def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--tolabel",
help="Preprocess images to create labels (out/tolabel)",
action="store_true",
default=False)
parser.add_argument("--augmentation",
help="Dataset augmentation (pass quantity)",
type=int)
parser.add_argument("--dataset",
help="Dataset name",
type=str,
default=constant.DATASET)
parser.add_argument("--train",
help="Train",
action="store_true",
default=False)
parser.add_argument("--test",
help="Predict",
action="store_true",
default=False)
parser.add_argument("--arch",
help="Neural Network architecture",
type=str,
default=constant.MODEL)
parser.add_argument("--dip",
help="Method for image processing",
type=str,
default=constant.IMG_PROCESSING)
parser.add_argument("--gpu",
help="Enable GPU mode",
action="store_true",
default=False)
args = parser.parse_args()
environment.setup(args)
exist = lambda x: len(x) > 0 and path.exist(path.data(x, mkdir=False))
if (args.tolabel):
generator.tolabel()
elif args.dataset is not None and exist(args.dataset):
if (args.augmentation):
generator.augmentation(args.augmentation)
elif (args.train):
nn.train()
elif (args.test):
nn.test()
else:
print("\n>> Dataset not found\n")
def main():
dataset = "crackconcrete"
train = True
test = False
environment.setup()
exist = lambda x: len(x) > 0 and path.exist(path.data(x, mkdir=False))
if dataset is not None and exist(dataset):
if train:
nn.train()
elif test:
nn.test()
else:
print("\n>> Dataset not found\n")
#!/usr/bin/env/python
# coding: utf-8
from nn import nn
from random import seed
seed(1)
dataset = [[2.7810836, 2.550537003, 0], [1.465489372, 2.362125076, 0],
[3.396561688, 4.400293529, 0], [1.38807019, 1.850220317, 0],
[3.06407232, 3.005305973, 0], [7.627531214, 2.759262235, 1],
[5.332441248, 2.088626775, 1], [6.922596716, 1.77106367, 1],
[8.675418651, -0.242068655, 1], [7.673756466, 3.508563011, 1]]
n_inputs = len(dataset[0]) - 1
n_outputs = len(set([row[-1] for row in dataset]))
nn = nn.NN(n_inputs, 2, n_outputs)
nn.train(dataset, 0.5, 20)
network = nn.get_network()
for layer in network:
print('{}\n'.format(layer))