Пример #1
0
        [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)

    # 4개의 배치 사이즈로 내부 신경망을 초기화
    learner.init(8)

    # 처음 정확도 출력하기
    print(learner.findAccuracy())

    # 학습
    learner.learn(1000, True)

    # 결과
    print(learner.findAccuracy())
Пример #2
0
import sys
sys.path.append(__file__.replace('\\NN\\example\\ex3_mnist1.py',''))

import NN as nn
import tensor
import pickle

network = nn.NetworkPlus(28*28)
network.append_affine(25).append_batchnormalization().append_relu()
network.append_affine(10).append_softmax()

optimizer = nn.optimizer.SGD()

with open(__file__.replace('ex3_mnist1.py', 'tensor_train_img.pkl'), 'rb') as f:
    train_img = pickle.load(f)
with open(__file__.replace('ex3_mnist1.py', 'tensor_train_label.pkl'), 'rb') as f:
    train_label = pickle.load(f)

train_img.shape = [train_img.shape[0], 28 * 28]

learner = nn.Learner(train_img, train_label, network, optimizer, 100)

learner.init()
learner.learn(10, True)

print(learner.findAccuracy())