 98, 49, train_ds.outdim, bias=True, hiddenclass=TanhLayer)
else:
net = buildNetwork(train_ds.indim, 98, train_ds.outdim, bias=True)
trainer = BackpropTrainer(net, train_ds)
print "start training"
for i in range(5):
trainer.trainEpochs(1)
print "epoch: %4d" % trainer.totalepochs
testNetwork(net)
NetworkWriter.writeToFile(net, file)
bar = np.random.randint(9) + 1
for j in range(32, 64):
if inpt[j] < bar:
inpt[j] = 0
else:
inpt[j] = 1
for j in range(64, 96):
inpt[j] = 0
denom = sum(inpt)
if denom == 0:
denom = 1
outpt = sum(inpt[:32]) / denom
ds.addSample(inpt, outpt)
#train network
trainer = BackpropTrainer(net, ds)
trainer.train()
#save network
NetworkWriter.writeToFile(net, 'synapsemon.xml')
NetworkWriter.writeToFile(net, 'synapsemon_copy.xml')
# test the network
# for i in range(10):
# a = np.random.randint(2, size=96)
# for j in range(64, 96):
# a[j] = 0
# outpt = sum(a[:32]) / sum(a)
# print(outpt)
# print(net.activate(a))
from pybrain.structure.modules import SoftmaxLayer
from pybrain.tools.customxml.networkwriter import NetworkWriter
classes = 3
new_data = genfromtxt('NerfGunConradTrainingPhotos/nerfgundata.csv',delimiter=',').transpose()
new_target = genfromtxt('NerfGunConradTrainingPhotos/target.csv',delimiter=',')
numsamples,numvals = new_data.shape #number of datapoints at which an image is analysed, number of images to be analysed
X,y = new_data,new_target#switch to matrix terminology for math portion
ds = ClassificationDataSet(numvals, 1 , nb_classes=classes)#get the numpy array parsed into a classification dataset
for k in range(len(X)):#iterate over each sample
ds.addSample(ravel(X[k]),y[k])#add each sample to the new ClassificationDataSet
tstdatatmp, trndatatmp = ds.splitWithProportion( 0.25 )
tstdata = ClassificationDataSet(numvals, 1, nb_classes=classes)
for n in range(0, tstdatatmp.getLength()):
tstdata.addSample( tstdatatmp.getSample(n)[0], tstdatatmp.getSample(n)[1] )
trndata = ClassificationDataSet(numvals, 1, nb_classes=classes)
for n in range(0, trndatatmp.getLength()):
trndata.addSample( trndatatmp.getSample(n)[0], trndatatmp.getSample(n)[1] )
print(type(ds))
trndata._convertToOneOfMany()
tstdata._convertToOneOfMany()
fnn = buildNetwork(trndata.indim, 64 , trndata.outdim, outclass=SoftmaxLayer)#construct a network with random initial values, first value is the dimension of input vectors, second value is dimension of hidden layers, third value is dimension of output
trainer = BackpropTrainer(fnn, dataset=trndata, momentum=0.1, learningrate=.001 , verbose=True, weightdecay=0.01)
for i in range (0,200):#run for 5000 epochs
print('Percent Error on Test dataset after training for epochs: ', i, ' ' , percentError( trainer.testOnClassData (dataset=tstdata ), tstdata['class'] ))#return percent error
log = open('finallogfile.txt','a')
log.write(str(percentError(trainer.testOnClassData (dataset=tstdata ), tstdata['class'])))
log.write('\n')
log.close()
trainer.trainEpochs(1)#train one at a time to get percent error at each step
NetworkWriter.writeToFile(fnn,'nerfgunnetwork.xml')