def __init__(self,
data=nn.Dataset(tensor.Tensor([1], [1]),
tensor.Tensor([1], [1])),
network=nn.Network(),
optimizer=nn.optimizer.SGD(0.01)):
self.data = data
self.network = network
self.optimizer = optimizer
import NN as nn
import tensor
import time
print('start')
# 다른 프로세스가 중복 실행을 방지
if __name__ == '__main__':
# xor 데이터
x = tensor.Tensor(
[0., 0., 0., 1., 1., 0., 1., 1., 0., 0., 0., 1., 1., 0., 1., 1.],
[8, 2])
y = tensor.Tensor(
[0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 0., 1., 0., 0., 1.],
[8, 2])
data = nn.Dataset(x, y)
# 입력층 뉴련이 2개인 네트워크 생성
network = nn.Network(2)
# 뉴런이 4개인 히든층
network.append_affine(
4).append_batchnormalization().append_shift().append_sigmoid()
# 뉴런이 2개인 출력층
network.append_affine(2).append_softmax()
# 옵티마이저
optimizer = nn.optimizer.SGD(0.01)
# 학습을 위한 객체 생성
learner = nn.Learner(data, network, optimizer)
from __future__ import absolute_import, division, print_function, unicode_literals
import utils
import preprocessing as pre
import numpy as np
import time
import os
import numpy as np
import matplotlib.pyplot as plt
import random
import NN as nn
train_data_dir, train_labels_path = "data/gtsrb-german-traffic-sign/Train", "./data/gtsrb-german-traffic-sign/Train.csv"
train_data_set = nn.Dataset(train_data_dir,
train_labels_path,
data='train',
n_samples=2000)
test_data_dir, test_labels_path = "data/gtsrb-german-traffic-sign/Test", "./data/gtsrb-german-traffic-sign/Test.csv"
test_data_set = nn.Dataset(test_data_dir, test_labels_path, data='test')
reshaped_train_images = []
for image in train_data_set.X:
dst = pre.eqHist(image)
dst = pre.reshape(dst)
reshaped_train_images.append(dst)
train_data_set.X = reshaped_train_images
reshaped_test_images = []
for image in test_data_set.X:
dst = pre.eqHist(image)