def main():
print "Select Network Type"
print "1. MLP"
print "2. Radial Basis"
nn_type = raw_input()
nn = NeuralNetwork()
if nn_type == "1":
num_inputs = int(raw_input("Enter number inputs"))
num_hidden = int(raw_input("Enter number hidden layers"))
nn = MLPNN(num_inputs, num_hidden)
elif nn_type == "2":
num_inputs = int(raw_input("Enter number inputs"))
num_centers = int(raw_input("Enter number radial basis functions"))
nn = RBNN(num_inputs, num_centers)
trainer = Trainer(nn)
tester = Tester(nn)
if nn_type == "1":
trainer.trainMLP(str(num_inputs))
tester.test(str(num_inputs))
elif nn_type == "2":
trainer.trainRB(str(num_inputs))
tester.testRB(str(num_inputs))
def main():
ANDperceptron = SigmoidNeuron([random.randint(-2,2),random.randint(-2,2)],random.randint(-2,2),0.1)
train_set_sizes = range(1,1000,10)
presitions = []
tester = Tester()
#entrenar
for train_size in train_set_sizes:
for i in range(train_size):
trainAND(ANDperceptron)
presitions.append(tester.test(ANDperceptron,[[0,0],[0,1],[1,0],[1,1]],[0,0,0,1],ifhalf))
tester.plot(train_set_sizes,presitions, "Neurona Sigmoide entrenada con AND")
def main():
XORperceptron = Perceptron(
[random.randint(-2, 2), random.randint(-2, 2)], random.randint(-2,
2), 0.1)
train_set_sizes = range(1, 1000, 10)
presitions = []
tester = Tester()
#entrenar
for train_size in train_set_sizes:
for i in range(train_size):
trainXOR(XORperceptron)
presitions.append(
tester.test(XORperceptron, [[0, 0], [0, 1], [1, 0], [1, 1]],
[0, 1, 1, 0]))
tester.plot(train_set_sizes, presitions, "Perceptron entrenado con XOR")
def main():
tester = Tester()
line = SimpleLine(1.5,10)
train_set = generateRandomPoints(1000)
valid_set = generateRandomPoints(1000)
results_valid_set = []
for (x,y) in valid_set:
results_valid_set.append(line.isUpperLine(x,y)>0.5)
train_set_sizes = range(1,1000,10) #[10,50,100,250,500,750,1000]
learning_rates = [0.1,0.5,1.5]
for lr in learning_rates:
precisions = []
for train_set_size in train_set_sizes:
perceptron = SigmoidNeuron([2,2],2,lr)
for index in range(train_set_size):
(x,y) = train_set[index]
perceptron.trainLonely([x,y],line.isUpperLine(x,y))
precisions.append(tester.test(perceptron,valid_set,results_valid_set,ifhalf))
tester.plot(train_set_sizes,precisions,"Presiciones por numero de muestras de entrenamiento, lr %.1f" % (lr))
def main():
tester = Tester(fitnessFunction, alphabeth, genSize, populationSize,
genSize)
tester.test()
print "vector original"
print real
opts, args = getopt.getopt(
sys.argv[1:], "c:i:td",
["classifier-dir=", "img-dir=", "teach", "debug"])
except getopt.GetoptError:
print help()
sys.exit(2)
if len(sys.argv) < 3:
print help()
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'):
print help()
sys.exit()
elif opt in ("-c", "--classifier-dir"):
classifier_dir = arg
elif opt in ("-i", "--img-dir"):
img_dir = arg
elif opt in ("-t", "--teach"):
teach = True
elif opt in ("-d", "--debug"):
debug = True
if teach:
teacher = Teacher(img_dir, classifier_dir, debug)
teacher.teach()
else:
tester = Tester(img_dir, classifier_dir, debug)
tester.test()
def upload():
if request.method == 'POST' and 'photo' in request.files:
filename = photos.save(request.files['photo'])
return Tester.test('static/img/' + filename, 'hundred', 'data')
return render_template('upload.html')
# Custom modules
from verbose_print import vprint
from Dataloader import Dataloader
from Trainer import Trainer
from VanillaLSTMTransModel import VanillaLSTMTransModel
#from HierLSTMTransModel import HierLSTMTransModel
#from AttenHierLSTMTransModel import AttenHierLSTMTransModel
from Tester import Tester
# Which model you would like to test? Maybe I should make it a commandline argument..
model_abbr = "V"
# The directory in which generated files reside
directory = "../RUN_" + model_abbr
try:
with open(os.path.join(directory, 'args.pkl'), 'r+') as f:
saved_args = pickle.load(f)
# Don't forget this line below. It tells the model that rebuilding it is not for training.
saved_args.continue_training = False
except Exception as e:
print e
raise ValueError("The specified model is either not trained, damaged,\
or in a wrong path. It should be ../RUN_" + model_abbr +
"/args.pkl")
tester = Tester(args=saved_args, model_choice="V", test_batch_size=32)
tester.test(printout=True, if_testing=True)
print "\nTotal loss = " + str(tester.get_total_loss())
print "\nLoss on each sequence: "
print tester.get_loss_on_each_seq()