강의 3기 AI개론 9차시 — Multinomial

9장 다중 분류 (Mulitnomial classification)

• “부록3 매트플롯립 입문”에서 한글 폰트를 올바르게 출력하기 위한 설치 방법을 설명했다. 설치 방법은 다음과 같다.

# 한글 폰트 설치
 
!sudo apt-get install -y fonts-nanum* | tail -n 1
!sudo fc-cache -fv
!rm -rf ~/.cache/matplotlib

0 upgraded, 0 newly installed, 0 to remove and 19 not upgraded.
/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs
/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs
/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs
/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs
/usr/share/fonts/truetype/nanum: caching, new cache contents: 39 fonts, 0 dirs
/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs
/root/.local/share/fonts: skipping, no such directory
/root/.fonts: skipping, no such directory
/usr/share/fonts/truetype: skipping, looped directory detected
/usr/share/fonts/truetype/humor-sans: skipping, looped directory detected
/usr/share/fonts/truetype/liberation: skipping, looped directory detected
/usr/share/fonts/truetype/nanum: skipping, looped directory detected
/var/cache/fontconfig: cleaning cache directory
/root/.cache/fontconfig: not cleaning non-existent cache directory
/root/.fontconfig: not cleaning non-existent cache directory
fc-cache: succeeded

# 필요 라이브러리 설치
 
!pip install torchviz | tail -n 1
!pip install torchinfo | tail -n 1

Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch->torchviz) (3.0.2)
Requirement already satisfied: torchinfo in /usr/local/lib/python3.11/dist-packages (1.8.0)

• 모든 설치가 끝나면 한글 폰트를 바르게 출력하기 위해 [런타임] -> **[런타임 다시시작]**을 클릭한 다음, 아래 셀부터 코드를 실행해 주십시오.

# 라이브러리 임포트
 
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
 
# 폰트 관련 용도
import matplotlib.font_manager as fm
 
# Colab, Linux
# 나눔 고딕 폰트의 경로 명시
path = '/usr/share/fonts/truetype/nanum/NanumGothic.ttf'
font_name = fm.FontProperties(fname=path, size=10).get_name()
 
# Window
# font_name = "NanumBarunGothic"
 
# Mac
# font_name = "AppleGothic"

# 파이토치 관련 라이브러리
import torch
from torch import nn, optim
import torch.nn.functional as F
from torchviz import make_dot
from torchinfo import summary
 
# Iris dataset
import pandas  as pd
# from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

# 기본 폰트 설정
plt.rcParams['font.family'] = font_name
 
# 기본 폰트 사이즈 변경
plt.rcParams['font.size'] = 14
 
# 기본 그래프 사이즈 변경
plt.rcParams['figure.figsize'] = (6,6)
 
# 기본 그리드 표시
# 필요에 따라 설정할 때는, plt.grid()
plt.rcParams['axes.grid'] = True
plt.rcParams["grid.linestyle"] = ":"
 
# 마이너스 기호 정상 출력
plt.rcParams['axes.unicode_minus'] = False
 
# 넘파이 부동소수점 자릿수 표시
np.set_printoptions(suppress=True, precision=4)

# warning 표시 끄기
import warnings
warnings.simplefilter('ignore')

Iris data

데이터 불러오기

# 학습용 데이터 준비
 
# 라이브러리 임포트
# from sklearn.datasets import load_iris
 
# 데이터 불러오기
iris = load_iris()
print("iris = \n", iris)
print('iris keys = \n', iris.keys())
print("target_names = \n", iris["target_names"])

iris = 
 {'data': array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2],
       [4.7, 3.2, 1.3, 0.2],
       [4.6, 3.1, 1.5, 0.2],
       [5. , 3.6, 1.4, 0.2],
       [5.4, 3.9, 1.7, 0.4],
       [4.6, 3.4, 1.4, 0.3],
       [5. , 3.4, 1.5, 0.2],
       [4.4, 2.9, 1.4, 0.2],
       [4.9, 3.1, 1.5, 0.1],
       [5.4, 3.7, 1.5, 0.2],
       [4.8, 3.4, 1.6, 0.2],
       [4.8, 3. , 1.4, 0.1],
       [4.3, 3. , 1.1, 0.1],
       [5.8, 4. , 1.2, 0.2],
       [5.7, 4.4, 1.5, 0.4],
       [5.4, 3.9, 1.3, 0.4],
       [5.1, 3.5, 1.4, 0.3],
       [5.7, 3.8, 1.7, 0.3],
       [5.1, 3.8, 1.5, 0.3],
       [5.4, 3.4, 1.7, 0.2],
       [5.1, 3.7, 1.5, 0.4],
       [4.6, 3.6, 1. , 0.2],
       [5.1, 3.3, 1.7, 0.5],
       [4.8, 3.4, 1.9, 0.2],
       [5. , 3. , 1.6, 0.2],
       [5. , 3.4, 1.6, 0.4],
       [5.2, 3.5, 1.5, 0.2],
       [5.2, 3.4, 1.4, 0.2],
       [4.7, 3.2, 1.6, 0.2],
       [4.8, 3.1, 1.6, 0.2],
       [5.4, 3.4, 1.5, 0.4],
       [5.2, 4.1, 1.5, 0.1],
       [5.5, 4.2, 1.4, 0.2],
       [4.9, 3.1, 1.5, 0.2],
       [5. , 3.2, 1.2, 0.2],
       [5.5, 3.5, 1.3, 0.2],
       [4.9, 3.6, 1.4, 0.1],
       [4.4, 3. , 1.3, 0.2],
       [5.1, 3.4, 1.5, 0.2],
       [5. , 3.5, 1.3, 0.3],
       [4.5, 2.3, 1.3, 0.3],
       [4.4, 3.2, 1.3, 0.2],
       [5. , 3.5, 1.6, 0.6],
       [5.1, 3.8, 1.9, 0.4],
       [4.8, 3. , 1.4, 0.3],
       [5.1, 3.8, 1.6, 0.2],
       [4.6, 3.2, 1.4, 0.2],
       [5.3, 3.7, 1.5, 0.2],
       [5. , 3.3, 1.4, 0.2],
       [7. , 3.2, 4.7, 1.4],
       [6.4, 3.2, 4.5, 1.5],
       [6.9, 3.1, 4.9, 1.5],
       [5.5, 2.3, 4. , 1.3],
       [6.5, 2.8, 4.6, 1.5],
       [5.7, 2.8, 4.5, 1.3],
       [6.3, 3.3, 4.7, 1.6],
       [4.9, 2.4, 3.3, 1. ],
       [6.6, 2.9, 4.6, 1.3],
       [5.2, 2.7, 3.9, 1.4],
       [5. , 2. , 3.5, 1. ],
       [5.9, 3. , 4.2, 1.5],
       [6. , 2.2, 4. , 1. ],
       [6.1, 2.9, 4.7, 1.4],
       [5.6, 2.9, 3.6, 1.3],
       [6.7, 3.1, 4.4, 1.4],
       [5.6, 3. , 4.5, 1.5],
       [5.8, 2.7, 4.1, 1. ],
       [6.2, 2.2, 4.5, 1.5],
       [5.6, 2.5, 3.9, 1.1],
       [5.9, 3.2, 4.8, 1.8],
       [6.1, 2.8, 4. , 1.3],
       [6.3, 2.5, 4.9, 1.5],
       [6.1, 2.8, 4.7, 1.2],
       [6.4, 2.9, 4.3, 1.3],
       [6.6, 3. , 4.4, 1.4],
       [6.8, 2.8, 4.8, 1.4],
       [6.7, 3. , 5. , 1.7],
       [6. , 2.9, 4.5, 1.5],
       [5.7, 2.6, 3.5, 1. ],
       [5.5, 2.4, 3.8, 1.1],
       [5.5, 2.4, 3.7, 1. ],
       [5.8, 2.7, 3.9, 1.2],
       [6. , 2.7, 5.1, 1.6],
       [5.4, 3. , 4.5, 1.5],
       [6. , 3.4, 4.5, 1.6],
       [6.7, 3.1, 4.7, 1.5],
       [6.3, 2.3, 4.4, 1.3],
       [5.6, 3. , 4.1, 1.3],
       [5.5, 2.5, 4. , 1.3],
       [5.5, 2.6, 4.4, 1.2],
       [6.1, 3. , 4.6, 1.4],
       [5.8, 2.6, 4. , 1.2],
       [5. , 2.3, 3.3, 1. ],
       [5.6, 2.7, 4.2, 1.3],
       [5.7, 3. , 4.2, 1.2],
       [5.7, 2.9, 4.2, 1.3],
       [6.2, 2.9, 4.3, 1.3],
       [5.1, 2.5, 3. , 1.1],
       [5.7, 2.8, 4.1, 1.3],
       [6.3, 3.3, 6. , 2.5],
       [5.8, 2.7, 5.1, 1.9],
       [7.1, 3. , 5.9, 2.1],
       [6.3, 2.9, 5.6, 1.8],
       [6.5, 3. , 5.8, 2.2],
       [7.6, 3. , 6.6, 2.1],
       [4.9, 2.5, 4.5, 1.7],
       [7.3, 2.9, 6.3, 1.8],
       [6.7, 2.5, 5.8, 1.8],
       [7.2, 3.6, 6.1, 2.5],
       [6.5, 3.2, 5.1, 2. ],
       [6.4, 2.7, 5.3, 1.9],
       [6.8, 3. , 5.5, 2.1],
       [5.7, 2.5, 5. , 2. ],
       [5.8, 2.8, 5.1, 2.4],
       [6.4, 3.2, 5.3, 2.3],
       [6.5, 3. , 5.5, 1.8],
       [7.7, 3.8, 6.7, 2.2],
       [7.7, 2.6, 6.9, 2.3],
       [6. , 2.2, 5. , 1.5],
       [6.9, 3.2, 5.7, 2.3],
       [5.6, 2.8, 4.9, 2. ],
       [7.7, 2.8, 6.7, 2. ],
       [6.3, 2.7, 4.9, 1.8],
       [6.7, 3.3, 5.7, 2.1],
       [7.2, 3.2, 6. , 1.8],
       [6.2, 2.8, 4.8, 1.8],
       [6.1, 3. , 4.9, 1.8],
       [6.4, 2.8, 5.6, 2.1],
       [7.2, 3. , 5.8, 1.6],
       [7.4, 2.8, 6.1, 1.9],
       [7.9, 3.8, 6.4, 2. ],
       [6.4, 2.8, 5.6, 2.2],
       [6.3, 2.8, 5.1, 1.5],
       [6.1, 2.6, 5.6, 1.4],
       [7.7, 3. , 6.1, 2.3],
       [6.3, 3.4, 5.6, 2.4],
       [6.4, 3.1, 5.5, 1.8],
       [6. , 3. , 4.8, 1.8],
       [6.9, 3.1, 5.4, 2.1],
       [6.7, 3.1, 5.6, 2.4],
       [6.9, 3.1, 5.1, 2.3],
       [5.8, 2.7, 5.1, 1.9],
       [6.8, 3.2, 5.9, 2.3],
       [6.7, 3.3, 5.7, 2.5],
       [6.7, 3. , 5.2, 2.3],
       [6.3, 2.5, 5. , 1.9],
       [6.5, 3. , 5.2, 2. ],
       [6.2, 3.4, 5.4, 2.3],
       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'frame': None, 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\n\nIris plants dataset\n--------------------\n\n**Data Set Characteristics:**\n\n:Number of Instances: 150 (50 in each of three classes)\n:Number of Attributes: 4 numeric, predictive attributes and the class\n:Attribute Information:\n    - sepal length in cm\n    - sepal width in cm\n    - petal length in cm\n    - petal width in cm\n    - class:\n            - Iris-Setosa\n            - Iris-Versicolour\n            - Iris-Virginica\n\n:Summary Statistics:\n\n============== ==== ==== ======= ===== ====================\n                Min  Max   Mean    SD   Class Correlation\n============== ==== ==== ======= ===== ====================\nsepal length:   4.3  7.9   5.84   0.83    0.7826\nsepal width:    2.0  4.4   3.05   0.43   -0.4194\npetal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\npetal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n============== ==== ==== ======= ===== ====================\n\n:Missing Attribute Values: None\n:Class Distribution: 33.3% for each of 3 classes.\n:Creator: R.A. Fisher\n:Donor: Michael Marshall (MARSHALL%[email protected])\n:Date: July, 1988\n\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\nfrom Fisher\'s paper. Note that it\'s the same as in R, but not as in the UCI\nMachine Learning Repository, which has two wrong data points.\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher\'s paper is a classic in the field and\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\n.. dropdown:: References\n\n  - Fisher, R.A. "The use of multiple measurements in taxonomic problems"\n    Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n    Mathematical Statistics" (John Wiley, NY, 1950).\n  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n  - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n    Structure and Classification Rule for Recognition in Partially Exposed\n    Environments".  IEEE Transactions on Pattern Analysis and Machine\n    Intelligence, Vol. PAMI-2, No. 1, 67-71.\n  - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions\n    on Information Theory, May 1972, 431-433.\n  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II\n    conceptual clustering system finds 3 classes in the data.\n  - Many, many more ...\n', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
iris keys = 
 dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])
target_names = 
 ['setosa' 'versicolor' 'virginica']

# 입력 데이터와 정답 데이터
x_org, y_org = iris.data, iris.target
 
# 결과 확인
print('원본 데이터 타입 :', type(x_org), type(y_org))
print('원본 데이터 크기 :', x_org.shape, y_org.shape)

원본 데이터 타입 : <class 'numpy.ndarray'> <class 'numpy.ndarray'>
원본 데이터 크기 : (150, 4) (150,)

데이터 추출

# 입력 데이터로 sepal(꽃받침) length(0)와 petal(꽃잎) length(2)를 추출
x_select = x_org[:,[0,2]]
 
# 결과 확인
print('원본 데이터', x_select.shape, y_org.shape)

원본 데이터 (150, 2) (150,)

훈련 데이터와 검증 데이터 분할

# 훈련 데이터와 검증 데이터로 분할(셔플도 동시에 실시함)
from sklearn.model_selection import train_test_split
 
x_train, x_test, y_train, y_test = train_test_split(
    x_select, y_org, train_size=75, test_size=75,
    random_state=123)
 
print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)

(75, 2) (75, 2) (75,) (75,)

훈련 데이터의 산포도 출력

# 데이터를 정답별로 분할
 
x_t0 = x_train[y_train == 0]
x_t1 = x_train[y_train == 1]
x_t2 = x_train[y_train == 2]

# 산포도 출력
 
plt.scatter(x_t0[:,0], x_t0[:,1], marker='x', c='k', s=50, label='0 (setosa)')
plt.scatter(x_t1[:,0], x_t1[:,1], marker='o', c='b', s=50, label='1 (versicolor)')
plt.scatter(x_t2[:,0], x_t2[:,1], marker='^', c='r', s=50, label='2 (virginica)')
plt.xlabel('sepal_length')
plt.ylabel('petal_length')
plt.legend()
plt.show()

모델 정의

# 학습용 파라미터 설정
 
# 입력 차원수
n_input = x_train.shape[1]
 
# 출력 차원수
# 분류 클래스 수, 여기서는 3
n_output = len(list(set(y_train)))
 
# 결과 확인
print(f'n_input: {n_input}  n_output: {n_output}')

n_input: 2  n_output: 3

# 모델 정의
# 2입력 3출력 로지스틱 회귀 모델
 
class Net(nn.Module):
    def __init__(self, n_input, n_output):
        super().__init__()
        self.l1 = nn.Linear(n_input, n_output)
 
        # 초깃값을 모두 1로 함
        # "딥러닝을 위한 수학"과 조건을 맞추기 위한 목적
        self.l1.weight.data.fill_(1.0)
        self.l1.bias.data.fill_(1.0)
 
    def forward(self, x):
        x1 = self.l1(x)
        return x1
 
# 인스턴스 생성
net = Net(n_input, n_output)
# list(net.parameters())

모델 확인

# 모델 내부 파라미터 확인
# l1.weight는 행렬, l1.bias는 벡터
 
for parameter in net.named_parameters():
    print(parameter)
 

('l1.weight', Parameter containing:
tensor([[1., 1.],
        [1., 1.],
        [1., 1.]], requires_grad=True))
('l1.bias', Parameter containing:
tensor([1., 1., 1.], requires_grad=True))

# 모델 개요 표시 1
 
print(net)

Net(
  (l1): Linear(in_features=2, out_features=3, bias=True)
)

# 모델 개요 표시 2
 
summary(net, (2,), device = 'cpu')

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
Net                                      [3]                       --
├─Linear: 1-1                            [3]                       9
==========================================================================================
Total params: 9
Trainable params: 9
Non-trainable params: 0
Total mult-adds (Units.MEGABYTES): 0.00
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.00
==========================================================================================

최적화 알고리즘과 손실 함수의 정의

# 손실 함수： 교차 엔트로피 함수
criterion = nn.CrossEntropyLoss()
 
# 학습률
lr = 0.01
 
# 최적화 함수: 경사 하강법
optimizer = optim.SGD(net.parameters(), lr=lr)

경사 하강법

# 입력 데이터 x_train과 정답 데이터 y_train의 텐서 변수화
 
inputs = torch.tensor(x_train).float()
labels = torch.tensor(y_train).long()
 
# 검증 데이터의 텐서 변수화
 
inputs_test = torch.tensor(x_test).float()
labels_test = torch.tensor(y_test).long()

손실의 계산 그래프 시각화

# 예측 계산
outputs = net(inputs)
 
# 손실 계산
loss = criterion(outputs, labels)
 
# 손실의 계산 그래프 시각화
g = make_dot(loss, params=dict(net.named_parameters()))
display(g)

예측 라벨을 얻는 방법

# torch.max 함수 호출
# 2번째 인수는 축을 의미함. 1이면 행별로 집계
print(torch.max(outputs, 1))
# print(torch.argmax(outputs, 1))
 
# 예측 라벨 리스트를 취득
torch.max(outputs, 1)[1]

torch.return_types.max(
values=tensor([12.0000, 12.7000,  7.6000, 13.0000, 12.3000,  7.6000,  7.3000, 11.1000,
        12.1000, 13.3000,  8.0000,  7.0000, 10.3000,  7.6000, 11.7000, 13.3000,
         7.4000, 13.5000,  8.2000,  8.4000, 12.7000,  6.6000,  7.9000, 12.2000,
        14.6000, 12.0000, 10.2000, 10.5000,  7.1000,  7.3000, 12.6000, 12.7000,
         7.4000,  7.7000, 10.8000, 11.5000, 11.5000, 14.0000, 12.8000, 10.8000,
        10.8000, 15.2000,  7.5000,  7.8000, 11.1000, 13.6000, 12.9000, 14.2000,
        12.7000,  7.6000, 10.9000,  7.0000, 10.9000, 11.2000,  7.4000, 11.7000,
        13.3000, 11.5000, 13.4000, 12.7000,  7.7000, 11.8000,  7.0000, 12.6000,
        11.7000, 10.9000,  9.2000, 12.2000, 10.4000, 12.1000,  7.5000,  9.1000,
        11.1000, 12.0000, 14.3000], grad_fn=<MaxBackward0>),
indices=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0]))





tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0])

반복 계산

# 학습률
lr = 0.01
 
# 초기화
net = Net(n_input, n_output)
 
# 손실 함수： 교차 엔트로피 함수
criterion = nn.CrossEntropyLoss()
 
# 최적화 함수: 경사 하강법
optimizer = optim.SGD(net.parameters(), lr=lr)
 
# 반복 횟수
num_epochs = 10000
 
# 평가 결과 기록
history = np.zeros((0,5))

# 반복 계산 메인 루프
 
for epoch in range(num_epochs):
 
    # 훈련 페이즈
 
    # 경사 초기화
    optimizer.zero_grad()
 
    # 예측 계산
    outputs = net(inputs)
 
    # 손실 계산
    loss = criterion(outputs, labels)
 
    # 경사 계산
    loss.backward()
 
    # 파라미터 수정
    optimizer.step()
 
    # 예측 라벨 산출
    predicted = torch.max(outputs, 1)[1]
 
    # 손실과 정확도 계산
    train_loss = loss.item()
    train_acc = (predicted == labels).sum()  / len(labels)
 
    # 예측 페이즈
 
    # 예측 계산
    outputs_test = net(inputs_test)
 
    # 손실 계산
    loss_test = criterion(outputs_test, labels_test)
 
    # 예측 라벨 산출
    predicted_test = torch.max(outputs_test, 1)[1]
 
    # 손실과 정확도 계산
    val_loss =  loss_test.item()
    val_acc =  (predicted_test == labels_test).sum() / len(labels_test)
 
    if ((epoch) % 10 == 0):
        print (f'Epoch [{epoch}/{num_epochs}], loss: {train_loss:.5f} acc: {train_acc:.5f} val_loss: {val_loss:.5f}, val_acc: {val_acc:.5f}')
        item = np.array([epoch, train_loss, train_acc, val_loss, val_acc])
        history = np.vstack((history, item))

Epoch [0/10000], loss: 1.09861 acc: 0.30667 val_loss: 1.09263, val_acc: 0.26667
Epoch [10/10000], loss: 1.03580 acc: 0.40000 val_loss: 1.06403, val_acc: 0.26667
Epoch [20/10000], loss: 1.00477 acc: 0.40000 val_loss: 1.03347, val_acc: 0.26667
Epoch [30/10000], loss: 0.97672 acc: 0.40000 val_loss: 1.00264, val_acc: 0.26667
Epoch [40/10000], loss: 0.95057 acc: 0.41333 val_loss: 0.97351, val_acc: 0.26667
Epoch [50/10000], loss: 0.92616 acc: 0.48000 val_loss: 0.94631, val_acc: 0.38667
Epoch [60/10000], loss: 0.90338 acc: 0.69333 val_loss: 0.92098, val_acc: 0.56000
Epoch [70/10000], loss: 0.88212 acc: 0.70667 val_loss: 0.89740, val_acc: 0.60000
Epoch [80/10000], loss: 0.86227 acc: 0.70667 val_loss: 0.87545, val_acc: 0.61333
Epoch [90/10000], loss: 0.84373 acc: 0.70667 val_loss: 0.85500, val_acc: 0.62667
Epoch [100/10000], loss: 0.82640 acc: 0.70667 val_loss: 0.83594, val_acc: 0.62667
Epoch [110/10000], loss: 0.81019 acc: 0.72000 val_loss: 0.81815, val_acc: 0.62667
Epoch [120/10000], loss: 0.79500 acc: 0.72000 val_loss: 0.80153, val_acc: 0.62667
Epoch [130/10000], loss: 0.78077 acc: 0.73333 val_loss: 0.78599, val_acc: 0.62667
Epoch [140/10000], loss: 0.76741 acc: 0.74667 val_loss: 0.77142, val_acc: 0.64000
Epoch [150/10000], loss: 0.75485 acc: 0.74667 val_loss: 0.75777, val_acc: 0.65333
Epoch [160/10000], loss: 0.74303 acc: 0.74667 val_loss: 0.74494, val_acc: 0.68000
Epoch [170/10000], loss: 0.73189 acc: 0.76000 val_loss: 0.73288, val_acc: 0.70667
Epoch [180/10000], loss: 0.72138 acc: 0.77333 val_loss: 0.72151, val_acc: 0.76000
Epoch [190/10000], loss: 0.71145 acc: 0.82667 val_loss: 0.71079, val_acc: 0.78667
Epoch [200/10000], loss: 0.70205 acc: 0.82667 val_loss: 0.70067, val_acc: 0.78667
Epoch [210/10000], loss: 0.69315 acc: 0.84000 val_loss: 0.69109, val_acc: 0.80000
Epoch [220/10000], loss: 0.68470 acc: 0.84000 val_loss: 0.68202, val_acc: 0.80000
Epoch [230/10000], loss: 0.67667 acc: 0.86667 val_loss: 0.67341, val_acc: 0.81333
Epoch [240/10000], loss: 0.66904 acc: 0.86667 val_loss: 0.66524, val_acc: 0.81333
Epoch [250/10000], loss: 0.66176 acc: 0.86667 val_loss: 0.65746, val_acc: 0.82667
Epoch [260/10000], loss: 0.65483 acc: 0.85333 val_loss: 0.65005, val_acc: 0.82667
Epoch [270/10000], loss: 0.64820 acc: 0.85333 val_loss: 0.64299, val_acc: 0.82667
Epoch [280/10000], loss: 0.64187 acc: 0.85333 val_loss: 0.63625, val_acc: 0.82667
Epoch [290/10000], loss: 0.63581 acc: 0.86667 val_loss: 0.62980, val_acc: 0.82667
Epoch [300/10000], loss: 0.63000 acc: 0.88000 val_loss: 0.62363, val_acc: 0.82667
Epoch [310/10000], loss: 0.62443 acc: 0.89333 val_loss: 0.61772, val_acc: 0.82667
Epoch [320/10000], loss: 0.61909 acc: 0.89333 val_loss: 0.61205, val_acc: 0.82667
Epoch [330/10000], loss: 0.61394 acc: 0.89333 val_loss: 0.60661, val_acc: 0.82667
Epoch [340/10000], loss: 0.60900 acc: 0.89333 val_loss: 0.60138, val_acc: 0.84000
Epoch [350/10000], loss: 0.60423 acc: 0.89333 val_loss: 0.59635, val_acc: 0.84000
Epoch [360/10000], loss: 0.59964 acc: 0.90667 val_loss: 0.59150, val_acc: 0.85333
Epoch [370/10000], loss: 0.59521 acc: 0.92000 val_loss: 0.58683, val_acc: 0.86667
Epoch [380/10000], loss: 0.59093 acc: 0.92000 val_loss: 0.58232, val_acc: 0.86667
Epoch [390/10000], loss: 0.58679 acc: 0.92000 val_loss: 0.57797, val_acc: 0.86667
Epoch [400/10000], loss: 0.58279 acc: 0.92000 val_loss: 0.57377, val_acc: 0.86667
Epoch [410/10000], loss: 0.57891 acc: 0.92000 val_loss: 0.56970, val_acc: 0.86667
Epoch [420/10000], loss: 0.57516 acc: 0.92000 val_loss: 0.56576, val_acc: 0.86667
Epoch [430/10000], loss: 0.57152 acc: 0.90667 val_loss: 0.56195, val_acc: 0.86667
Epoch [440/10000], loss: 0.56799 acc: 0.90667 val_loss: 0.55825, val_acc: 0.86667
Epoch [450/10000], loss: 0.56456 acc: 0.90667 val_loss: 0.55466, val_acc: 0.86667
Epoch [460/10000], loss: 0.56123 acc: 0.90667 val_loss: 0.55118, val_acc: 0.86667
Epoch [470/10000], loss: 0.55799 acc: 0.90667 val_loss: 0.54779, val_acc: 0.88000
Epoch [480/10000], loss: 0.55484 acc: 0.90667 val_loss: 0.54451, val_acc: 0.88000
Epoch [490/10000], loss: 0.55177 acc: 0.90667 val_loss: 0.54131, val_acc: 0.88000
Epoch [500/10000], loss: 0.54878 acc: 0.90667 val_loss: 0.53819, val_acc: 0.88000
Epoch [510/10000], loss: 0.54587 acc: 0.90667 val_loss: 0.53516, val_acc: 0.88000
Epoch [520/10000], loss: 0.54303 acc: 0.90667 val_loss: 0.53221, val_acc: 0.88000
Epoch [530/10000], loss: 0.54026 acc: 0.90667 val_loss: 0.52933, val_acc: 0.88000
Epoch [540/10000], loss: 0.53755 acc: 0.90667 val_loss: 0.52652, val_acc: 0.88000
Epoch [550/10000], loss: 0.53491 acc: 0.90667 val_loss: 0.52377, val_acc: 0.88000
Epoch [560/10000], loss: 0.53233 acc: 0.90667 val_loss: 0.52110, val_acc: 0.88000
Epoch [570/10000], loss: 0.52981 acc: 0.90667 val_loss: 0.51848, val_acc: 0.88000
Epoch [580/10000], loss: 0.52734 acc: 0.90667 val_loss: 0.51592, val_acc: 0.88000
Epoch [590/10000], loss: 0.52493 acc: 0.90667 val_loss: 0.51342, val_acc: 0.88000
Epoch [600/10000], loss: 0.52256 acc: 0.90667 val_loss: 0.51098, val_acc: 0.88000
Epoch [610/10000], loss: 0.52025 acc: 0.90667 val_loss: 0.50859, val_acc: 0.88000
Epoch [620/10000], loss: 0.51798 acc: 0.90667 val_loss: 0.50624, val_acc: 0.88000
Epoch [630/10000], loss: 0.51576 acc: 0.90667 val_loss: 0.50395, val_acc: 0.88000
Epoch [640/10000], loss: 0.51358 acc: 0.90667 val_loss: 0.50170, val_acc: 0.88000
Epoch [650/10000], loss: 0.51144 acc: 0.90667 val_loss: 0.49949, val_acc: 0.88000
Epoch [660/10000], loss: 0.50934 acc: 0.90667 val_loss: 0.49733, val_acc: 0.89333
Epoch [670/10000], loss: 0.50728 acc: 0.90667 val_loss: 0.49521, val_acc: 0.90667
Epoch [680/10000], loss: 0.50526 acc: 0.90667 val_loss: 0.49313, val_acc: 0.90667
Epoch [690/10000], loss: 0.50328 acc: 0.90667 val_loss: 0.49109, val_acc: 0.90667
Epoch [700/10000], loss: 0.50133 acc: 0.90667 val_loss: 0.48908, val_acc: 0.90667
Epoch [710/10000], loss: 0.49941 acc: 0.90667 val_loss: 0.48711, val_acc: 0.90667
Epoch [720/10000], loss: 0.49752 acc: 0.90667 val_loss: 0.48517, val_acc: 0.90667
Epoch [730/10000], loss: 0.49567 acc: 0.90667 val_loss: 0.48327, val_acc: 0.90667
Epoch [740/10000], loss: 0.49385 acc: 0.90667 val_loss: 0.48140, val_acc: 0.90667
Epoch [750/10000], loss: 0.49205 acc: 0.90667 val_loss: 0.47956, val_acc: 0.90667
Epoch [760/10000], loss: 0.49029 acc: 0.90667 val_loss: 0.47775, val_acc: 0.90667
Epoch [770/10000], loss: 0.48855 acc: 0.90667 val_loss: 0.47597, val_acc: 0.90667
Epoch [780/10000], loss: 0.48684 acc: 0.90667 val_loss: 0.47422, val_acc: 0.90667
Epoch [790/10000], loss: 0.48515 acc: 0.89333 val_loss: 0.47249, val_acc: 0.92000
Epoch [800/10000], loss: 0.48349 acc: 0.89333 val_loss: 0.47079, val_acc: 0.92000
Epoch [810/10000], loss: 0.48186 acc: 0.89333 val_loss: 0.46912, val_acc: 0.92000
Epoch [820/10000], loss: 0.48024 acc: 0.89333 val_loss: 0.46747, val_acc: 0.92000
Epoch [830/10000], loss: 0.47865 acc: 0.89333 val_loss: 0.46585, val_acc: 0.92000
Epoch [840/10000], loss: 0.47709 acc: 0.89333 val_loss: 0.46425, val_acc: 0.92000
Epoch [850/10000], loss: 0.47554 acc: 0.89333 val_loss: 0.46267, val_acc: 0.92000
Epoch [860/10000], loss: 0.47402 acc: 0.89333 val_loss: 0.46111, val_acc: 0.92000
Epoch [870/10000], loss: 0.47251 acc: 0.89333 val_loss: 0.45958, val_acc: 0.92000
Epoch [880/10000], loss: 0.47103 acc: 0.89333 val_loss: 0.45806, val_acc: 0.92000
Epoch [890/10000], loss: 0.46956 acc: 0.89333 val_loss: 0.45657, val_acc: 0.92000
Epoch [900/10000], loss: 0.46811 acc: 0.89333 val_loss: 0.45509, val_acc: 0.92000
Epoch [910/10000], loss: 0.46668 acc: 0.89333 val_loss: 0.45364, val_acc: 0.92000
Epoch [920/10000], loss: 0.46527 acc: 0.89333 val_loss: 0.45220, val_acc: 0.92000
Epoch [930/10000], loss: 0.46388 acc: 0.89333 val_loss: 0.45078, val_acc: 0.92000
Epoch [940/10000], loss: 0.46250 acc: 0.89333 val_loss: 0.44938, val_acc: 0.92000
Epoch [950/10000], loss: 0.46114 acc: 0.89333 val_loss: 0.44800, val_acc: 0.92000
Epoch [960/10000], loss: 0.45980 acc: 0.89333 val_loss: 0.44663, val_acc: 0.92000
Epoch [970/10000], loss: 0.45847 acc: 0.89333 val_loss: 0.44528, val_acc: 0.92000
Epoch [980/10000], loss: 0.45716 acc: 0.89333 val_loss: 0.44395, val_acc: 0.92000
Epoch [990/10000], loss: 0.45586 acc: 0.89333 val_loss: 0.44263, val_acc: 0.92000
Epoch [1000/10000], loss: 0.45458 acc: 0.89333 val_loss: 0.44133, val_acc: 0.92000
Epoch [1010/10000], loss: 0.45331 acc: 0.89333 val_loss: 0.44004, val_acc: 0.92000
Epoch [1020/10000], loss: 0.45205 acc: 0.89333 val_loss: 0.43877, val_acc: 0.92000
Epoch [1030/10000], loss: 0.45081 acc: 0.89333 val_loss: 0.43751, val_acc: 0.92000
Epoch [1040/10000], loss: 0.44958 acc: 0.89333 val_loss: 0.43626, val_acc: 0.92000
Epoch [1050/10000], loss: 0.44836 acc: 0.89333 val_loss: 0.43503, val_acc: 0.92000
Epoch [1060/10000], loss: 0.44716 acc: 0.89333 val_loss: 0.43381, val_acc: 0.92000
Epoch [1070/10000], loss: 0.44597 acc: 0.89333 val_loss: 0.43260, val_acc: 0.92000
Epoch [1080/10000], loss: 0.44479 acc: 0.89333 val_loss: 0.43141, val_acc: 0.92000
Epoch [1090/10000], loss: 0.44363 acc: 0.89333 val_loss: 0.43023, val_acc: 0.92000
Epoch [1100/10000], loss: 0.44247 acc: 0.89333 val_loss: 0.42906, val_acc: 0.92000
Epoch [1110/10000], loss: 0.44133 acc: 0.89333 val_loss: 0.42790, val_acc: 0.92000
Epoch [1120/10000], loss: 0.44020 acc: 0.89333 val_loss: 0.42676, val_acc: 0.92000
Epoch [1130/10000], loss: 0.43908 acc: 0.89333 val_loss: 0.42562, val_acc: 0.92000
Epoch [1140/10000], loss: 0.43797 acc: 0.89333 val_loss: 0.42450, val_acc: 0.92000
Epoch [1150/10000], loss: 0.43687 acc: 0.89333 val_loss: 0.42339, val_acc: 0.92000
Epoch [1160/10000], loss: 0.43578 acc: 0.89333 val_loss: 0.42229, val_acc: 0.92000
Epoch [1170/10000], loss: 0.43470 acc: 0.89333 val_loss: 0.42120, val_acc: 0.92000
Epoch [1180/10000], loss: 0.43363 acc: 0.89333 val_loss: 0.42012, val_acc: 0.92000
Epoch [1190/10000], loss: 0.43257 acc: 0.89333 val_loss: 0.41905, val_acc: 0.92000
Epoch [1200/10000], loss: 0.43152 acc: 0.89333 val_loss: 0.41799, val_acc: 0.92000
Epoch [1210/10000], loss: 0.43048 acc: 0.89333 val_loss: 0.41694, val_acc: 0.92000
Epoch [1220/10000], loss: 0.42945 acc: 0.89333 val_loss: 0.41590, val_acc: 0.92000
Epoch [1230/10000], loss: 0.42843 acc: 0.89333 val_loss: 0.41487, val_acc: 0.92000
Epoch [1240/10000], loss: 0.42742 acc: 0.89333 val_loss: 0.41384, val_acc: 0.92000
Epoch [1250/10000], loss: 0.42641 acc: 0.89333 val_loss: 0.41283, val_acc: 0.92000
Epoch [1260/10000], loss: 0.42542 acc: 0.89333 val_loss: 0.41182, val_acc: 0.92000
Epoch [1270/10000], loss: 0.42443 acc: 0.89333 val_loss: 0.41083, val_acc: 0.92000
Epoch [1280/10000], loss: 0.42345 acc: 0.89333 val_loss: 0.40984, val_acc: 0.92000
Epoch [1290/10000], loss: 0.42248 acc: 0.89333 val_loss: 0.40886, val_acc: 0.92000
Epoch [1300/10000], loss: 0.42152 acc: 0.89333 val_loss: 0.40789, val_acc: 0.92000
Epoch [1310/10000], loss: 0.42056 acc: 0.89333 val_loss: 0.40693, val_acc: 0.92000
Epoch [1320/10000], loss: 0.41962 acc: 0.89333 val_loss: 0.40598, val_acc: 0.92000
Epoch [1330/10000], loss: 0.41868 acc: 0.89333 val_loss: 0.40503, val_acc: 0.93333
Epoch [1340/10000], loss: 0.41775 acc: 0.89333 val_loss: 0.40409, val_acc: 0.93333
Epoch [1350/10000], loss: 0.41682 acc: 0.89333 val_loss: 0.40316, val_acc: 0.93333
Epoch [1360/10000], loss: 0.41590 acc: 0.89333 val_loss: 0.40224, val_acc: 0.93333
Epoch [1370/10000], loss: 0.41499 acc: 0.89333 val_loss: 0.40132, val_acc: 0.93333
Epoch [1380/10000], loss: 0.41409 acc: 0.89333 val_loss: 0.40041, val_acc: 0.93333
Epoch [1390/10000], loss: 0.41320 acc: 0.89333 val_loss: 0.39951, val_acc: 0.93333
Epoch [1400/10000], loss: 0.41231 acc: 0.89333 val_loss: 0.39861, val_acc: 0.93333
Epoch [1410/10000], loss: 0.41143 acc: 0.89333 val_loss: 0.39773, val_acc: 0.93333
Epoch [1420/10000], loss: 0.41055 acc: 0.89333 val_loss: 0.39685, val_acc: 0.93333
Epoch [1430/10000], loss: 0.40968 acc: 0.89333 val_loss: 0.39597, val_acc: 0.93333
Epoch [1440/10000], loss: 0.40882 acc: 0.89333 val_loss: 0.39510, val_acc: 0.93333
Epoch [1450/10000], loss: 0.40796 acc: 0.89333 val_loss: 0.39424, val_acc: 0.93333
Epoch [1460/10000], loss: 0.40711 acc: 0.89333 val_loss: 0.39339, val_acc: 0.93333
Epoch [1470/10000], loss: 0.40627 acc: 0.89333 val_loss: 0.39254, val_acc: 0.93333
Epoch [1480/10000], loss: 0.40543 acc: 0.90667 val_loss: 0.39170, val_acc: 0.93333
Epoch [1490/10000], loss: 0.40460 acc: 0.90667 val_loss: 0.39086, val_acc: 0.93333
Epoch [1500/10000], loss: 0.40378 acc: 0.90667 val_loss: 0.39003, val_acc: 0.93333
Epoch [1510/10000], loss: 0.40296 acc: 0.90667 val_loss: 0.38921, val_acc: 0.93333
Epoch [1520/10000], loss: 0.40214 acc: 0.90667 val_loss: 0.38839, val_acc: 0.93333
Epoch [1530/10000], loss: 0.40134 acc: 0.90667 val_loss: 0.38758, val_acc: 0.93333
Epoch [1540/10000], loss: 0.40053 acc: 0.90667 val_loss: 0.38677, val_acc: 0.93333
Epoch [1550/10000], loss: 0.39974 acc: 0.90667 val_loss: 0.38597, val_acc: 0.93333
Epoch [1560/10000], loss: 0.39894 acc: 0.90667 val_loss: 0.38517, val_acc: 0.94667
Epoch [1570/10000], loss: 0.39816 acc: 0.90667 val_loss: 0.38438, val_acc: 0.94667
Epoch [1580/10000], loss: 0.39738 acc: 0.90667 val_loss: 0.38360, val_acc: 0.94667
Epoch [1590/10000], loss: 0.39660 acc: 0.90667 val_loss: 0.38282, val_acc: 0.94667
Epoch [1600/10000], loss: 0.39583 acc: 0.90667 val_loss: 0.38204, val_acc: 0.94667
Epoch [1610/10000], loss: 0.39507 acc: 0.90667 val_loss: 0.38128, val_acc: 0.94667
Epoch [1620/10000], loss: 0.39431 acc: 0.90667 val_loss: 0.38051, val_acc: 0.94667
Epoch [1630/10000], loss: 0.39355 acc: 0.90667 val_loss: 0.37975, val_acc: 0.94667
Epoch [1640/10000], loss: 0.39280 acc: 0.90667 val_loss: 0.37900, val_acc: 0.94667
Epoch [1650/10000], loss: 0.39206 acc: 0.90667 val_loss: 0.37825, val_acc: 0.94667
Epoch [1660/10000], loss: 0.39132 acc: 0.90667 val_loss: 0.37751, val_acc: 0.94667
Epoch [1670/10000], loss: 0.39058 acc: 0.90667 val_loss: 0.37677, val_acc: 0.94667
Epoch [1680/10000], loss: 0.38985 acc: 0.90667 val_loss: 0.37604, val_acc: 0.94667
Epoch [1690/10000], loss: 0.38913 acc: 0.90667 val_loss: 0.37531, val_acc: 0.94667
Epoch [1700/10000], loss: 0.38841 acc: 0.90667 val_loss: 0.37458, val_acc: 0.94667
Epoch [1710/10000], loss: 0.38769 acc: 0.90667 val_loss: 0.37386, val_acc: 0.94667
Epoch [1720/10000], loss: 0.38698 acc: 0.90667 val_loss: 0.37315, val_acc: 0.94667
Epoch [1730/10000], loss: 0.38627 acc: 0.90667 val_loss: 0.37244, val_acc: 0.94667
Epoch [1740/10000], loss: 0.38557 acc: 0.90667 val_loss: 0.37173, val_acc: 0.94667
Epoch [1750/10000], loss: 0.38487 acc: 0.90667 val_loss: 0.37103, val_acc: 0.94667
Epoch [1760/10000], loss: 0.38417 acc: 0.90667 val_loss: 0.37033, val_acc: 0.94667
Epoch [1770/10000], loss: 0.38348 acc: 0.90667 val_loss: 0.36964, val_acc: 0.94667
Epoch [1780/10000], loss: 0.38280 acc: 0.90667 val_loss: 0.36895, val_acc: 0.94667
Epoch [1790/10000], loss: 0.38212 acc: 0.90667 val_loss: 0.36826, val_acc: 0.94667
Epoch [1800/10000], loss: 0.38144 acc: 0.90667 val_loss: 0.36758, val_acc: 0.94667
Epoch [1810/10000], loss: 0.38076 acc: 0.90667 val_loss: 0.36690, val_acc: 0.94667
Epoch [1820/10000], loss: 0.38009 acc: 0.90667 val_loss: 0.36623, val_acc: 0.94667
Epoch [1830/10000], loss: 0.37943 acc: 0.90667 val_loss: 0.36556, val_acc: 0.94667
Epoch [1840/10000], loss: 0.37877 acc: 0.90667 val_loss: 0.36490, val_acc: 0.94667
Epoch [1850/10000], loss: 0.37811 acc: 0.90667 val_loss: 0.36424, val_acc: 0.94667
Epoch [1860/10000], loss: 0.37746 acc: 0.90667 val_loss: 0.36358, val_acc: 0.94667
Epoch [1870/10000], loss: 0.37681 acc: 0.90667 val_loss: 0.36293, val_acc: 0.94667
Epoch [1880/10000], loss: 0.37616 acc: 0.90667 val_loss: 0.36228, val_acc: 0.94667
Epoch [1890/10000], loss: 0.37552 acc: 0.90667 val_loss: 0.36163, val_acc: 0.94667
Epoch [1900/10000], loss: 0.37488 acc: 0.90667 val_loss: 0.36099, val_acc: 0.94667
Epoch [1910/10000], loss: 0.37424 acc: 0.90667 val_loss: 0.36035, val_acc: 0.94667
Epoch [1920/10000], loss: 0.37361 acc: 0.90667 val_loss: 0.35972, val_acc: 0.94667
Epoch [1930/10000], loss: 0.37298 acc: 0.90667 val_loss: 0.35909, val_acc: 0.94667
Epoch [1940/10000], loss: 0.37236 acc: 0.90667 val_loss: 0.35846, val_acc: 0.94667
Epoch [1950/10000], loss: 0.37174 acc: 0.90667 val_loss: 0.35784, val_acc: 0.94667
Epoch [1960/10000], loss: 0.37112 acc: 0.90667 val_loss: 0.35722, val_acc: 0.94667
Epoch [1970/10000], loss: 0.37051 acc: 0.90667 val_loss: 0.35660, val_acc: 0.94667
Epoch [1980/10000], loss: 0.36990 acc: 0.90667 val_loss: 0.35599, val_acc: 0.94667
Epoch [1990/10000], loss: 0.36929 acc: 0.90667 val_loss: 0.35538, val_acc: 0.94667
Epoch [2000/10000], loss: 0.36869 acc: 0.90667 val_loss: 0.35477, val_acc: 0.94667
Epoch [2010/10000], loss: 0.36809 acc: 0.90667 val_loss: 0.35417, val_acc: 0.94667
Epoch [2020/10000], loss: 0.36749 acc: 0.90667 val_loss: 0.35357, val_acc: 0.94667
Epoch [2030/10000], loss: 0.36690 acc: 0.90667 val_loss: 0.35298, val_acc: 0.94667
Epoch [2040/10000], loss: 0.36631 acc: 0.90667 val_loss: 0.35238, val_acc: 0.94667
Epoch [2050/10000], loss: 0.36572 acc: 0.90667 val_loss: 0.35179, val_acc: 0.94667
Epoch [2060/10000], loss: 0.36514 acc: 0.90667 val_loss: 0.35121, val_acc: 0.94667
Epoch [2070/10000], loss: 0.36455 acc: 0.90667 val_loss: 0.35062, val_acc: 0.94667
Epoch [2080/10000], loss: 0.36398 acc: 0.90667 val_loss: 0.35004, val_acc: 0.94667
Epoch [2090/10000], loss: 0.36340 acc: 0.90667 val_loss: 0.34947, val_acc: 0.94667
Epoch [2100/10000], loss: 0.36283 acc: 0.90667 val_loss: 0.34889, val_acc: 0.94667
Epoch [2110/10000], loss: 0.36226 acc: 0.90667 val_loss: 0.34832, val_acc: 0.94667
Epoch [2120/10000], loss: 0.36170 acc: 0.90667 val_loss: 0.34775, val_acc: 0.94667
Epoch [2130/10000], loss: 0.36114 acc: 0.90667 val_loss: 0.34719, val_acc: 0.94667
Epoch [2140/10000], loss: 0.36058 acc: 0.90667 val_loss: 0.34663, val_acc: 0.94667
Epoch [2150/10000], loss: 0.36002 acc: 0.90667 val_loss: 0.34607, val_acc: 0.94667
Epoch [2160/10000], loss: 0.35947 acc: 0.90667 val_loss: 0.34551, val_acc: 0.94667
Epoch [2170/10000], loss: 0.35892 acc: 0.90667 val_loss: 0.34496, val_acc: 0.94667
Epoch [2180/10000], loss: 0.35837 acc: 0.90667 val_loss: 0.34441, val_acc: 0.94667
Epoch [2190/10000], loss: 0.35782 acc: 0.90667 val_loss: 0.34386, val_acc: 0.94667
Epoch [2200/10000], loss: 0.35728 acc: 0.90667 val_loss: 0.34331, val_acc: 0.94667
Epoch [2210/10000], loss: 0.35674 acc: 0.90667 val_loss: 0.34277, val_acc: 0.94667
Epoch [2220/10000], loss: 0.35621 acc: 0.90667 val_loss: 0.34223, val_acc: 0.94667
Epoch [2230/10000], loss: 0.35567 acc: 0.90667 val_loss: 0.34170, val_acc: 0.94667
Epoch [2240/10000], loss: 0.35514 acc: 0.90667 val_loss: 0.34116, val_acc: 0.94667
Epoch [2250/10000], loss: 0.35461 acc: 0.90667 val_loss: 0.34063, val_acc: 0.94667
Epoch [2260/10000], loss: 0.35409 acc: 0.90667 val_loss: 0.34010, val_acc: 0.94667
Epoch [2270/10000], loss: 0.35356 acc: 0.90667 val_loss: 0.33958, val_acc: 0.94667
Epoch [2280/10000], loss: 0.35304 acc: 0.90667 val_loss: 0.33905, val_acc: 0.94667
Epoch [2290/10000], loss: 0.35253 acc: 0.90667 val_loss: 0.33853, val_acc: 0.94667
Epoch [2300/10000], loss: 0.35201 acc: 0.90667 val_loss: 0.33802, val_acc: 0.94667
Epoch [2310/10000], loss: 0.35150 acc: 0.90667 val_loss: 0.33750, val_acc: 0.94667
Epoch [2320/10000], loss: 0.35099 acc: 0.90667 val_loss: 0.33699, val_acc: 0.94667
Epoch [2330/10000], loss: 0.35048 acc: 0.90667 val_loss: 0.33648, val_acc: 0.94667
Epoch [2340/10000], loss: 0.34998 acc: 0.90667 val_loss: 0.33597, val_acc: 0.94667
Epoch [2350/10000], loss: 0.34947 acc: 0.90667 val_loss: 0.33546, val_acc: 0.94667
Epoch [2360/10000], loss: 0.34897 acc: 0.90667 val_loss: 0.33496, val_acc: 0.94667
Epoch [2370/10000], loss: 0.34848 acc: 0.90667 val_loss: 0.33446, val_acc: 0.94667
Epoch [2380/10000], loss: 0.34798 acc: 0.90667 val_loss: 0.33396, val_acc: 0.94667
Epoch [2390/10000], loss: 0.34749 acc: 0.90667 val_loss: 0.33347, val_acc: 0.94667
Epoch [2400/10000], loss: 0.34700 acc: 0.90667 val_loss: 0.33297, val_acc: 0.94667
Epoch [2410/10000], loss: 0.34651 acc: 0.90667 val_loss: 0.33248, val_acc: 0.94667
Epoch [2420/10000], loss: 0.34602 acc: 0.90667 val_loss: 0.33199, val_acc: 0.94667
Epoch [2430/10000], loss: 0.34554 acc: 0.90667 val_loss: 0.33151, val_acc: 0.94667
Epoch [2440/10000], loss: 0.34506 acc: 0.90667 val_loss: 0.33102, val_acc: 0.94667
Epoch [2450/10000], loss: 0.34458 acc: 0.90667 val_loss: 0.33054, val_acc: 0.94667
Epoch [2460/10000], loss: 0.34411 acc: 0.90667 val_loss: 0.33006, val_acc: 0.94667
Epoch [2470/10000], loss: 0.34363 acc: 0.90667 val_loss: 0.32959, val_acc: 0.94667
Epoch [2480/10000], loss: 0.34316 acc: 0.90667 val_loss: 0.32911, val_acc: 0.94667
Epoch [2490/10000], loss: 0.34269 acc: 0.90667 val_loss: 0.32864, val_acc: 0.94667
Epoch [2500/10000], loss: 0.34222 acc: 0.90667 val_loss: 0.32817, val_acc: 0.94667
Epoch [2510/10000], loss: 0.34176 acc: 0.90667 val_loss: 0.32770, val_acc: 0.94667
Epoch [2520/10000], loss: 0.34130 acc: 0.90667 val_loss: 0.32723, val_acc: 0.94667
Epoch [2530/10000], loss: 0.34083 acc: 0.90667 val_loss: 0.32677, val_acc: 0.94667
Epoch [2540/10000], loss: 0.34038 acc: 0.90667 val_loss: 0.32631, val_acc: 0.94667
Epoch [2550/10000], loss: 0.33992 acc: 0.90667 val_loss: 0.32585, val_acc: 0.94667
Epoch [2560/10000], loss: 0.33947 acc: 0.90667 val_loss: 0.32539, val_acc: 0.94667
Epoch [2570/10000], loss: 0.33901 acc: 0.90667 val_loss: 0.32493, val_acc: 0.94667
Epoch [2580/10000], loss: 0.33856 acc: 0.90667 val_loss: 0.32448, val_acc: 0.94667
Epoch [2590/10000], loss: 0.33812 acc: 0.90667 val_loss: 0.32403, val_acc: 0.94667
Epoch [2600/10000], loss: 0.33767 acc: 0.90667 val_loss: 0.32358, val_acc: 0.94667
Epoch [2610/10000], loss: 0.33723 acc: 0.90667 val_loss: 0.32313, val_acc: 0.94667
Epoch [2620/10000], loss: 0.33678 acc: 0.90667 val_loss: 0.32269, val_acc: 0.94667
Epoch [2630/10000], loss: 0.33634 acc: 0.90667 val_loss: 0.32225, val_acc: 0.94667
Epoch [2640/10000], loss: 0.33591 acc: 0.90667 val_loss: 0.32180, val_acc: 0.94667
Epoch [2650/10000], loss: 0.33547 acc: 0.90667 val_loss: 0.32136, val_acc: 0.94667
Epoch [2660/10000], loss: 0.33504 acc: 0.90667 val_loss: 0.32093, val_acc: 0.94667
Epoch [2670/10000], loss: 0.33460 acc: 0.90667 val_loss: 0.32049, val_acc: 0.94667
Epoch [2680/10000], loss: 0.33417 acc: 0.90667 val_loss: 0.32006, val_acc: 0.94667
Epoch [2690/10000], loss: 0.33375 acc: 0.90667 val_loss: 0.31963, val_acc: 0.94667
Epoch [2700/10000], loss: 0.33332 acc: 0.90667 val_loss: 0.31920, val_acc: 0.94667
Epoch [2710/10000], loss: 0.33290 acc: 0.90667 val_loss: 0.31877, val_acc: 0.94667
Epoch [2720/10000], loss: 0.33247 acc: 0.90667 val_loss: 0.31834, val_acc: 0.94667
Epoch [2730/10000], loss: 0.33205 acc: 0.90667 val_loss: 0.31792, val_acc: 0.94667
Epoch [2740/10000], loss: 0.33164 acc: 0.90667 val_loss: 0.31750, val_acc: 0.94667
Epoch [2750/10000], loss: 0.33122 acc: 0.90667 val_loss: 0.31708, val_acc: 0.94667
Epoch [2760/10000], loss: 0.33080 acc: 0.90667 val_loss: 0.31666, val_acc: 0.94667
Epoch [2770/10000], loss: 0.33039 acc: 0.90667 val_loss: 0.31624, val_acc: 0.94667
Epoch [2780/10000], loss: 0.32998 acc: 0.90667 val_loss: 0.31583, val_acc: 0.94667
Epoch [2790/10000], loss: 0.32957 acc: 0.90667 val_loss: 0.31542, val_acc: 0.94667
Epoch [2800/10000], loss: 0.32916 acc: 0.90667 val_loss: 0.31500, val_acc: 0.94667
Epoch [2810/10000], loss: 0.32876 acc: 0.90667 val_loss: 0.31460, val_acc: 0.94667
Epoch [2820/10000], loss: 0.32835 acc: 0.90667 val_loss: 0.31419, val_acc: 0.94667
Epoch [2830/10000], loss: 0.32795 acc: 0.90667 val_loss: 0.31378, val_acc: 0.94667
Epoch [2840/10000], loss: 0.32755 acc: 0.90667 val_loss: 0.31338, val_acc: 0.94667
Epoch [2850/10000], loss: 0.32715 acc: 0.90667 val_loss: 0.31297, val_acc: 0.94667
Epoch [2860/10000], loss: 0.32675 acc: 0.90667 val_loss: 0.31257, val_acc: 0.94667
Epoch [2870/10000], loss: 0.32636 acc: 0.90667 val_loss: 0.31217, val_acc: 0.94667
Epoch [2880/10000], loss: 0.32597 acc: 0.90667 val_loss: 0.31178, val_acc: 0.94667
Epoch [2890/10000], loss: 0.32557 acc: 0.90667 val_loss: 0.31138, val_acc: 0.94667
Epoch [2900/10000], loss: 0.32518 acc: 0.90667 val_loss: 0.31099, val_acc: 0.94667
Epoch [2910/10000], loss: 0.32480 acc: 0.90667 val_loss: 0.31060, val_acc: 0.94667
Epoch [2920/10000], loss: 0.32441 acc: 0.90667 val_loss: 0.31020, val_acc: 0.94667
Epoch [2930/10000], loss: 0.32402 acc: 0.90667 val_loss: 0.30982, val_acc: 0.94667
Epoch [2940/10000], loss: 0.32364 acc: 0.90667 val_loss: 0.30943, val_acc: 0.94667
Epoch [2950/10000], loss: 0.32326 acc: 0.90667 val_loss: 0.30904, val_acc: 0.94667
Epoch [2960/10000], loss: 0.32288 acc: 0.90667 val_loss: 0.30866, val_acc: 0.94667
Epoch [2970/10000], loss: 0.32250 acc: 0.90667 val_loss: 0.30827, val_acc: 0.94667
Epoch [2980/10000], loss: 0.32212 acc: 0.90667 val_loss: 0.30789, val_acc: 0.94667
Epoch [2990/10000], loss: 0.32175 acc: 0.90667 val_loss: 0.30751, val_acc: 0.94667
Epoch [3000/10000], loss: 0.32137 acc: 0.90667 val_loss: 0.30714, val_acc: 0.94667
Epoch [3010/10000], loss: 0.32100 acc: 0.90667 val_loss: 0.30676, val_acc: 0.94667
Epoch [3020/10000], loss: 0.32063 acc: 0.90667 val_loss: 0.30638, val_acc: 0.94667
Epoch [3030/10000], loss: 0.32026 acc: 0.90667 val_loss: 0.30601, val_acc: 0.94667
Epoch [3040/10000], loss: 0.31989 acc: 0.90667 val_loss: 0.30564, val_acc: 0.94667
Epoch [3050/10000], loss: 0.31952 acc: 0.90667 val_loss: 0.30527, val_acc: 0.94667
Epoch [3060/10000], loss: 0.31916 acc: 0.90667 val_loss: 0.30490, val_acc: 0.94667
Epoch [3070/10000], loss: 0.31880 acc: 0.90667 val_loss: 0.30453, val_acc: 0.94667
Epoch [3080/10000], loss: 0.31844 acc: 0.90667 val_loss: 0.30417, val_acc: 0.94667
Epoch [3090/10000], loss: 0.31807 acc: 0.90667 val_loss: 0.30380, val_acc: 0.94667
Epoch [3100/10000], loss: 0.31772 acc: 0.90667 val_loss: 0.30344, val_acc: 0.94667
Epoch [3110/10000], loss: 0.31736 acc: 0.90667 val_loss: 0.30308, val_acc: 0.94667
Epoch [3120/10000], loss: 0.31700 acc: 0.90667 val_loss: 0.30272, val_acc: 0.94667
Epoch [3130/10000], loss: 0.31665 acc: 0.90667 val_loss: 0.30236, val_acc: 0.94667
Epoch [3140/10000], loss: 0.31630 acc: 0.90667 val_loss: 0.30200, val_acc: 0.94667
Epoch [3150/10000], loss: 0.31594 acc: 0.90667 val_loss: 0.30165, val_acc: 0.94667
Epoch [3160/10000], loss: 0.31559 acc: 0.90667 val_loss: 0.30129, val_acc: 0.94667
Epoch [3170/10000], loss: 0.31525 acc: 0.90667 val_loss: 0.30094, val_acc: 0.94667
Epoch [3180/10000], loss: 0.31490 acc: 0.90667 val_loss: 0.30059, val_acc: 0.94667
Epoch [3190/10000], loss: 0.31455 acc: 0.90667 val_loss: 0.30024, val_acc: 0.94667
Epoch [3200/10000], loss: 0.31421 acc: 0.90667 val_loss: 0.29989, val_acc: 0.94667
Epoch [3210/10000], loss: 0.31386 acc: 0.90667 val_loss: 0.29954, val_acc: 0.94667
Epoch [3220/10000], loss: 0.31352 acc: 0.90667 val_loss: 0.29919, val_acc: 0.94667
Epoch [3230/10000], loss: 0.31318 acc: 0.90667 val_loss: 0.29885, val_acc: 0.94667
Epoch [3240/10000], loss: 0.31284 acc: 0.90667 val_loss: 0.29851, val_acc: 0.94667
Epoch [3250/10000], loss: 0.31251 acc: 0.90667 val_loss: 0.29816, val_acc: 0.94667
Epoch [3260/10000], loss: 0.31217 acc: 0.90667 val_loss: 0.29782, val_acc: 0.94667
Epoch [3270/10000], loss: 0.31183 acc: 0.90667 val_loss: 0.29748, val_acc: 0.94667
Epoch [3280/10000], loss: 0.31150 acc: 0.90667 val_loss: 0.29715, val_acc: 0.94667
Epoch [3290/10000], loss: 0.31117 acc: 0.90667 val_loss: 0.29681, val_acc: 0.94667
Epoch [3300/10000], loss: 0.31084 acc: 0.90667 val_loss: 0.29647, val_acc: 0.94667
Epoch [3310/10000], loss: 0.31051 acc: 0.90667 val_loss: 0.29614, val_acc: 0.94667
Epoch [3320/10000], loss: 0.31018 acc: 0.90667 val_loss: 0.29581, val_acc: 0.94667
Epoch [3330/10000], loss: 0.30985 acc: 0.90667 val_loss: 0.29548, val_acc: 0.94667
Epoch [3340/10000], loss: 0.30953 acc: 0.90667 val_loss: 0.29515, val_acc: 0.94667
Epoch [3350/10000], loss: 0.30920 acc: 0.90667 val_loss: 0.29482, val_acc: 0.94667
Epoch [3360/10000], loss: 0.30888 acc: 0.90667 val_loss: 0.29449, val_acc: 0.94667
Epoch [3370/10000], loss: 0.30856 acc: 0.90667 val_loss: 0.29416, val_acc: 0.94667
Epoch [3380/10000], loss: 0.30824 acc: 0.90667 val_loss: 0.29384, val_acc: 0.94667
Epoch [3390/10000], loss: 0.30792 acc: 0.90667 val_loss: 0.29351, val_acc: 0.94667
Epoch [3400/10000], loss: 0.30760 acc: 0.90667 val_loss: 0.29319, val_acc: 0.94667
Epoch [3410/10000], loss: 0.30728 acc: 0.90667 val_loss: 0.29287, val_acc: 0.94667
Epoch [3420/10000], loss: 0.30696 acc: 0.90667 val_loss: 0.29255, val_acc: 0.94667
Epoch [3430/10000], loss: 0.30665 acc: 0.90667 val_loss: 0.29223, val_acc: 0.94667
Epoch [3440/10000], loss: 0.30634 acc: 0.90667 val_loss: 0.29191, val_acc: 0.94667
Epoch [3450/10000], loss: 0.30602 acc: 0.90667 val_loss: 0.29159, val_acc: 0.94667
Epoch [3460/10000], loss: 0.30571 acc: 0.90667 val_loss: 0.29128, val_acc: 0.94667
Epoch [3470/10000], loss: 0.30540 acc: 0.90667 val_loss: 0.29096, val_acc: 0.94667
Epoch [3480/10000], loss: 0.30509 acc: 0.90667 val_loss: 0.29065, val_acc: 0.94667
Epoch [3490/10000], loss: 0.30479 acc: 0.90667 val_loss: 0.29034, val_acc: 0.94667
Epoch [3500/10000], loss: 0.30448 acc: 0.90667 val_loss: 0.29003, val_acc: 0.94667
Epoch [3510/10000], loss: 0.30417 acc: 0.90667 val_loss: 0.28972, val_acc: 0.94667
Epoch [3520/10000], loss: 0.30387 acc: 0.90667 val_loss: 0.28941, val_acc: 0.94667
Epoch [3530/10000], loss: 0.30357 acc: 0.90667 val_loss: 0.28910, val_acc: 0.94667
Epoch [3540/10000], loss: 0.30327 acc: 0.90667 val_loss: 0.28879, val_acc: 0.94667
Epoch [3550/10000], loss: 0.30297 acc: 0.90667 val_loss: 0.28849, val_acc: 0.94667
Epoch [3560/10000], loss: 0.30267 acc: 0.90667 val_loss: 0.28818, val_acc: 0.94667
Epoch [3570/10000], loss: 0.30237 acc: 0.90667 val_loss: 0.28788, val_acc: 0.94667
Epoch [3580/10000], loss: 0.30207 acc: 0.90667 val_loss: 0.28758, val_acc: 0.94667
Epoch [3590/10000], loss: 0.30177 acc: 0.90667 val_loss: 0.28728, val_acc: 0.94667
Epoch [3600/10000], loss: 0.30148 acc: 0.90667 val_loss: 0.28698, val_acc: 0.94667
Epoch [3610/10000], loss: 0.30119 acc: 0.90667 val_loss: 0.28668, val_acc: 0.94667
Epoch [3620/10000], loss: 0.30089 acc: 0.90667 val_loss: 0.28638, val_acc: 0.96000
Epoch [3630/10000], loss: 0.30060 acc: 0.90667 val_loss: 0.28608, val_acc: 0.96000
Epoch [3640/10000], loss: 0.30031 acc: 0.90667 val_loss: 0.28579, val_acc: 0.96000
Epoch [3650/10000], loss: 0.30002 acc: 0.90667 val_loss: 0.28549, val_acc: 0.96000
Epoch [3660/10000], loss: 0.29973 acc: 0.90667 val_loss: 0.28520, val_acc: 0.96000
Epoch [3670/10000], loss: 0.29944 acc: 0.90667 val_loss: 0.28491, val_acc: 0.96000
Epoch [3680/10000], loss: 0.29916 acc: 0.90667 val_loss: 0.28462, val_acc: 0.96000
Epoch [3690/10000], loss: 0.29887 acc: 0.90667 val_loss: 0.28433, val_acc: 0.96000
Epoch [3700/10000], loss: 0.29859 acc: 0.90667 val_loss: 0.28404, val_acc: 0.96000
Epoch [3710/10000], loss: 0.29830 acc: 0.90667 val_loss: 0.28375, val_acc: 0.96000
Epoch [3720/10000], loss: 0.29802 acc: 0.90667 val_loss: 0.28346, val_acc: 0.96000
Epoch [3730/10000], loss: 0.29774 acc: 0.90667 val_loss: 0.28318, val_acc: 0.96000
Epoch [3740/10000], loss: 0.29746 acc: 0.90667 val_loss: 0.28289, val_acc: 0.96000
Epoch [3750/10000], loss: 0.29718 acc: 0.90667 val_loss: 0.28261, val_acc: 0.96000
Epoch [3760/10000], loss: 0.29690 acc: 0.90667 val_loss: 0.28232, val_acc: 0.96000
Epoch [3770/10000], loss: 0.29663 acc: 0.90667 val_loss: 0.28204, val_acc: 0.96000
Epoch [3780/10000], loss: 0.29635 acc: 0.90667 val_loss: 0.28176, val_acc: 0.96000
Epoch [3790/10000], loss: 0.29607 acc: 0.90667 val_loss: 0.28148, val_acc: 0.96000
Epoch [3800/10000], loss: 0.29580 acc: 0.90667 val_loss: 0.28120, val_acc: 0.96000
Epoch [3810/10000], loss: 0.29553 acc: 0.90667 val_loss: 0.28092, val_acc: 0.96000
Epoch [3820/10000], loss: 0.29525 acc: 0.90667 val_loss: 0.28064, val_acc: 0.96000
Epoch [3830/10000], loss: 0.29498 acc: 0.90667 val_loss: 0.28037, val_acc: 0.96000
Epoch [3840/10000], loss: 0.29471 acc: 0.90667 val_loss: 0.28009, val_acc: 0.96000
Epoch [3850/10000], loss: 0.29444 acc: 0.90667 val_loss: 0.27982, val_acc: 0.96000
Epoch [3860/10000], loss: 0.29418 acc: 0.90667 val_loss: 0.27954, val_acc: 0.96000
Epoch [3870/10000], loss: 0.29391 acc: 0.90667 val_loss: 0.27927, val_acc: 0.96000
Epoch [3880/10000], loss: 0.29364 acc: 0.90667 val_loss: 0.27900, val_acc: 0.96000
Epoch [3890/10000], loss: 0.29338 acc: 0.90667 val_loss: 0.27873, val_acc: 0.96000
Epoch [3900/10000], loss: 0.29311 acc: 0.90667 val_loss: 0.27846, val_acc: 0.96000
Epoch [3910/10000], loss: 0.29285 acc: 0.90667 val_loss: 0.27819, val_acc: 0.96000
Epoch [3920/10000], loss: 0.29258 acc: 0.90667 val_loss: 0.27792, val_acc: 0.96000
Epoch [3930/10000], loss: 0.29232 acc: 0.90667 val_loss: 0.27766, val_acc: 0.96000
Epoch [3940/10000], loss: 0.29206 acc: 0.90667 val_loss: 0.27739, val_acc: 0.96000
Epoch [3950/10000], loss: 0.29180 acc: 0.90667 val_loss: 0.27712, val_acc: 0.96000
Epoch [3960/10000], loss: 0.29154 acc: 0.90667 val_loss: 0.27686, val_acc: 0.96000
Epoch [3970/10000], loss: 0.29128 acc: 0.90667 val_loss: 0.27660, val_acc: 0.96000
Epoch [3980/10000], loss: 0.29103 acc: 0.90667 val_loss: 0.27633, val_acc: 0.96000
Epoch [3990/10000], loss: 0.29077 acc: 0.90667 val_loss: 0.27607, val_acc: 0.96000
Epoch [4000/10000], loss: 0.29052 acc: 0.90667 val_loss: 0.27581, val_acc: 0.96000
Epoch [4010/10000], loss: 0.29026 acc: 0.90667 val_loss: 0.27555, val_acc: 0.96000
Epoch [4020/10000], loss: 0.29001 acc: 0.90667 val_loss: 0.27529, val_acc: 0.96000
Epoch [4030/10000], loss: 0.28975 acc: 0.90667 val_loss: 0.27504, val_acc: 0.96000
Epoch [4040/10000], loss: 0.28950 acc: 0.90667 val_loss: 0.27478, val_acc: 0.96000
Epoch [4050/10000], loss: 0.28925 acc: 0.90667 val_loss: 0.27452, val_acc: 0.96000
Epoch [4060/10000], loss: 0.28900 acc: 0.90667 val_loss: 0.27427, val_acc: 0.96000
Epoch [4070/10000], loss: 0.28875 acc: 0.90667 val_loss: 0.27401, val_acc: 0.96000
Epoch [4080/10000], loss: 0.28850 acc: 0.90667 val_loss: 0.27376, val_acc: 0.96000
Epoch [4090/10000], loss: 0.28826 acc: 0.90667 val_loss: 0.27351, val_acc: 0.96000
Epoch [4100/10000], loss: 0.28801 acc: 0.90667 val_loss: 0.27325, val_acc: 0.96000
Epoch [4110/10000], loss: 0.28776 acc: 0.90667 val_loss: 0.27300, val_acc: 0.96000
Epoch [4120/10000], loss: 0.28752 acc: 0.90667 val_loss: 0.27275, val_acc: 0.96000
Epoch [4130/10000], loss: 0.28727 acc: 0.90667 val_loss: 0.27250, val_acc: 0.96000
Epoch [4140/10000], loss: 0.28703 acc: 0.90667 val_loss: 0.27225, val_acc: 0.96000
Epoch [4150/10000], loss: 0.28679 acc: 0.90667 val_loss: 0.27200, val_acc: 0.96000
Epoch [4160/10000], loss: 0.28654 acc: 0.90667 val_loss: 0.27176, val_acc: 0.96000
Epoch [4170/10000], loss: 0.28630 acc: 0.90667 val_loss: 0.27151, val_acc: 0.96000
Epoch [4180/10000], loss: 0.28606 acc: 0.90667 val_loss: 0.27127, val_acc: 0.96000
Epoch [4190/10000], loss: 0.28582 acc: 0.90667 val_loss: 0.27102, val_acc: 0.96000
Epoch [4200/10000], loss: 0.28559 acc: 0.90667 val_loss: 0.27078, val_acc: 0.96000
Epoch [4210/10000], loss: 0.28535 acc: 0.90667 val_loss: 0.27053, val_acc: 0.96000
Epoch [4220/10000], loss: 0.28511 acc: 0.90667 val_loss: 0.27029, val_acc: 0.96000
Epoch [4230/10000], loss: 0.28487 acc: 0.90667 val_loss: 0.27005, val_acc: 0.96000
Epoch [4240/10000], loss: 0.28464 acc: 0.90667 val_loss: 0.26981, val_acc: 0.96000
Epoch [4250/10000], loss: 0.28440 acc: 0.90667 val_loss: 0.26957, val_acc: 0.96000
Epoch [4260/10000], loss: 0.28417 acc: 0.90667 val_loss: 0.26933, val_acc: 0.96000
Epoch [4270/10000], loss: 0.28394 acc: 0.90667 val_loss: 0.26909, val_acc: 0.96000
Epoch [4280/10000], loss: 0.28370 acc: 0.90667 val_loss: 0.26885, val_acc: 0.96000
Epoch [4290/10000], loss: 0.28347 acc: 0.90667 val_loss: 0.26862, val_acc: 0.96000
Epoch [4300/10000], loss: 0.28324 acc: 0.90667 val_loss: 0.26838, val_acc: 0.96000
Epoch [4310/10000], loss: 0.28301 acc: 0.90667 val_loss: 0.26815, val_acc: 0.96000
Epoch [4320/10000], loss: 0.28278 acc: 0.90667 val_loss: 0.26791, val_acc: 0.96000
Epoch [4330/10000], loss: 0.28255 acc: 0.90667 val_loss: 0.26768, val_acc: 0.96000
Epoch [4340/10000], loss: 0.28233 acc: 0.90667 val_loss: 0.26744, val_acc: 0.96000
Epoch [4350/10000], loss: 0.28210 acc: 0.90667 val_loss: 0.26721, val_acc: 0.96000
Epoch [4360/10000], loss: 0.28187 acc: 0.90667 val_loss: 0.26698, val_acc: 0.96000
Epoch [4370/10000], loss: 0.28165 acc: 0.90667 val_loss: 0.26675, val_acc: 0.96000
Epoch [4380/10000], loss: 0.28142 acc: 0.90667 val_loss: 0.26652, val_acc: 0.96000
Epoch [4390/10000], loss: 0.28120 acc: 0.90667 val_loss: 0.26629, val_acc: 0.96000
Epoch [4400/10000], loss: 0.28098 acc: 0.90667 val_loss: 0.26606, val_acc: 0.96000
Epoch [4410/10000], loss: 0.28075 acc: 0.90667 val_loss: 0.26583, val_acc: 0.96000
Epoch [4420/10000], loss: 0.28053 acc: 0.90667 val_loss: 0.26560, val_acc: 0.96000
Epoch [4430/10000], loss: 0.28031 acc: 0.90667 val_loss: 0.26538, val_acc: 0.96000
Epoch [4440/10000], loss: 0.28009 acc: 0.90667 val_loss: 0.26515, val_acc: 0.96000
Epoch [4450/10000], loss: 0.27987 acc: 0.90667 val_loss: 0.26493, val_acc: 0.96000
Epoch [4460/10000], loss: 0.27965 acc: 0.90667 val_loss: 0.26470, val_acc: 0.96000
Epoch [4470/10000], loss: 0.27943 acc: 0.90667 val_loss: 0.26448, val_acc: 0.96000
Epoch [4480/10000], loss: 0.27922 acc: 0.90667 val_loss: 0.26425, val_acc: 0.96000
Epoch [4490/10000], loss: 0.27900 acc: 0.90667 val_loss: 0.26403, val_acc: 0.96000
Epoch [4500/10000], loss: 0.27878 acc: 0.90667 val_loss: 0.26381, val_acc: 0.96000
Epoch [4510/10000], loss: 0.27857 acc: 0.90667 val_loss: 0.26359, val_acc: 0.96000
Epoch [4520/10000], loss: 0.27835 acc: 0.90667 val_loss: 0.26337, val_acc: 0.96000
Epoch [4530/10000], loss: 0.27814 acc: 0.90667 val_loss: 0.26315, val_acc: 0.96000
Epoch [4540/10000], loss: 0.27792 acc: 0.90667 val_loss: 0.26293, val_acc: 0.96000
Epoch [4550/10000], loss: 0.27771 acc: 0.90667 val_loss: 0.26271, val_acc: 0.96000
Epoch [4560/10000], loss: 0.27750 acc: 0.90667 val_loss: 0.26249, val_acc: 0.96000
Epoch [4570/10000], loss: 0.27729 acc: 0.90667 val_loss: 0.26228, val_acc: 0.96000
Epoch [4580/10000], loss: 0.27708 acc: 0.90667 val_loss: 0.26206, val_acc: 0.96000
Epoch [4590/10000], loss: 0.27687 acc: 0.90667 val_loss: 0.26185, val_acc: 0.96000
Epoch [4600/10000], loss: 0.27666 acc: 0.90667 val_loss: 0.26163, val_acc: 0.96000
Epoch [4610/10000], loss: 0.27645 acc: 0.90667 val_loss: 0.26142, val_acc: 0.96000
Epoch [4620/10000], loss: 0.27624 acc: 0.90667 val_loss: 0.26120, val_acc: 0.96000
Epoch [4630/10000], loss: 0.27603 acc: 0.90667 val_loss: 0.26099, val_acc: 0.96000
Epoch [4640/10000], loss: 0.27583 acc: 0.90667 val_loss: 0.26078, val_acc: 0.96000
Epoch [4650/10000], loss: 0.27562 acc: 0.90667 val_loss: 0.26057, val_acc: 0.96000
Epoch [4660/10000], loss: 0.27541 acc: 0.90667 val_loss: 0.26035, val_acc: 0.96000
Epoch [4670/10000], loss: 0.27521 acc: 0.90667 val_loss: 0.26014, val_acc: 0.96000
Epoch [4680/10000], loss: 0.27500 acc: 0.90667 val_loss: 0.25993, val_acc: 0.96000
Epoch [4690/10000], loss: 0.27480 acc: 0.90667 val_loss: 0.25973, val_acc: 0.96000
Epoch [4700/10000], loss: 0.27460 acc: 0.90667 val_loss: 0.25952, val_acc: 0.96000
Epoch [4710/10000], loss: 0.27440 acc: 0.90667 val_loss: 0.25931, val_acc: 0.96000
Epoch [4720/10000], loss: 0.27419 acc: 0.90667 val_loss: 0.25910, val_acc: 0.96000
Epoch [4730/10000], loss: 0.27399 acc: 0.90667 val_loss: 0.25889, val_acc: 0.96000
Epoch [4740/10000], loss: 0.27379 acc: 0.90667 val_loss: 0.25869, val_acc: 0.96000
Epoch [4750/10000], loss: 0.27359 acc: 0.90667 val_loss: 0.25848, val_acc: 0.96000
Epoch [4760/10000], loss: 0.27339 acc: 0.90667 val_loss: 0.25828, val_acc: 0.96000
Epoch [4770/10000], loss: 0.27319 acc: 0.90667 val_loss: 0.25807, val_acc: 0.96000
Epoch [4780/10000], loss: 0.27300 acc: 0.90667 val_loss: 0.25787, val_acc: 0.96000
Epoch [4790/10000], loss: 0.27280 acc: 0.90667 val_loss: 0.25767, val_acc: 0.96000
Epoch [4800/10000], loss: 0.27260 acc: 0.90667 val_loss: 0.25746, val_acc: 0.96000
Epoch [4810/10000], loss: 0.27241 acc: 0.90667 val_loss: 0.25726, val_acc: 0.96000
Epoch [4820/10000], loss: 0.27221 acc: 0.90667 val_loss: 0.25706, val_acc: 0.96000
Epoch [4830/10000], loss: 0.27202 acc: 0.90667 val_loss: 0.25686, val_acc: 0.96000
Epoch [4840/10000], loss: 0.27182 acc: 0.90667 val_loss: 0.25666, val_acc: 0.96000
Epoch [4850/10000], loss: 0.27163 acc: 0.90667 val_loss: 0.25646, val_acc: 0.96000
Epoch [4860/10000], loss: 0.27143 acc: 0.90667 val_loss: 0.25626, val_acc: 0.96000
Epoch [4870/10000], loss: 0.27124 acc: 0.90667 val_loss: 0.25606, val_acc: 0.96000
Epoch [4880/10000], loss: 0.27105 acc: 0.90667 val_loss: 0.25587, val_acc: 0.96000
Epoch [4890/10000], loss: 0.27086 acc: 0.90667 val_loss: 0.25567, val_acc: 0.96000
Epoch [4900/10000], loss: 0.27067 acc: 0.90667 val_loss: 0.25547, val_acc: 0.96000
Epoch [4910/10000], loss: 0.27048 acc: 0.90667 val_loss: 0.25528, val_acc: 0.96000
Epoch [4920/10000], loss: 0.27029 acc: 0.90667 val_loss: 0.25508, val_acc: 0.96000
Epoch [4930/10000], loss: 0.27010 acc: 0.90667 val_loss: 0.25489, val_acc: 0.96000
Epoch [4940/10000], loss: 0.26991 acc: 0.90667 val_loss: 0.25469, val_acc: 0.96000
Epoch [4950/10000], loss: 0.26972 acc: 0.90667 val_loss: 0.25450, val_acc: 0.96000
Epoch [4960/10000], loss: 0.26953 acc: 0.90667 val_loss: 0.25431, val_acc: 0.96000
Epoch [4970/10000], loss: 0.26935 acc: 0.90667 val_loss: 0.25411, val_acc: 0.96000
Epoch [4980/10000], loss: 0.26916 acc: 0.90667 val_loss: 0.25392, val_acc: 0.96000
Epoch [4990/10000], loss: 0.26897 acc: 0.90667 val_loss: 0.25373, val_acc: 0.96000
Epoch [5000/10000], loss: 0.26879 acc: 0.90667 val_loss: 0.25354, val_acc: 0.96000
Epoch [5010/10000], loss: 0.26860 acc: 0.90667 val_loss: 0.25335, val_acc: 0.96000
Epoch [5020/10000], loss: 0.26842 acc: 0.90667 val_loss: 0.25316, val_acc: 0.96000
Epoch [5030/10000], loss: 0.26824 acc: 0.90667 val_loss: 0.25297, val_acc: 0.96000
Epoch [5040/10000], loss: 0.26805 acc: 0.90667 val_loss: 0.25278, val_acc: 0.96000
Epoch [5050/10000], loss: 0.26787 acc: 0.90667 val_loss: 0.25259, val_acc: 0.96000
Epoch [5060/10000], loss: 0.26769 acc: 0.90667 val_loss: 0.25240, val_acc: 0.96000
Epoch [5070/10000], loss: 0.26751 acc: 0.90667 val_loss: 0.25222, val_acc: 0.96000
Epoch [5080/10000], loss: 0.26733 acc: 0.90667 val_loss: 0.25203, val_acc: 0.96000
Epoch [5090/10000], loss: 0.26715 acc: 0.90667 val_loss: 0.25184, val_acc: 0.96000
Epoch [5100/10000], loss: 0.26697 acc: 0.90667 val_loss: 0.25166, val_acc: 0.96000
Epoch [5110/10000], loss: 0.26679 acc: 0.90667 val_loss: 0.25147, val_acc: 0.96000
Epoch [5120/10000], loss: 0.26661 acc: 0.90667 val_loss: 0.25129, val_acc: 0.96000
Epoch [5130/10000], loss: 0.26643 acc: 0.90667 val_loss: 0.25111, val_acc: 0.96000
Epoch [5140/10000], loss: 0.26625 acc: 0.90667 val_loss: 0.25092, val_acc: 0.96000
Epoch [5150/10000], loss: 0.26608 acc: 0.90667 val_loss: 0.25074, val_acc: 0.96000
Epoch [5160/10000], loss: 0.26590 acc: 0.90667 val_loss: 0.25056, val_acc: 0.96000
Epoch [5170/10000], loss: 0.26572 acc: 0.90667 val_loss: 0.25037, val_acc: 0.96000
Epoch [5180/10000], loss: 0.26555 acc: 0.90667 val_loss: 0.25019, val_acc: 0.96000
Epoch [5190/10000], loss: 0.26537 acc: 0.90667 val_loss: 0.25001, val_acc: 0.96000
Epoch [5200/10000], loss: 0.26520 acc: 0.90667 val_loss: 0.24983, val_acc: 0.96000
Epoch [5210/10000], loss: 0.26502 acc: 0.90667 val_loss: 0.24965, val_acc: 0.96000
Epoch [5220/10000], loss: 0.26485 acc: 0.90667 val_loss: 0.24947, val_acc: 0.96000
Epoch [5230/10000], loss: 0.26468 acc: 0.90667 val_loss: 0.24929, val_acc: 0.96000
Epoch [5240/10000], loss: 0.26450 acc: 0.90667 val_loss: 0.24912, val_acc: 0.96000
Epoch [5250/10000], loss: 0.26433 acc: 0.90667 val_loss: 0.24894, val_acc: 0.96000
Epoch [5260/10000], loss: 0.26416 acc: 0.90667 val_loss: 0.24876, val_acc: 0.96000
Epoch [5270/10000], loss: 0.26399 acc: 0.90667 val_loss: 0.24858, val_acc: 0.96000
Epoch [5280/10000], loss: 0.26382 acc: 0.90667 val_loss: 0.24841, val_acc: 0.96000
Epoch [5290/10000], loss: 0.26365 acc: 0.90667 val_loss: 0.24823, val_acc: 0.96000
Epoch [5300/10000], loss: 0.26348 acc: 0.90667 val_loss: 0.24806, val_acc: 0.96000
Epoch [5310/10000], loss: 0.26331 acc: 0.90667 val_loss: 0.24788, val_acc: 0.96000
Epoch [5320/10000], loss: 0.26314 acc: 0.90667 val_loss: 0.24771, val_acc: 0.96000
Epoch [5330/10000], loss: 0.26297 acc: 0.90667 val_loss: 0.24753, val_acc: 0.96000
Epoch [5340/10000], loss: 0.26280 acc: 0.90667 val_loss: 0.24736, val_acc: 0.96000
Epoch [5350/10000], loss: 0.26264 acc: 0.90667 val_loss: 0.24719, val_acc: 0.96000
Epoch [5360/10000], loss: 0.26247 acc: 0.90667 val_loss: 0.24701, val_acc: 0.96000
Epoch [5370/10000], loss: 0.26230 acc: 0.90667 val_loss: 0.24684, val_acc: 0.96000
Epoch [5380/10000], loss: 0.26214 acc: 0.90667 val_loss: 0.24667, val_acc: 0.96000
Epoch [5390/10000], loss: 0.26197 acc: 0.90667 val_loss: 0.24650, val_acc: 0.96000
Epoch [5400/10000], loss: 0.26181 acc: 0.90667 val_loss: 0.24633, val_acc: 0.96000
Epoch [5410/10000], loss: 0.26164 acc: 0.90667 val_loss: 0.24616, val_acc: 0.96000
Epoch [5420/10000], loss: 0.26148 acc: 0.90667 val_loss: 0.24599, val_acc: 0.96000
Epoch [5430/10000], loss: 0.26131 acc: 0.90667 val_loss: 0.24582, val_acc: 0.96000
Epoch [5440/10000], loss: 0.26115 acc: 0.90667 val_loss: 0.24565, val_acc: 0.96000
Epoch [5450/10000], loss: 0.26099 acc: 0.90667 val_loss: 0.24548, val_acc: 0.96000
Epoch [5460/10000], loss: 0.26083 acc: 0.90667 val_loss: 0.24531, val_acc: 0.96000
Epoch [5470/10000], loss: 0.26066 acc: 0.90667 val_loss: 0.24514, val_acc: 0.96000
Epoch [5480/10000], loss: 0.26050 acc: 0.90667 val_loss: 0.24498, val_acc: 0.96000
Epoch [5490/10000], loss: 0.26034 acc: 0.90667 val_loss: 0.24481, val_acc: 0.96000
Epoch [5500/10000], loss: 0.26018 acc: 0.90667 val_loss: 0.24464, val_acc: 0.96000
Epoch [5510/10000], loss: 0.26002 acc: 0.90667 val_loss: 0.24448, val_acc: 0.96000
Epoch [5520/10000], loss: 0.25986 acc: 0.90667 val_loss: 0.24431, val_acc: 0.96000
Epoch [5530/10000], loss: 0.25970 acc: 0.90667 val_loss: 0.24415, val_acc: 0.96000
Epoch [5540/10000], loss: 0.25954 acc: 0.90667 val_loss: 0.24398, val_acc: 0.96000
Epoch [5550/10000], loss: 0.25939 acc: 0.90667 val_loss: 0.24382, val_acc: 0.96000
Epoch [5560/10000], loss: 0.25923 acc: 0.90667 val_loss: 0.24366, val_acc: 0.96000
Epoch [5570/10000], loss: 0.25907 acc: 0.90667 val_loss: 0.24349, val_acc: 0.96000
Epoch [5580/10000], loss: 0.25891 acc: 0.90667 val_loss: 0.24333, val_acc: 0.96000
Epoch [5590/10000], loss: 0.25876 acc: 0.90667 val_loss: 0.24317, val_acc: 0.96000
Epoch [5600/10000], loss: 0.25860 acc: 0.90667 val_loss: 0.24301, val_acc: 0.96000
Epoch [5610/10000], loss: 0.25845 acc: 0.90667 val_loss: 0.24285, val_acc: 0.96000
Epoch [5620/10000], loss: 0.25829 acc: 0.90667 val_loss: 0.24268, val_acc: 0.96000
Epoch [5630/10000], loss: 0.25814 acc: 0.90667 val_loss: 0.24252, val_acc: 0.96000
Epoch [5640/10000], loss: 0.25798 acc: 0.90667 val_loss: 0.24236, val_acc: 0.96000
Epoch [5650/10000], loss: 0.25783 acc: 0.90667 val_loss: 0.24220, val_acc: 0.96000
Epoch [5660/10000], loss: 0.25767 acc: 0.90667 val_loss: 0.24205, val_acc: 0.96000
Epoch [5670/10000], loss: 0.25752 acc: 0.90667 val_loss: 0.24189, val_acc: 0.96000
Epoch [5680/10000], loss: 0.25737 acc: 0.90667 val_loss: 0.24173, val_acc: 0.96000
Epoch [5690/10000], loss: 0.25722 acc: 0.90667 val_loss: 0.24157, val_acc: 0.96000
Epoch [5700/10000], loss: 0.25706 acc: 0.90667 val_loss: 0.24141, val_acc: 0.96000
Epoch [5710/10000], loss: 0.25691 acc: 0.90667 val_loss: 0.24126, val_acc: 0.96000
Epoch [5720/10000], loss: 0.25676 acc: 0.90667 val_loss: 0.24110, val_acc: 0.96000
Epoch [5730/10000], loss: 0.25661 acc: 0.90667 val_loss: 0.24094, val_acc: 0.96000
Epoch [5740/10000], loss: 0.25646 acc: 0.90667 val_loss: 0.24079, val_acc: 0.96000
Epoch [5750/10000], loss: 0.25631 acc: 0.90667 val_loss: 0.24063, val_acc: 0.96000
Epoch [5760/10000], loss: 0.25616 acc: 0.90667 val_loss: 0.24048, val_acc: 0.96000
Epoch [5770/10000], loss: 0.25601 acc: 0.90667 val_loss: 0.24032, val_acc: 0.96000
Epoch [5780/10000], loss: 0.25586 acc: 0.90667 val_loss: 0.24017, val_acc: 0.96000
Epoch [5790/10000], loss: 0.25571 acc: 0.90667 val_loss: 0.24001, val_acc: 0.96000
Epoch [5800/10000], loss: 0.25557 acc: 0.90667 val_loss: 0.23986, val_acc: 0.96000
Epoch [5810/10000], loss: 0.25542 acc: 0.90667 val_loss: 0.23971, val_acc: 0.96000
Epoch [5820/10000], loss: 0.25527 acc: 0.90667 val_loss: 0.23955, val_acc: 0.96000
Epoch [5830/10000], loss: 0.25513 acc: 0.90667 val_loss: 0.23940, val_acc: 0.96000
Epoch [5840/10000], loss: 0.25498 acc: 0.90667 val_loss: 0.23925, val_acc: 0.96000
Epoch [5850/10000], loss: 0.25483 acc: 0.90667 val_loss: 0.23910, val_acc: 0.96000
Epoch [5860/10000], loss: 0.25469 acc: 0.90667 val_loss: 0.23895, val_acc: 0.96000
Epoch [5870/10000], loss: 0.25454 acc: 0.90667 val_loss: 0.23879, val_acc: 0.96000
Epoch [5880/10000], loss: 0.25440 acc: 0.90667 val_loss: 0.23864, val_acc: 0.96000
Epoch [5890/10000], loss: 0.25425 acc: 0.90667 val_loss: 0.23849, val_acc: 0.96000
Epoch [5900/10000], loss: 0.25411 acc: 0.90667 val_loss: 0.23834, val_acc: 0.96000
Epoch [5910/10000], loss: 0.25397 acc: 0.90667 val_loss: 0.23819, val_acc: 0.96000
Epoch [5920/10000], loss: 0.25382 acc: 0.90667 val_loss: 0.23805, val_acc: 0.96000
Epoch [5930/10000], loss: 0.25368 acc: 0.90667 val_loss: 0.23790, val_acc: 0.96000
Epoch [5940/10000], loss: 0.25354 acc: 0.90667 val_loss: 0.23775, val_acc: 0.96000
Epoch [5950/10000], loss: 0.25340 acc: 0.90667 val_loss: 0.23760, val_acc: 0.96000
Epoch [5960/10000], loss: 0.25325 acc: 0.90667 val_loss: 0.23745, val_acc: 0.96000
Epoch [5970/10000], loss: 0.25311 acc: 0.90667 val_loss: 0.23731, val_acc: 0.96000
Epoch [5980/10000], loss: 0.25297 acc: 0.90667 val_loss: 0.23716, val_acc: 0.96000
Epoch [5990/10000], loss: 0.25283 acc: 0.90667 val_loss: 0.23701, val_acc: 0.96000
Epoch [6000/10000], loss: 0.25269 acc: 0.90667 val_loss: 0.23687, val_acc: 0.96000
Epoch [6010/10000], loss: 0.25255 acc: 0.90667 val_loss: 0.23672, val_acc: 0.96000
Epoch [6020/10000], loss: 0.25241 acc: 0.90667 val_loss: 0.23658, val_acc: 0.96000
Epoch [6030/10000], loss: 0.25227 acc: 0.90667 val_loss: 0.23643, val_acc: 0.96000
Epoch [6040/10000], loss: 0.25213 acc: 0.90667 val_loss: 0.23629, val_acc: 0.96000
Epoch [6050/10000], loss: 0.25199 acc: 0.90667 val_loss: 0.23614, val_acc: 0.96000
Epoch [6060/10000], loss: 0.25186 acc: 0.90667 val_loss: 0.23600, val_acc: 0.96000
Epoch [6070/10000], loss: 0.25172 acc: 0.90667 val_loss: 0.23586, val_acc: 0.96000
Epoch [6080/10000], loss: 0.25158 acc: 0.90667 val_loss: 0.23571, val_acc: 0.96000
Epoch [6090/10000], loss: 0.25144 acc: 0.90667 val_loss: 0.23557, val_acc: 0.96000
Epoch [6100/10000], loss: 0.25131 acc: 0.90667 val_loss: 0.23543, val_acc: 0.96000
Epoch [6110/10000], loss: 0.25117 acc: 0.90667 val_loss: 0.23529, val_acc: 0.96000
Epoch [6120/10000], loss: 0.25104 acc: 0.90667 val_loss: 0.23514, val_acc: 0.96000
Epoch [6130/10000], loss: 0.25090 acc: 0.90667 val_loss: 0.23500, val_acc: 0.96000
Epoch [6140/10000], loss: 0.25076 acc: 0.90667 val_loss: 0.23486, val_acc: 0.96000
Epoch [6150/10000], loss: 0.25063 acc: 0.90667 val_loss: 0.23472, val_acc: 0.96000
Epoch [6160/10000], loss: 0.25049 acc: 0.90667 val_loss: 0.23458, val_acc: 0.96000
Epoch [6170/10000], loss: 0.25036 acc: 0.90667 val_loss: 0.23444, val_acc: 0.96000
Epoch [6180/10000], loss: 0.25023 acc: 0.90667 val_loss: 0.23430, val_acc: 0.96000
Epoch [6190/10000], loss: 0.25009 acc: 0.90667 val_loss: 0.23416, val_acc: 0.96000
Epoch [6200/10000], loss: 0.24996 acc: 0.90667 val_loss: 0.23402, val_acc: 0.96000
Epoch [6210/10000], loss: 0.24983 acc: 0.90667 val_loss: 0.23388, val_acc: 0.96000
Epoch [6220/10000], loss: 0.24969 acc: 0.90667 val_loss: 0.23375, val_acc: 0.96000
Epoch [6230/10000], loss: 0.24956 acc: 0.90667 val_loss: 0.23361, val_acc: 0.96000
Epoch [6240/10000], loss: 0.24943 acc: 0.90667 val_loss: 0.23347, val_acc: 0.96000
Epoch [6250/10000], loss: 0.24930 acc: 0.90667 val_loss: 0.23333, val_acc: 0.96000
Epoch [6260/10000], loss: 0.24917 acc: 0.90667 val_loss: 0.23320, val_acc: 0.96000
Epoch [6270/10000], loss: 0.24904 acc: 0.90667 val_loss: 0.23306, val_acc: 0.96000
Epoch [6280/10000], loss: 0.24891 acc: 0.90667 val_loss: 0.23292, val_acc: 0.96000
Epoch [6290/10000], loss: 0.24878 acc: 0.90667 val_loss: 0.23279, val_acc: 0.96000
Epoch [6300/10000], loss: 0.24865 acc: 0.90667 val_loss: 0.23265, val_acc: 0.96000
Epoch [6310/10000], loss: 0.24852 acc: 0.90667 val_loss: 0.23252, val_acc: 0.96000
Epoch [6320/10000], loss: 0.24839 acc: 0.90667 val_loss: 0.23238, val_acc: 0.96000
Epoch [6330/10000], loss: 0.24826 acc: 0.90667 val_loss: 0.23225, val_acc: 0.96000
Epoch [6340/10000], loss: 0.24813 acc: 0.90667 val_loss: 0.23211, val_acc: 0.96000
Epoch [6350/10000], loss: 0.24800 acc: 0.90667 val_loss: 0.23198, val_acc: 0.96000
Epoch [6360/10000], loss: 0.24787 acc: 0.90667 val_loss: 0.23184, val_acc: 0.96000
Epoch [6370/10000], loss: 0.24775 acc: 0.90667 val_loss: 0.23171, val_acc: 0.96000
Epoch [6380/10000], loss: 0.24762 acc: 0.90667 val_loss: 0.23158, val_acc: 0.96000
Epoch [6390/10000], loss: 0.24749 acc: 0.90667 val_loss: 0.23145, val_acc: 0.96000
Epoch [6400/10000], loss: 0.24736 acc: 0.90667 val_loss: 0.23131, val_acc: 0.96000
Epoch [6410/10000], loss: 0.24724 acc: 0.90667 val_loss: 0.23118, val_acc: 0.96000
Epoch [6420/10000], loss: 0.24711 acc: 0.90667 val_loss: 0.23105, val_acc: 0.96000
Epoch [6430/10000], loss: 0.24699 acc: 0.90667 val_loss: 0.23092, val_acc: 0.96000
Epoch [6440/10000], loss: 0.24686 acc: 0.90667 val_loss: 0.23079, val_acc: 0.96000
Epoch [6450/10000], loss: 0.24674 acc: 0.90667 val_loss: 0.23066, val_acc: 0.96000
Epoch [6460/10000], loss: 0.24661 acc: 0.90667 val_loss: 0.23053, val_acc: 0.96000
Epoch [6470/10000], loss: 0.24649 acc: 0.90667 val_loss: 0.23040, val_acc: 0.96000
Epoch [6480/10000], loss: 0.24636 acc: 0.90667 val_loss: 0.23027, val_acc: 0.96000
Epoch [6490/10000], loss: 0.24624 acc: 0.90667 val_loss: 0.23014, val_acc: 0.96000
Epoch [6500/10000], loss: 0.24611 acc: 0.90667 val_loss: 0.23001, val_acc: 0.96000
Epoch [6510/10000], loss: 0.24599 acc: 0.90667 val_loss: 0.22988, val_acc: 0.96000
Epoch [6520/10000], loss: 0.24587 acc: 0.90667 val_loss: 0.22975, val_acc: 0.96000
Epoch [6530/10000], loss: 0.24575 acc: 0.90667 val_loss: 0.22962, val_acc: 0.96000
Epoch [6540/10000], loss: 0.24562 acc: 0.90667 val_loss: 0.22949, val_acc: 0.96000
Epoch [6550/10000], loss: 0.24550 acc: 0.90667 val_loss: 0.22936, val_acc: 0.96000
Epoch [6560/10000], loss: 0.24538 acc: 0.90667 val_loss: 0.22924, val_acc: 0.96000
Epoch [6570/10000], loss: 0.24526 acc: 0.90667 val_loss: 0.22911, val_acc: 0.96000
Epoch [6580/10000], loss: 0.24514 acc: 0.90667 val_loss: 0.22898, val_acc: 0.96000
Epoch [6590/10000], loss: 0.24502 acc: 0.90667 val_loss: 0.22886, val_acc: 0.96000
Epoch [6600/10000], loss: 0.24489 acc: 0.90667 val_loss: 0.22873, val_acc: 0.96000
Epoch [6610/10000], loss: 0.24477 acc: 0.90667 val_loss: 0.22860, val_acc: 0.96000
Epoch [6620/10000], loss: 0.24465 acc: 0.90667 val_loss: 0.22848, val_acc: 0.96000
Epoch [6630/10000], loss: 0.24453 acc: 0.90667 val_loss: 0.22835, val_acc: 0.96000
Epoch [6640/10000], loss: 0.24441 acc: 0.90667 val_loss: 0.22823, val_acc: 0.96000
Epoch [6650/10000], loss: 0.24430 acc: 0.90667 val_loss: 0.22810, val_acc: 0.96000
Epoch [6660/10000], loss: 0.24418 acc: 0.90667 val_loss: 0.22798, val_acc: 0.96000
Epoch [6670/10000], loss: 0.24406 acc: 0.90667 val_loss: 0.22785, val_acc: 0.96000
Epoch [6680/10000], loss: 0.24394 acc: 0.90667 val_loss: 0.22773, val_acc: 0.96000
Epoch [6690/10000], loss: 0.24382 acc: 0.90667 val_loss: 0.22761, val_acc: 0.96000
Epoch [6700/10000], loss: 0.24370 acc: 0.90667 val_loss: 0.22748, val_acc: 0.96000
Epoch [6710/10000], loss: 0.24359 acc: 0.90667 val_loss: 0.22736, val_acc: 0.96000
Epoch [6720/10000], loss: 0.24347 acc: 0.90667 val_loss: 0.22724, val_acc: 0.96000
Epoch [6730/10000], loss: 0.24335 acc: 0.90667 val_loss: 0.22711, val_acc: 0.96000
Epoch [6740/10000], loss: 0.24324 acc: 0.90667 val_loss: 0.22699, val_acc: 0.96000
Epoch [6750/10000], loss: 0.24312 acc: 0.90667 val_loss: 0.22687, val_acc: 0.96000
Epoch [6760/10000], loss: 0.24300 acc: 0.90667 val_loss: 0.22675, val_acc: 0.96000
Epoch [6770/10000], loss: 0.24289 acc: 0.90667 val_loss: 0.22663, val_acc: 0.96000
Epoch [6780/10000], loss: 0.24277 acc: 0.90667 val_loss: 0.22651, val_acc: 0.96000
Epoch [6790/10000], loss: 0.24266 acc: 0.90667 val_loss: 0.22638, val_acc: 0.96000
Epoch [6800/10000], loss: 0.24254 acc: 0.90667 val_loss: 0.22626, val_acc: 0.96000
Epoch [6810/10000], loss: 0.24243 acc: 0.90667 val_loss: 0.22614, val_acc: 0.96000
Epoch [6820/10000], loss: 0.24231 acc: 0.90667 val_loss: 0.22602, val_acc: 0.96000
Epoch [6830/10000], loss: 0.24220 acc: 0.90667 val_loss: 0.22590, val_acc: 0.96000
Epoch [6840/10000], loss: 0.24208 acc: 0.90667 val_loss: 0.22578, val_acc: 0.96000
Epoch [6850/10000], loss: 0.24197 acc: 0.90667 val_loss: 0.22566, val_acc: 0.96000
Epoch [6860/10000], loss: 0.24186 acc: 0.90667 val_loss: 0.22555, val_acc: 0.96000
Epoch [6870/10000], loss: 0.24174 acc: 0.90667 val_loss: 0.22543, val_acc: 0.96000
Epoch [6880/10000], loss: 0.24163 acc: 0.90667 val_loss: 0.22531, val_acc: 0.96000
Epoch [6890/10000], loss: 0.24152 acc: 0.90667 val_loss: 0.22519, val_acc: 0.96000
Epoch [6900/10000], loss: 0.24141 acc: 0.90667 val_loss: 0.22507, val_acc: 0.96000
Epoch [6910/10000], loss: 0.24129 acc: 0.90667 val_loss: 0.22495, val_acc: 0.96000
Epoch [6920/10000], loss: 0.24118 acc: 0.90667 val_loss: 0.22484, val_acc: 0.96000
Epoch [6930/10000], loss: 0.24107 acc: 0.90667 val_loss: 0.22472, val_acc: 0.96000
Epoch [6940/10000], loss: 0.24096 acc: 0.90667 val_loss: 0.22460, val_acc: 0.96000
Epoch [6950/10000], loss: 0.24085 acc: 0.90667 val_loss: 0.22449, val_acc: 0.96000
Epoch [6960/10000], loss: 0.24074 acc: 0.90667 val_loss: 0.22437, val_acc: 0.96000
Epoch [6970/10000], loss: 0.24063 acc: 0.90667 val_loss: 0.22425, val_acc: 0.96000
Epoch [6980/10000], loss: 0.24052 acc: 0.90667 val_loss: 0.22414, val_acc: 0.96000
Epoch [6990/10000], loss: 0.24041 acc: 0.90667 val_loss: 0.22402, val_acc: 0.96000
Epoch [7000/10000], loss: 0.24030 acc: 0.90667 val_loss: 0.22391, val_acc: 0.96000
Epoch [7010/10000], loss: 0.24019 acc: 0.90667 val_loss: 0.22379, val_acc: 0.96000
Epoch [7020/10000], loss: 0.24008 acc: 0.90667 val_loss: 0.22368, val_acc: 0.96000
Epoch [7030/10000], loss: 0.23997 acc: 0.90667 val_loss: 0.22356, val_acc: 0.96000
Epoch [7040/10000], loss: 0.23986 acc: 0.90667 val_loss: 0.22345, val_acc: 0.96000
Epoch [7050/10000], loss: 0.23975 acc: 0.90667 val_loss: 0.22333, val_acc: 0.96000
Epoch [7060/10000], loss: 0.23964 acc: 0.90667 val_loss: 0.22322, val_acc: 0.96000
Epoch [7070/10000], loss: 0.23953 acc: 0.90667 val_loss: 0.22311, val_acc: 0.96000
Epoch [7080/10000], loss: 0.23943 acc: 0.90667 val_loss: 0.22299, val_acc: 0.96000
Epoch [7090/10000], loss: 0.23932 acc: 0.90667 val_loss: 0.22288, val_acc: 0.96000
Epoch [7100/10000], loss: 0.23921 acc: 0.90667 val_loss: 0.22277, val_acc: 0.96000
Epoch [7110/10000], loss: 0.23910 acc: 0.90667 val_loss: 0.22265, val_acc: 0.96000
Epoch [7120/10000], loss: 0.23900 acc: 0.90667 val_loss: 0.22254, val_acc: 0.96000
Epoch [7130/10000], loss: 0.23889 acc: 0.90667 val_loss: 0.22243, val_acc: 0.96000
Epoch [7140/10000], loss: 0.23879 acc: 0.90667 val_loss: 0.22232, val_acc: 0.96000
Epoch [7150/10000], loss: 0.23868 acc: 0.90667 val_loss: 0.22221, val_acc: 0.96000
Epoch [7160/10000], loss: 0.23857 acc: 0.90667 val_loss: 0.22209, val_acc: 0.96000
Epoch [7170/10000], loss: 0.23847 acc: 0.90667 val_loss: 0.22198, val_acc: 0.96000
Epoch [7180/10000], loss: 0.23836 acc: 0.90667 val_loss: 0.22187, val_acc: 0.96000
Epoch [7190/10000], loss: 0.23826 acc: 0.90667 val_loss: 0.22176, val_acc: 0.96000
Epoch [7200/10000], loss: 0.23815 acc: 0.90667 val_loss: 0.22165, val_acc: 0.96000
Epoch [7210/10000], loss: 0.23805 acc: 0.90667 val_loss: 0.22154, val_acc: 0.96000
Epoch [7220/10000], loss: 0.23794 acc: 0.90667 val_loss: 0.22143, val_acc: 0.96000
Epoch [7230/10000], loss: 0.23784 acc: 0.90667 val_loss: 0.22132, val_acc: 0.96000
Epoch [7240/10000], loss: 0.23774 acc: 0.90667 val_loss: 0.22121, val_acc: 0.96000
Epoch [7250/10000], loss: 0.23763 acc: 0.90667 val_loss: 0.22110, val_acc: 0.96000
Epoch [7260/10000], loss: 0.23753 acc: 0.90667 val_loss: 0.22099, val_acc: 0.96000
Epoch [7270/10000], loss: 0.23742 acc: 0.90667 val_loss: 0.22088, val_acc: 0.96000
Epoch [7280/10000], loss: 0.23732 acc: 0.90667 val_loss: 0.22078, val_acc: 0.96000
Epoch [7290/10000], loss: 0.23722 acc: 0.90667 val_loss: 0.22067, val_acc: 0.96000
Epoch [7300/10000], loss: 0.23712 acc: 0.90667 val_loss: 0.22056, val_acc: 0.96000
Epoch [7310/10000], loss: 0.23701 acc: 0.90667 val_loss: 0.22045, val_acc: 0.96000
Epoch [7320/10000], loss: 0.23691 acc: 0.90667 val_loss: 0.22034, val_acc: 0.96000
Epoch [7330/10000], loss: 0.23681 acc: 0.90667 val_loss: 0.22024, val_acc: 0.96000
Epoch [7340/10000], loss: 0.23671 acc: 0.90667 val_loss: 0.22013, val_acc: 0.96000
Epoch [7350/10000], loss: 0.23661 acc: 0.90667 val_loss: 0.22002, val_acc: 0.96000
Epoch [7360/10000], loss: 0.23651 acc: 0.90667 val_loss: 0.21992, val_acc: 0.96000
Epoch [7370/10000], loss: 0.23640 acc: 0.90667 val_loss: 0.21981, val_acc: 0.96000
Epoch [7380/10000], loss: 0.23630 acc: 0.90667 val_loss: 0.21970, val_acc: 0.96000
Epoch [7390/10000], loss: 0.23620 acc: 0.90667 val_loss: 0.21960, val_acc: 0.96000
Epoch [7400/10000], loss: 0.23610 acc: 0.90667 val_loss: 0.21949, val_acc: 0.96000
Epoch [7410/10000], loss: 0.23600 acc: 0.90667 val_loss: 0.21939, val_acc: 0.96000
Epoch [7420/10000], loss: 0.23590 acc: 0.90667 val_loss: 0.21928, val_acc: 0.96000
Epoch [7430/10000], loss: 0.23580 acc: 0.90667 val_loss: 0.21918, val_acc: 0.96000
Epoch [7440/10000], loss: 0.23570 acc: 0.90667 val_loss: 0.21907, val_acc: 0.96000
Epoch [7450/10000], loss: 0.23560 acc: 0.90667 val_loss: 0.21897, val_acc: 0.96000
Epoch [7460/10000], loss: 0.23551 acc: 0.90667 val_loss: 0.21886, val_acc: 0.96000
Epoch [7470/10000], loss: 0.23541 acc: 0.90667 val_loss: 0.21876, val_acc: 0.96000
Epoch [7480/10000], loss: 0.23531 acc: 0.90667 val_loss: 0.21865, val_acc: 0.96000
Epoch [7490/10000], loss: 0.23521 acc: 0.90667 val_loss: 0.21855, val_acc: 0.96000
Epoch [7500/10000], loss: 0.23511 acc: 0.90667 val_loss: 0.21845, val_acc: 0.96000
Epoch [7510/10000], loss: 0.23501 acc: 0.90667 val_loss: 0.21834, val_acc: 0.96000
Epoch [7520/10000], loss: 0.23492 acc: 0.90667 val_loss: 0.21824, val_acc: 0.96000
Epoch [7530/10000], loss: 0.23482 acc: 0.90667 val_loss: 0.21814, val_acc: 0.96000
Epoch [7540/10000], loss: 0.23472 acc: 0.90667 val_loss: 0.21803, val_acc: 0.96000
Epoch [7550/10000], loss: 0.23462 acc: 0.90667 val_loss: 0.21793, val_acc: 0.96000
Epoch [7560/10000], loss: 0.23453 acc: 0.90667 val_loss: 0.21783, val_acc: 0.96000
Epoch [7570/10000], loss: 0.23443 acc: 0.90667 val_loss: 0.21773, val_acc: 0.96000
Epoch [7580/10000], loss: 0.23433 acc: 0.90667 val_loss: 0.21762, val_acc: 0.96000
Epoch [7590/10000], loss: 0.23424 acc: 0.90667 val_loss: 0.21752, val_acc: 0.96000
Epoch [7600/10000], loss: 0.23414 acc: 0.90667 val_loss: 0.21742, val_acc: 0.96000
Epoch [7610/10000], loss: 0.23405 acc: 0.90667 val_loss: 0.21732, val_acc: 0.96000
Epoch [7620/10000], loss: 0.23395 acc: 0.90667 val_loss: 0.21722, val_acc: 0.96000
Epoch [7630/10000], loss: 0.23385 acc: 0.90667 val_loss: 0.21712, val_acc: 0.96000
Epoch [7640/10000], loss: 0.23376 acc: 0.90667 val_loss: 0.21702, val_acc: 0.96000
Epoch [7650/10000], loss: 0.23366 acc: 0.90667 val_loss: 0.21692, val_acc: 0.96000
Epoch [7660/10000], loss: 0.23357 acc: 0.90667 val_loss: 0.21682, val_acc: 0.96000
Epoch [7670/10000], loss: 0.23348 acc: 0.90667 val_loss: 0.21672, val_acc: 0.96000
Epoch [7680/10000], loss: 0.23338 acc: 0.90667 val_loss: 0.21662, val_acc: 0.96000
Epoch [7690/10000], loss: 0.23329 acc: 0.90667 val_loss: 0.21652, val_acc: 0.96000
Epoch [7700/10000], loss: 0.23319 acc: 0.90667 val_loss: 0.21642, val_acc: 0.96000
Epoch [7710/10000], loss: 0.23310 acc: 0.90667 val_loss: 0.21632, val_acc: 0.96000
Epoch [7720/10000], loss: 0.23301 acc: 0.90667 val_loss: 0.21622, val_acc: 0.96000
Epoch [7730/10000], loss: 0.23291 acc: 0.90667 val_loss: 0.21612, val_acc: 0.96000
Epoch [7740/10000], loss: 0.23282 acc: 0.90667 val_loss: 0.21602, val_acc: 0.96000
Epoch [7750/10000], loss: 0.23273 acc: 0.90667 val_loss: 0.21592, val_acc: 0.96000
Epoch [7760/10000], loss: 0.23263 acc: 0.90667 val_loss: 0.21582, val_acc: 0.96000
Epoch [7770/10000], loss: 0.23254 acc: 0.90667 val_loss: 0.21573, val_acc: 0.96000
Epoch [7780/10000], loss: 0.23245 acc: 0.90667 val_loss: 0.21563, val_acc: 0.96000
Epoch [7790/10000], loss: 0.23236 acc: 0.90667 val_loss: 0.21553, val_acc: 0.96000
Epoch [7800/10000], loss: 0.23226 acc: 0.90667 val_loss: 0.21543, val_acc: 0.96000
Epoch [7810/10000], loss: 0.23217 acc: 0.90667 val_loss: 0.21534, val_acc: 0.96000
Epoch [7820/10000], loss: 0.23208 acc: 0.90667 val_loss: 0.21524, val_acc: 0.96000
Epoch [7830/10000], loss: 0.23199 acc: 0.90667 val_loss: 0.21514, val_acc: 0.96000
Epoch [7840/10000], loss: 0.23190 acc: 0.90667 val_loss: 0.21505, val_acc: 0.96000
Epoch [7850/10000], loss: 0.23181 acc: 0.90667 val_loss: 0.21495, val_acc: 0.96000
Epoch [7860/10000], loss: 0.23172 acc: 0.90667 val_loss: 0.21485, val_acc: 0.96000
Epoch [7870/10000], loss: 0.23162 acc: 0.90667 val_loss: 0.21476, val_acc: 0.96000
Epoch [7880/10000], loss: 0.23153 acc: 0.90667 val_loss: 0.21466, val_acc: 0.96000
Epoch [7890/10000], loss: 0.23144 acc: 0.90667 val_loss: 0.21457, val_acc: 0.96000
Epoch [7900/10000], loss: 0.23135 acc: 0.90667 val_loss: 0.21447, val_acc: 0.96000
Epoch [7910/10000], loss: 0.23126 acc: 0.90667 val_loss: 0.21437, val_acc: 0.96000
Epoch [7920/10000], loss: 0.23117 acc: 0.90667 val_loss: 0.21428, val_acc: 0.96000
Epoch [7930/10000], loss: 0.23108 acc: 0.90667 val_loss: 0.21418, val_acc: 0.96000
Epoch [7940/10000], loss: 0.23099 acc: 0.90667 val_loss: 0.21409, val_acc: 0.96000
Epoch [7950/10000], loss: 0.23091 acc: 0.90667 val_loss: 0.21400, val_acc: 0.96000
Epoch [7960/10000], loss: 0.23082 acc: 0.90667 val_loss: 0.21390, val_acc: 0.96000
Epoch [7970/10000], loss: 0.23073 acc: 0.90667 val_loss: 0.21381, val_acc: 0.96000
Epoch [7980/10000], loss: 0.23064 acc: 0.90667 val_loss: 0.21371, val_acc: 0.96000
Epoch [7990/10000], loss: 0.23055 acc: 0.90667 val_loss: 0.21362, val_acc: 0.96000
Epoch [8000/10000], loss: 0.23046 acc: 0.90667 val_loss: 0.21353, val_acc: 0.96000
Epoch [8010/10000], loss: 0.23037 acc: 0.90667 val_loss: 0.21343, val_acc: 0.96000
Epoch [8020/10000], loss: 0.23029 acc: 0.90667 val_loss: 0.21334, val_acc: 0.96000
Epoch [8030/10000], loss: 0.23020 acc: 0.90667 val_loss: 0.21325, val_acc: 0.96000
Epoch [8040/10000], loss: 0.23011 acc: 0.90667 val_loss: 0.21315, val_acc: 0.96000
Epoch [8050/10000], loss: 0.23002 acc: 0.90667 val_loss: 0.21306, val_acc: 0.96000
Epoch [8060/10000], loss: 0.22994 acc: 0.90667 val_loss: 0.21297, val_acc: 0.96000
Epoch [8070/10000], loss: 0.22985 acc: 0.90667 val_loss: 0.21288, val_acc: 0.96000
Epoch [8080/10000], loss: 0.22976 acc: 0.90667 val_loss: 0.21278, val_acc: 0.96000
Epoch [8090/10000], loss: 0.22968 acc: 0.90667 val_loss: 0.21269, val_acc: 0.96000
Epoch [8100/10000], loss: 0.22959 acc: 0.90667 val_loss: 0.21260, val_acc: 0.96000
Epoch [8110/10000], loss: 0.22950 acc: 0.90667 val_loss: 0.21251, val_acc: 0.96000
Epoch [8120/10000], loss: 0.22942 acc: 0.90667 val_loss: 0.21242, val_acc: 0.96000
Epoch [8130/10000], loss: 0.22933 acc: 0.90667 val_loss: 0.21233, val_acc: 0.96000
Epoch [8140/10000], loss: 0.22925 acc: 0.90667 val_loss: 0.21223, val_acc: 0.96000
Epoch [8150/10000], loss: 0.22916 acc: 0.90667 val_loss: 0.21214, val_acc: 0.96000
Epoch [8160/10000], loss: 0.22907 acc: 0.90667 val_loss: 0.21205, val_acc: 0.96000
Epoch [8170/10000], loss: 0.22899 acc: 0.90667 val_loss: 0.21196, val_acc: 0.96000
Epoch [8180/10000], loss: 0.22890 acc: 0.90667 val_loss: 0.21187, val_acc: 0.96000
Epoch [8190/10000], loss: 0.22882 acc: 0.90667 val_loss: 0.21178, val_acc: 0.96000
Epoch [8200/10000], loss: 0.22873 acc: 0.90667 val_loss: 0.21169, val_acc: 0.96000
Epoch [8210/10000], loss: 0.22865 acc: 0.90667 val_loss: 0.21160, val_acc: 0.96000
Epoch [8220/10000], loss: 0.22857 acc: 0.90667 val_loss: 0.21151, val_acc: 0.96000
Epoch [8230/10000], loss: 0.22848 acc: 0.90667 val_loss: 0.21142, val_acc: 0.96000
Epoch [8240/10000], loss: 0.22840 acc: 0.90667 val_loss: 0.21133, val_acc: 0.96000
Epoch [8250/10000], loss: 0.22831 acc: 0.90667 val_loss: 0.21124, val_acc: 0.96000
Epoch [8260/10000], loss: 0.22823 acc: 0.90667 val_loss: 0.21115, val_acc: 0.96000
Epoch [8270/10000], loss: 0.22815 acc: 0.90667 val_loss: 0.21107, val_acc: 0.96000
Epoch [8280/10000], loss: 0.22806 acc: 0.90667 val_loss: 0.21098, val_acc: 0.96000
Epoch [8290/10000], loss: 0.22798 acc: 0.90667 val_loss: 0.21089, val_acc: 0.96000
Epoch [8300/10000], loss: 0.22790 acc: 0.90667 val_loss: 0.21080, val_acc: 0.96000
Epoch [8310/10000], loss: 0.22781 acc: 0.90667 val_loss: 0.21071, val_acc: 0.96000
Epoch [8320/10000], loss: 0.22773 acc: 0.90667 val_loss: 0.21062, val_acc: 0.96000
Epoch [8330/10000], loss: 0.22765 acc: 0.90667 val_loss: 0.21054, val_acc: 0.96000
Epoch [8340/10000], loss: 0.22757 acc: 0.90667 val_loss: 0.21045, val_acc: 0.96000
Epoch [8350/10000], loss: 0.22748 acc: 0.90667 val_loss: 0.21036, val_acc: 0.96000
Epoch [8360/10000], loss: 0.22740 acc: 0.90667 val_loss: 0.21027, val_acc: 0.96000
Epoch [8370/10000], loss: 0.22732 acc: 0.90667 val_loss: 0.21019, val_acc: 0.96000
Epoch [8380/10000], loss: 0.22724 acc: 0.90667 val_loss: 0.21010, val_acc: 0.96000
Epoch [8390/10000], loss: 0.22716 acc: 0.90667 val_loss: 0.21001, val_acc: 0.96000
Epoch [8400/10000], loss: 0.22708 acc: 0.90667 val_loss: 0.20993, val_acc: 0.96000
Epoch [8410/10000], loss: 0.22699 acc: 0.90667 val_loss: 0.20984, val_acc: 0.96000
Epoch [8420/10000], loss: 0.22691 acc: 0.90667 val_loss: 0.20975, val_acc: 0.96000
Epoch [8430/10000], loss: 0.22683 acc: 0.90667 val_loss: 0.20967, val_acc: 0.96000
Epoch [8440/10000], loss: 0.22675 acc: 0.90667 val_loss: 0.20958, val_acc: 0.96000
Epoch [8450/10000], loss: 0.22667 acc: 0.90667 val_loss: 0.20949, val_acc: 0.96000
Epoch [8460/10000], loss: 0.22659 acc: 0.90667 val_loss: 0.20941, val_acc: 0.96000
Epoch [8470/10000], loss: 0.22651 acc: 0.90667 val_loss: 0.20932, val_acc: 0.96000
Epoch [8480/10000], loss: 0.22643 acc: 0.90667 val_loss: 0.20924, val_acc: 0.96000
Epoch [8490/10000], loss: 0.22635 acc: 0.90667 val_loss: 0.20915, val_acc: 0.96000
Epoch [8500/10000], loss: 0.22627 acc: 0.90667 val_loss: 0.20907, val_acc: 0.96000
Epoch [8510/10000], loss: 0.22619 acc: 0.90667 val_loss: 0.20898, val_acc: 0.96000
Epoch [8520/10000], loss: 0.22611 acc: 0.90667 val_loss: 0.20890, val_acc: 0.96000
Epoch [8530/10000], loss: 0.22603 acc: 0.90667 val_loss: 0.20881, val_acc: 0.96000
Epoch [8540/10000], loss: 0.22595 acc: 0.90667 val_loss: 0.20873, val_acc: 0.96000
Epoch [8550/10000], loss: 0.22587 acc: 0.90667 val_loss: 0.20865, val_acc: 0.96000
Epoch [8560/10000], loss: 0.22579 acc: 0.90667 val_loss: 0.20856, val_acc: 0.96000
Epoch [8570/10000], loss: 0.22572 acc: 0.90667 val_loss: 0.20848, val_acc: 0.96000
Epoch [8580/10000], loss: 0.22564 acc: 0.90667 val_loss: 0.20839, val_acc: 0.96000
Epoch [8590/10000], loss: 0.22556 acc: 0.90667 val_loss: 0.20831, val_acc: 0.96000
Epoch [8600/10000], loss: 0.22548 acc: 0.90667 val_loss: 0.20823, val_acc: 0.96000
Epoch [8610/10000], loss: 0.22540 acc: 0.90667 val_loss: 0.20814, val_acc: 0.96000
Epoch [8620/10000], loss: 0.22532 acc: 0.90667 val_loss: 0.20806, val_acc: 0.96000
Epoch [8630/10000], loss: 0.22525 acc: 0.90667 val_loss: 0.20798, val_acc: 0.96000
Epoch [8640/10000], loss: 0.22517 acc: 0.90667 val_loss: 0.20789, val_acc: 0.96000
Epoch [8650/10000], loss: 0.22509 acc: 0.90667 val_loss: 0.20781, val_acc: 0.96000
Epoch [8660/10000], loss: 0.22501 acc: 0.90667 val_loss: 0.20773, val_acc: 0.96000
Epoch [8670/10000], loss: 0.22494 acc: 0.90667 val_loss: 0.20765, val_acc: 0.96000
Epoch [8680/10000], loss: 0.22486 acc: 0.90667 val_loss: 0.20756, val_acc: 0.96000
Epoch [8690/10000], loss: 0.22478 acc: 0.90667 val_loss: 0.20748, val_acc: 0.96000
Epoch [8700/10000], loss: 0.22471 acc: 0.90667 val_loss: 0.20740, val_acc: 0.96000
Epoch [8710/10000], loss: 0.22463 acc: 0.90667 val_loss: 0.20732, val_acc: 0.96000
Epoch [8720/10000], loss: 0.22455 acc: 0.90667 val_loss: 0.20724, val_acc: 0.96000
Epoch [8730/10000], loss: 0.22448 acc: 0.90667 val_loss: 0.20716, val_acc: 0.96000
Epoch [8740/10000], loss: 0.22440 acc: 0.90667 val_loss: 0.20707, val_acc: 0.96000
Epoch [8750/10000], loss: 0.22432 acc: 0.90667 val_loss: 0.20699, val_acc: 0.96000
Epoch [8760/10000], loss: 0.22425 acc: 0.90667 val_loss: 0.20691, val_acc: 0.96000
Epoch [8770/10000], loss: 0.22417 acc: 0.90667 val_loss: 0.20683, val_acc: 0.96000
Epoch [8780/10000], loss: 0.22410 acc: 0.90667 val_loss: 0.20675, val_acc: 0.96000
Epoch [8790/10000], loss: 0.22402 acc: 0.90667 val_loss: 0.20667, val_acc: 0.96000
Epoch [8800/10000], loss: 0.22395 acc: 0.90667 val_loss: 0.20659, val_acc: 0.96000
Epoch [8810/10000], loss: 0.22387 acc: 0.90667 val_loss: 0.20651, val_acc: 0.96000
Epoch [8820/10000], loss: 0.22380 acc: 0.90667 val_loss: 0.20643, val_acc: 0.96000
Epoch [8830/10000], loss: 0.22372 acc: 0.90667 val_loss: 0.20635, val_acc: 0.96000
Epoch [8840/10000], loss: 0.22365 acc: 0.90667 val_loss: 0.20627, val_acc: 0.96000
Epoch [8850/10000], loss: 0.22357 acc: 0.90667 val_loss: 0.20619, val_acc: 0.96000
Epoch [8860/10000], loss: 0.22350 acc: 0.90667 val_loss: 0.20611, val_acc: 0.96000
Epoch [8870/10000], loss: 0.22342 acc: 0.90667 val_loss: 0.20603, val_acc: 0.96000
Epoch [8880/10000], loss: 0.22335 acc: 0.90667 val_loss: 0.20595, val_acc: 0.96000
Epoch [8890/10000], loss: 0.22327 acc: 0.90667 val_loss: 0.20587, val_acc: 0.96000
Epoch [8900/10000], loss: 0.22320 acc: 0.90667 val_loss: 0.20579, val_acc: 0.96000
Epoch [8910/10000], loss: 0.22313 acc: 0.90667 val_loss: 0.20571, val_acc: 0.96000
Epoch [8920/10000], loss: 0.22305 acc: 0.90667 val_loss: 0.20563, val_acc: 0.96000
Epoch [8930/10000], loss: 0.22298 acc: 0.90667 val_loss: 0.20556, val_acc: 0.96000
Epoch [8940/10000], loss: 0.22291 acc: 0.90667 val_loss: 0.20548, val_acc: 0.96000
Epoch [8950/10000], loss: 0.22283 acc: 0.90667 val_loss: 0.20540, val_acc: 0.96000
Epoch [8960/10000], loss: 0.22276 acc: 0.90667 val_loss: 0.20532, val_acc: 0.96000
Epoch [8970/10000], loss: 0.22269 acc: 0.90667 val_loss: 0.20524, val_acc: 0.96000
Epoch [8980/10000], loss: 0.22261 acc: 0.90667 val_loss: 0.20517, val_acc: 0.96000
Epoch [8990/10000], loss: 0.22254 acc: 0.90667 val_loss: 0.20509, val_acc: 0.96000
Epoch [9000/10000], loss: 0.22247 acc: 0.90667 val_loss: 0.20501, val_acc: 0.96000
Epoch [9010/10000], loss: 0.22240 acc: 0.90667 val_loss: 0.20493, val_acc: 0.96000
Epoch [9020/10000], loss: 0.22232 acc: 0.90667 val_loss: 0.20485, val_acc: 0.96000
Epoch [9030/10000], loss: 0.22225 acc: 0.90667 val_loss: 0.20478, val_acc: 0.96000
Epoch [9040/10000], loss: 0.22218 acc: 0.90667 val_loss: 0.20470, val_acc: 0.96000
Epoch [9050/10000], loss: 0.22211 acc: 0.90667 val_loss: 0.20462, val_acc: 0.96000
Epoch [9060/10000], loss: 0.22204 acc: 0.90667 val_loss: 0.20455, val_acc: 0.96000
Epoch [9070/10000], loss: 0.22196 acc: 0.90667 val_loss: 0.20447, val_acc: 0.96000
Epoch [9080/10000], loss: 0.22189 acc: 0.90667 val_loss: 0.20439, val_acc: 0.96000
Epoch [9090/10000], loss: 0.22182 acc: 0.90667 val_loss: 0.20432, val_acc: 0.96000
Epoch [9100/10000], loss: 0.22175 acc: 0.90667 val_loss: 0.20424, val_acc: 0.96000
Epoch [9110/10000], loss: 0.22168 acc: 0.90667 val_loss: 0.20416, val_acc: 0.96000
Epoch [9120/10000], loss: 0.22161 acc: 0.90667 val_loss: 0.20409, val_acc: 0.96000
Epoch [9130/10000], loss: 0.22154 acc: 0.90667 val_loss: 0.20401, val_acc: 0.96000
Epoch [9140/10000], loss: 0.22147 acc: 0.90667 val_loss: 0.20394, val_acc: 0.96000
Epoch [9150/10000], loss: 0.22140 acc: 0.90667 val_loss: 0.20386, val_acc: 0.96000
Epoch [9160/10000], loss: 0.22133 acc: 0.90667 val_loss: 0.20379, val_acc: 0.96000
Epoch [9170/10000], loss: 0.22126 acc: 0.90667 val_loss: 0.20371, val_acc: 0.96000
Epoch [9180/10000], loss: 0.22118 acc: 0.90667 val_loss: 0.20364, val_acc: 0.96000
Epoch [9190/10000], loss: 0.22111 acc: 0.90667 val_loss: 0.20356, val_acc: 0.96000
Epoch [9200/10000], loss: 0.22105 acc: 0.90667 val_loss: 0.20349, val_acc: 0.96000
Epoch [9210/10000], loss: 0.22098 acc: 0.90667 val_loss: 0.20341, val_acc: 0.96000
Epoch [9220/10000], loss: 0.22091 acc: 0.90667 val_loss: 0.20334, val_acc: 0.96000
Epoch [9230/10000], loss: 0.22084 acc: 0.90667 val_loss: 0.20326, val_acc: 0.96000
Epoch [9240/10000], loss: 0.22077 acc: 0.90667 val_loss: 0.20319, val_acc: 0.96000
Epoch [9250/10000], loss: 0.22070 acc: 0.90667 val_loss: 0.20311, val_acc: 0.96000
Epoch [9260/10000], loss: 0.22063 acc: 0.90667 val_loss: 0.20304, val_acc: 0.96000
Epoch [9270/10000], loss: 0.22056 acc: 0.90667 val_loss: 0.20296, val_acc: 0.96000
Epoch [9280/10000], loss: 0.22049 acc: 0.90667 val_loss: 0.20289, val_acc: 0.96000
Epoch [9290/10000], loss: 0.22042 acc: 0.90667 val_loss: 0.20282, val_acc: 0.96000
Epoch [9300/10000], loss: 0.22035 acc: 0.90667 val_loss: 0.20274, val_acc: 0.96000
Epoch [9310/10000], loss: 0.22028 acc: 0.90667 val_loss: 0.20267, val_acc: 0.96000
Epoch [9320/10000], loss: 0.22022 acc: 0.90667 val_loss: 0.20260, val_acc: 0.96000
Epoch [9330/10000], loss: 0.22015 acc: 0.90667 val_loss: 0.20252, val_acc: 0.96000
Epoch [9340/10000], loss: 0.22008 acc: 0.90667 val_loss: 0.20245, val_acc: 0.96000
Epoch [9350/10000], loss: 0.22001 acc: 0.90667 val_loss: 0.20238, val_acc: 0.96000
Epoch [9360/10000], loss: 0.21994 acc: 0.90667 val_loss: 0.20230, val_acc: 0.96000
Epoch [9370/10000], loss: 0.21988 acc: 0.90667 val_loss: 0.20223, val_acc: 0.96000
Epoch [9380/10000], loss: 0.21981 acc: 0.90667 val_loss: 0.20216, val_acc: 0.96000
Epoch [9390/10000], loss: 0.21974 acc: 0.90667 val_loss: 0.20209, val_acc: 0.96000
Epoch [9400/10000], loss: 0.21967 acc: 0.90667 val_loss: 0.20201, val_acc: 0.96000
Epoch [9410/10000], loss: 0.21961 acc: 0.90667 val_loss: 0.20194, val_acc: 0.96000
Epoch [9420/10000], loss: 0.21954 acc: 0.90667 val_loss: 0.20187, val_acc: 0.96000
Epoch [9430/10000], loss: 0.21947 acc: 0.90667 val_loss: 0.20180, val_acc: 0.96000
Epoch [9440/10000], loss: 0.21940 acc: 0.90667 val_loss: 0.20173, val_acc: 0.96000
Epoch [9450/10000], loss: 0.21934 acc: 0.90667 val_loss: 0.20165, val_acc: 0.96000
Epoch [9460/10000], loss: 0.21927 acc: 0.90667 val_loss: 0.20158, val_acc: 0.96000
Epoch [9470/10000], loss: 0.21920 acc: 0.90667 val_loss: 0.20151, val_acc: 0.96000
Epoch [9480/10000], loss: 0.21914 acc: 0.90667 val_loss: 0.20144, val_acc: 0.96000
Epoch [9490/10000], loss: 0.21907 acc: 0.90667 val_loss: 0.20137, val_acc: 0.96000
Epoch [9500/10000], loss: 0.21901 acc: 0.90667 val_loss: 0.20130, val_acc: 0.96000
Epoch [9510/10000], loss: 0.21894 acc: 0.90667 val_loss: 0.20123, val_acc: 0.96000
Epoch [9520/10000], loss: 0.21887 acc: 0.90667 val_loss: 0.20115, val_acc: 0.96000
Epoch [9530/10000], loss: 0.21881 acc: 0.90667 val_loss: 0.20108, val_acc: 0.96000
Epoch [9540/10000], loss: 0.21874 acc: 0.90667 val_loss: 0.20101, val_acc: 0.96000
Epoch [9550/10000], loss: 0.21868 acc: 0.90667 val_loss: 0.20094, val_acc: 0.96000
Epoch [9560/10000], loss: 0.21861 acc: 0.90667 val_loss: 0.20087, val_acc: 0.96000
Epoch [9570/10000], loss: 0.21854 acc: 0.90667 val_loss: 0.20080, val_acc: 0.96000
Epoch [9580/10000], loss: 0.21848 acc: 0.90667 val_loss: 0.20073, val_acc: 0.96000
Epoch [9590/10000], loss: 0.21841 acc: 0.90667 val_loss: 0.20066, val_acc: 0.96000
Epoch [9600/10000], loss: 0.21835 acc: 0.90667 val_loss: 0.20059, val_acc: 0.96000
Epoch [9610/10000], loss: 0.21828 acc: 0.90667 val_loss: 0.20052, val_acc: 0.96000
Epoch [9620/10000], loss: 0.21822 acc: 0.90667 val_loss: 0.20045, val_acc: 0.96000
Epoch [9630/10000], loss: 0.21815 acc: 0.90667 val_loss: 0.20038, val_acc: 0.96000
Epoch [9640/10000], loss: 0.21809 acc: 0.90667 val_loss: 0.20031, val_acc: 0.96000
Epoch [9650/10000], loss: 0.21803 acc: 0.90667 val_loss: 0.20024, val_acc: 0.96000
Epoch [9660/10000], loss: 0.21796 acc: 0.90667 val_loss: 0.20017, val_acc: 0.96000
Epoch [9670/10000], loss: 0.21790 acc: 0.90667 val_loss: 0.20010, val_acc: 0.96000
Epoch [9680/10000], loss: 0.21783 acc: 0.90667 val_loss: 0.20004, val_acc: 0.96000
Epoch [9690/10000], loss: 0.21777 acc: 0.90667 val_loss: 0.19997, val_acc: 0.96000
Epoch [9700/10000], loss: 0.21770 acc: 0.90667 val_loss: 0.19990, val_acc: 0.96000
Epoch [9710/10000], loss: 0.21764 acc: 0.90667 val_loss: 0.19983, val_acc: 0.96000
Epoch [9720/10000], loss: 0.21758 acc: 0.90667 val_loss: 0.19976, val_acc: 0.96000
Epoch [9730/10000], loss: 0.21751 acc: 0.90667 val_loss: 0.19969, val_acc: 0.96000
Epoch [9740/10000], loss: 0.21745 acc: 0.90667 val_loss: 0.19962, val_acc: 0.96000
Epoch [9750/10000], loss: 0.21739 acc: 0.90667 val_loss: 0.19956, val_acc: 0.96000
Epoch [9760/10000], loss: 0.21732 acc: 0.90667 val_loss: 0.19949, val_acc: 0.96000
Epoch [9770/10000], loss: 0.21726 acc: 0.90667 val_loss: 0.19942, val_acc: 0.96000
Epoch [9780/10000], loss: 0.21720 acc: 0.90667 val_loss: 0.19935, val_acc: 0.96000
Epoch [9790/10000], loss: 0.21713 acc: 0.90667 val_loss: 0.19928, val_acc: 0.96000
Epoch [9800/10000], loss: 0.21707 acc: 0.90667 val_loss: 0.19922, val_acc: 0.96000
Epoch [9810/10000], loss: 0.21701 acc: 0.90667 val_loss: 0.19915, val_acc: 0.96000
Epoch [9820/10000], loss: 0.21695 acc: 0.90667 val_loss: 0.19908, val_acc: 0.96000
Epoch [9830/10000], loss: 0.21688 acc: 0.90667 val_loss: 0.19901, val_acc: 0.96000
Epoch [9840/10000], loss: 0.21682 acc: 0.90667 val_loss: 0.19895, val_acc: 0.96000
Epoch [9850/10000], loss: 0.21676 acc: 0.90667 val_loss: 0.19888, val_acc: 0.96000
Epoch [9860/10000], loss: 0.21670 acc: 0.90667 val_loss: 0.19881, val_acc: 0.96000
Epoch [9870/10000], loss: 0.21663 acc: 0.90667 val_loss: 0.19874, val_acc: 0.96000
Epoch [9880/10000], loss: 0.21657 acc: 0.90667 val_loss: 0.19868, val_acc: 0.96000
Epoch [9890/10000], loss: 0.21651 acc: 0.90667 val_loss: 0.19861, val_acc: 0.96000
Epoch [9900/10000], loss: 0.21645 acc: 0.90667 val_loss: 0.19854, val_acc: 0.96000
Epoch [9910/10000], loss: 0.21639 acc: 0.90667 val_loss: 0.19848, val_acc: 0.96000
Epoch [9920/10000], loss: 0.21633 acc: 0.90667 val_loss: 0.19841, val_acc: 0.96000
Epoch [9930/10000], loss: 0.21626 acc: 0.90667 val_loss: 0.19835, val_acc: 0.96000
Epoch [9940/10000], loss: 0.21620 acc: 0.90667 val_loss: 0.19828, val_acc: 0.96000
Epoch [9950/10000], loss: 0.21614 acc: 0.90667 val_loss: 0.19821, val_acc: 0.96000
Epoch [9960/10000], loss: 0.21608 acc: 0.90667 val_loss: 0.19815, val_acc: 0.96000
Epoch [9970/10000], loss: 0.21602 acc: 0.90667 val_loss: 0.19808, val_acc: 0.96000
Epoch [9980/10000], loss: 0.21596 acc: 0.90667 val_loss: 0.19802, val_acc: 0.96000
Epoch [9990/10000], loss: 0.21590 acc: 0.90667 val_loss: 0.19795, val_acc: 0.96000

결과 확인

# 손실과 정확도 확인
 
print(f'초기상태 : 손실 : {history[0,3]:.5f}  정확도 : {history[0,4]:.5f}' )
print(f'최종상태 : 손실 : {history[-1,3]:.5f}  정확도 : {history[-1,4]:.5f}' )

초기상태 : 손실 : 1.09263  정확도 : 0.26667
최종상태 : 손실 : 0.19795  정확도 : 0.96000

# 학습 곡선 출력(손실)
 
plt.plot(history[:,0], history[:,1], 'b', label='훈련')
plt.plot(history[:,0], history[:,3], 'k', label='검증')
plt.xlabel('반복 횟수')
plt.ylabel('손실')
plt.title('학습 곡선(손실)')
plt.legend()
plt.show()

모델 출력 확인

# 정답 데이터의 0번째, 2번째, 3번째를 추출
 
print(labels[[0,2,3]])
 
# 이에 해당하는 입력값을 추출
print("="*50)
i3 = inputs[[0,2,3],:]
print(i3.data.numpy())

tensor([1, 0, 2])
==================================================
[[6.3 4.7]
 [5.  1.6]
 [6.4 5.6]]

# 출력값에 소프트맥스 함수를 적용한 결과를 취득
 
softmax = torch.nn.Softmax(dim=1)
o3 = net(i3)
k3 = softmax(o3)
print(o3.data.numpy())
print(k3.data.numpy())

[[ 8.8071 14.1937 12.9986]
 [12.8262  9.8     0.1734]
 [ 6.7954 15.0928 17.1111]]
[[0.0035 0.765  0.2315]
 [0.9537 0.0463 0.    ]
 [0.     0.1173 0.8827]]

가중치 행렬과 바이어스 값

# 가중치 행렬
print(net.l1.weight.data)
 
# 바이어스
print(net.l1.bias.data)

tensor([[ 3.0452, -2.5735],
        [ 1.3573,  0.8481],
        [-1.4026,  4.7253]])
tensor([ 1.7178,  1.6563, -0.3741])

입력 변수 4개 사용하기

# 훈련 데이터와 검증 데이터로 분할(셔플도 동시에 실시함)
 
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(
    x_org, y_org, train_size=75, test_size=75,
    random_state=123)
print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)
 
# 입력 차원수
n_input = x_train.shape[1]

(75, 4) (75, 4) (75,) (75,)

print('입력 데이터(x)')
print(x_train[:5,:])
print(f'입력 차원수: {n_input}')

입력 데이터(x)
[[6.3 3.3 4.7 1.6]
 [7.  3.2 4.7 1.4]
 [5.  3.  1.6 0.2]
 [6.4 2.8 5.6 2.1]
 [6.3 2.5 5.  1.9]]
입력 차원수: 4

# 입력 데이터 x_train과 정답 데이터 y_train의 텐서 변수화
inputs = torch.tensor(x_train).float()
labels = torch.tensor(y_train).long()
 
# 검증용 데이터의 텐서 변수화
inputs_test = torch.tensor(x_test).float()
labels_test = torch.tensor(y_test).long()

# 학습률
lr = 0.01
 
# 초기화
net = Net(n_input, n_output)
 
# 손실 함수： 교차 엔트로피 함수
criterion = nn.CrossEntropyLoss()
 
# 최적화 알고리즘: 경사 하강법
optimizer = optim.SGD(net.parameters(), lr=lr)
 
# 반복 횟수
num_epochs = 10000
 
# 평가 결과 기록
history = np.zeros((0,5))

for epoch in range(num_epochs):
 
    # 훈련 페이즈
 
    # 경사 초기화
    optimizer.zero_grad()
 
    # 예측 계산
    outputs = net(inputs)
 
    # 손실 계산
    loss = criterion(outputs, labels)
 
    # 경사 계산
    loss.backward()
 
    # 파라미터 수정
    optimizer.step()
 
    # 예측 라벨 산출
    predicted = torch.max(outputs, 1)[1]
 
    # 손실과 정확도 계산
    train_loss = loss.item()
    train_acc = (predicted == labels).sum()  / len(labels)
 
    # 예측 페이즈
 
    # 예측 계산
    outputs_test = net(inputs_test)
 
    # 손실 계산
    loss_test = criterion(outputs_test, labels_test)
 
    # 예측 라벨 산출
    predicted_test = torch.max(outputs_test, 1)[1]
 
    # 손실과 정확도 계산
    val_loss =  loss_test.item()
    val_acc =  (predicted_test == labels_test).sum() / len(labels_test)
 
    if ( epoch % 10 == 0):
        print (f'Epoch [{epoch}/{num_epochs}], loss: {train_loss:.5f} acc: {train_acc:.5f} val_loss: {val_loss:.5f}, val_acc: {val_acc:.5f}')
        item = np.array([epoch , train_loss, train_acc, val_loss, val_acc])
        history = np.vstack((history, item))

Epoch [0/10000], loss: 1.09861 acc: 0.30667 val_loss: 1.09158, val_acc: 0.26667
Epoch [10/10000], loss: 1.01848 acc: 0.40000 val_loss: 1.04171, val_acc: 0.26667
Epoch [20/10000], loss: 0.96854 acc: 0.40000 val_loss: 0.98850, val_acc: 0.26667
Epoch [30/10000], loss: 0.92459 acc: 0.65333 val_loss: 0.93996, val_acc: 0.57333
Epoch [40/10000], loss: 0.88568 acc: 0.70667 val_loss: 0.89704, val_acc: 0.62667
Epoch [50/10000], loss: 0.85120 acc: 0.70667 val_loss: 0.85918, val_acc: 0.62667
Epoch [60/10000], loss: 0.82059 acc: 0.70667 val_loss: 0.82572, val_acc: 0.62667
Epoch [70/10000], loss: 0.79335 acc: 0.72000 val_loss: 0.79607, val_acc: 0.62667
Epoch [80/10000], loss: 0.76900 acc: 0.72000 val_loss: 0.76968, val_acc: 0.65333
Epoch [90/10000], loss: 0.74717 acc: 0.72000 val_loss: 0.74610, val_acc: 0.65333
Epoch [100/10000], loss: 0.72750 acc: 0.76000 val_loss: 0.72494, val_acc: 0.69333
Epoch [110/10000], loss: 0.70970 acc: 0.77333 val_loss: 0.70585, val_acc: 0.74667
Epoch [120/10000], loss: 0.69354 acc: 0.81333 val_loss: 0.68856, val_acc: 0.76000
Epoch [130/10000], loss: 0.67878 acc: 0.84000 val_loss: 0.67283, val_acc: 0.76000
Epoch [140/10000], loss: 0.66526 acc: 0.84000 val_loss: 0.65846, val_acc: 0.78667
Epoch [150/10000], loss: 0.65283 acc: 0.86667 val_loss: 0.64528, val_acc: 0.78667
Epoch [160/10000], loss: 0.64135 acc: 0.88000 val_loss: 0.63313, val_acc: 0.78667
Epoch [170/10000], loss: 0.63070 acc: 0.89333 val_loss: 0.62190, val_acc: 0.81333
Epoch [180/10000], loss: 0.62081 acc: 0.90667 val_loss: 0.61149, val_acc: 0.81333
Epoch [190/10000], loss: 0.61157 acc: 0.90667 val_loss: 0.60179, val_acc: 0.84000
Epoch [200/10000], loss: 0.60292 acc: 0.90667 val_loss: 0.59273, val_acc: 0.84000
Epoch [210/10000], loss: 0.59481 acc: 0.90667 val_loss: 0.58425, val_acc: 0.88000
Epoch [220/10000], loss: 0.58717 acc: 0.93333 val_loss: 0.57628, val_acc: 0.88000
Epoch [230/10000], loss: 0.57996 acc: 0.93333 val_loss: 0.56877, val_acc: 0.89333
Epoch [240/10000], loss: 0.57313 acc: 0.93333 val_loss: 0.56169, val_acc: 0.90667
Epoch [250/10000], loss: 0.56666 acc: 0.93333 val_loss: 0.55498, val_acc: 0.90667
Epoch [260/10000], loss: 0.56051 acc: 0.92000 val_loss: 0.54862, val_acc: 0.90667
Epoch [270/10000], loss: 0.55465 acc: 0.92000 val_loss: 0.54257, val_acc: 0.90667
Epoch [280/10000], loss: 0.54906 acc: 0.92000 val_loss: 0.53681, val_acc: 0.90667
Epoch [290/10000], loss: 0.54371 acc: 0.92000 val_loss: 0.53131, val_acc: 0.90667
Epoch [300/10000], loss: 0.53859 acc: 0.93333 val_loss: 0.52605, val_acc: 0.90667
Epoch [310/10000], loss: 0.53368 acc: 0.93333 val_loss: 0.52102, val_acc: 0.90667
Epoch [320/10000], loss: 0.52896 acc: 0.93333 val_loss: 0.51619, val_acc: 0.90667
Epoch [330/10000], loss: 0.52442 acc: 0.93333 val_loss: 0.51155, val_acc: 0.90667
Epoch [340/10000], loss: 0.52004 acc: 0.93333 val_loss: 0.50709, val_acc: 0.90667
Epoch [350/10000], loss: 0.51582 acc: 0.93333 val_loss: 0.50280, val_acc: 0.90667
Epoch [360/10000], loss: 0.51173 acc: 0.93333 val_loss: 0.49865, val_acc: 0.90667
Epoch [370/10000], loss: 0.50779 acc: 0.93333 val_loss: 0.49465, val_acc: 0.90667
Epoch [380/10000], loss: 0.50397 acc: 0.93333 val_loss: 0.49078, val_acc: 0.90667
Epoch [390/10000], loss: 0.50026 acc: 0.93333 val_loss: 0.48703, val_acc: 0.90667
Epoch [400/10000], loss: 0.49666 acc: 0.94667 val_loss: 0.48340, val_acc: 0.90667
Epoch [410/10000], loss: 0.49317 acc: 0.94667 val_loss: 0.47988, val_acc: 0.90667
Epoch [420/10000], loss: 0.48978 acc: 0.94667 val_loss: 0.47647, val_acc: 0.90667
Epoch [430/10000], loss: 0.48647 acc: 0.96000 val_loss: 0.47315, val_acc: 0.90667
Epoch [440/10000], loss: 0.48326 acc: 0.96000 val_loss: 0.46992, val_acc: 0.90667
Epoch [450/10000], loss: 0.48012 acc: 0.96000 val_loss: 0.46678, val_acc: 0.90667
Epoch [460/10000], loss: 0.47706 acc: 0.96000 val_loss: 0.46372, val_acc: 0.90667
Epoch [470/10000], loss: 0.47408 acc: 0.96000 val_loss: 0.46073, val_acc: 0.90667
Epoch [480/10000], loss: 0.47116 acc: 0.96000 val_loss: 0.45783, val_acc: 0.90667
Epoch [490/10000], loss: 0.46831 acc: 0.96000 val_loss: 0.45499, val_acc: 0.90667
Epoch [500/10000], loss: 0.46553 acc: 0.96000 val_loss: 0.45221, val_acc: 0.90667
Epoch [510/10000], loss: 0.46280 acc: 0.96000 val_loss: 0.44951, val_acc: 0.90667
Epoch [520/10000], loss: 0.46013 acc: 0.96000 val_loss: 0.44686, val_acc: 0.90667
Epoch [530/10000], loss: 0.45752 acc: 0.96000 val_loss: 0.44426, val_acc: 0.90667
Epoch [540/10000], loss: 0.45496 acc: 0.96000 val_loss: 0.44173, val_acc: 0.90667
Epoch [550/10000], loss: 0.45245 acc: 0.96000 val_loss: 0.43924, val_acc: 0.90667
Epoch [560/10000], loss: 0.44998 acc: 0.96000 val_loss: 0.43681, val_acc: 0.90667
Epoch [570/10000], loss: 0.44757 acc: 0.96000 val_loss: 0.43442, val_acc: 0.90667
Epoch [580/10000], loss: 0.44519 acc: 0.96000 val_loss: 0.43208, val_acc: 0.90667
Epoch [590/10000], loss: 0.44286 acc: 0.96000 val_loss: 0.42979, val_acc: 0.92000
Epoch [600/10000], loss: 0.44057 acc: 0.96000 val_loss: 0.42753, val_acc: 0.92000
Epoch [610/10000], loss: 0.43832 acc: 0.96000 val_loss: 0.42532, val_acc: 0.92000
Epoch [620/10000], loss: 0.43611 acc: 0.96000 val_loss: 0.42315, val_acc: 0.92000
Epoch [630/10000], loss: 0.43393 acc: 0.96000 val_loss: 0.42101, val_acc: 0.92000
Epoch [640/10000], loss: 0.43179 acc: 0.96000 val_loss: 0.41891, val_acc: 0.92000
Epoch [650/10000], loss: 0.42968 acc: 0.96000 val_loss: 0.41685, val_acc: 0.92000
Epoch [660/10000], loss: 0.42761 acc: 0.96000 val_loss: 0.41482, val_acc: 0.92000
Epoch [670/10000], loss: 0.42556 acc: 0.96000 val_loss: 0.41282, val_acc: 0.92000
Epoch [680/10000], loss: 0.42355 acc: 0.96000 val_loss: 0.41085, val_acc: 0.92000
Epoch [690/10000], loss: 0.42157 acc: 0.96000 val_loss: 0.40892, val_acc: 0.92000
Epoch [700/10000], loss: 0.41961 acc: 0.96000 val_loss: 0.40701, val_acc: 0.92000
Epoch [710/10000], loss: 0.41768 acc: 0.96000 val_loss: 0.40513, val_acc: 0.92000
Epoch [720/10000], loss: 0.41578 acc: 0.96000 val_loss: 0.40329, val_acc: 0.92000
Epoch [730/10000], loss: 0.41391 acc: 0.96000 val_loss: 0.40146, val_acc: 0.92000
Epoch [740/10000], loss: 0.41206 acc: 0.96000 val_loss: 0.39967, val_acc: 0.92000
Epoch [750/10000], loss: 0.41024 acc: 0.96000 val_loss: 0.39789, val_acc: 0.92000
Epoch [760/10000], loss: 0.40844 acc: 0.96000 val_loss: 0.39615, val_acc: 0.92000
Epoch [770/10000], loss: 0.40666 acc: 0.96000 val_loss: 0.39443, val_acc: 0.93333
Epoch [780/10000], loss: 0.40491 acc: 0.96000 val_loss: 0.39273, val_acc: 0.93333
Epoch [790/10000], loss: 0.40317 acc: 0.96000 val_loss: 0.39105, val_acc: 0.93333
Epoch [800/10000], loss: 0.40146 acc: 0.96000 val_loss: 0.38939, val_acc: 0.93333
Epoch [810/10000], loss: 0.39977 acc: 0.96000 val_loss: 0.38776, val_acc: 0.93333
Epoch [820/10000], loss: 0.39810 acc: 0.96000 val_loss: 0.38615, val_acc: 0.93333
Epoch [830/10000], loss: 0.39646 acc: 0.96000 val_loss: 0.38456, val_acc: 0.93333
Epoch [840/10000], loss: 0.39483 acc: 0.96000 val_loss: 0.38298, val_acc: 0.93333
Epoch [850/10000], loss: 0.39321 acc: 0.97333 val_loss: 0.38143, val_acc: 0.94667
Epoch [860/10000], loss: 0.39162 acc: 0.97333 val_loss: 0.37990, val_acc: 0.94667
Epoch [870/10000], loss: 0.39005 acc: 0.97333 val_loss: 0.37838, val_acc: 0.94667
Epoch [880/10000], loss: 0.38849 acc: 0.97333 val_loss: 0.37688, val_acc: 0.94667
Epoch [890/10000], loss: 0.38695 acc: 0.97333 val_loss: 0.37540, val_acc: 0.94667
Epoch [900/10000], loss: 0.38543 acc: 0.97333 val_loss: 0.37394, val_acc: 0.94667
Epoch [910/10000], loss: 0.38392 acc: 0.97333 val_loss: 0.37249, val_acc: 0.94667
Epoch [920/10000], loss: 0.38243 acc: 0.97333 val_loss: 0.37106, val_acc: 0.94667
Epoch [930/10000], loss: 0.38096 acc: 0.97333 val_loss: 0.36965, val_acc: 0.94667
Epoch [940/10000], loss: 0.37950 acc: 0.97333 val_loss: 0.36825, val_acc: 0.94667
Epoch [950/10000], loss: 0.37806 acc: 0.97333 val_loss: 0.36686, val_acc: 0.94667
Epoch [960/10000], loss: 0.37663 acc: 0.97333 val_loss: 0.36550, val_acc: 0.96000
Epoch [970/10000], loss: 0.37522 acc: 0.97333 val_loss: 0.36414, val_acc: 0.96000
Epoch [980/10000], loss: 0.37382 acc: 0.97333 val_loss: 0.36280, val_acc: 0.96000
Epoch [990/10000], loss: 0.37243 acc: 0.97333 val_loss: 0.36148, val_acc: 0.96000
Epoch [1000/10000], loss: 0.37106 acc: 0.97333 val_loss: 0.36017, val_acc: 0.96000
Epoch [1010/10000], loss: 0.36970 acc: 0.97333 val_loss: 0.35887, val_acc: 0.96000
Epoch [1020/10000], loss: 0.36836 acc: 0.97333 val_loss: 0.35758, val_acc: 0.96000
Epoch [1030/10000], loss: 0.36703 acc: 0.97333 val_loss: 0.35631, val_acc: 0.96000
Epoch [1040/10000], loss: 0.36571 acc: 0.97333 val_loss: 0.35505, val_acc: 0.96000
Epoch [1050/10000], loss: 0.36440 acc: 0.97333 val_loss: 0.35381, val_acc: 0.96000
Epoch [1060/10000], loss: 0.36311 acc: 0.97333 val_loss: 0.35258, val_acc: 0.96000
Epoch [1070/10000], loss: 0.36183 acc: 0.97333 val_loss: 0.35135, val_acc: 0.96000
Epoch [1080/10000], loss: 0.36056 acc: 0.97333 val_loss: 0.35014, val_acc: 0.96000
Epoch [1090/10000], loss: 0.35930 acc: 0.97333 val_loss: 0.34895, val_acc: 0.96000
Epoch [1100/10000], loss: 0.35805 acc: 0.97333 val_loss: 0.34776, val_acc: 0.96000
Epoch [1110/10000], loss: 0.35682 acc: 0.97333 val_loss: 0.34659, val_acc: 0.96000
Epoch [1120/10000], loss: 0.35559 acc: 0.97333 val_loss: 0.34542, val_acc: 0.96000
Epoch [1130/10000], loss: 0.35438 acc: 0.97333 val_loss: 0.34427, val_acc: 0.96000
Epoch [1140/10000], loss: 0.35318 acc: 0.97333 val_loss: 0.34313, val_acc: 0.96000
Epoch [1150/10000], loss: 0.35199 acc: 0.97333 val_loss: 0.34199, val_acc: 0.96000
Epoch [1160/10000], loss: 0.35081 acc: 0.97333 val_loss: 0.34087, val_acc: 0.96000
Epoch [1170/10000], loss: 0.34964 acc: 0.97333 val_loss: 0.33976, val_acc: 0.96000
Epoch [1180/10000], loss: 0.34848 acc: 0.97333 val_loss: 0.33866, val_acc: 0.96000
Epoch [1190/10000], loss: 0.34732 acc: 0.97333 val_loss: 0.33757, val_acc: 0.96000
Epoch [1200/10000], loss: 0.34618 acc: 0.97333 val_loss: 0.33649, val_acc: 0.96000
Epoch [1210/10000], loss: 0.34505 acc: 0.97333 val_loss: 0.33542, val_acc: 0.96000
Epoch [1220/10000], loss: 0.34393 acc: 0.97333 val_loss: 0.33435, val_acc: 0.96000
Epoch [1230/10000], loss: 0.34282 acc: 0.97333 val_loss: 0.33330, val_acc: 0.96000
Epoch [1240/10000], loss: 0.34172 acc: 0.97333 val_loss: 0.33226, val_acc: 0.96000
Epoch [1250/10000], loss: 0.34062 acc: 0.97333 val_loss: 0.33122, val_acc: 0.96000
Epoch [1260/10000], loss: 0.33954 acc: 0.97333 val_loss: 0.33020, val_acc: 0.96000
Epoch [1270/10000], loss: 0.33846 acc: 0.97333 val_loss: 0.32918, val_acc: 0.96000
Epoch [1280/10000], loss: 0.33740 acc: 0.97333 val_loss: 0.32817, val_acc: 0.96000
Epoch [1290/10000], loss: 0.33634 acc: 0.97333 val_loss: 0.32717, val_acc: 0.96000
Epoch [1300/10000], loss: 0.33529 acc: 0.97333 val_loss: 0.32618, val_acc: 0.96000
Epoch [1310/10000], loss: 0.33425 acc: 0.97333 val_loss: 0.32520, val_acc: 0.96000
Epoch [1320/10000], loss: 0.33321 acc: 0.97333 val_loss: 0.32422, val_acc: 0.96000
Epoch [1330/10000], loss: 0.33219 acc: 0.97333 val_loss: 0.32325, val_acc: 0.96000
Epoch [1340/10000], loss: 0.33117 acc: 0.97333 val_loss: 0.32229, val_acc: 0.96000
Epoch [1350/10000], loss: 0.33016 acc: 0.97333 val_loss: 0.32134, val_acc: 0.96000
Epoch [1360/10000], loss: 0.32916 acc: 0.97333 val_loss: 0.32040, val_acc: 0.96000
Epoch [1370/10000], loss: 0.32817 acc: 0.97333 val_loss: 0.31946, val_acc: 0.96000
Epoch [1380/10000], loss: 0.32719 acc: 0.97333 val_loss: 0.31853, val_acc: 0.96000
Epoch [1390/10000], loss: 0.32621 acc: 0.97333 val_loss: 0.31761, val_acc: 0.96000
Epoch [1400/10000], loss: 0.32524 acc: 0.97333 val_loss: 0.31670, val_acc: 0.96000
Epoch [1410/10000], loss: 0.32428 acc: 0.97333 val_loss: 0.31579, val_acc: 0.96000
Epoch [1420/10000], loss: 0.32332 acc: 0.97333 val_loss: 0.31490, val_acc: 0.96000
Epoch [1430/10000], loss: 0.32237 acc: 0.97333 val_loss: 0.31400, val_acc: 0.96000
Epoch [1440/10000], loss: 0.32143 acc: 0.97333 val_loss: 0.31312, val_acc: 0.96000
Epoch [1450/10000], loss: 0.32050 acc: 0.97333 val_loss: 0.31224, val_acc: 0.96000
Epoch [1460/10000], loss: 0.31957 acc: 0.97333 val_loss: 0.31137, val_acc: 0.96000
Epoch [1470/10000], loss: 0.31865 acc: 0.97333 val_loss: 0.31050, val_acc: 0.96000
Epoch [1480/10000], loss: 0.31774 acc: 0.97333 val_loss: 0.30964, val_acc: 0.96000
Epoch [1490/10000], loss: 0.31683 acc: 0.97333 val_loss: 0.30879, val_acc: 0.96000
Epoch [1500/10000], loss: 0.31593 acc: 0.97333 val_loss: 0.30795, val_acc: 0.96000
Epoch [1510/10000], loss: 0.31504 acc: 0.97333 val_loss: 0.30711, val_acc: 0.96000
Epoch [1520/10000], loss: 0.31415 acc: 0.97333 val_loss: 0.30628, val_acc: 0.96000
Epoch [1530/10000], loss: 0.31327 acc: 0.97333 val_loss: 0.30545, val_acc: 0.96000
Epoch [1540/10000], loss: 0.31240 acc: 0.97333 val_loss: 0.30463, val_acc: 0.96000
Epoch [1550/10000], loss: 0.31153 acc: 0.97333 val_loss: 0.30382, val_acc: 0.96000
Epoch [1560/10000], loss: 0.31067 acc: 0.97333 val_loss: 0.30301, val_acc: 0.96000
Epoch [1570/10000], loss: 0.30981 acc: 0.97333 val_loss: 0.30221, val_acc: 0.96000
Epoch [1580/10000], loss: 0.30896 acc: 0.97333 val_loss: 0.30141, val_acc: 0.96000
Epoch [1590/10000], loss: 0.30812 acc: 0.97333 val_loss: 0.30062, val_acc: 0.96000
Epoch [1600/10000], loss: 0.30728 acc: 0.97333 val_loss: 0.29984, val_acc: 0.96000
Epoch [1610/10000], loss: 0.30645 acc: 0.97333 val_loss: 0.29906, val_acc: 0.96000
Epoch [1620/10000], loss: 0.30562 acc: 0.97333 val_loss: 0.29828, val_acc: 0.96000
Epoch [1630/10000], loss: 0.30480 acc: 0.97333 val_loss: 0.29752, val_acc: 0.96000
Epoch [1640/10000], loss: 0.30399 acc: 0.97333 val_loss: 0.29675, val_acc: 0.96000
Epoch [1650/10000], loss: 0.30318 acc: 0.97333 val_loss: 0.29600, val_acc: 0.96000
Epoch [1660/10000], loss: 0.30237 acc: 0.97333 val_loss: 0.29525, val_acc: 0.96000
Epoch [1670/10000], loss: 0.30158 acc: 0.97333 val_loss: 0.29450, val_acc: 0.96000
Epoch [1680/10000], loss: 0.30078 acc: 0.97333 val_loss: 0.29376, val_acc: 0.96000
Epoch [1690/10000], loss: 0.30000 acc: 0.97333 val_loss: 0.29302, val_acc: 0.96000
Epoch [1700/10000], loss: 0.29922 acc: 0.97333 val_loss: 0.29229, val_acc: 0.96000
Epoch [1710/10000], loss: 0.29844 acc: 0.97333 val_loss: 0.29157, val_acc: 0.96000
Epoch [1720/10000], loss: 0.29767 acc: 0.97333 val_loss: 0.29085, val_acc: 0.96000
Epoch [1730/10000], loss: 0.29690 acc: 0.97333 val_loss: 0.29013, val_acc: 0.96000
Epoch [1740/10000], loss: 0.29614 acc: 0.97333 val_loss: 0.28942, val_acc: 0.96000
Epoch [1750/10000], loss: 0.29538 acc: 0.97333 val_loss: 0.28872, val_acc: 0.96000
Epoch [1760/10000], loss: 0.29463 acc: 0.97333 val_loss: 0.28801, val_acc: 0.96000
Epoch [1770/10000], loss: 0.29389 acc: 0.97333 val_loss: 0.28732, val_acc: 0.96000
Epoch [1780/10000], loss: 0.29315 acc: 0.97333 val_loss: 0.28663, val_acc: 0.96000
Epoch [1790/10000], loss: 0.29241 acc: 0.97333 val_loss: 0.28594, val_acc: 0.96000
Epoch [1800/10000], loss: 0.29168 acc: 0.97333 val_loss: 0.28526, val_acc: 0.96000
Epoch [1810/10000], loss: 0.29095 acc: 0.97333 val_loss: 0.28458, val_acc: 0.96000
Epoch [1820/10000], loss: 0.29023 acc: 0.97333 val_loss: 0.28391, val_acc: 0.96000
Epoch [1830/10000], loss: 0.28951 acc: 0.97333 val_loss: 0.28324, val_acc: 0.96000
Epoch [1840/10000], loss: 0.28880 acc: 0.97333 val_loss: 0.28258, val_acc: 0.96000
Epoch [1850/10000], loss: 0.28809 acc: 0.97333 val_loss: 0.28192, val_acc: 0.96000
Epoch [1860/10000], loss: 0.28739 acc: 0.97333 val_loss: 0.28126, val_acc: 0.96000
Epoch [1870/10000], loss: 0.28669 acc: 0.97333 val_loss: 0.28061, val_acc: 0.96000
Epoch [1880/10000], loss: 0.28599 acc: 0.97333 val_loss: 0.27996, val_acc: 0.96000
Epoch [1890/10000], loss: 0.28530 acc: 0.97333 val_loss: 0.27932, val_acc: 0.96000
Epoch [1900/10000], loss: 0.28462 acc: 0.97333 val_loss: 0.27868, val_acc: 0.96000
Epoch [1910/10000], loss: 0.28394 acc: 0.97333 val_loss: 0.27805, val_acc: 0.96000
Epoch [1920/10000], loss: 0.28326 acc: 0.97333 val_loss: 0.27742, val_acc: 0.96000
Epoch [1930/10000], loss: 0.28258 acc: 0.97333 val_loss: 0.27679, val_acc: 0.96000
Epoch [1940/10000], loss: 0.28192 acc: 0.97333 val_loss: 0.27617, val_acc: 0.96000
Epoch [1950/10000], loss: 0.28125 acc: 0.97333 val_loss: 0.27555, val_acc: 0.96000
Epoch [1960/10000], loss: 0.28059 acc: 0.97333 val_loss: 0.27494, val_acc: 0.96000
Epoch [1970/10000], loss: 0.27993 acc: 0.97333 val_loss: 0.27433, val_acc: 0.96000
Epoch [1980/10000], loss: 0.27928 acc: 0.97333 val_loss: 0.27372, val_acc: 0.96000
Epoch [1990/10000], loss: 0.27863 acc: 0.97333 val_loss: 0.27312, val_acc: 0.96000
Epoch [2000/10000], loss: 0.27799 acc: 0.97333 val_loss: 0.27252, val_acc: 0.96000
Epoch [2010/10000], loss: 0.27735 acc: 0.97333 val_loss: 0.27193, val_acc: 0.96000
Epoch [2020/10000], loss: 0.27671 acc: 0.97333 val_loss: 0.27134, val_acc: 0.96000
Epoch [2030/10000], loss: 0.27608 acc: 0.97333 val_loss: 0.27075, val_acc: 0.96000
Epoch [2040/10000], loss: 0.27545 acc: 0.97333 val_loss: 0.27016, val_acc: 0.96000
Epoch [2050/10000], loss: 0.27482 acc: 0.97333 val_loss: 0.26958, val_acc: 0.96000
Epoch [2060/10000], loss: 0.27420 acc: 0.97333 val_loss: 0.26901, val_acc: 0.96000
Epoch [2070/10000], loss: 0.27358 acc: 0.97333 val_loss: 0.26843, val_acc: 0.96000
Epoch [2080/10000], loss: 0.27297 acc: 0.97333 val_loss: 0.26786, val_acc: 0.96000
Epoch [2090/10000], loss: 0.27236 acc: 0.97333 val_loss: 0.26730, val_acc: 0.96000
Epoch [2100/10000], loss: 0.27175 acc: 0.97333 val_loss: 0.26674, val_acc: 0.96000
Epoch [2110/10000], loss: 0.27115 acc: 0.97333 val_loss: 0.26618, val_acc: 0.96000
Epoch [2120/10000], loss: 0.27055 acc: 0.97333 val_loss: 0.26562, val_acc: 0.96000
Epoch [2130/10000], loss: 0.26995 acc: 0.97333 val_loss: 0.26507, val_acc: 0.96000
Epoch [2140/10000], loss: 0.26936 acc: 0.97333 val_loss: 0.26452, val_acc: 0.96000
Epoch [2150/10000], loss: 0.26877 acc: 0.97333 val_loss: 0.26397, val_acc: 0.96000
Epoch [2160/10000], loss: 0.26818 acc: 0.97333 val_loss: 0.26343, val_acc: 0.96000
Epoch [2170/10000], loss: 0.26760 acc: 0.97333 val_loss: 0.26289, val_acc: 0.96000
Epoch [2180/10000], loss: 0.26702 acc: 0.97333 val_loss: 0.26236, val_acc: 0.96000
Epoch [2190/10000], loss: 0.26644 acc: 0.97333 val_loss: 0.26182, val_acc: 0.96000
Epoch [2200/10000], loss: 0.26587 acc: 0.97333 val_loss: 0.26129, val_acc: 0.96000
Epoch [2210/10000], loss: 0.26530 acc: 0.97333 val_loss: 0.26077, val_acc: 0.96000
Epoch [2220/10000], loss: 0.26473 acc: 0.97333 val_loss: 0.26024, val_acc: 0.96000
Epoch [2230/10000], loss: 0.26417 acc: 0.97333 val_loss: 0.25972, val_acc: 0.96000
Epoch [2240/10000], loss: 0.26361 acc: 0.97333 val_loss: 0.25921, val_acc: 0.96000
Epoch [2250/10000], loss: 0.26305 acc: 0.97333 val_loss: 0.25869, val_acc: 0.96000
Epoch [2260/10000], loss: 0.26250 acc: 0.97333 val_loss: 0.25818, val_acc: 0.96000
Epoch [2270/10000], loss: 0.26195 acc: 0.97333 val_loss: 0.25767, val_acc: 0.96000
Epoch [2280/10000], loss: 0.26140 acc: 0.97333 val_loss: 0.25717, val_acc: 0.96000
Epoch [2290/10000], loss: 0.26086 acc: 0.97333 val_loss: 0.25666, val_acc: 0.96000
Epoch [2300/10000], loss: 0.26032 acc: 0.97333 val_loss: 0.25616, val_acc: 0.96000
Epoch [2310/10000], loss: 0.25978 acc: 0.97333 val_loss: 0.25567, val_acc: 0.96000
Epoch [2320/10000], loss: 0.25924 acc: 0.97333 val_loss: 0.25517, val_acc: 0.96000
Epoch [2330/10000], loss: 0.25871 acc: 0.97333 val_loss: 0.25468, val_acc: 0.96000
Epoch [2340/10000], loss: 0.25818 acc: 0.97333 val_loss: 0.25419, val_acc: 0.96000
Epoch [2350/10000], loss: 0.25766 acc: 0.97333 val_loss: 0.25371, val_acc: 0.96000
Epoch [2360/10000], loss: 0.25713 acc: 0.97333 val_loss: 0.25322, val_acc: 0.96000
Epoch [2370/10000], loss: 0.25661 acc: 0.97333 val_loss: 0.25274, val_acc: 0.96000
Epoch [2380/10000], loss: 0.25609 acc: 0.97333 val_loss: 0.25227, val_acc: 0.96000
Epoch [2390/10000], loss: 0.25558 acc: 0.97333 val_loss: 0.25179, val_acc: 0.96000
Epoch [2400/10000], loss: 0.25507 acc: 0.97333 val_loss: 0.25132, val_acc: 0.96000
Epoch [2410/10000], loss: 0.25456 acc: 0.97333 val_loss: 0.25085, val_acc: 0.96000
Epoch [2420/10000], loss: 0.25405 acc: 0.97333 val_loss: 0.25038, val_acc: 0.96000
Epoch [2430/10000], loss: 0.25355 acc: 0.97333 val_loss: 0.24992, val_acc: 0.96000
Epoch [2440/10000], loss: 0.25304 acc: 0.97333 val_loss: 0.24946, val_acc: 0.96000
Epoch [2450/10000], loss: 0.25255 acc: 0.97333 val_loss: 0.24900, val_acc: 0.96000
Epoch [2460/10000], loss: 0.25205 acc: 0.97333 val_loss: 0.24854, val_acc: 0.96000
Epoch [2470/10000], loss: 0.25156 acc: 0.97333 val_loss: 0.24809, val_acc: 0.96000
Epoch [2480/10000], loss: 0.25107 acc: 0.97333 val_loss: 0.24764, val_acc: 0.96000
Epoch [2490/10000], loss: 0.25058 acc: 0.97333 val_loss: 0.24719, val_acc: 0.96000
Epoch [2500/10000], loss: 0.25009 acc: 0.97333 val_loss: 0.24674, val_acc: 0.96000
Epoch [2510/10000], loss: 0.24961 acc: 0.97333 val_loss: 0.24630, val_acc: 0.96000
Epoch [2520/10000], loss: 0.24913 acc: 0.97333 val_loss: 0.24585, val_acc: 0.96000
Epoch [2530/10000], loss: 0.24865 acc: 0.97333 val_loss: 0.24541, val_acc: 0.96000
Epoch [2540/10000], loss: 0.24818 acc: 0.97333 val_loss: 0.24498, val_acc: 0.96000
Epoch [2550/10000], loss: 0.24770 acc: 0.97333 val_loss: 0.24454, val_acc: 0.96000
Epoch [2560/10000], loss: 0.24723 acc: 0.97333 val_loss: 0.24411, val_acc: 0.96000
Epoch [2570/10000], loss: 0.24676 acc: 0.97333 val_loss: 0.24368, val_acc: 0.96000
Epoch [2580/10000], loss: 0.24630 acc: 0.98667 val_loss: 0.24325, val_acc: 0.96000
Epoch [2590/10000], loss: 0.24584 acc: 0.98667 val_loss: 0.24283, val_acc: 0.96000
Epoch [2600/10000], loss: 0.24537 acc: 0.98667 val_loss: 0.24240, val_acc: 0.96000
Epoch [2610/10000], loss: 0.24492 acc: 0.98667 val_loss: 0.24198, val_acc: 0.96000
Epoch [2620/10000], loss: 0.24446 acc: 0.98667 val_loss: 0.24156, val_acc: 0.96000
Epoch [2630/10000], loss: 0.24401 acc: 0.98667 val_loss: 0.24115, val_acc: 0.96000
Epoch [2640/10000], loss: 0.24355 acc: 0.98667 val_loss: 0.24073, val_acc: 0.96000
Epoch [2650/10000], loss: 0.24311 acc: 0.98667 val_loss: 0.24032, val_acc: 0.96000
Epoch [2660/10000], loss: 0.24266 acc: 0.98667 val_loss: 0.23991, val_acc: 0.96000
Epoch [2670/10000], loss: 0.24221 acc: 0.98667 val_loss: 0.23950, val_acc: 0.96000
Epoch [2680/10000], loss: 0.24177 acc: 0.98667 val_loss: 0.23909, val_acc: 0.96000
Epoch [2690/10000], loss: 0.24133 acc: 0.98667 val_loss: 0.23869, val_acc: 0.96000
Epoch [2700/10000], loss: 0.24089 acc: 0.98667 val_loss: 0.23829, val_acc: 0.96000
Epoch [2710/10000], loss: 0.24046 acc: 0.98667 val_loss: 0.23789, val_acc: 0.96000
Epoch [2720/10000], loss: 0.24002 acc: 0.98667 val_loss: 0.23749, val_acc: 0.96000
Epoch [2730/10000], loss: 0.23959 acc: 0.98667 val_loss: 0.23710, val_acc: 0.96000
Epoch [2740/10000], loss: 0.23916 acc: 0.98667 val_loss: 0.23670, val_acc: 0.96000
Epoch [2750/10000], loss: 0.23874 acc: 0.98667 val_loss: 0.23631, val_acc: 0.96000
Epoch [2760/10000], loss: 0.23831 acc: 0.98667 val_loss: 0.23592, val_acc: 0.96000
Epoch [2770/10000], loss: 0.23789 acc: 0.98667 val_loss: 0.23553, val_acc: 0.96000
Epoch [2780/10000], loss: 0.23747 acc: 0.98667 val_loss: 0.23515, val_acc: 0.96000
Epoch [2790/10000], loss: 0.23705 acc: 0.98667 val_loss: 0.23476, val_acc: 0.96000
Epoch [2800/10000], loss: 0.23663 acc: 0.98667 val_loss: 0.23438, val_acc: 0.96000
Epoch [2810/10000], loss: 0.23622 acc: 0.98667 val_loss: 0.23400, val_acc: 0.96000
Epoch [2820/10000], loss: 0.23580 acc: 0.98667 val_loss: 0.23363, val_acc: 0.96000
Epoch [2830/10000], loss: 0.23539 acc: 0.98667 val_loss: 0.23325, val_acc: 0.96000
Epoch [2840/10000], loss: 0.23498 acc: 0.98667 val_loss: 0.23287, val_acc: 0.96000
Epoch [2850/10000], loss: 0.23458 acc: 0.98667 val_loss: 0.23250, val_acc: 0.96000
Epoch [2860/10000], loss: 0.23417 acc: 0.98667 val_loss: 0.23213, val_acc: 0.96000
Epoch [2870/10000], loss: 0.23377 acc: 0.98667 val_loss: 0.23176, val_acc: 0.96000
Epoch [2880/10000], loss: 0.23337 acc: 0.98667 val_loss: 0.23140, val_acc: 0.96000
Epoch [2890/10000], loss: 0.23297 acc: 0.98667 val_loss: 0.23103, val_acc: 0.96000
Epoch [2900/10000], loss: 0.23257 acc: 0.98667 val_loss: 0.23067, val_acc: 0.96000
Epoch [2910/10000], loss: 0.23218 acc: 0.98667 val_loss: 0.23031, val_acc: 0.96000
Epoch [2920/10000], loss: 0.23178 acc: 0.98667 val_loss: 0.22995, val_acc: 0.96000
Epoch [2930/10000], loss: 0.23139 acc: 0.98667 val_loss: 0.22959, val_acc: 0.96000
Epoch [2940/10000], loss: 0.23100 acc: 0.98667 val_loss: 0.22923, val_acc: 0.96000
Epoch [2950/10000], loss: 0.23061 acc: 0.98667 val_loss: 0.22888, val_acc: 0.96000
Epoch [2960/10000], loss: 0.23023 acc: 0.98667 val_loss: 0.22853, val_acc: 0.96000
Epoch [2970/10000], loss: 0.22984 acc: 0.98667 val_loss: 0.22818, val_acc: 0.96000
Epoch [2980/10000], loss: 0.22946 acc: 0.98667 val_loss: 0.22783, val_acc: 0.96000
Epoch [2990/10000], loss: 0.22908 acc: 0.98667 val_loss: 0.22748, val_acc: 0.96000
Epoch [3000/10000], loss: 0.22870 acc: 0.98667 val_loss: 0.22713, val_acc: 0.96000
Epoch [3010/10000], loss: 0.22832 acc: 0.98667 val_loss: 0.22679, val_acc: 0.96000
Epoch [3020/10000], loss: 0.22795 acc: 0.98667 val_loss: 0.22645, val_acc: 0.96000
Epoch [3030/10000], loss: 0.22757 acc: 0.98667 val_loss: 0.22610, val_acc: 0.96000
Epoch [3040/10000], loss: 0.22720 acc: 0.98667 val_loss: 0.22577, val_acc: 0.96000
Epoch [3050/10000], loss: 0.22683 acc: 0.98667 val_loss: 0.22543, val_acc: 0.96000
Epoch [3060/10000], loss: 0.22646 acc: 0.98667 val_loss: 0.22509, val_acc: 0.96000
Epoch [3070/10000], loss: 0.22610 acc: 0.98667 val_loss: 0.22476, val_acc: 0.96000
Epoch [3080/10000], loss: 0.22573 acc: 0.98667 val_loss: 0.22442, val_acc: 0.96000
Epoch [3090/10000], loss: 0.22537 acc: 0.98667 val_loss: 0.22409, val_acc: 0.96000
Epoch [3100/10000], loss: 0.22501 acc: 0.98667 val_loss: 0.22376, val_acc: 0.96000
Epoch [3110/10000], loss: 0.22465 acc: 0.98667 val_loss: 0.22343, val_acc: 0.96000
Epoch [3120/10000], loss: 0.22429 acc: 0.98667 val_loss: 0.22311, val_acc: 0.96000
Epoch [3130/10000], loss: 0.22393 acc: 0.98667 val_loss: 0.22278, val_acc: 0.96000
Epoch [3140/10000], loss: 0.22358 acc: 0.98667 val_loss: 0.22246, val_acc: 0.96000
Epoch [3150/10000], loss: 0.22322 acc: 0.98667 val_loss: 0.22214, val_acc: 0.96000
Epoch [3160/10000], loss: 0.22287 acc: 0.98667 val_loss: 0.22181, val_acc: 0.96000
Epoch [3170/10000], loss: 0.22252 acc: 0.98667 val_loss: 0.22150, val_acc: 0.96000
Epoch [3180/10000], loss: 0.22217 acc: 0.98667 val_loss: 0.22118, val_acc: 0.96000
Epoch [3190/10000], loss: 0.22182 acc: 0.98667 val_loss: 0.22086, val_acc: 0.96000
Epoch [3200/10000], loss: 0.22148 acc: 0.98667 val_loss: 0.22055, val_acc: 0.96000
Epoch [3210/10000], loss: 0.22113 acc: 0.98667 val_loss: 0.22023, val_acc: 0.96000
Epoch [3220/10000], loss: 0.22079 acc: 0.98667 val_loss: 0.21992, val_acc: 0.96000
Epoch [3230/10000], loss: 0.22045 acc: 0.98667 val_loss: 0.21961, val_acc: 0.96000
Epoch [3240/10000], loss: 0.22011 acc: 0.98667 val_loss: 0.21930, val_acc: 0.96000
Epoch [3250/10000], loss: 0.21977 acc: 0.98667 val_loss: 0.21899, val_acc: 0.96000
Epoch [3260/10000], loss: 0.21943 acc: 0.98667 val_loss: 0.21869, val_acc: 0.96000
Epoch [3270/10000], loss: 0.21910 acc: 0.98667 val_loss: 0.21838, val_acc: 0.96000
Epoch [3280/10000], loss: 0.21876 acc: 0.98667 val_loss: 0.21808, val_acc: 0.96000
Epoch [3290/10000], loss: 0.21843 acc: 0.98667 val_loss: 0.21778, val_acc: 0.96000
Epoch [3300/10000], loss: 0.21810 acc: 0.98667 val_loss: 0.21747, val_acc: 0.96000
Epoch [3310/10000], loss: 0.21777 acc: 0.98667 val_loss: 0.21717, val_acc: 0.96000
Epoch [3320/10000], loss: 0.21744 acc: 0.98667 val_loss: 0.21688, val_acc: 0.96000
Epoch [3330/10000], loss: 0.21711 acc: 0.98667 val_loss: 0.21658, val_acc: 0.96000
Epoch [3340/10000], loss: 0.21679 acc: 0.98667 val_loss: 0.21628, val_acc: 0.96000
Epoch [3350/10000], loss: 0.21646 acc: 0.98667 val_loss: 0.21599, val_acc: 0.96000
Epoch [3360/10000], loss: 0.21614 acc: 0.98667 val_loss: 0.21570, val_acc: 0.96000
Epoch [3370/10000], loss: 0.21582 acc: 0.98667 val_loss: 0.21540, val_acc: 0.96000
Epoch [3380/10000], loss: 0.21550 acc: 0.98667 val_loss: 0.21511, val_acc: 0.96000
Epoch [3390/10000], loss: 0.21518 acc: 0.98667 val_loss: 0.21483, val_acc: 0.96000
Epoch [3400/10000], loss: 0.21487 acc: 0.98667 val_loss: 0.21454, val_acc: 0.96000
Epoch [3410/10000], loss: 0.21455 acc: 0.98667 val_loss: 0.21425, val_acc: 0.96000
Epoch [3420/10000], loss: 0.21424 acc: 0.98667 val_loss: 0.21396, val_acc: 0.96000
Epoch [3430/10000], loss: 0.21392 acc: 0.98667 val_loss: 0.21368, val_acc: 0.96000
Epoch [3440/10000], loss: 0.21361 acc: 0.98667 val_loss: 0.21340, val_acc: 0.96000
Epoch [3450/10000], loss: 0.21330 acc: 0.98667 val_loss: 0.21312, val_acc: 0.96000
Epoch [3460/10000], loss: 0.21299 acc: 0.98667 val_loss: 0.21284, val_acc: 0.96000
Epoch [3470/10000], loss: 0.21268 acc: 0.98667 val_loss: 0.21256, val_acc: 0.96000
Epoch [3480/10000], loss: 0.21238 acc: 0.98667 val_loss: 0.21228, val_acc: 0.96000
Epoch [3490/10000], loss: 0.21207 acc: 0.98667 val_loss: 0.21200, val_acc: 0.96000
Epoch [3500/10000], loss: 0.21177 acc: 0.98667 val_loss: 0.21173, val_acc: 0.96000
Epoch [3510/10000], loss: 0.21146 acc: 0.98667 val_loss: 0.21145, val_acc: 0.96000
Epoch [3520/10000], loss: 0.21116 acc: 0.98667 val_loss: 0.21118, val_acc: 0.96000
Epoch [3530/10000], loss: 0.21086 acc: 0.98667 val_loss: 0.21091, val_acc: 0.96000
Epoch [3540/10000], loss: 0.21056 acc: 0.98667 val_loss: 0.21064, val_acc: 0.96000
Epoch [3550/10000], loss: 0.21026 acc: 0.98667 val_loss: 0.21037, val_acc: 0.96000
Epoch [3560/10000], loss: 0.20997 acc: 0.98667 val_loss: 0.21010, val_acc: 0.96000
Epoch [3570/10000], loss: 0.20967 acc: 0.98667 val_loss: 0.20983, val_acc: 0.96000
Epoch [3580/10000], loss: 0.20938 acc: 0.98667 val_loss: 0.20956, val_acc: 0.96000
Epoch [3590/10000], loss: 0.20909 acc: 0.98667 val_loss: 0.20930, val_acc: 0.96000
Epoch [3600/10000], loss: 0.20879 acc: 0.98667 val_loss: 0.20903, val_acc: 0.96000
Epoch [3610/10000], loss: 0.20850 acc: 0.98667 val_loss: 0.20877, val_acc: 0.96000
Epoch [3620/10000], loss: 0.20821 acc: 0.98667 val_loss: 0.20851, val_acc: 0.96000
Epoch [3630/10000], loss: 0.20793 acc: 0.98667 val_loss: 0.20825, val_acc: 0.96000
Epoch [3640/10000], loss: 0.20764 acc: 0.98667 val_loss: 0.20799, val_acc: 0.96000
Epoch [3650/10000], loss: 0.20735 acc: 0.98667 val_loss: 0.20773, val_acc: 0.96000
Epoch [3660/10000], loss: 0.20707 acc: 0.98667 val_loss: 0.20747, val_acc: 0.96000
Epoch [3670/10000], loss: 0.20678 acc: 0.98667 val_loss: 0.20721, val_acc: 0.96000
Epoch [3680/10000], loss: 0.20650 acc: 0.98667 val_loss: 0.20696, val_acc: 0.96000
Epoch [3690/10000], loss: 0.20622 acc: 0.98667 val_loss: 0.20670, val_acc: 0.96000
Epoch [3700/10000], loss: 0.20594 acc: 0.98667 val_loss: 0.20645, val_acc: 0.96000
Epoch [3710/10000], loss: 0.20566 acc: 0.98667 val_loss: 0.20620, val_acc: 0.96000
Epoch [3720/10000], loss: 0.20538 acc: 0.98667 val_loss: 0.20595, val_acc: 0.96000
Epoch [3730/10000], loss: 0.20511 acc: 0.98667 val_loss: 0.20570, val_acc: 0.96000
Epoch [3740/10000], loss: 0.20483 acc: 0.98667 val_loss: 0.20545, val_acc: 0.96000
Epoch [3750/10000], loss: 0.20455 acc: 0.98667 val_loss: 0.20520, val_acc: 0.96000
Epoch [3760/10000], loss: 0.20428 acc: 0.98667 val_loss: 0.20495, val_acc: 0.96000
Epoch [3770/10000], loss: 0.20401 acc: 0.98667 val_loss: 0.20471, val_acc: 0.96000
Epoch [3780/10000], loss: 0.20374 acc: 0.98667 val_loss: 0.20446, val_acc: 0.96000
Epoch [3790/10000], loss: 0.20347 acc: 0.98667 val_loss: 0.20422, val_acc: 0.96000
Epoch [3800/10000], loss: 0.20320 acc: 0.98667 val_loss: 0.20397, val_acc: 0.96000
Epoch [3810/10000], loss: 0.20293 acc: 0.98667 val_loss: 0.20373, val_acc: 0.96000
Epoch [3820/10000], loss: 0.20266 acc: 0.98667 val_loss: 0.20349, val_acc: 0.96000
Epoch [3830/10000], loss: 0.20239 acc: 0.98667 val_loss: 0.20325, val_acc: 0.96000
Epoch [3840/10000], loss: 0.20213 acc: 0.98667 val_loss: 0.20301, val_acc: 0.96000
Epoch [3850/10000], loss: 0.20186 acc: 0.98667 val_loss: 0.20277, val_acc: 0.96000
Epoch [3860/10000], loss: 0.20160 acc: 0.98667 val_loss: 0.20253, val_acc: 0.96000
Epoch [3870/10000], loss: 0.20134 acc: 0.98667 val_loss: 0.20230, val_acc: 0.96000
Epoch [3880/10000], loss: 0.20108 acc: 0.98667 val_loss: 0.20206, val_acc: 0.96000
Epoch [3890/10000], loss: 0.20082 acc: 0.98667 val_loss: 0.20183, val_acc: 0.96000
Epoch [3900/10000], loss: 0.20056 acc: 0.98667 val_loss: 0.20159, val_acc: 0.96000
Epoch [3910/10000], loss: 0.20030 acc: 0.98667 val_loss: 0.20136, val_acc: 0.96000
Epoch [3920/10000], loss: 0.20004 acc: 0.98667 val_loss: 0.20113, val_acc: 0.96000
Epoch [3930/10000], loss: 0.19979 acc: 0.98667 val_loss: 0.20090, val_acc: 0.96000
Epoch [3940/10000], loss: 0.19953 acc: 0.98667 val_loss: 0.20067, val_acc: 0.96000
Epoch [3950/10000], loss: 0.19928 acc: 0.98667 val_loss: 0.20044, val_acc: 0.96000
Epoch [3960/10000], loss: 0.19902 acc: 0.98667 val_loss: 0.20021, val_acc: 0.96000
Epoch [3970/10000], loss: 0.19877 acc: 0.98667 val_loss: 0.19998, val_acc: 0.96000
Epoch [3980/10000], loss: 0.19852 acc: 0.98667 val_loss: 0.19976, val_acc: 0.96000
Epoch [3990/10000], loss: 0.19827 acc: 0.98667 val_loss: 0.19953, val_acc: 0.96000
Epoch [4000/10000], loss: 0.19802 acc: 0.98667 val_loss: 0.19931, val_acc: 0.96000
Epoch [4010/10000], loss: 0.19777 acc: 0.98667 val_loss: 0.19908, val_acc: 0.96000
Epoch [4020/10000], loss: 0.19752 acc: 0.98667 val_loss: 0.19886, val_acc: 0.96000
Epoch [4030/10000], loss: 0.19728 acc: 0.98667 val_loss: 0.19864, val_acc: 0.96000
Epoch [4040/10000], loss: 0.19703 acc: 0.98667 val_loss: 0.19842, val_acc: 0.96000
Epoch [4050/10000], loss: 0.19679 acc: 0.98667 val_loss: 0.19820, val_acc: 0.96000
Epoch [4060/10000], loss: 0.19654 acc: 0.98667 val_loss: 0.19798, val_acc: 0.96000
Epoch [4070/10000], loss: 0.19630 acc: 0.98667 val_loss: 0.19776, val_acc: 0.96000
Epoch [4080/10000], loss: 0.19606 acc: 0.98667 val_loss: 0.19754, val_acc: 0.96000
Epoch [4090/10000], loss: 0.19582 acc: 0.98667 val_loss: 0.19732, val_acc: 0.96000
Epoch [4100/10000], loss: 0.19557 acc: 0.98667 val_loss: 0.19711, val_acc: 0.96000
Epoch [4110/10000], loss: 0.19534 acc: 0.98667 val_loss: 0.19689, val_acc: 0.96000
Epoch [4120/10000], loss: 0.19510 acc: 0.98667 val_loss: 0.19668, val_acc: 0.96000
Epoch [4130/10000], loss: 0.19486 acc: 0.98667 val_loss: 0.19646, val_acc: 0.96000
Epoch [4140/10000], loss: 0.19462 acc: 0.98667 val_loss: 0.19625, val_acc: 0.96000
Epoch [4150/10000], loss: 0.19439 acc: 0.98667 val_loss: 0.19604, val_acc: 0.96000
Epoch [4160/10000], loss: 0.19415 acc: 0.98667 val_loss: 0.19583, val_acc: 0.96000
Epoch [4170/10000], loss: 0.19392 acc: 0.98667 val_loss: 0.19562, val_acc: 0.96000
Epoch [4180/10000], loss: 0.19368 acc: 0.98667 val_loss: 0.19541, val_acc: 0.96000
Epoch [4190/10000], loss: 0.19345 acc: 0.98667 val_loss: 0.19520, val_acc: 0.96000
Epoch [4200/10000], loss: 0.19322 acc: 0.98667 val_loss: 0.19499, val_acc: 0.96000
Epoch [4210/10000], loss: 0.19299 acc: 0.98667 val_loss: 0.19478, val_acc: 0.96000
Epoch [4220/10000], loss: 0.19276 acc: 0.98667 val_loss: 0.19457, val_acc: 0.96000
Epoch [4230/10000], loss: 0.19253 acc: 0.98667 val_loss: 0.19437, val_acc: 0.96000
Epoch [4240/10000], loss: 0.19230 acc: 0.98667 val_loss: 0.19416, val_acc: 0.96000
Epoch [4250/10000], loss: 0.19207 acc: 0.98667 val_loss: 0.19396, val_acc: 0.96000
Epoch [4260/10000], loss: 0.19184 acc: 0.98667 val_loss: 0.19376, val_acc: 0.96000
Epoch [4270/10000], loss: 0.19162 acc: 0.98667 val_loss: 0.19355, val_acc: 0.96000
Epoch [4280/10000], loss: 0.19139 acc: 0.98667 val_loss: 0.19335, val_acc: 0.96000
Epoch [4290/10000], loss: 0.19117 acc: 0.98667 val_loss: 0.19315, val_acc: 0.96000
Epoch [4300/10000], loss: 0.19094 acc: 0.98667 val_loss: 0.19295, val_acc: 0.96000
Epoch [4310/10000], loss: 0.19072 acc: 0.98667 val_loss: 0.19275, val_acc: 0.96000
Epoch [4320/10000], loss: 0.19050 acc: 0.98667 val_loss: 0.19255, val_acc: 0.96000
Epoch [4330/10000], loss: 0.19028 acc: 0.98667 val_loss: 0.19235, val_acc: 0.96000
Epoch [4340/10000], loss: 0.19006 acc: 0.98667 val_loss: 0.19215, val_acc: 0.96000
Epoch [4350/10000], loss: 0.18984 acc: 0.98667 val_loss: 0.19196, val_acc: 0.96000
Epoch [4360/10000], loss: 0.18962 acc: 0.98667 val_loss: 0.19176, val_acc: 0.96000
Epoch [4370/10000], loss: 0.18940 acc: 0.98667 val_loss: 0.19156, val_acc: 0.96000
Epoch [4380/10000], loss: 0.18918 acc: 0.98667 val_loss: 0.19137, val_acc: 0.96000
Epoch [4390/10000], loss: 0.18897 acc: 0.98667 val_loss: 0.19118, val_acc: 0.96000
Epoch [4400/10000], loss: 0.18875 acc: 0.98667 val_loss: 0.19098, val_acc: 0.96000
Epoch [4410/10000], loss: 0.18853 acc: 0.98667 val_loss: 0.19079, val_acc: 0.96000
Epoch [4420/10000], loss: 0.18832 acc: 0.98667 val_loss: 0.19060, val_acc: 0.96000
Epoch [4430/10000], loss: 0.18811 acc: 0.98667 val_loss: 0.19041, val_acc: 0.96000
Epoch [4440/10000], loss: 0.18789 acc: 0.98667 val_loss: 0.19021, val_acc: 0.96000
Epoch [4450/10000], loss: 0.18768 acc: 0.98667 val_loss: 0.19002, val_acc: 0.96000
Epoch [4460/10000], loss: 0.18747 acc: 0.98667 val_loss: 0.18984, val_acc: 0.96000
Epoch [4470/10000], loss: 0.18726 acc: 0.98667 val_loss: 0.18965, val_acc: 0.96000
Epoch [4480/10000], loss: 0.18705 acc: 0.98667 val_loss: 0.18946, val_acc: 0.96000
Epoch [4490/10000], loss: 0.18684 acc: 0.98667 val_loss: 0.18927, val_acc: 0.96000
Epoch [4500/10000], loss: 0.18663 acc: 0.98667 val_loss: 0.18908, val_acc: 0.96000
Epoch [4510/10000], loss: 0.18642 acc: 0.98667 val_loss: 0.18890, val_acc: 0.96000
Epoch [4520/10000], loss: 0.18622 acc: 0.98667 val_loss: 0.18871, val_acc: 0.96000
Epoch [4530/10000], loss: 0.18601 acc: 0.98667 val_loss: 0.18853, val_acc: 0.96000
Epoch [4540/10000], loss: 0.18580 acc: 0.98667 val_loss: 0.18834, val_acc: 0.96000
Epoch [4550/10000], loss: 0.18560 acc: 0.98667 val_loss: 0.18816, val_acc: 0.96000
Epoch [4560/10000], loss: 0.18539 acc: 0.98667 val_loss: 0.18798, val_acc: 0.96000
Epoch [4570/10000], loss: 0.18519 acc: 0.98667 val_loss: 0.18780, val_acc: 0.96000
Epoch [4580/10000], loss: 0.18499 acc: 0.98667 val_loss: 0.18762, val_acc: 0.96000
Epoch [4590/10000], loss: 0.18478 acc: 0.98667 val_loss: 0.18743, val_acc: 0.96000
Epoch [4600/10000], loss: 0.18458 acc: 0.98667 val_loss: 0.18725, val_acc: 0.96000
Epoch [4610/10000], loss: 0.18438 acc: 0.98667 val_loss: 0.18707, val_acc: 0.96000
Epoch [4620/10000], loss: 0.18418 acc: 0.98667 val_loss: 0.18690, val_acc: 0.96000
Epoch [4630/10000], loss: 0.18398 acc: 0.98667 val_loss: 0.18672, val_acc: 0.96000
Epoch [4640/10000], loss: 0.18378 acc: 0.98667 val_loss: 0.18654, val_acc: 0.96000
Epoch [4650/10000], loss: 0.18358 acc: 0.98667 val_loss: 0.18636, val_acc: 0.96000
Epoch [4660/10000], loss: 0.18339 acc: 0.98667 val_loss: 0.18619, val_acc: 0.96000
Epoch [4670/10000], loss: 0.18319 acc: 0.98667 val_loss: 0.18601, val_acc: 0.96000
Epoch [4680/10000], loss: 0.18299 acc: 0.98667 val_loss: 0.18583, val_acc: 0.96000
Epoch [4690/10000], loss: 0.18280 acc: 0.98667 val_loss: 0.18566, val_acc: 0.96000
Epoch [4700/10000], loss: 0.18260 acc: 0.98667 val_loss: 0.18549, val_acc: 0.96000
Epoch [4710/10000], loss: 0.18241 acc: 0.98667 val_loss: 0.18531, val_acc: 0.96000
Epoch [4720/10000], loss: 0.18221 acc: 0.98667 val_loss: 0.18514, val_acc: 0.96000
Epoch [4730/10000], loss: 0.18202 acc: 0.98667 val_loss: 0.18497, val_acc: 0.96000
Epoch [4740/10000], loss: 0.18183 acc: 0.98667 val_loss: 0.18479, val_acc: 0.96000
Epoch [4750/10000], loss: 0.18164 acc: 0.98667 val_loss: 0.18462, val_acc: 0.96000
Epoch [4760/10000], loss: 0.18144 acc: 0.98667 val_loss: 0.18445, val_acc: 0.96000
Epoch [4770/10000], loss: 0.18125 acc: 0.98667 val_loss: 0.18428, val_acc: 0.96000
Epoch [4780/10000], loss: 0.18106 acc: 0.98667 val_loss: 0.18411, val_acc: 0.96000
Epoch [4790/10000], loss: 0.18087 acc: 0.98667 val_loss: 0.18395, val_acc: 0.96000
Epoch [4800/10000], loss: 0.18068 acc: 0.98667 val_loss: 0.18378, val_acc: 0.96000
Epoch [4810/10000], loss: 0.18050 acc: 0.98667 val_loss: 0.18361, val_acc: 0.96000
Epoch [4820/10000], loss: 0.18031 acc: 0.98667 val_loss: 0.18344, val_acc: 0.96000
Epoch [4830/10000], loss: 0.18012 acc: 0.98667 val_loss: 0.18328, val_acc: 0.96000
Epoch [4840/10000], loss: 0.17994 acc: 0.98667 val_loss: 0.18311, val_acc: 0.96000
Epoch [4850/10000], loss: 0.17975 acc: 0.98667 val_loss: 0.18294, val_acc: 0.96000
Epoch [4860/10000], loss: 0.17956 acc: 0.98667 val_loss: 0.18278, val_acc: 0.96000
Epoch [4870/10000], loss: 0.17938 acc: 0.98667 val_loss: 0.18261, val_acc: 0.96000
Epoch [4880/10000], loss: 0.17920 acc: 0.98667 val_loss: 0.18245, val_acc: 0.96000
Epoch [4890/10000], loss: 0.17901 acc: 0.98667 val_loss: 0.18229, val_acc: 0.96000
Epoch [4900/10000], loss: 0.17883 acc: 0.98667 val_loss: 0.18212, val_acc: 0.96000
Epoch [4910/10000], loss: 0.17865 acc: 0.98667 val_loss: 0.18196, val_acc: 0.96000
Epoch [4920/10000], loss: 0.17846 acc: 0.98667 val_loss: 0.18180, val_acc: 0.96000
Epoch [4930/10000], loss: 0.17828 acc: 0.98667 val_loss: 0.18164, val_acc: 0.96000
Epoch [4940/10000], loss: 0.17810 acc: 0.98667 val_loss: 0.18148, val_acc: 0.96000
Epoch [4950/10000], loss: 0.17792 acc: 0.98667 val_loss: 0.18132, val_acc: 0.96000
Epoch [4960/10000], loss: 0.17774 acc: 0.98667 val_loss: 0.18116, val_acc: 0.96000
Epoch [4970/10000], loss: 0.17756 acc: 0.98667 val_loss: 0.18100, val_acc: 0.96000
Epoch [4980/10000], loss: 0.17739 acc: 0.98667 val_loss: 0.18084, val_acc: 0.96000
Epoch [4990/10000], loss: 0.17721 acc: 0.98667 val_loss: 0.18068, val_acc: 0.96000
Epoch [5000/10000], loss: 0.17703 acc: 0.98667 val_loss: 0.18053, val_acc: 0.96000
Epoch [5010/10000], loss: 0.17685 acc: 0.98667 val_loss: 0.18037, val_acc: 0.96000
Epoch [5020/10000], loss: 0.17668 acc: 0.98667 val_loss: 0.18021, val_acc: 0.96000
Epoch [5030/10000], loss: 0.17650 acc: 0.98667 val_loss: 0.18006, val_acc: 0.96000
Epoch [5040/10000], loss: 0.17633 acc: 0.98667 val_loss: 0.17990, val_acc: 0.96000
Epoch [5050/10000], loss: 0.17615 acc: 0.98667 val_loss: 0.17975, val_acc: 0.96000
Epoch [5060/10000], loss: 0.17598 acc: 0.98667 val_loss: 0.17959, val_acc: 0.96000
Epoch [5070/10000], loss: 0.17581 acc: 0.98667 val_loss: 0.17944, val_acc: 0.96000
Epoch [5080/10000], loss: 0.17563 acc: 0.98667 val_loss: 0.17928, val_acc: 0.96000
Epoch [5090/10000], loss: 0.17546 acc: 0.98667 val_loss: 0.17913, val_acc: 0.96000
Epoch [5100/10000], loss: 0.17529 acc: 0.98667 val_loss: 0.17898, val_acc: 0.96000
Epoch [5110/10000], loss: 0.17512 acc: 0.98667 val_loss: 0.17883, val_acc: 0.96000
Epoch [5120/10000], loss: 0.17495 acc: 0.98667 val_loss: 0.17867, val_acc: 0.96000
Epoch [5130/10000], loss: 0.17478 acc: 0.98667 val_loss: 0.17852, val_acc: 0.96000
Epoch [5140/10000], loss: 0.17461 acc: 0.98667 val_loss: 0.17837, val_acc: 0.96000
Epoch [5150/10000], loss: 0.17444 acc: 0.98667 val_loss: 0.17822, val_acc: 0.96000
Epoch [5160/10000], loss: 0.17427 acc: 0.98667 val_loss: 0.17807, val_acc: 0.96000
Epoch [5170/10000], loss: 0.17410 acc: 0.98667 val_loss: 0.17792, val_acc: 0.96000
Epoch [5180/10000], loss: 0.17393 acc: 0.98667 val_loss: 0.17778, val_acc: 0.96000
Epoch [5190/10000], loss: 0.17377 acc: 0.98667 val_loss: 0.17763, val_acc: 0.96000
Epoch [5200/10000], loss: 0.17360 acc: 0.98667 val_loss: 0.17748, val_acc: 0.96000
Epoch [5210/10000], loss: 0.17343 acc: 0.98667 val_loss: 0.17733, val_acc: 0.96000
Epoch [5220/10000], loss: 0.17327 acc: 0.98667 val_loss: 0.17719, val_acc: 0.96000
Epoch [5230/10000], loss: 0.17310 acc: 0.98667 val_loss: 0.17704, val_acc: 0.96000
Epoch [5240/10000], loss: 0.17294 acc: 0.98667 val_loss: 0.17689, val_acc: 0.96000
Epoch [5250/10000], loss: 0.17277 acc: 0.98667 val_loss: 0.17675, val_acc: 0.96000
Epoch [5260/10000], loss: 0.17261 acc: 0.98667 val_loss: 0.17660, val_acc: 0.96000
Epoch [5270/10000], loss: 0.17245 acc: 0.98667 val_loss: 0.17646, val_acc: 0.96000
Epoch [5280/10000], loss: 0.17229 acc: 0.98667 val_loss: 0.17631, val_acc: 0.96000
Epoch [5290/10000], loss: 0.17212 acc: 0.98667 val_loss: 0.17617, val_acc: 0.96000
Epoch [5300/10000], loss: 0.17196 acc: 0.98667 val_loss: 0.17603, val_acc: 0.96000
Epoch [5310/10000], loss: 0.17180 acc: 0.98667 val_loss: 0.17589, val_acc: 0.96000
Epoch [5320/10000], loss: 0.17164 acc: 0.98667 val_loss: 0.17574, val_acc: 0.96000
Epoch [5330/10000], loss: 0.17148 acc: 0.98667 val_loss: 0.17560, val_acc: 0.96000
Epoch [5340/10000], loss: 0.17132 acc: 0.98667 val_loss: 0.17546, val_acc: 0.96000
Epoch [5350/10000], loss: 0.17116 acc: 0.98667 val_loss: 0.17532, val_acc: 0.96000
Epoch [5360/10000], loss: 0.17100 acc: 0.98667 val_loss: 0.17518, val_acc: 0.96000
Epoch [5370/10000], loss: 0.17084 acc: 0.98667 val_loss: 0.17504, val_acc: 0.96000
Epoch [5380/10000], loss: 0.17068 acc: 0.98667 val_loss: 0.17490, val_acc: 0.96000
Epoch [5390/10000], loss: 0.17053 acc: 0.98667 val_loss: 0.17476, val_acc: 0.96000
Epoch [5400/10000], loss: 0.17037 acc: 0.98667 val_loss: 0.17462, val_acc: 0.96000
Epoch [5410/10000], loss: 0.17021 acc: 0.98667 val_loss: 0.17448, val_acc: 0.96000
Epoch [5420/10000], loss: 0.17006 acc: 0.98667 val_loss: 0.17434, val_acc: 0.96000
Epoch [5430/10000], loss: 0.16990 acc: 0.98667 val_loss: 0.17421, val_acc: 0.96000
Epoch [5440/10000], loss: 0.16975 acc: 0.98667 val_loss: 0.17407, val_acc: 0.96000
Epoch [5450/10000], loss: 0.16959 acc: 0.98667 val_loss: 0.17393, val_acc: 0.96000
Epoch [5460/10000], loss: 0.16944 acc: 0.98667 val_loss: 0.17380, val_acc: 0.96000
Epoch [5470/10000], loss: 0.16928 acc: 0.98667 val_loss: 0.17366, val_acc: 0.96000
Epoch [5480/10000], loss: 0.16913 acc: 0.98667 val_loss: 0.17352, val_acc: 0.96000
Epoch [5490/10000], loss: 0.16898 acc: 0.98667 val_loss: 0.17339, val_acc: 0.96000
Epoch [5500/10000], loss: 0.16883 acc: 0.98667 val_loss: 0.17325, val_acc: 0.96000
Epoch [5510/10000], loss: 0.16867 acc: 0.98667 val_loss: 0.17312, val_acc: 0.96000
Epoch [5520/10000], loss: 0.16852 acc: 0.98667 val_loss: 0.17299, val_acc: 0.96000
Epoch [5530/10000], loss: 0.16837 acc: 0.98667 val_loss: 0.17285, val_acc: 0.96000
Epoch [5540/10000], loss: 0.16822 acc: 0.98667 val_loss: 0.17272, val_acc: 0.96000
Epoch [5550/10000], loss: 0.16807 acc: 0.98667 val_loss: 0.17259, val_acc: 0.96000
Epoch [5560/10000], loss: 0.16792 acc: 0.98667 val_loss: 0.17246, val_acc: 0.96000
Epoch [5570/10000], loss: 0.16777 acc: 0.98667 val_loss: 0.17232, val_acc: 0.96000
Epoch [5580/10000], loss: 0.16762 acc: 0.98667 val_loss: 0.17219, val_acc: 0.96000
Epoch [5590/10000], loss: 0.16747 acc: 0.98667 val_loss: 0.17206, val_acc: 0.96000
Epoch [5600/10000], loss: 0.16732 acc: 0.98667 val_loss: 0.17193, val_acc: 0.96000
Epoch [5610/10000], loss: 0.16718 acc: 0.98667 val_loss: 0.17180, val_acc: 0.96000
Epoch [5620/10000], loss: 0.16703 acc: 0.98667 val_loss: 0.17167, val_acc: 0.96000
Epoch [5630/10000], loss: 0.16688 acc: 0.98667 val_loss: 0.17154, val_acc: 0.96000
Epoch [5640/10000], loss: 0.16674 acc: 0.98667 val_loss: 0.17141, val_acc: 0.96000
Epoch [5650/10000], loss: 0.16659 acc: 0.98667 val_loss: 0.17128, val_acc: 0.96000
Epoch [5660/10000], loss: 0.16644 acc: 0.98667 val_loss: 0.17115, val_acc: 0.96000
Epoch [5670/10000], loss: 0.16630 acc: 0.98667 val_loss: 0.17103, val_acc: 0.96000
Epoch [5680/10000], loss: 0.16615 acc: 0.98667 val_loss: 0.17090, val_acc: 0.96000
Epoch [5690/10000], loss: 0.16601 acc: 0.98667 val_loss: 0.17077, val_acc: 0.96000
Epoch [5700/10000], loss: 0.16587 acc: 0.98667 val_loss: 0.17064, val_acc: 0.96000
Epoch [5710/10000], loss: 0.16572 acc: 0.98667 val_loss: 0.17052, val_acc: 0.96000
Epoch [5720/10000], loss: 0.16558 acc: 0.98667 val_loss: 0.17039, val_acc: 0.96000
Epoch [5730/10000], loss: 0.16544 acc: 0.98667 val_loss: 0.17027, val_acc: 0.96000
Epoch [5740/10000], loss: 0.16529 acc: 0.98667 val_loss: 0.17014, val_acc: 0.96000
Epoch [5750/10000], loss: 0.16515 acc: 0.98667 val_loss: 0.17001, val_acc: 0.96000
Epoch [5760/10000], loss: 0.16501 acc: 0.98667 val_loss: 0.16989, val_acc: 0.96000
Epoch [5770/10000], loss: 0.16487 acc: 0.98667 val_loss: 0.16977, val_acc: 0.96000
Epoch [5780/10000], loss: 0.16473 acc: 0.98667 val_loss: 0.16964, val_acc: 0.96000
Epoch [5790/10000], loss: 0.16459 acc: 0.98667 val_loss: 0.16952, val_acc: 0.96000
Epoch [5800/10000], loss: 0.16445 acc: 0.98667 val_loss: 0.16939, val_acc: 0.96000
Epoch [5810/10000], loss: 0.16431 acc: 0.98667 val_loss: 0.16927, val_acc: 0.96000
Epoch [5820/10000], loss: 0.16417 acc: 0.98667 val_loss: 0.16915, val_acc: 0.96000
Epoch [5830/10000], loss: 0.16403 acc: 0.98667 val_loss: 0.16903, val_acc: 0.96000
Epoch [5840/10000], loss: 0.16389 acc: 0.98667 val_loss: 0.16891, val_acc: 0.96000
Epoch [5850/10000], loss: 0.16375 acc: 0.98667 val_loss: 0.16878, val_acc: 0.96000
Epoch [5860/10000], loss: 0.16361 acc: 0.98667 val_loss: 0.16866, val_acc: 0.96000
Epoch [5870/10000], loss: 0.16348 acc: 0.98667 val_loss: 0.16854, val_acc: 0.96000
Epoch [5880/10000], loss: 0.16334 acc: 0.98667 val_loss: 0.16842, val_acc: 0.96000
Epoch [5890/10000], loss: 0.16320 acc: 0.98667 val_loss: 0.16830, val_acc: 0.96000
Epoch [5900/10000], loss: 0.16307 acc: 0.98667 val_loss: 0.16818, val_acc: 0.96000
Epoch [5910/10000], loss: 0.16293 acc: 0.98667 val_loss: 0.16806, val_acc: 0.96000
Epoch [5920/10000], loss: 0.16280 acc: 0.98667 val_loss: 0.16794, val_acc: 0.96000
Epoch [5930/10000], loss: 0.16266 acc: 0.98667 val_loss: 0.16782, val_acc: 0.96000
Epoch [5940/10000], loss: 0.16253 acc: 0.98667 val_loss: 0.16771, val_acc: 0.96000
Epoch [5950/10000], loss: 0.16239 acc: 0.98667 val_loss: 0.16759, val_acc: 0.96000
Epoch [5960/10000], loss: 0.16226 acc: 0.98667 val_loss: 0.16747, val_acc: 0.96000
Epoch [5970/10000], loss: 0.16212 acc: 0.98667 val_loss: 0.16735, val_acc: 0.96000
Epoch [5980/10000], loss: 0.16199 acc: 0.98667 val_loss: 0.16724, val_acc: 0.96000
Epoch [5990/10000], loss: 0.16186 acc: 0.98667 val_loss: 0.16712, val_acc: 0.96000
Epoch [6000/10000], loss: 0.16172 acc: 0.98667 val_loss: 0.16700, val_acc: 0.96000
Epoch [6010/10000], loss: 0.16159 acc: 0.98667 val_loss: 0.16689, val_acc: 0.96000
Epoch [6020/10000], loss: 0.16146 acc: 0.98667 val_loss: 0.16677, val_acc: 0.96000
Epoch [6030/10000], loss: 0.16133 acc: 0.98667 val_loss: 0.16665, val_acc: 0.96000
Epoch [6040/10000], loss: 0.16120 acc: 0.98667 val_loss: 0.16654, val_acc: 0.96000
Epoch [6050/10000], loss: 0.16107 acc: 0.98667 val_loss: 0.16642, val_acc: 0.96000
Epoch [6060/10000], loss: 0.16094 acc: 0.98667 val_loss: 0.16631, val_acc: 0.96000
Epoch [6070/10000], loss: 0.16080 acc: 0.98667 val_loss: 0.16620, val_acc: 0.96000
Epoch [6080/10000], loss: 0.16067 acc: 0.98667 val_loss: 0.16608, val_acc: 0.96000
Epoch [6090/10000], loss: 0.16055 acc: 0.98667 val_loss: 0.16597, val_acc: 0.96000
Epoch [6100/10000], loss: 0.16042 acc: 0.98667 val_loss: 0.16585, val_acc: 0.96000
Epoch [6110/10000], loss: 0.16029 acc: 0.98667 val_loss: 0.16574, val_acc: 0.96000
Epoch [6120/10000], loss: 0.16016 acc: 0.98667 val_loss: 0.16563, val_acc: 0.96000
Epoch [6130/10000], loss: 0.16003 acc: 0.98667 val_loss: 0.16552, val_acc: 0.96000
Epoch [6140/10000], loss: 0.15990 acc: 0.98667 val_loss: 0.16540, val_acc: 0.96000
Epoch [6150/10000], loss: 0.15978 acc: 0.98667 val_loss: 0.16529, val_acc: 0.96000
Epoch [6160/10000], loss: 0.15965 acc: 0.98667 val_loss: 0.16518, val_acc: 0.96000
Epoch [6170/10000], loss: 0.15952 acc: 0.98667 val_loss: 0.16507, val_acc: 0.96000
Epoch [6180/10000], loss: 0.15939 acc: 0.98667 val_loss: 0.16496, val_acc: 0.96000
Epoch [6190/10000], loss: 0.15927 acc: 0.98667 val_loss: 0.16485, val_acc: 0.96000
Epoch [6200/10000], loss: 0.15914 acc: 0.98667 val_loss: 0.16474, val_acc: 0.96000
Epoch [6210/10000], loss: 0.15902 acc: 0.98667 val_loss: 0.16463, val_acc: 0.96000
Epoch [6220/10000], loss: 0.15889 acc: 0.98667 val_loss: 0.16452, val_acc: 0.96000
Epoch [6230/10000], loss: 0.15877 acc: 0.98667 val_loss: 0.16441, val_acc: 0.96000
Epoch [6240/10000], loss: 0.15864 acc: 0.98667 val_loss: 0.16430, val_acc: 0.96000
Epoch [6250/10000], loss: 0.15852 acc: 0.98667 val_loss: 0.16419, val_acc: 0.96000
Epoch [6260/10000], loss: 0.15839 acc: 0.98667 val_loss: 0.16408, val_acc: 0.96000
Epoch [6270/10000], loss: 0.15827 acc: 0.98667 val_loss: 0.16398, val_acc: 0.96000
Epoch [6280/10000], loss: 0.15815 acc: 0.98667 val_loss: 0.16387, val_acc: 0.96000
Epoch [6290/10000], loss: 0.15802 acc: 0.98667 val_loss: 0.16376, val_acc: 0.96000
Epoch [6300/10000], loss: 0.15790 acc: 0.98667 val_loss: 0.16365, val_acc: 0.96000
Epoch [6310/10000], loss: 0.15778 acc: 0.98667 val_loss: 0.16355, val_acc: 0.96000
Epoch [6320/10000], loss: 0.15766 acc: 0.98667 val_loss: 0.16344, val_acc: 0.96000
Epoch [6330/10000], loss: 0.15754 acc: 0.98667 val_loss: 0.16333, val_acc: 0.96000
Epoch [6340/10000], loss: 0.15741 acc: 0.98667 val_loss: 0.16323, val_acc: 0.96000
Epoch [6350/10000], loss: 0.15729 acc: 0.98667 val_loss: 0.16312, val_acc: 0.96000
Epoch [6360/10000], loss: 0.15717 acc: 0.98667 val_loss: 0.16302, val_acc: 0.96000
Epoch [6370/10000], loss: 0.15705 acc: 0.98667 val_loss: 0.16291, val_acc: 0.96000
Epoch [6380/10000], loss: 0.15693 acc: 0.98667 val_loss: 0.16281, val_acc: 0.96000
Epoch [6390/10000], loss: 0.15681 acc: 0.98667 val_loss: 0.16270, val_acc: 0.96000
Epoch [6400/10000], loss: 0.15669 acc: 0.98667 val_loss: 0.16260, val_acc: 0.96000
Epoch [6410/10000], loss: 0.15657 acc: 0.98667 val_loss: 0.16249, val_acc: 0.96000
Epoch [6420/10000], loss: 0.15645 acc: 0.98667 val_loss: 0.16239, val_acc: 0.96000
Epoch [6430/10000], loss: 0.15634 acc: 0.98667 val_loss: 0.16228, val_acc: 0.96000
Epoch [6440/10000], loss: 0.15622 acc: 0.98667 val_loss: 0.16218, val_acc: 0.96000
Epoch [6450/10000], loss: 0.15610 acc: 0.98667 val_loss: 0.16208, val_acc: 0.96000
Epoch [6460/10000], loss: 0.15598 acc: 0.98667 val_loss: 0.16198, val_acc: 0.96000
Epoch [6470/10000], loss: 0.15586 acc: 0.98667 val_loss: 0.16187, val_acc: 0.96000
Epoch [6480/10000], loss: 0.15575 acc: 0.98667 val_loss: 0.16177, val_acc: 0.96000
Epoch [6490/10000], loss: 0.15563 acc: 0.98667 val_loss: 0.16167, val_acc: 0.96000
Epoch [6500/10000], loss: 0.15551 acc: 0.98667 val_loss: 0.16157, val_acc: 0.96000
Epoch [6510/10000], loss: 0.15540 acc: 0.98667 val_loss: 0.16147, val_acc: 0.96000
Epoch [6520/10000], loss: 0.15528 acc: 0.98667 val_loss: 0.16136, val_acc: 0.96000
Epoch [6530/10000], loss: 0.15516 acc: 0.98667 val_loss: 0.16126, val_acc: 0.96000
Epoch [6540/10000], loss: 0.15505 acc: 0.98667 val_loss: 0.16116, val_acc: 0.96000
Epoch [6550/10000], loss: 0.15493 acc: 0.98667 val_loss: 0.16106, val_acc: 0.96000
Epoch [6560/10000], loss: 0.15482 acc: 0.98667 val_loss: 0.16096, val_acc: 0.96000
Epoch [6570/10000], loss: 0.15470 acc: 0.98667 val_loss: 0.16086, val_acc: 0.96000
Epoch [6580/10000], loss: 0.15459 acc: 0.98667 val_loss: 0.16076, val_acc: 0.96000
Epoch [6590/10000], loss: 0.15448 acc: 0.98667 val_loss: 0.16066, val_acc: 0.96000
Epoch [6600/10000], loss: 0.15436 acc: 0.98667 val_loss: 0.16056, val_acc: 0.96000
Epoch [6610/10000], loss: 0.15425 acc: 0.98667 val_loss: 0.16047, val_acc: 0.96000
Epoch [6620/10000], loss: 0.15414 acc: 0.98667 val_loss: 0.16037, val_acc: 0.96000
Epoch [6630/10000], loss: 0.15402 acc: 0.98667 val_loss: 0.16027, val_acc: 0.96000
Epoch [6640/10000], loss: 0.15391 acc: 0.98667 val_loss: 0.16017, val_acc: 0.96000
Epoch [6650/10000], loss: 0.15380 acc: 0.98667 val_loss: 0.16007, val_acc: 0.96000
Epoch [6660/10000], loss: 0.15369 acc: 0.98667 val_loss: 0.15998, val_acc: 0.96000
Epoch [6670/10000], loss: 0.15357 acc: 0.98667 val_loss: 0.15988, val_acc: 0.96000
Epoch [6680/10000], loss: 0.15346 acc: 0.98667 val_loss: 0.15978, val_acc: 0.96000
Epoch [6690/10000], loss: 0.15335 acc: 0.98667 val_loss: 0.15968, val_acc: 0.96000
Epoch [6700/10000], loss: 0.15324 acc: 0.98667 val_loss: 0.15959, val_acc: 0.96000
Epoch [6710/10000], loss: 0.15313 acc: 0.98667 val_loss: 0.15949, val_acc: 0.96000
Epoch [6720/10000], loss: 0.15302 acc: 0.98667 val_loss: 0.15939, val_acc: 0.96000
Epoch [6730/10000], loss: 0.15291 acc: 0.98667 val_loss: 0.15930, val_acc: 0.96000
Epoch [6740/10000], loss: 0.15280 acc: 0.98667 val_loss: 0.15920, val_acc: 0.96000
Epoch [6750/10000], loss: 0.15269 acc: 0.98667 val_loss: 0.15911, val_acc: 0.96000
Epoch [6760/10000], loss: 0.15258 acc: 0.98667 val_loss: 0.15901, val_acc: 0.96000
Epoch [6770/10000], loss: 0.15247 acc: 0.98667 val_loss: 0.15892, val_acc: 0.96000
Epoch [6780/10000], loss: 0.15236 acc: 0.98667 val_loss: 0.15882, val_acc: 0.96000
Epoch [6790/10000], loss: 0.15225 acc: 0.98667 val_loss: 0.15873, val_acc: 0.96000
Epoch [6800/10000], loss: 0.15215 acc: 0.98667 val_loss: 0.15863, val_acc: 0.96000
Epoch [6810/10000], loss: 0.15204 acc: 0.98667 val_loss: 0.15854, val_acc: 0.96000
Epoch [6820/10000], loss: 0.15193 acc: 0.98667 val_loss: 0.15845, val_acc: 0.96000
Epoch [6830/10000], loss: 0.15182 acc: 0.98667 val_loss: 0.15835, val_acc: 0.96000
Epoch [6840/10000], loss: 0.15171 acc: 0.98667 val_loss: 0.15826, val_acc: 0.96000
Epoch [6850/10000], loss: 0.15161 acc: 0.98667 val_loss: 0.15817, val_acc: 0.96000
Epoch [6860/10000], loss: 0.15150 acc: 0.98667 val_loss: 0.15807, val_acc: 0.96000
Epoch [6870/10000], loss: 0.15139 acc: 0.98667 val_loss: 0.15798, val_acc: 0.96000
Epoch [6880/10000], loss: 0.15129 acc: 0.98667 val_loss: 0.15789, val_acc: 0.96000
Epoch [6890/10000], loss: 0.15118 acc: 0.98667 val_loss: 0.15780, val_acc: 0.96000
Epoch [6900/10000], loss: 0.15108 acc: 0.98667 val_loss: 0.15771, val_acc: 0.96000
Epoch [6910/10000], loss: 0.15097 acc: 0.98667 val_loss: 0.15761, val_acc: 0.96000
Epoch [6920/10000], loss: 0.15086 acc: 0.98667 val_loss: 0.15752, val_acc: 0.96000
Epoch [6930/10000], loss: 0.15076 acc: 0.98667 val_loss: 0.15743, val_acc: 0.96000
Epoch [6940/10000], loss: 0.15065 acc: 0.98667 val_loss: 0.15734, val_acc: 0.96000
Epoch [6950/10000], loss: 0.15055 acc: 0.98667 val_loss: 0.15725, val_acc: 0.96000
Epoch [6960/10000], loss: 0.15045 acc: 0.98667 val_loss: 0.15716, val_acc: 0.96000
Epoch [6970/10000], loss: 0.15034 acc: 0.98667 val_loss: 0.15707, val_acc: 0.96000
Epoch [6980/10000], loss: 0.15024 acc: 0.98667 val_loss: 0.15698, val_acc: 0.96000
Epoch [6990/10000], loss: 0.15013 acc: 0.98667 val_loss: 0.15689, val_acc: 0.96000
Epoch [7000/10000], loss: 0.15003 acc: 0.98667 val_loss: 0.15680, val_acc: 0.96000
Epoch [7010/10000], loss: 0.14993 acc: 0.98667 val_loss: 0.15671, val_acc: 0.96000
Epoch [7020/10000], loss: 0.14983 acc: 0.98667 val_loss: 0.15662, val_acc: 0.96000
Epoch [7030/10000], loss: 0.14972 acc: 0.98667 val_loss: 0.15653, val_acc: 0.96000
Epoch [7040/10000], loss: 0.14962 acc: 0.98667 val_loss: 0.15644, val_acc: 0.96000
Epoch [7050/10000], loss: 0.14952 acc: 0.98667 val_loss: 0.15636, val_acc: 0.96000
Epoch [7060/10000], loss: 0.14942 acc: 0.98667 val_loss: 0.15627, val_acc: 0.96000
Epoch [7070/10000], loss: 0.14931 acc: 0.98667 val_loss: 0.15618, val_acc: 0.96000
Epoch [7080/10000], loss: 0.14921 acc: 0.98667 val_loss: 0.15609, val_acc: 0.96000
Epoch [7090/10000], loss: 0.14911 acc: 0.98667 val_loss: 0.15600, val_acc: 0.96000
Epoch [7100/10000], loss: 0.14901 acc: 0.98667 val_loss: 0.15592, val_acc: 0.96000
Epoch [7110/10000], loss: 0.14891 acc: 0.98667 val_loss: 0.15583, val_acc: 0.96000
Epoch [7120/10000], loss: 0.14881 acc: 0.98667 val_loss: 0.15574, val_acc: 0.96000
Epoch [7130/10000], loss: 0.14871 acc: 0.98667 val_loss: 0.15565, val_acc: 0.96000
Epoch [7140/10000], loss: 0.14861 acc: 0.98667 val_loss: 0.15557, val_acc: 0.96000
Epoch [7150/10000], loss: 0.14851 acc: 0.98667 val_loss: 0.15548, val_acc: 0.96000
Epoch [7160/10000], loss: 0.14841 acc: 0.98667 val_loss: 0.15540, val_acc: 0.96000
Epoch [7170/10000], loss: 0.14831 acc: 0.98667 val_loss: 0.15531, val_acc: 0.96000
Epoch [7180/10000], loss: 0.14821 acc: 0.98667 val_loss: 0.15522, val_acc: 0.96000
Epoch [7190/10000], loss: 0.14811 acc: 0.98667 val_loss: 0.15514, val_acc: 0.96000
Epoch [7200/10000], loss: 0.14801 acc: 0.98667 val_loss: 0.15505, val_acc: 0.96000
Epoch [7210/10000], loss: 0.14792 acc: 0.98667 val_loss: 0.15497, val_acc: 0.96000
Epoch [7220/10000], loss: 0.14782 acc: 0.98667 val_loss: 0.15488, val_acc: 0.96000
Epoch [7230/10000], loss: 0.14772 acc: 0.98667 val_loss: 0.15480, val_acc: 0.96000
Epoch [7240/10000], loss: 0.14762 acc: 0.98667 val_loss: 0.15471, val_acc: 0.96000
Epoch [7250/10000], loss: 0.14752 acc: 0.98667 val_loss: 0.15463, val_acc: 0.96000
Epoch [7260/10000], loss: 0.14743 acc: 0.98667 val_loss: 0.15455, val_acc: 0.96000
Epoch [7270/10000], loss: 0.14733 acc: 0.98667 val_loss: 0.15446, val_acc: 0.96000
Epoch [7280/10000], loss: 0.14723 acc: 0.98667 val_loss: 0.15438, val_acc: 0.96000
Epoch [7290/10000], loss: 0.14714 acc: 0.98667 val_loss: 0.15429, val_acc: 0.96000
Epoch [7300/10000], loss: 0.14704 acc: 0.98667 val_loss: 0.15421, val_acc: 0.96000
Epoch [7310/10000], loss: 0.14694 acc: 0.98667 val_loss: 0.15413, val_acc: 0.96000
Epoch [7320/10000], loss: 0.14685 acc: 0.98667 val_loss: 0.15404, val_acc: 0.96000
Epoch [7330/10000], loss: 0.14675 acc: 0.98667 val_loss: 0.15396, val_acc: 0.96000
Epoch [7340/10000], loss: 0.14666 acc: 0.98667 val_loss: 0.15388, val_acc: 0.96000
Epoch [7350/10000], loss: 0.14656 acc: 0.98667 val_loss: 0.15380, val_acc: 0.96000
Epoch [7360/10000], loss: 0.14646 acc: 0.98667 val_loss: 0.15371, val_acc: 0.96000
Epoch [7370/10000], loss: 0.14637 acc: 0.98667 val_loss: 0.15363, val_acc: 0.96000
Epoch [7380/10000], loss: 0.14627 acc: 0.98667 val_loss: 0.15355, val_acc: 0.96000
Epoch [7390/10000], loss: 0.14618 acc: 0.98667 val_loss: 0.15347, val_acc: 0.96000
Epoch [7400/10000], loss: 0.14609 acc: 0.98667 val_loss: 0.15339, val_acc: 0.96000
Epoch [7410/10000], loss: 0.14599 acc: 0.98667 val_loss: 0.15331, val_acc: 0.96000
Epoch [7420/10000], loss: 0.14590 acc: 0.98667 val_loss: 0.15323, val_acc: 0.96000
Epoch [7430/10000], loss: 0.14580 acc: 0.98667 val_loss: 0.15314, val_acc: 0.96000
Epoch [7440/10000], loss: 0.14571 acc: 0.98667 val_loss: 0.15306, val_acc: 0.96000
Epoch [7450/10000], loss: 0.14562 acc: 0.98667 val_loss: 0.15298, val_acc: 0.96000
Epoch [7460/10000], loss: 0.14552 acc: 0.98667 val_loss: 0.15290, val_acc: 0.96000
Epoch [7470/10000], loss: 0.14543 acc: 0.98667 val_loss: 0.15282, val_acc: 0.96000
Epoch [7480/10000], loss: 0.14534 acc: 0.98667 val_loss: 0.15274, val_acc: 0.96000
Epoch [7490/10000], loss: 0.14525 acc: 0.98667 val_loss: 0.15266, val_acc: 0.96000
Epoch [7500/10000], loss: 0.14515 acc: 0.98667 val_loss: 0.15258, val_acc: 0.96000
Epoch [7510/10000], loss: 0.14506 acc: 0.98667 val_loss: 0.15250, val_acc: 0.96000
Epoch [7520/10000], loss: 0.14497 acc: 0.98667 val_loss: 0.15243, val_acc: 0.96000
Epoch [7530/10000], loss: 0.14488 acc: 0.98667 val_loss: 0.15235, val_acc: 0.96000
Epoch [7540/10000], loss: 0.14479 acc: 0.98667 val_loss: 0.15227, val_acc: 0.96000
Epoch [7550/10000], loss: 0.14470 acc: 0.98667 val_loss: 0.15219, val_acc: 0.96000
Epoch [7560/10000], loss: 0.14460 acc: 0.98667 val_loss: 0.15211, val_acc: 0.96000
Epoch [7570/10000], loss: 0.14451 acc: 0.98667 val_loss: 0.15203, val_acc: 0.96000
Epoch [7580/10000], loss: 0.14442 acc: 0.98667 val_loss: 0.15195, val_acc: 0.96000
Epoch [7590/10000], loss: 0.14433 acc: 0.98667 val_loss: 0.15188, val_acc: 0.96000
Epoch [7600/10000], loss: 0.14424 acc: 0.98667 val_loss: 0.15180, val_acc: 0.96000
Epoch [7610/10000], loss: 0.14415 acc: 0.98667 val_loss: 0.15172, val_acc: 0.96000
Epoch [7620/10000], loss: 0.14406 acc: 0.98667 val_loss: 0.15164, val_acc: 0.96000
Epoch [7630/10000], loss: 0.14397 acc: 0.98667 val_loss: 0.15157, val_acc: 0.96000
Epoch [7640/10000], loss: 0.14388 acc: 0.98667 val_loss: 0.15149, val_acc: 0.96000
Epoch [7650/10000], loss: 0.14379 acc: 0.98667 val_loss: 0.15141, val_acc: 0.96000
Epoch [7660/10000], loss: 0.14370 acc: 0.98667 val_loss: 0.15134, val_acc: 0.96000
Epoch [7670/10000], loss: 0.14362 acc: 0.98667 val_loss: 0.15126, val_acc: 0.96000
Epoch [7680/10000], loss: 0.14353 acc: 0.98667 val_loss: 0.15118, val_acc: 0.96000
Epoch [7690/10000], loss: 0.14344 acc: 0.98667 val_loss: 0.15111, val_acc: 0.96000
Epoch [7700/10000], loss: 0.14335 acc: 0.98667 val_loss: 0.15103, val_acc: 0.96000
Epoch [7710/10000], loss: 0.14326 acc: 0.98667 val_loss: 0.15096, val_acc: 0.96000
Epoch [7720/10000], loss: 0.14317 acc: 0.98667 val_loss: 0.15088, val_acc: 0.96000
Epoch [7730/10000], loss: 0.14309 acc: 0.98667 val_loss: 0.15080, val_acc: 0.96000
Epoch [7740/10000], loss: 0.14300 acc: 0.98667 val_loss: 0.15073, val_acc: 0.96000
Epoch [7750/10000], loss: 0.14291 acc: 0.98667 val_loss: 0.15065, val_acc: 0.96000
Epoch [7760/10000], loss: 0.14282 acc: 0.98667 val_loss: 0.15058, val_acc: 0.96000
Epoch [7770/10000], loss: 0.14274 acc: 0.98667 val_loss: 0.15050, val_acc: 0.96000
Epoch [7780/10000], loss: 0.14265 acc: 0.98667 val_loss: 0.15043, val_acc: 0.96000
Epoch [7790/10000], loss: 0.14256 acc: 0.98667 val_loss: 0.15036, val_acc: 0.96000
Epoch [7800/10000], loss: 0.14248 acc: 0.98667 val_loss: 0.15028, val_acc: 0.96000
Epoch [7810/10000], loss: 0.14239 acc: 0.98667 val_loss: 0.15021, val_acc: 0.96000
Epoch [7820/10000], loss: 0.14230 acc: 0.98667 val_loss: 0.15013, val_acc: 0.96000
Epoch [7830/10000], loss: 0.14222 acc: 0.98667 val_loss: 0.15006, val_acc: 0.96000
Epoch [7840/10000], loss: 0.14213 acc: 0.98667 val_loss: 0.14999, val_acc: 0.96000
Epoch [7850/10000], loss: 0.14205 acc: 0.98667 val_loss: 0.14991, val_acc: 0.96000
Epoch [7860/10000], loss: 0.14196 acc: 0.98667 val_loss: 0.14984, val_acc: 0.96000
Epoch [7870/10000], loss: 0.14188 acc: 0.98667 val_loss: 0.14977, val_acc: 0.96000
Epoch [7880/10000], loss: 0.14179 acc: 0.98667 val_loss: 0.14969, val_acc: 0.96000
Epoch [7890/10000], loss: 0.14171 acc: 0.98667 val_loss: 0.14962, val_acc: 0.96000
Epoch [7900/10000], loss: 0.14162 acc: 0.98667 val_loss: 0.14955, val_acc: 0.96000
Epoch [7910/10000], loss: 0.14154 acc: 0.98667 val_loss: 0.14948, val_acc: 0.96000
Epoch [7920/10000], loss: 0.14145 acc: 0.98667 val_loss: 0.14940, val_acc: 0.96000
Epoch [7930/10000], loss: 0.14137 acc: 0.98667 val_loss: 0.14933, val_acc: 0.96000
Epoch [7940/10000], loss: 0.14128 acc: 0.98667 val_loss: 0.14926, val_acc: 0.96000
Epoch [7950/10000], loss: 0.14120 acc: 0.98667 val_loss: 0.14919, val_acc: 0.96000
Epoch [7960/10000], loss: 0.14112 acc: 0.98667 val_loss: 0.14912, val_acc: 0.96000
Epoch [7970/10000], loss: 0.14103 acc: 0.98667 val_loss: 0.14904, val_acc: 0.96000
Epoch [7980/10000], loss: 0.14095 acc: 0.98667 val_loss: 0.14897, val_acc: 0.96000
Epoch [7990/10000], loss: 0.14087 acc: 0.98667 val_loss: 0.14890, val_acc: 0.96000
Epoch [8000/10000], loss: 0.14078 acc: 0.98667 val_loss: 0.14883, val_acc: 0.96000
Epoch [8010/10000], loss: 0.14070 acc: 0.98667 val_loss: 0.14876, val_acc: 0.96000
Epoch [8020/10000], loss: 0.14062 acc: 0.98667 val_loss: 0.14869, val_acc: 0.96000
Epoch [8030/10000], loss: 0.14054 acc: 0.98667 val_loss: 0.14862, val_acc: 0.96000
Epoch [8040/10000], loss: 0.14045 acc: 0.98667 val_loss: 0.14855, val_acc: 0.96000
Epoch [8050/10000], loss: 0.14037 acc: 0.98667 val_loss: 0.14848, val_acc: 0.96000
Epoch [8060/10000], loss: 0.14029 acc: 0.98667 val_loss: 0.14841, val_acc: 0.96000
Epoch [8070/10000], loss: 0.14021 acc: 0.98667 val_loss: 0.14834, val_acc: 0.96000
Epoch [8080/10000], loss: 0.14013 acc: 0.98667 val_loss: 0.14827, val_acc: 0.96000
Epoch [8090/10000], loss: 0.14004 acc: 0.98667 val_loss: 0.14820, val_acc: 0.96000
Epoch [8100/10000], loss: 0.13996 acc: 0.98667 val_loss: 0.14813, val_acc: 0.96000
Epoch [8110/10000], loss: 0.13988 acc: 0.98667 val_loss: 0.14806, val_acc: 0.96000
Epoch [8120/10000], loss: 0.13980 acc: 0.98667 val_loss: 0.14799, val_acc: 0.96000
Epoch [8130/10000], loss: 0.13972 acc: 0.98667 val_loss: 0.14792, val_acc: 0.96000
Epoch [8140/10000], loss: 0.13964 acc: 0.98667 val_loss: 0.14785, val_acc: 0.96000
Epoch [8150/10000], loss: 0.13956 acc: 0.98667 val_loss: 0.14778, val_acc: 0.96000
Epoch [8160/10000], loss: 0.13948 acc: 0.98667 val_loss: 0.14771, val_acc: 0.96000
Epoch [8170/10000], loss: 0.13940 acc: 0.98667 val_loss: 0.14765, val_acc: 0.96000
Epoch [8180/10000], loss: 0.13932 acc: 0.98667 val_loss: 0.14758, val_acc: 0.96000
Epoch [8190/10000], loss: 0.13924 acc: 0.98667 val_loss: 0.14751, val_acc: 0.96000
Epoch [8200/10000], loss: 0.13916 acc: 0.98667 val_loss: 0.14744, val_acc: 0.96000
Epoch [8210/10000], loss: 0.13908 acc: 0.98667 val_loss: 0.14737, val_acc: 0.96000
Epoch [8220/10000], loss: 0.13900 acc: 0.98667 val_loss: 0.14731, val_acc: 0.96000
Epoch [8230/10000], loss: 0.13892 acc: 0.98667 val_loss: 0.14724, val_acc: 0.96000
Epoch [8240/10000], loss: 0.13884 acc: 0.98667 val_loss: 0.14717, val_acc: 0.96000
Epoch [8250/10000], loss: 0.13876 acc: 0.98667 val_loss: 0.14710, val_acc: 0.96000
Epoch [8260/10000], loss: 0.13869 acc: 0.98667 val_loss: 0.14704, val_acc: 0.96000
Epoch [8270/10000], loss: 0.13861 acc: 0.98667 val_loss: 0.14697, val_acc: 0.96000
Epoch [8280/10000], loss: 0.13853 acc: 0.98667 val_loss: 0.14690, val_acc: 0.96000
Epoch [8290/10000], loss: 0.13845 acc: 0.98667 val_loss: 0.14684, val_acc: 0.96000
Epoch [8300/10000], loss: 0.13837 acc: 0.98667 val_loss: 0.14677, val_acc: 0.96000
Epoch [8310/10000], loss: 0.13829 acc: 0.98667 val_loss: 0.14670, val_acc: 0.96000
Epoch [8320/10000], loss: 0.13822 acc: 0.98667 val_loss: 0.14664, val_acc: 0.96000
Epoch [8330/10000], loss: 0.13814 acc: 0.98667 val_loss: 0.14657, val_acc: 0.96000
Epoch [8340/10000], loss: 0.13806 acc: 0.98667 val_loss: 0.14650, val_acc: 0.96000
Epoch [8350/10000], loss: 0.13798 acc: 0.98667 val_loss: 0.14644, val_acc: 0.96000
Epoch [8360/10000], loss: 0.13791 acc: 0.98667 val_loss: 0.14637, val_acc: 0.96000
Epoch [8370/10000], loss: 0.13783 acc: 0.98667 val_loss: 0.14631, val_acc: 0.96000
Epoch [8380/10000], loss: 0.13775 acc: 0.98667 val_loss: 0.14624, val_acc: 0.96000
Epoch [8390/10000], loss: 0.13768 acc: 0.98667 val_loss: 0.14618, val_acc: 0.96000
Epoch [8400/10000], loss: 0.13760 acc: 0.98667 val_loss: 0.14611, val_acc: 0.96000
Epoch [8410/10000], loss: 0.13752 acc: 0.98667 val_loss: 0.14605, val_acc: 0.96000
Epoch [8420/10000], loss: 0.13745 acc: 0.98667 val_loss: 0.14598, val_acc: 0.96000
Epoch [8430/10000], loss: 0.13737 acc: 0.98667 val_loss: 0.14592, val_acc: 0.96000
Epoch [8440/10000], loss: 0.13730 acc: 0.98667 val_loss: 0.14585, val_acc: 0.96000
Epoch [8450/10000], loss: 0.13722 acc: 0.98667 val_loss: 0.14579, val_acc: 0.96000
Epoch [8460/10000], loss: 0.13714 acc: 0.98667 val_loss: 0.14572, val_acc: 0.96000
Epoch [8470/10000], loss: 0.13707 acc: 0.98667 val_loss: 0.14566, val_acc: 0.96000
Epoch [8480/10000], loss: 0.13699 acc: 0.98667 val_loss: 0.14559, val_acc: 0.96000
Epoch [8490/10000], loss: 0.13692 acc: 0.98667 val_loss: 0.14553, val_acc: 0.96000
Epoch [8500/10000], loss: 0.13684 acc: 0.98667 val_loss: 0.14547, val_acc: 0.96000
Epoch [8510/10000], loss: 0.13677 acc: 0.98667 val_loss: 0.14540, val_acc: 0.96000
Epoch [8520/10000], loss: 0.13669 acc: 0.98667 val_loss: 0.14534, val_acc: 0.96000
Epoch [8530/10000], loss: 0.13662 acc: 0.98667 val_loss: 0.14528, val_acc: 0.96000
Epoch [8540/10000], loss: 0.13654 acc: 0.98667 val_loss: 0.14521, val_acc: 0.96000
Epoch [8550/10000], loss: 0.13647 acc: 0.98667 val_loss: 0.14515, val_acc: 0.96000
Epoch [8560/10000], loss: 0.13640 acc: 0.98667 val_loss: 0.14509, val_acc: 0.96000
Epoch [8570/10000], loss: 0.13632 acc: 0.98667 val_loss: 0.14502, val_acc: 0.96000
Epoch [8580/10000], loss: 0.13625 acc: 0.98667 val_loss: 0.14496, val_acc: 0.96000
Epoch [8590/10000], loss: 0.13617 acc: 0.98667 val_loss: 0.14490, val_acc: 0.96000
Epoch [8600/10000], loss: 0.13610 acc: 0.98667 val_loss: 0.14483, val_acc: 0.96000
Epoch [8610/10000], loss: 0.13603 acc: 0.98667 val_loss: 0.14477, val_acc: 0.96000
Epoch [8620/10000], loss: 0.13595 acc: 0.98667 val_loss: 0.14471, val_acc: 0.96000
Epoch [8630/10000], loss: 0.13588 acc: 0.98667 val_loss: 0.14465, val_acc: 0.96000
Epoch [8640/10000], loss: 0.13581 acc: 0.98667 val_loss: 0.14459, val_acc: 0.96000
Epoch [8650/10000], loss: 0.13574 acc: 0.98667 val_loss: 0.14452, val_acc: 0.96000
Epoch [8660/10000], loss: 0.13566 acc: 0.98667 val_loss: 0.14446, val_acc: 0.96000
Epoch [8670/10000], loss: 0.13559 acc: 0.98667 val_loss: 0.14440, val_acc: 0.96000
Epoch [8680/10000], loss: 0.13552 acc: 0.98667 val_loss: 0.14434, val_acc: 0.96000
Epoch [8690/10000], loss: 0.13545 acc: 0.98667 val_loss: 0.14428, val_acc: 0.96000
Epoch [8700/10000], loss: 0.13537 acc: 0.98667 val_loss: 0.14422, val_acc: 0.96000
Epoch [8710/10000], loss: 0.13530 acc: 0.98667 val_loss: 0.14415, val_acc: 0.96000
Epoch [8720/10000], loss: 0.13523 acc: 0.98667 val_loss: 0.14409, val_acc: 0.96000
Epoch [8730/10000], loss: 0.13516 acc: 0.98667 val_loss: 0.14403, val_acc: 0.96000
Epoch [8740/10000], loss: 0.13509 acc: 0.98667 val_loss: 0.14397, val_acc: 0.96000
Epoch [8750/10000], loss: 0.13501 acc: 0.98667 val_loss: 0.14391, val_acc: 0.96000
Epoch [8760/10000], loss: 0.13494 acc: 0.98667 val_loss: 0.14385, val_acc: 0.96000
Epoch [8770/10000], loss: 0.13487 acc: 0.98667 val_loss: 0.14379, val_acc: 0.96000
Epoch [8780/10000], loss: 0.13480 acc: 0.98667 val_loss: 0.14373, val_acc: 0.96000
Epoch [8790/10000], loss: 0.13473 acc: 0.98667 val_loss: 0.14367, val_acc: 0.96000
Epoch [8800/10000], loss: 0.13466 acc: 0.98667 val_loss: 0.14361, val_acc: 0.96000
Epoch [8810/10000], loss: 0.13459 acc: 0.98667 val_loss: 0.14355, val_acc: 0.96000
Epoch [8820/10000], loss: 0.13452 acc: 0.98667 val_loss: 0.14349, val_acc: 0.96000
Epoch [8830/10000], loss: 0.13445 acc: 0.98667 val_loss: 0.14343, val_acc: 0.96000
Epoch [8840/10000], loss: 0.13438 acc: 0.98667 val_loss: 0.14337, val_acc: 0.96000
Epoch [8850/10000], loss: 0.13431 acc: 0.98667 val_loss: 0.14331, val_acc: 0.96000
Epoch [8860/10000], loss: 0.13424 acc: 0.98667 val_loss: 0.14325, val_acc: 0.96000
Epoch [8870/10000], loss: 0.13417 acc: 0.98667 val_loss: 0.14319, val_acc: 0.96000
Epoch [8880/10000], loss: 0.13410 acc: 0.98667 val_loss: 0.14313, val_acc: 0.96000
Epoch [8890/10000], loss: 0.13403 acc: 0.98667 val_loss: 0.14308, val_acc: 0.96000
Epoch [8900/10000], loss: 0.13396 acc: 0.98667 val_loss: 0.14302, val_acc: 0.96000
Epoch [8910/10000], loss: 0.13389 acc: 0.98667 val_loss: 0.14296, val_acc: 0.96000
Epoch [8920/10000], loss: 0.13382 acc: 0.98667 val_loss: 0.14290, val_acc: 0.96000
Epoch [8930/10000], loss: 0.13375 acc: 0.98667 val_loss: 0.14284, val_acc: 0.96000
Epoch [8940/10000], loss: 0.13368 acc: 0.98667 val_loss: 0.14278, val_acc: 0.96000
Epoch [8950/10000], loss: 0.13361 acc: 0.98667 val_loss: 0.14272, val_acc: 0.96000
Epoch [8960/10000], loss: 0.13354 acc: 0.98667 val_loss: 0.14266, val_acc: 0.96000
Epoch [8970/10000], loss: 0.13347 acc: 0.98667 val_loss: 0.14261, val_acc: 0.96000
Epoch [8980/10000], loss: 0.13341 acc: 0.98667 val_loss: 0.14255, val_acc: 0.96000
Epoch [8990/10000], loss: 0.13334 acc: 0.98667 val_loss: 0.14249, val_acc: 0.96000
Epoch [9000/10000], loss: 0.13327 acc: 0.98667 val_loss: 0.14243, val_acc: 0.96000
Epoch [9010/10000], loss: 0.13320 acc: 0.98667 val_loss: 0.14238, val_acc: 0.96000
Epoch [9020/10000], loss: 0.13313 acc: 0.98667 val_loss: 0.14232, val_acc: 0.96000
Epoch [9030/10000], loss: 0.13307 acc: 0.98667 val_loss: 0.14226, val_acc: 0.96000
Epoch [9040/10000], loss: 0.13300 acc: 0.98667 val_loss: 0.14220, val_acc: 0.96000
Epoch [9050/10000], loss: 0.13293 acc: 0.98667 val_loss: 0.14215, val_acc: 0.96000
Epoch [9060/10000], loss: 0.13286 acc: 0.98667 val_loss: 0.14209, val_acc: 0.96000
Epoch [9070/10000], loss: 0.13280 acc: 0.98667 val_loss: 0.14203, val_acc: 0.96000
Epoch [9080/10000], loss: 0.13273 acc: 0.98667 val_loss: 0.14198, val_acc: 0.96000
Epoch [9090/10000], loss: 0.13266 acc: 0.98667 val_loss: 0.14192, val_acc: 0.96000
Epoch [9100/10000], loss: 0.13259 acc: 0.98667 val_loss: 0.14186, val_acc: 0.96000
Epoch [9110/10000], loss: 0.13253 acc: 0.98667 val_loss: 0.14181, val_acc: 0.96000
Epoch [9120/10000], loss: 0.13246 acc: 0.98667 val_loss: 0.14175, val_acc: 0.96000
Epoch [9130/10000], loss: 0.13239 acc: 0.98667 val_loss: 0.14169, val_acc: 0.96000
Epoch [9140/10000], loss: 0.13233 acc: 0.98667 val_loss: 0.14164, val_acc: 0.96000
Epoch [9150/10000], loss: 0.13226 acc: 0.98667 val_loss: 0.14158, val_acc: 0.96000
Epoch [9160/10000], loss: 0.13220 acc: 0.98667 val_loss: 0.14153, val_acc: 0.96000
Epoch [9170/10000], loss: 0.13213 acc: 0.98667 val_loss: 0.14147, val_acc: 0.96000
Epoch [9180/10000], loss: 0.13206 acc: 0.98667 val_loss: 0.14141, val_acc: 0.96000
Epoch [9190/10000], loss: 0.13200 acc: 0.98667 val_loss: 0.14136, val_acc: 0.96000
Epoch [9200/10000], loss: 0.13193 acc: 0.98667 val_loss: 0.14130, val_acc: 0.96000
Epoch [9210/10000], loss: 0.13187 acc: 0.98667 val_loss: 0.14125, val_acc: 0.96000
Epoch [9220/10000], loss: 0.13180 acc: 0.98667 val_loss: 0.14119, val_acc: 0.96000
Epoch [9230/10000], loss: 0.13174 acc: 0.98667 val_loss: 0.14114, val_acc: 0.96000
Epoch [9240/10000], loss: 0.13167 acc: 0.98667 val_loss: 0.14108, val_acc: 0.96000
Epoch [9250/10000], loss: 0.13160 acc: 0.98667 val_loss: 0.14103, val_acc: 0.96000
Epoch [9260/10000], loss: 0.13154 acc: 0.98667 val_loss: 0.14097, val_acc: 0.96000
Epoch [9270/10000], loss: 0.13147 acc: 0.98667 val_loss: 0.14092, val_acc: 0.96000
Epoch [9280/10000], loss: 0.13141 acc: 0.98667 val_loss: 0.14086, val_acc: 0.96000
Epoch [9290/10000], loss: 0.13135 acc: 0.98667 val_loss: 0.14081, val_acc: 0.96000
Epoch [9300/10000], loss: 0.13128 acc: 0.98667 val_loss: 0.14075, val_acc: 0.96000
Epoch [9310/10000], loss: 0.13122 acc: 0.98667 val_loss: 0.14070, val_acc: 0.96000
Epoch [9320/10000], loss: 0.13115 acc: 0.98667 val_loss: 0.14065, val_acc: 0.96000
Epoch [9330/10000], loss: 0.13109 acc: 0.98667 val_loss: 0.14059, val_acc: 0.96000
Epoch [9340/10000], loss: 0.13102 acc: 0.98667 val_loss: 0.14054, val_acc: 0.96000
Epoch [9350/10000], loss: 0.13096 acc: 0.98667 val_loss: 0.14048, val_acc: 0.96000
Epoch [9360/10000], loss: 0.13090 acc: 0.98667 val_loss: 0.14043, val_acc: 0.96000
Epoch [9370/10000], loss: 0.13083 acc: 0.98667 val_loss: 0.14038, val_acc: 0.96000
Epoch [9380/10000], loss: 0.13077 acc: 0.98667 val_loss: 0.14032, val_acc: 0.96000
Epoch [9390/10000], loss: 0.13070 acc: 0.98667 val_loss: 0.14027, val_acc: 0.96000
Epoch [9400/10000], loss: 0.13064 acc: 0.98667 val_loss: 0.14022, val_acc: 0.96000
Epoch [9410/10000], loss: 0.13058 acc: 0.98667 val_loss: 0.14016, val_acc: 0.96000
Epoch [9420/10000], loss: 0.13051 acc: 0.98667 val_loss: 0.14011, val_acc: 0.96000
Epoch [9430/10000], loss: 0.13045 acc: 0.98667 val_loss: 0.14006, val_acc: 0.96000
Epoch [9440/10000], loss: 0.13039 acc: 0.98667 val_loss: 0.14000, val_acc: 0.96000
Epoch [9450/10000], loss: 0.13033 acc: 0.98667 val_loss: 0.13995, val_acc: 0.96000
Epoch [9460/10000], loss: 0.13026 acc: 0.98667 val_loss: 0.13990, val_acc: 0.96000
Epoch [9470/10000], loss: 0.13020 acc: 0.98667 val_loss: 0.13984, val_acc: 0.96000
Epoch [9480/10000], loss: 0.13014 acc: 0.98667 val_loss: 0.13979, val_acc: 0.96000
Epoch [9490/10000], loss: 0.13008 acc: 0.98667 val_loss: 0.13974, val_acc: 0.96000
Epoch [9500/10000], loss: 0.13001 acc: 0.98667 val_loss: 0.13969, val_acc: 0.96000
Epoch [9510/10000], loss: 0.12995 acc: 0.98667 val_loss: 0.13963, val_acc: 0.96000
Epoch [9520/10000], loss: 0.12989 acc: 0.98667 val_loss: 0.13958, val_acc: 0.96000
Epoch [9530/10000], loss: 0.12983 acc: 0.98667 val_loss: 0.13953, val_acc: 0.96000
Epoch [9540/10000], loss: 0.12976 acc: 0.98667 val_loss: 0.13948, val_acc: 0.96000
Epoch [9550/10000], loss: 0.12970 acc: 0.98667 val_loss: 0.13943, val_acc: 0.96000
Epoch [9560/10000], loss: 0.12964 acc: 0.98667 val_loss: 0.13937, val_acc: 0.96000
Epoch [9570/10000], loss: 0.12958 acc: 0.98667 val_loss: 0.13932, val_acc: 0.96000
Epoch [9580/10000], loss: 0.12952 acc: 0.98667 val_loss: 0.13927, val_acc: 0.96000
Epoch [9590/10000], loss: 0.12946 acc: 0.98667 val_loss: 0.13922, val_acc: 0.96000
Epoch [9600/10000], loss: 0.12940 acc: 0.98667 val_loss: 0.13917, val_acc: 0.96000
Epoch [9610/10000], loss: 0.12933 acc: 0.98667 val_loss: 0.13912, val_acc: 0.96000
Epoch [9620/10000], loss: 0.12927 acc: 0.98667 val_loss: 0.13907, val_acc: 0.96000
Epoch [9630/10000], loss: 0.12921 acc: 0.98667 val_loss: 0.13901, val_acc: 0.96000
Epoch [9640/10000], loss: 0.12915 acc: 0.98667 val_loss: 0.13896, val_acc: 0.96000
Epoch [9650/10000], loss: 0.12909 acc: 0.98667 val_loss: 0.13891, val_acc: 0.96000
Epoch [9660/10000], loss: 0.12903 acc: 0.98667 val_loss: 0.13886, val_acc: 0.96000
Epoch [9670/10000], loss: 0.12897 acc: 0.98667 val_loss: 0.13881, val_acc: 0.96000
Epoch [9680/10000], loss: 0.12891 acc: 0.98667 val_loss: 0.13876, val_acc: 0.96000
Epoch [9690/10000], loss: 0.12885 acc: 0.98667 val_loss: 0.13871, val_acc: 0.96000
Epoch [9700/10000], loss: 0.12879 acc: 0.98667 val_loss: 0.13866, val_acc: 0.96000
Epoch [9710/10000], loss: 0.12873 acc: 0.98667 val_loss: 0.13861, val_acc: 0.96000
Epoch [9720/10000], loss: 0.12867 acc: 0.98667 val_loss: 0.13856, val_acc: 0.96000
Epoch [9730/10000], loss: 0.12861 acc: 0.98667 val_loss: 0.13851, val_acc: 0.96000
Epoch [9740/10000], loss: 0.12855 acc: 0.98667 val_loss: 0.13846, val_acc: 0.96000
Epoch [9750/10000], loss: 0.12849 acc: 0.98667 val_loss: 0.13841, val_acc: 0.96000
Epoch [9760/10000], loss: 0.12843 acc: 0.98667 val_loss: 0.13836, val_acc: 0.96000
Epoch [9770/10000], loss: 0.12837 acc: 0.98667 val_loss: 0.13831, val_acc: 0.96000
Epoch [9780/10000], loss: 0.12831 acc: 0.98667 val_loss: 0.13826, val_acc: 0.96000
Epoch [9790/10000], loss: 0.12825 acc: 0.98667 val_loss: 0.13821, val_acc: 0.96000
Epoch [9800/10000], loss: 0.12819 acc: 0.98667 val_loss: 0.13816, val_acc: 0.96000
Epoch [9810/10000], loss: 0.12813 acc: 0.98667 val_loss: 0.13811, val_acc: 0.96000
Epoch [9820/10000], loss: 0.12808 acc: 0.98667 val_loss: 0.13806, val_acc: 0.96000
Epoch [9830/10000], loss: 0.12802 acc: 0.98667 val_loss: 0.13801, val_acc: 0.96000
Epoch [9840/10000], loss: 0.12796 acc: 0.98667 val_loss: 0.13796, val_acc: 0.96000
Epoch [9850/10000], loss: 0.12790 acc: 0.98667 val_loss: 0.13791, val_acc: 0.96000
Epoch [9860/10000], loss: 0.12784 acc: 0.98667 val_loss: 0.13786, val_acc: 0.96000
Epoch [9870/10000], loss: 0.12778 acc: 0.98667 val_loss: 0.13782, val_acc: 0.96000
Epoch [9880/10000], loss: 0.12772 acc: 0.98667 val_loss: 0.13777, val_acc: 0.96000
Epoch [9890/10000], loss: 0.12767 acc: 0.98667 val_loss: 0.13772, val_acc: 0.96000
Epoch [9900/10000], loss: 0.12761 acc: 0.98667 val_loss: 0.13767, val_acc: 0.96000
Epoch [9910/10000], loss: 0.12755 acc: 0.98667 val_loss: 0.13762, val_acc: 0.96000
Epoch [9920/10000], loss: 0.12749 acc: 0.98667 val_loss: 0.13757, val_acc: 0.96000
Epoch [9930/10000], loss: 0.12743 acc: 0.98667 val_loss: 0.13752, val_acc: 0.96000
Epoch [9940/10000], loss: 0.12738 acc: 0.98667 val_loss: 0.13748, val_acc: 0.96000
Epoch [9950/10000], loss: 0.12732 acc: 0.98667 val_loss: 0.13743, val_acc: 0.96000
Epoch [9960/10000], loss: 0.12726 acc: 0.98667 val_loss: 0.13738, val_acc: 0.96000
Epoch [9970/10000], loss: 0.12720 acc: 0.98667 val_loss: 0.13733, val_acc: 0.96000
Epoch [9980/10000], loss: 0.12715 acc: 0.98667 val_loss: 0.13728, val_acc: 0.96000
Epoch [9990/10000], loss: 0.12709 acc: 0.98667 val_loss: 0.13724, val_acc: 0.96000

# 손실과 정확도 확인
 
print(f'초기상태 : 손실 : {history[0,3]:.5f}  정확도 : {history[0,4]:.5f}' )
print(f'최종상태 : 손실 : {history[-1,3]:.5f}  정확도 : {history[-1,4]:.5f}' )

초기상태 : 손실 : 1.09158  정확도 : 0.26667
최종상태 : 손실 : 0.13724  정확도 : 0.96000

# 학습 곡선 출력(손실)
 
plt.plot(history[:,0], history[:,1], 'b', label='훈련')
plt.plot(history[:,0], history[:,3], 'k', label='검증')
plt.xlabel('반복 횟수')
plt.ylabel('손실')
plt.title('학습 곡선(손실)')
plt.legend()
plt.show()

# 학습 곡선 출력(정확도)
 
plt.plot(history[:,0], history[:,2], 'b', label='훈련')
plt.plot(history[:,0], history[:,4], 'k', label='검증')
plt.xlabel('반복 횟수')
plt.ylabel('정확도')
plt.title('학습 곡선(정확도)')
plt.legend()
plt.show()

NLLLoss 함수 이해 하기

# 입력 변수 준비
 
# 더미 출력 데이터
outputs_np = np.array(range(1, 13)).reshape((4,3))
# 더미 정답 데이터
labels_np = np.array([0, 1, 2, 0])
 
# 텐서화
outputs_dummy = torch.tensor(outputs_np).float()
labels_dummy = torch.tensor(labels_np).long()
 
# 결과 확인
print(outputs_dummy.data)
print(labels_dummy.data)

tensor([[ 1.,  2.,  3.],
        [ 4.,  5.,  6.],
        [ 7.,  8.,  9.],
        [10., 11., 12.]])
tensor([0, 1, 2, 0])

# NLLLoss 함수 호출
 
nllloss = nn.NLLLoss()
loss = nllloss(outputs_dummy, labels_dummy) # -(1 + 5 + 9 + 10)/4 = -6.25
print(loss.item())

-6.25

모델 클래스측에 LogSoftmax 함수를 포함

# 모델 정의
# 2입력 3출력 로지스틱 회귀 모델
 
class Net(nn.Module):
    def __init__(self, n_input, n_output):
        super().__init__()
        self.l1 = nn.Linear(n_input, n_output)
        # logsoftmax 함수 정의
        self.logsoftmax = nn.LogSoftmax(dim=1)
 
        # 초깃값을 모두 1로 함
        # "딥러닝을 위한 수학"과 조건을 맞추기 위한 목적
        # self.l1.weight.data.fill_(1.0)
        # self.l1.bias.data.fill_(1.0)
 
    def forward(self, x):
        x1 = self.l1(x)
        x2 = self.logsoftmax(x1)
        return x2

# 학습률
lr = 0.01
 
# 초기화
net = Net(n_input, n_output)
 
# 손실 함수： NLLLoss 함수
criterion = nn.NLLLoss()
 
# 최적화 함수: 경사 하강법
optimizer = optim.SGD(net.parameters(), lr=lr)

# 예측 계산
outputs = net(inputs)
 
# 손실 계산
loss = criterion(outputs, labels)
 
# 손실의 계산 그래프 시각화
g = make_dot(loss, params=dict(net.named_parameters()))
display(g)

# 학습률
lr = 0.01
 
# 초기화
net = Net(n_input, n_output)
 
# 손실 함수： NLLLoss 함수
criterion = nn.NLLLoss()
 
# 최적화 함수: 경사 하강법
optimizer = optim.SGD(net.parameters(), lr=lr)
 
# 반복 횟수
num_epochs = 10000
 
# 평가 결과 기록
history = np.zeros((0,5))

for epoch in range(num_epochs):
 
    # 훈련 페이즈
 
    # 경사 초기화
    optimizer.zero_grad()
 
    # 예측 계산
    outputs = net(inputs)
 
    # 손실 계산
    loss = criterion(outputs, labels)
 
    # 경사 계산
    loss.backward()
 
    # 파라미터 수정
    optimizer.step()
 
    # 예측 라벨 산출
    predicted = torch.max(outputs, 1)[1]
 
    # 손실과 정확도 계산
    train_loss = loss.item()
    train_acc = (predicted == labels).sum()  / len(labels)
 
    # 예측 페이즈
 
    # 예측 계산
    outputs_test = net(inputs_test)
 
    # 손실 계산
    loss_test = criterion(outputs_test, labels_test)
 
    # 예측 라벨 산출
    predicted_test = torch.max(outputs_test, 1)[1]
 
    # 손실과 정확도 계산
    val_loss =  loss_test.item()
    val_acc =  (predicted_test == labels_test).sum() / len(labels_test)
 
    if ( epoch % 10 == 0):
        print (f'Epoch [{epoch}/{num_epochs}], loss: {train_loss:.5f} acc: {train_acc:.5f} val_loss: {val_loss:.5f}, val_acc: {val_acc:.5f}')
        item = np.array([epoch , train_loss, train_acc, val_loss, val_acc])
        history = np.vstack((history, item))

Epoch [0/10000], loss: 4.88003 acc: 0.30667 val_loss: 3.70707, val_acc: 0.36000
Epoch [10/10000], loss: 1.80466 acc: 0.00000 val_loss: 1.44230, val_acc: 0.00000
Epoch [20/10000], loss: 1.18552 acc: 0.09333 val_loss: 1.19227, val_acc: 0.29333
Epoch [30/10000], loss: 1.10535 acc: 0.40000 val_loss: 1.13996, val_acc: 0.26667
Epoch [40/10000], loss: 1.04555 acc: 0.40000 val_loss: 1.07464, val_acc: 0.26667
Epoch [50/10000], loss: 0.99253 acc: 0.44000 val_loss: 1.01507, val_acc: 0.36000
Epoch [60/10000], loss: 0.94560 acc: 0.60000 val_loss: 0.96248, val_acc: 0.48000
Epoch [70/10000], loss: 0.90413 acc: 0.70667 val_loss: 0.91625, val_acc: 0.61333
Epoch [80/10000], loss: 0.86747 acc: 0.73333 val_loss: 0.87559, val_acc: 0.62667
Epoch [90/10000], loss: 0.83500 acc: 0.74667 val_loss: 0.83976, val_acc: 0.65333
Epoch [100/10000], loss: 0.80617 acc: 0.76000 val_loss: 0.80809, val_acc: 0.66667
Epoch [110/10000], loss: 0.78049 acc: 0.74667 val_loss: 0.78000, val_acc: 0.68000
Epoch [120/10000], loss: 0.75752 acc: 0.74667 val_loss: 0.75497, val_acc: 0.70667
Epoch [130/10000], loss: 0.73688 acc: 0.73333 val_loss: 0.73256, val_acc: 0.74667
Epoch [140/10000], loss: 0.71825 acc: 0.73333 val_loss: 0.71242, val_acc: 0.76000
Epoch [150/10000], loss: 0.70138 acc: 0.74667 val_loss: 0.69422, val_acc: 0.77333
Epoch [160/10000], loss: 0.68601 acc: 0.74667 val_loss: 0.67771, val_acc: 0.78667
Epoch [170/10000], loss: 0.67197 acc: 0.77333 val_loss: 0.66267, val_acc: 0.80000
Epoch [180/10000], loss: 0.65907 acc: 0.78667 val_loss: 0.64890, val_acc: 0.80000
Epoch [190/10000], loss: 0.64719 acc: 0.80000 val_loss: 0.63624, val_acc: 0.80000
Epoch [200/10000], loss: 0.63621 acc: 0.80000 val_loss: 0.62457, val_acc: 0.81333
Epoch [210/10000], loss: 0.62601 acc: 0.80000 val_loss: 0.61376, val_acc: 0.84000
Epoch [220/10000], loss: 0.61650 acc: 0.82667 val_loss: 0.60372, val_acc: 0.84000
Epoch [230/10000], loss: 0.60763 acc: 0.84000 val_loss: 0.59437, val_acc: 0.84000
Epoch [240/10000], loss: 0.59930 acc: 0.84000 val_loss: 0.58562, val_acc: 0.84000
Epoch [250/10000], loss: 0.59148 acc: 0.85333 val_loss: 0.57741, val_acc: 0.84000
Epoch [260/10000], loss: 0.58411 acc: 0.85333 val_loss: 0.56970, val_acc: 0.84000
Epoch [270/10000], loss: 0.57714 acc: 0.85333 val_loss: 0.56243, val_acc: 0.84000
Epoch [280/10000], loss: 0.57055 acc: 0.86667 val_loss: 0.55555, val_acc: 0.84000
Epoch [290/10000], loss: 0.56428 acc: 0.88000 val_loss: 0.54904, val_acc: 0.84000
Epoch [300/10000], loss: 0.55832 acc: 0.88000 val_loss: 0.54286, val_acc: 0.84000
Epoch [310/10000], loss: 0.55264 acc: 0.89333 val_loss: 0.53699, val_acc: 0.84000
Epoch [320/10000], loss: 0.54722 acc: 0.89333 val_loss: 0.53138, val_acc: 0.84000
Epoch [330/10000], loss: 0.54203 acc: 0.89333 val_loss: 0.52603, val_acc: 0.84000
Epoch [340/10000], loss: 0.53705 acc: 0.89333 val_loss: 0.52092, val_acc: 0.84000
Epoch [350/10000], loss: 0.53227 acc: 0.89333 val_loss: 0.51601, val_acc: 0.84000
Epoch [360/10000], loss: 0.52768 acc: 0.89333 val_loss: 0.51131, val_acc: 0.84000
Epoch [370/10000], loss: 0.52326 acc: 0.89333 val_loss: 0.50679, val_acc: 0.84000
Epoch [380/10000], loss: 0.51900 acc: 0.89333 val_loss: 0.50244, val_acc: 0.84000
Epoch [390/10000], loss: 0.51488 acc: 0.89333 val_loss: 0.49824, val_acc: 0.84000
Epoch [400/10000], loss: 0.51090 acc: 0.89333 val_loss: 0.49420, val_acc: 0.85333
Epoch [410/10000], loss: 0.50705 acc: 0.89333 val_loss: 0.49029, val_acc: 0.85333
Epoch [420/10000], loss: 0.50333 acc: 0.89333 val_loss: 0.48651, val_acc: 0.85333
Epoch [430/10000], loss: 0.49971 acc: 0.89333 val_loss: 0.48285, val_acc: 0.88000
Epoch [440/10000], loss: 0.49620 acc: 0.89333 val_loss: 0.47931, val_acc: 0.88000
Epoch [450/10000], loss: 0.49279 acc: 0.89333 val_loss: 0.47587, val_acc: 0.88000
Epoch [460/10000], loss: 0.48947 acc: 0.89333 val_loss: 0.47253, val_acc: 0.88000
Epoch [470/10000], loss: 0.48624 acc: 0.89333 val_loss: 0.46928, val_acc: 0.88000
Epoch [480/10000], loss: 0.48310 acc: 0.90667 val_loss: 0.46613, val_acc: 0.88000
Epoch [490/10000], loss: 0.48003 acc: 0.90667 val_loss: 0.46305, val_acc: 0.88000
Epoch [500/10000], loss: 0.47704 acc: 0.90667 val_loss: 0.46006, val_acc: 0.89333
Epoch [510/10000], loss: 0.47412 acc: 0.90667 val_loss: 0.45714, val_acc: 0.89333
Epoch [520/10000], loss: 0.47127 acc: 0.90667 val_loss: 0.45430, val_acc: 0.89333
Epoch [530/10000], loss: 0.46848 acc: 0.90667 val_loss: 0.45152, val_acc: 0.89333
Epoch [540/10000], loss: 0.46576 acc: 0.90667 val_loss: 0.44881, val_acc: 0.89333
Epoch [550/10000], loss: 0.46309 acc: 0.90667 val_loss: 0.44616, val_acc: 0.89333
Epoch [560/10000], loss: 0.46048 acc: 0.90667 val_loss: 0.44356, val_acc: 0.89333
Epoch [570/10000], loss: 0.45792 acc: 0.90667 val_loss: 0.44103, val_acc: 0.90667
Epoch [580/10000], loss: 0.45541 acc: 0.90667 val_loss: 0.43854, val_acc: 0.90667
Epoch [590/10000], loss: 0.45295 acc: 0.90667 val_loss: 0.43611, val_acc: 0.92000
Epoch [600/10000], loss: 0.45053 acc: 0.90667 val_loss: 0.43373, val_acc: 0.93333
Epoch [610/10000], loss: 0.44816 acc: 0.90667 val_loss: 0.43139, val_acc: 0.93333
Epoch [620/10000], loss: 0.44584 acc: 0.90667 val_loss: 0.42910, val_acc: 0.93333
Epoch [630/10000], loss: 0.44355 acc: 0.90667 val_loss: 0.42685, val_acc: 0.93333
Epoch [640/10000], loss: 0.44131 acc: 0.92000 val_loss: 0.42464, val_acc: 0.93333
Epoch [650/10000], loss: 0.43910 acc: 0.92000 val_loss: 0.42247, val_acc: 0.93333
Epoch [660/10000], loss: 0.43693 acc: 0.92000 val_loss: 0.42035, val_acc: 0.93333
Epoch [670/10000], loss: 0.43479 acc: 0.92000 val_loss: 0.41825, val_acc: 0.93333
Epoch [680/10000], loss: 0.43269 acc: 0.92000 val_loss: 0.41620, val_acc: 0.93333
Epoch [690/10000], loss: 0.43062 acc: 0.92000 val_loss: 0.41417, val_acc: 0.93333
Epoch [700/10000], loss: 0.42859 acc: 0.92000 val_loss: 0.41218, val_acc: 0.93333
Epoch [710/10000], loss: 0.42658 acc: 0.92000 val_loss: 0.41023, val_acc: 0.93333
Epoch [720/10000], loss: 0.42460 acc: 0.92000 val_loss: 0.40830, val_acc: 0.93333
Epoch [730/10000], loss: 0.42266 acc: 0.92000 val_loss: 0.40640, val_acc: 0.93333
Epoch [740/10000], loss: 0.42074 acc: 0.92000 val_loss: 0.40454, val_acc: 0.93333
Epoch [750/10000], loss: 0.41884 acc: 0.92000 val_loss: 0.40270, val_acc: 0.93333
Epoch [760/10000], loss: 0.41698 acc: 0.92000 val_loss: 0.40088, val_acc: 0.93333
Epoch [770/10000], loss: 0.41514 acc: 0.92000 val_loss: 0.39910, val_acc: 0.93333
Epoch [780/10000], loss: 0.41332 acc: 0.92000 val_loss: 0.39734, val_acc: 0.93333
Epoch [790/10000], loss: 0.41153 acc: 0.92000 val_loss: 0.39560, val_acc: 0.93333
Epoch [800/10000], loss: 0.40976 acc: 0.92000 val_loss: 0.39389, val_acc: 0.93333
Epoch [810/10000], loss: 0.40801 acc: 0.92000 val_loss: 0.39220, val_acc: 0.93333
Epoch [820/10000], loss: 0.40629 acc: 0.93333 val_loss: 0.39053, val_acc: 0.93333
Epoch [830/10000], loss: 0.40459 acc: 0.93333 val_loss: 0.38889, val_acc: 0.93333
Epoch [840/10000], loss: 0.40291 acc: 0.93333 val_loss: 0.38726, val_acc: 0.93333
Epoch [850/10000], loss: 0.40124 acc: 0.93333 val_loss: 0.38566, val_acc: 0.93333
Epoch [860/10000], loss: 0.39960 acc: 0.93333 val_loss: 0.38408, val_acc: 0.93333
Epoch [870/10000], loss: 0.39798 acc: 0.93333 val_loss: 0.38252, val_acc: 0.93333
Epoch [880/10000], loss: 0.39638 acc: 0.93333 val_loss: 0.38097, val_acc: 0.93333
Epoch [890/10000], loss: 0.39479 acc: 0.93333 val_loss: 0.37945, val_acc: 0.93333
Epoch [900/10000], loss: 0.39323 acc: 0.93333 val_loss: 0.37795, val_acc: 0.93333
Epoch [910/10000], loss: 0.39168 acc: 0.93333 val_loss: 0.37646, val_acc: 0.93333
Epoch [920/10000], loss: 0.39015 acc: 0.93333 val_loss: 0.37499, val_acc: 0.93333
Epoch [930/10000], loss: 0.38863 acc: 0.93333 val_loss: 0.37354, val_acc: 0.93333
Epoch [940/10000], loss: 0.38713 acc: 0.93333 val_loss: 0.37210, val_acc: 0.93333
Epoch [950/10000], loss: 0.38565 acc: 0.93333 val_loss: 0.37068, val_acc: 0.93333
Epoch [960/10000], loss: 0.38418 acc: 0.93333 val_loss: 0.36928, val_acc: 0.93333
Epoch [970/10000], loss: 0.38273 acc: 0.93333 val_loss: 0.36789, val_acc: 0.93333
Epoch [980/10000], loss: 0.38130 acc: 0.93333 val_loss: 0.36651, val_acc: 0.93333
Epoch [990/10000], loss: 0.37988 acc: 0.93333 val_loss: 0.36516, val_acc: 0.93333
Epoch [1000/10000], loss: 0.37847 acc: 0.93333 val_loss: 0.36381, val_acc: 0.93333
Epoch [1010/10000], loss: 0.37708 acc: 0.93333 val_loss: 0.36248, val_acc: 0.93333
Epoch [1020/10000], loss: 0.37570 acc: 0.93333 val_loss: 0.36117, val_acc: 0.93333
Epoch [1030/10000], loss: 0.37433 acc: 0.93333 val_loss: 0.35987, val_acc: 0.93333
Epoch [1040/10000], loss: 0.37298 acc: 0.93333 val_loss: 0.35858, val_acc: 0.93333
Epoch [1050/10000], loss: 0.37164 acc: 0.93333 val_loss: 0.35730, val_acc: 0.93333
Epoch [1060/10000], loss: 0.37032 acc: 0.93333 val_loss: 0.35604, val_acc: 0.93333
Epoch [1070/10000], loss: 0.36900 acc: 0.93333 val_loss: 0.35479, val_acc: 0.93333
Epoch [1080/10000], loss: 0.36770 acc: 0.93333 val_loss: 0.35356, val_acc: 0.93333
Epoch [1090/10000], loss: 0.36642 acc: 0.93333 val_loss: 0.35233, val_acc: 0.94667
Epoch [1100/10000], loss: 0.36514 acc: 0.93333 val_loss: 0.35112, val_acc: 0.94667
Epoch [1110/10000], loss: 0.36388 acc: 0.93333 val_loss: 0.34992, val_acc: 0.94667
Epoch [1120/10000], loss: 0.36262 acc: 0.93333 val_loss: 0.34873, val_acc: 0.94667
Epoch [1130/10000], loss: 0.36138 acc: 0.93333 val_loss: 0.34756, val_acc: 0.94667
Epoch [1140/10000], loss: 0.36015 acc: 0.93333 val_loss: 0.34639, val_acc: 0.94667
Epoch [1150/10000], loss: 0.35893 acc: 0.93333 val_loss: 0.34524, val_acc: 0.94667
Epoch [1160/10000], loss: 0.35773 acc: 0.93333 val_loss: 0.34409, val_acc: 0.94667
Epoch [1170/10000], loss: 0.35653 acc: 0.93333 val_loss: 0.34296, val_acc: 0.94667
Epoch [1180/10000], loss: 0.35534 acc: 0.93333 val_loss: 0.34183, val_acc: 0.94667
Epoch [1190/10000], loss: 0.35416 acc: 0.93333 val_loss: 0.34072, val_acc: 0.94667
Epoch [1200/10000], loss: 0.35300 acc: 0.93333 val_loss: 0.33962, val_acc: 0.94667
Epoch [1210/10000], loss: 0.35184 acc: 0.93333 val_loss: 0.33853, val_acc: 0.94667
Epoch [1220/10000], loss: 0.35070 acc: 0.93333 val_loss: 0.33744, val_acc: 0.94667
Epoch [1230/10000], loss: 0.34956 acc: 0.93333 val_loss: 0.33637, val_acc: 0.94667
Epoch [1240/10000], loss: 0.34843 acc: 0.93333 val_loss: 0.33531, val_acc: 0.94667
Epoch [1250/10000], loss: 0.34731 acc: 0.93333 val_loss: 0.33425, val_acc: 0.94667
Epoch [1260/10000], loss: 0.34621 acc: 0.93333 val_loss: 0.33321, val_acc: 0.94667
Epoch [1270/10000], loss: 0.34511 acc: 0.93333 val_loss: 0.33217, val_acc: 0.94667
Epoch [1280/10000], loss: 0.34402 acc: 0.93333 val_loss: 0.33115, val_acc: 0.94667
Epoch [1290/10000], loss: 0.34294 acc: 0.93333 val_loss: 0.33013, val_acc: 0.94667
Epoch [1300/10000], loss: 0.34186 acc: 0.93333 val_loss: 0.32912, val_acc: 0.94667
Epoch [1310/10000], loss: 0.34080 acc: 0.93333 val_loss: 0.32812, val_acc: 0.94667
Epoch [1320/10000], loss: 0.33975 acc: 0.93333 val_loss: 0.32712, val_acc: 0.94667
Epoch [1330/10000], loss: 0.33870 acc: 0.93333 val_loss: 0.32614, val_acc: 0.94667
Epoch [1340/10000], loss: 0.33766 acc: 0.93333 val_loss: 0.32516, val_acc: 0.94667
Epoch [1350/10000], loss: 0.33663 acc: 0.93333 val_loss: 0.32420, val_acc: 0.94667
Epoch [1360/10000], loss: 0.33561 acc: 0.93333 val_loss: 0.32324, val_acc: 0.94667
Epoch [1370/10000], loss: 0.33459 acc: 0.93333 val_loss: 0.32228, val_acc: 0.94667
Epoch [1380/10000], loss: 0.33359 acc: 0.93333 val_loss: 0.32134, val_acc: 0.94667
Epoch [1390/10000], loss: 0.33259 acc: 0.93333 val_loss: 0.32040, val_acc: 0.94667
Epoch [1400/10000], loss: 0.33160 acc: 0.93333 val_loss: 0.31947, val_acc: 0.94667
Epoch [1410/10000], loss: 0.33062 acc: 0.93333 val_loss: 0.31855, val_acc: 0.94667
Epoch [1420/10000], loss: 0.32964 acc: 0.93333 val_loss: 0.31764, val_acc: 0.94667
Epoch [1430/10000], loss: 0.32867 acc: 0.93333 val_loss: 0.31673, val_acc: 0.94667
Epoch [1440/10000], loss: 0.32771 acc: 0.93333 val_loss: 0.31583, val_acc: 0.94667
Epoch [1450/10000], loss: 0.32676 acc: 0.93333 val_loss: 0.31494, val_acc: 0.94667
Epoch [1460/10000], loss: 0.32581 acc: 0.93333 val_loss: 0.31405, val_acc: 0.94667
Epoch [1470/10000], loss: 0.32487 acc: 0.93333 val_loss: 0.31317, val_acc: 0.94667
Epoch [1480/10000], loss: 0.32394 acc: 0.93333 val_loss: 0.31230, val_acc: 0.94667
Epoch [1490/10000], loss: 0.32301 acc: 0.93333 val_loss: 0.31144, val_acc: 0.94667
Epoch [1500/10000], loss: 0.32210 acc: 0.93333 val_loss: 0.31058, val_acc: 0.94667
Epoch [1510/10000], loss: 0.32118 acc: 0.93333 val_loss: 0.30973, val_acc: 0.94667
Epoch [1520/10000], loss: 0.32028 acc: 0.93333 val_loss: 0.30888, val_acc: 0.94667
Epoch [1530/10000], loss: 0.31938 acc: 0.93333 val_loss: 0.30804, val_acc: 0.94667
Epoch [1540/10000], loss: 0.31849 acc: 0.93333 val_loss: 0.30721, val_acc: 0.94667
Epoch [1550/10000], loss: 0.31760 acc: 0.93333 val_loss: 0.30638, val_acc: 0.94667
Epoch [1560/10000], loss: 0.31672 acc: 0.93333 val_loss: 0.30556, val_acc: 0.94667
Epoch [1570/10000], loss: 0.31585 acc: 0.94667 val_loss: 0.30474, val_acc: 0.94667
Epoch [1580/10000], loss: 0.31498 acc: 0.94667 val_loss: 0.30394, val_acc: 0.94667
Epoch [1590/10000], loss: 0.31412 acc: 0.94667 val_loss: 0.30313, val_acc: 0.94667
Epoch [1600/10000], loss: 0.31326 acc: 0.94667 val_loss: 0.30234, val_acc: 0.94667
Epoch [1610/10000], loss: 0.31241 acc: 0.94667 val_loss: 0.30155, val_acc: 0.94667
Epoch [1620/10000], loss: 0.31157 acc: 0.94667 val_loss: 0.30076, val_acc: 0.94667
Epoch [1630/10000], loss: 0.31073 acc: 0.94667 val_loss: 0.29998, val_acc: 0.94667
Epoch [1640/10000], loss: 0.30990 acc: 0.94667 val_loss: 0.29921, val_acc: 0.94667
Epoch [1650/10000], loss: 0.30908 acc: 0.94667 val_loss: 0.29844, val_acc: 0.94667
Epoch [1660/10000], loss: 0.30826 acc: 0.94667 val_loss: 0.29768, val_acc: 0.94667
Epoch [1670/10000], loss: 0.30744 acc: 0.94667 val_loss: 0.29692, val_acc: 0.94667
Epoch [1680/10000], loss: 0.30663 acc: 0.94667 val_loss: 0.29617, val_acc: 0.94667
Epoch [1690/10000], loss: 0.30583 acc: 0.94667 val_loss: 0.29542, val_acc: 0.94667
Epoch [1700/10000], loss: 0.30503 acc: 0.94667 val_loss: 0.29468, val_acc: 0.94667
Epoch [1710/10000], loss: 0.30424 acc: 0.94667 val_loss: 0.29394, val_acc: 0.94667
Epoch [1720/10000], loss: 0.30345 acc: 0.94667 val_loss: 0.29321, val_acc: 0.94667
Epoch [1730/10000], loss: 0.30267 acc: 0.94667 val_loss: 0.29248, val_acc: 0.94667
Epoch [1740/10000], loss: 0.30189 acc: 0.94667 val_loss: 0.29176, val_acc: 0.94667
Epoch [1750/10000], loss: 0.30112 acc: 0.94667 val_loss: 0.29105, val_acc: 0.94667
Epoch [1760/10000], loss: 0.30035 acc: 0.94667 val_loss: 0.29033, val_acc: 0.94667
Epoch [1770/10000], loss: 0.29959 acc: 0.94667 val_loss: 0.28963, val_acc: 0.94667
Epoch [1780/10000], loss: 0.29883 acc: 0.94667 val_loss: 0.28893, val_acc: 0.94667
Epoch [1790/10000], loss: 0.29808 acc: 0.94667 val_loss: 0.28823, val_acc: 0.94667
Epoch [1800/10000], loss: 0.29734 acc: 0.96000 val_loss: 0.28754, val_acc: 0.94667
Epoch [1810/10000], loss: 0.29659 acc: 0.96000 val_loss: 0.28685, val_acc: 0.94667
Epoch [1820/10000], loss: 0.29586 acc: 0.96000 val_loss: 0.28617, val_acc: 0.94667
Epoch [1830/10000], loss: 0.29512 acc: 0.96000 val_loss: 0.28549, val_acc: 0.94667
Epoch [1840/10000], loss: 0.29440 acc: 0.96000 val_loss: 0.28482, val_acc: 0.94667
Epoch [1850/10000], loss: 0.29367 acc: 0.96000 val_loss: 0.28415, val_acc: 0.94667
Epoch [1860/10000], loss: 0.29295 acc: 0.96000 val_loss: 0.28348, val_acc: 0.94667
Epoch [1870/10000], loss: 0.29224 acc: 0.96000 val_loss: 0.28282, val_acc: 0.94667
Epoch [1880/10000], loss: 0.29153 acc: 0.96000 val_loss: 0.28216, val_acc: 0.94667
Epoch [1890/10000], loss: 0.29083 acc: 0.96000 val_loss: 0.28151, val_acc: 0.94667
Epoch [1900/10000], loss: 0.29013 acc: 0.96000 val_loss: 0.28087, val_acc: 0.94667
Epoch [1910/10000], loss: 0.28943 acc: 0.96000 val_loss: 0.28022, val_acc: 0.94667
Epoch [1920/10000], loss: 0.28874 acc: 0.96000 val_loss: 0.27958, val_acc: 0.94667
Epoch [1930/10000], loss: 0.28805 acc: 0.96000 val_loss: 0.27895, val_acc: 0.94667
Epoch [1940/10000], loss: 0.28737 acc: 0.96000 val_loss: 0.27832, val_acc: 0.94667
Epoch [1950/10000], loss: 0.28669 acc: 0.96000 val_loss: 0.27769, val_acc: 0.94667
Epoch [1960/10000], loss: 0.28601 acc: 0.96000 val_loss: 0.27707, val_acc: 0.94667
Epoch [1970/10000], loss: 0.28534 acc: 0.96000 val_loss: 0.27645, val_acc: 0.94667
Epoch [1980/10000], loss: 0.28467 acc: 0.96000 val_loss: 0.27583, val_acc: 0.94667
Epoch [1990/10000], loss: 0.28401 acc: 0.96000 val_loss: 0.27522, val_acc: 0.94667
Epoch [2000/10000], loss: 0.28335 acc: 0.96000 val_loss: 0.27461, val_acc: 0.94667
Epoch [2010/10000], loss: 0.28270 acc: 0.96000 val_loss: 0.27401, val_acc: 0.94667
Epoch [2020/10000], loss: 0.28205 acc: 0.96000 val_loss: 0.27341, val_acc: 0.94667
Epoch [2030/10000], loss: 0.28140 acc: 0.96000 val_loss: 0.27282, val_acc: 0.94667
Epoch [2040/10000], loss: 0.28076 acc: 0.96000 val_loss: 0.27222, val_acc: 0.94667
Epoch [2050/10000], loss: 0.28012 acc: 0.96000 val_loss: 0.27163, val_acc: 0.94667
Epoch [2060/10000], loss: 0.27948 acc: 0.96000 val_loss: 0.27105, val_acc: 0.94667
Epoch [2070/10000], loss: 0.27885 acc: 0.96000 val_loss: 0.27047, val_acc: 0.94667
Epoch [2080/10000], loss: 0.27823 acc: 0.96000 val_loss: 0.26989, val_acc: 0.94667
Epoch [2090/10000], loss: 0.27760 acc: 0.96000 val_loss: 0.26932, val_acc: 0.94667
Epoch [2100/10000], loss: 0.27698 acc: 0.96000 val_loss: 0.26875, val_acc: 0.94667
Epoch [2110/10000], loss: 0.27636 acc: 0.96000 val_loss: 0.26818, val_acc: 0.94667
Epoch [2120/10000], loss: 0.27575 acc: 0.96000 val_loss: 0.26761, val_acc: 0.94667
Epoch [2130/10000], loss: 0.27514 acc: 0.96000 val_loss: 0.26705, val_acc: 0.96000
Epoch [2140/10000], loss: 0.27454 acc: 0.96000 val_loss: 0.26650, val_acc: 0.96000
Epoch [2150/10000], loss: 0.27393 acc: 0.96000 val_loss: 0.26594, val_acc: 0.96000
Epoch [2160/10000], loss: 0.27333 acc: 0.96000 val_loss: 0.26539, val_acc: 0.96000
Epoch [2170/10000], loss: 0.27274 acc: 0.96000 val_loss: 0.26485, val_acc: 0.96000
Epoch [2180/10000], loss: 0.27215 acc: 0.96000 val_loss: 0.26430, val_acc: 0.96000
Epoch [2190/10000], loss: 0.27156 acc: 0.96000 val_loss: 0.26376, val_acc: 0.96000
Epoch [2200/10000], loss: 0.27097 acc: 0.96000 val_loss: 0.26323, val_acc: 0.96000
Epoch [2210/10000], loss: 0.27039 acc: 0.96000 val_loss: 0.26269, val_acc: 0.96000
Epoch [2220/10000], loss: 0.26981 acc: 0.96000 val_loss: 0.26216, val_acc: 0.96000
Epoch [2230/10000], loss: 0.26924 acc: 0.96000 val_loss: 0.26163, val_acc: 0.96000
Epoch [2240/10000], loss: 0.26866 acc: 0.96000 val_loss: 0.26111, val_acc: 0.96000
Epoch [2250/10000], loss: 0.26810 acc: 0.96000 val_loss: 0.26059, val_acc: 0.96000
Epoch [2260/10000], loss: 0.26753 acc: 0.96000 val_loss: 0.26007, val_acc: 0.96000
Epoch [2270/10000], loss: 0.26697 acc: 0.96000 val_loss: 0.25955, val_acc: 0.96000
Epoch [2280/10000], loss: 0.26641 acc: 0.96000 val_loss: 0.25904, val_acc: 0.96000
Epoch [2290/10000], loss: 0.26585 acc: 0.96000 val_loss: 0.25853, val_acc: 0.96000
Epoch [2300/10000], loss: 0.26530 acc: 0.96000 val_loss: 0.25802, val_acc: 0.96000
Epoch [2310/10000], loss: 0.26475 acc: 0.96000 val_loss: 0.25752, val_acc: 0.96000
Epoch [2320/10000], loss: 0.26420 acc: 0.96000 val_loss: 0.25702, val_acc: 0.96000
Epoch [2330/10000], loss: 0.26366 acc: 0.96000 val_loss: 0.25652, val_acc: 0.96000
Epoch [2340/10000], loss: 0.26311 acc: 0.96000 val_loss: 0.25602, val_acc: 0.96000
Epoch [2350/10000], loss: 0.26258 acc: 0.96000 val_loss: 0.25553, val_acc: 0.96000
Epoch [2360/10000], loss: 0.26204 acc: 0.96000 val_loss: 0.25504, val_acc: 0.96000
Epoch [2370/10000], loss: 0.26151 acc: 0.96000 val_loss: 0.25456, val_acc: 0.96000
Epoch [2380/10000], loss: 0.26098 acc: 0.96000 val_loss: 0.25407, val_acc: 0.96000
Epoch [2390/10000], loss: 0.26045 acc: 0.96000 val_loss: 0.25359, val_acc: 0.96000
Epoch [2400/10000], loss: 0.25993 acc: 0.96000 val_loss: 0.25311, val_acc: 0.96000
Epoch [2410/10000], loss: 0.25941 acc: 0.96000 val_loss: 0.25263, val_acc: 0.96000
Epoch [2420/10000], loss: 0.25889 acc: 0.96000 val_loss: 0.25216, val_acc: 0.96000
Epoch [2430/10000], loss: 0.25837 acc: 0.96000 val_loss: 0.25169, val_acc: 0.96000
Epoch [2440/10000], loss: 0.25786 acc: 0.96000 val_loss: 0.25122, val_acc: 0.96000
Epoch [2450/10000], loss: 0.25735 acc: 0.96000 val_loss: 0.25076, val_acc: 0.96000
Epoch [2460/10000], loss: 0.25685 acc: 0.96000 val_loss: 0.25029, val_acc: 0.96000
Epoch [2470/10000], loss: 0.25634 acc: 0.96000 val_loss: 0.24983, val_acc: 0.96000
Epoch [2480/10000], loss: 0.25584 acc: 0.96000 val_loss: 0.24937, val_acc: 0.96000
Epoch [2490/10000], loss: 0.25534 acc: 0.96000 val_loss: 0.24892, val_acc: 0.96000
Epoch [2500/10000], loss: 0.25484 acc: 0.96000 val_loss: 0.24847, val_acc: 0.96000
Epoch [2510/10000], loss: 0.25435 acc: 0.96000 val_loss: 0.24802, val_acc: 0.96000
Epoch [2520/10000], loss: 0.25386 acc: 0.96000 val_loss: 0.24757, val_acc: 0.96000
Epoch [2530/10000], loss: 0.25337 acc: 0.96000 val_loss: 0.24712, val_acc: 0.96000
Epoch [2540/10000], loss: 0.25288 acc: 0.96000 val_loss: 0.24668, val_acc: 0.96000
Epoch [2550/10000], loss: 0.25240 acc: 0.96000 val_loss: 0.24624, val_acc: 0.96000
Epoch [2560/10000], loss: 0.25192 acc: 0.96000 val_loss: 0.24580, val_acc: 0.96000
Epoch [2570/10000], loss: 0.25144 acc: 0.96000 val_loss: 0.24536, val_acc: 0.96000
Epoch [2580/10000], loss: 0.25096 acc: 0.96000 val_loss: 0.24493, val_acc: 0.96000
Epoch [2590/10000], loss: 0.25049 acc: 0.96000 val_loss: 0.24450, val_acc: 0.96000
Epoch [2600/10000], loss: 0.25002 acc: 0.96000 val_loss: 0.24407, val_acc: 0.96000
Epoch [2610/10000], loss: 0.24955 acc: 0.96000 val_loss: 0.24364, val_acc: 0.96000
Epoch [2620/10000], loss: 0.24908 acc: 0.96000 val_loss: 0.24322, val_acc: 0.96000
Epoch [2630/10000], loss: 0.24862 acc: 0.96000 val_loss: 0.24279, val_acc: 0.96000
Epoch [2640/10000], loss: 0.24815 acc: 0.96000 val_loss: 0.24237, val_acc: 0.96000
Epoch [2650/10000], loss: 0.24770 acc: 0.96000 val_loss: 0.24196, val_acc: 0.96000
Epoch [2660/10000], loss: 0.24724 acc: 0.96000 val_loss: 0.24154, val_acc: 0.96000
Epoch [2670/10000], loss: 0.24678 acc: 0.96000 val_loss: 0.24113, val_acc: 0.96000
Epoch [2680/10000], loss: 0.24633 acc: 0.96000 val_loss: 0.24071, val_acc: 0.96000
Epoch [2690/10000], loss: 0.24588 acc: 0.96000 val_loss: 0.24030, val_acc: 0.96000
Epoch [2700/10000], loss: 0.24543 acc: 0.96000 val_loss: 0.23990, val_acc: 0.96000
Epoch [2710/10000], loss: 0.24499 acc: 0.96000 val_loss: 0.23949, val_acc: 0.96000
Epoch [2720/10000], loss: 0.24454 acc: 0.96000 val_loss: 0.23909, val_acc: 0.96000
Epoch [2730/10000], loss: 0.24410 acc: 0.96000 val_loss: 0.23869, val_acc: 0.96000
Epoch [2740/10000], loss: 0.24366 acc: 0.96000 val_loss: 0.23829, val_acc: 0.96000
Epoch [2750/10000], loss: 0.24322 acc: 0.96000 val_loss: 0.23789, val_acc: 0.96000
Epoch [2760/10000], loss: 0.24279 acc: 0.96000 val_loss: 0.23750, val_acc: 0.96000
Epoch [2770/10000], loss: 0.24236 acc: 0.96000 val_loss: 0.23710, val_acc: 0.96000
Epoch [2780/10000], loss: 0.24192 acc: 0.96000 val_loss: 0.23671, val_acc: 0.96000
Epoch [2790/10000], loss: 0.24150 acc: 0.96000 val_loss: 0.23632, val_acc: 0.96000
Epoch [2800/10000], loss: 0.24107 acc: 0.96000 val_loss: 0.23594, val_acc: 0.96000
Epoch [2810/10000], loss: 0.24064 acc: 0.96000 val_loss: 0.23555, val_acc: 0.96000
Epoch [2820/10000], loss: 0.24022 acc: 0.96000 val_loss: 0.23517, val_acc: 0.96000
Epoch [2830/10000], loss: 0.23980 acc: 0.96000 val_loss: 0.23479, val_acc: 0.96000
Epoch [2840/10000], loss: 0.23938 acc: 0.96000 val_loss: 0.23441, val_acc: 0.96000
Epoch [2850/10000], loss: 0.23897 acc: 0.96000 val_loss: 0.23403, val_acc: 0.96000
Epoch [2860/10000], loss: 0.23855 acc: 0.96000 val_loss: 0.23365, val_acc: 0.96000
Epoch [2870/10000], loss: 0.23814 acc: 0.96000 val_loss: 0.23328, val_acc: 0.96000
Epoch [2880/10000], loss: 0.23773 acc: 0.96000 val_loss: 0.23291, val_acc: 0.96000
Epoch [2890/10000], loss: 0.23732 acc: 0.96000 val_loss: 0.23254, val_acc: 0.96000
Epoch [2900/10000], loss: 0.23691 acc: 0.96000 val_loss: 0.23217, val_acc: 0.96000
Epoch [2910/10000], loss: 0.23651 acc: 0.96000 val_loss: 0.23180, val_acc: 0.96000
Epoch [2920/10000], loss: 0.23611 acc: 0.96000 val_loss: 0.23144, val_acc: 0.96000
Epoch [2930/10000], loss: 0.23570 acc: 0.96000 val_loss: 0.23108, val_acc: 0.96000
Epoch [2940/10000], loss: 0.23531 acc: 0.96000 val_loss: 0.23071, val_acc: 0.96000
Epoch [2950/10000], loss: 0.23491 acc: 0.96000 val_loss: 0.23035, val_acc: 0.96000
Epoch [2960/10000], loss: 0.23451 acc: 0.96000 val_loss: 0.23000, val_acc: 0.96000
Epoch [2970/10000], loss: 0.23412 acc: 0.96000 val_loss: 0.22964, val_acc: 0.96000
Epoch [2980/10000], loss: 0.23373 acc: 0.96000 val_loss: 0.22929, val_acc: 0.96000
Epoch [2990/10000], loss: 0.23334 acc: 0.96000 val_loss: 0.22893, val_acc: 0.96000
Epoch [3000/10000], loss: 0.23295 acc: 0.96000 val_loss: 0.22858, val_acc: 0.96000
Epoch [3010/10000], loss: 0.23256 acc: 0.96000 val_loss: 0.22823, val_acc: 0.96000
Epoch [3020/10000], loss: 0.23218 acc: 0.96000 val_loss: 0.22789, val_acc: 0.96000
Epoch [3030/10000], loss: 0.23180 acc: 0.96000 val_loss: 0.22754, val_acc: 0.96000
Epoch [3040/10000], loss: 0.23142 acc: 0.96000 val_loss: 0.22720, val_acc: 0.96000
Epoch [3050/10000], loss: 0.23104 acc: 0.96000 val_loss: 0.22685, val_acc: 0.96000
Epoch [3060/10000], loss: 0.23066 acc: 0.96000 val_loss: 0.22651, val_acc: 0.96000
Epoch [3070/10000], loss: 0.23028 acc: 0.96000 val_loss: 0.22617, val_acc: 0.96000
Epoch [3080/10000], loss: 0.22991 acc: 0.96000 val_loss: 0.22584, val_acc: 0.96000
Epoch [3090/10000], loss: 0.22954 acc: 0.96000 val_loss: 0.22550, val_acc: 0.96000
Epoch [3100/10000], loss: 0.22917 acc: 0.96000 val_loss: 0.22517, val_acc: 0.96000
Epoch [3110/10000], loss: 0.22880 acc: 0.96000 val_loss: 0.22483, val_acc: 0.96000
Epoch [3120/10000], loss: 0.22843 acc: 0.96000 val_loss: 0.22450, val_acc: 0.96000
Epoch [3130/10000], loss: 0.22806 acc: 0.96000 val_loss: 0.22417, val_acc: 0.96000
Epoch [3140/10000], loss: 0.22770 acc: 0.96000 val_loss: 0.22384, val_acc: 0.96000
Epoch [3150/10000], loss: 0.22734 acc: 0.96000 val_loss: 0.22352, val_acc: 0.96000
Epoch [3160/10000], loss: 0.22698 acc: 0.96000 val_loss: 0.22319, val_acc: 0.96000
Epoch [3170/10000], loss: 0.22662 acc: 0.96000 val_loss: 0.22287, val_acc: 0.96000
Epoch [3180/10000], loss: 0.22626 acc: 0.96000 val_loss: 0.22255, val_acc: 0.96000
Epoch [3190/10000], loss: 0.22590 acc: 0.96000 val_loss: 0.22222, val_acc: 0.96000
Epoch [3200/10000], loss: 0.22555 acc: 0.96000 val_loss: 0.22190, val_acc: 0.96000
Epoch [3210/10000], loss: 0.22520 acc: 0.96000 val_loss: 0.22159, val_acc: 0.96000
Epoch [3220/10000], loss: 0.22485 acc: 0.96000 val_loss: 0.22127, val_acc: 0.96000
Epoch [3230/10000], loss: 0.22450 acc: 0.96000 val_loss: 0.22096, val_acc: 0.96000
Epoch [3240/10000], loss: 0.22415 acc: 0.96000 val_loss: 0.22064, val_acc: 0.96000
Epoch [3250/10000], loss: 0.22380 acc: 0.96000 val_loss: 0.22033, val_acc: 0.96000
Epoch [3260/10000], loss: 0.22346 acc: 0.96000 val_loss: 0.22002, val_acc: 0.96000
Epoch [3270/10000], loss: 0.22311 acc: 0.96000 val_loss: 0.21971, val_acc: 0.96000
Epoch [3280/10000], loss: 0.22277 acc: 0.96000 val_loss: 0.21940, val_acc: 0.96000
Epoch [3290/10000], loss: 0.22243 acc: 0.96000 val_loss: 0.21910, val_acc: 0.96000
Epoch [3300/10000], loss: 0.22209 acc: 0.96000 val_loss: 0.21879, val_acc: 0.96000
Epoch [3310/10000], loss: 0.22175 acc: 0.96000 val_loss: 0.21849, val_acc: 0.96000
Epoch [3320/10000], loss: 0.22142 acc: 0.96000 val_loss: 0.21818, val_acc: 0.96000
Epoch [3330/10000], loss: 0.22108 acc: 0.96000 val_loss: 0.21788, val_acc: 0.96000
Epoch [3340/10000], loss: 0.22075 acc: 0.96000 val_loss: 0.21758, val_acc: 0.96000
Epoch [3350/10000], loss: 0.22042 acc: 0.96000 val_loss: 0.21728, val_acc: 0.96000
Epoch [3360/10000], loss: 0.22009 acc: 0.96000 val_loss: 0.21699, val_acc: 0.96000
Epoch [3370/10000], loss: 0.21976 acc: 0.96000 val_loss: 0.21669, val_acc: 0.96000
Epoch [3380/10000], loss: 0.21943 acc: 0.96000 val_loss: 0.21640, val_acc: 0.96000
Epoch [3390/10000], loss: 0.21910 acc: 0.96000 val_loss: 0.21610, val_acc: 0.96000
Epoch [3400/10000], loss: 0.21878 acc: 0.96000 val_loss: 0.21581, val_acc: 0.96000
Epoch [3410/10000], loss: 0.21845 acc: 0.96000 val_loss: 0.21552, val_acc: 0.96000
Epoch [3420/10000], loss: 0.21813 acc: 0.96000 val_loss: 0.21523, val_acc: 0.96000
Epoch [3430/10000], loss: 0.21781 acc: 0.96000 val_loss: 0.21494, val_acc: 0.96000
Epoch [3440/10000], loss: 0.21749 acc: 0.96000 val_loss: 0.21466, val_acc: 0.96000
Epoch [3450/10000], loss: 0.21717 acc: 0.96000 val_loss: 0.21437, val_acc: 0.96000
Epoch [3460/10000], loss: 0.21685 acc: 0.96000 val_loss: 0.21409, val_acc: 0.96000
Epoch [3470/10000], loss: 0.21654 acc: 0.96000 val_loss: 0.21380, val_acc: 0.96000
Epoch [3480/10000], loss: 0.21622 acc: 0.96000 val_loss: 0.21352, val_acc: 0.96000
Epoch [3490/10000], loss: 0.21591 acc: 0.96000 val_loss: 0.21324, val_acc: 0.96000
Epoch [3500/10000], loss: 0.21560 acc: 0.96000 val_loss: 0.21296, val_acc: 0.96000
Epoch [3510/10000], loss: 0.21529 acc: 0.96000 val_loss: 0.21268, val_acc: 0.96000
Epoch [3520/10000], loss: 0.21498 acc: 0.96000 val_loss: 0.21241, val_acc: 0.96000
Epoch [3530/10000], loss: 0.21467 acc: 0.96000 val_loss: 0.21213, val_acc: 0.96000
Epoch [3540/10000], loss: 0.21437 acc: 0.96000 val_loss: 0.21185, val_acc: 0.96000
Epoch [3550/10000], loss: 0.21406 acc: 0.96000 val_loss: 0.21158, val_acc: 0.96000
Epoch [3560/10000], loss: 0.21376 acc: 0.96000 val_loss: 0.21131, val_acc: 0.96000
Epoch [3570/10000], loss: 0.21345 acc: 0.96000 val_loss: 0.21104, val_acc: 0.96000
Epoch [3580/10000], loss: 0.21315 acc: 0.96000 val_loss: 0.21077, val_acc: 0.96000
Epoch [3590/10000], loss: 0.21285 acc: 0.96000 val_loss: 0.21050, val_acc: 0.96000
Epoch [3600/10000], loss: 0.21255 acc: 0.96000 val_loss: 0.21023, val_acc: 0.96000
Epoch [3610/10000], loss: 0.21225 acc: 0.96000 val_loss: 0.20996, val_acc: 0.96000
Epoch [3620/10000], loss: 0.21196 acc: 0.96000 val_loss: 0.20970, val_acc: 0.96000
Epoch [3630/10000], loss: 0.21166 acc: 0.96000 val_loss: 0.20943, val_acc: 0.96000
Epoch [3640/10000], loss: 0.21137 acc: 0.96000 val_loss: 0.20917, val_acc: 0.96000
Epoch [3650/10000], loss: 0.21107 acc: 0.96000 val_loss: 0.20891, val_acc: 0.96000
Epoch [3660/10000], loss: 0.21078 acc: 0.96000 val_loss: 0.20865, val_acc: 0.96000
Epoch [3670/10000], loss: 0.21049 acc: 0.96000 val_loss: 0.20839, val_acc: 0.96000
Epoch [3680/10000], loss: 0.21020 acc: 0.96000 val_loss: 0.20813, val_acc: 0.96000
Epoch [3690/10000], loss: 0.20991 acc: 0.96000 val_loss: 0.20787, val_acc: 0.96000
Epoch [3700/10000], loss: 0.20963 acc: 0.96000 val_loss: 0.20761, val_acc: 0.96000
Epoch [3710/10000], loss: 0.20934 acc: 0.96000 val_loss: 0.20736, val_acc: 0.96000
Epoch [3720/10000], loss: 0.20905 acc: 0.96000 val_loss: 0.20710, val_acc: 0.96000
Epoch [3730/10000], loss: 0.20877 acc: 0.96000 val_loss: 0.20685, val_acc: 0.96000
Epoch [3740/10000], loss: 0.20849 acc: 0.96000 val_loss: 0.20659, val_acc: 0.96000
Epoch [3750/10000], loss: 0.20821 acc: 0.96000 val_loss: 0.20634, val_acc: 0.96000
Epoch [3760/10000], loss: 0.20792 acc: 0.96000 val_loss: 0.20609, val_acc: 0.96000
Epoch [3770/10000], loss: 0.20765 acc: 0.96000 val_loss: 0.20584, val_acc: 0.96000
Epoch [3780/10000], loss: 0.20737 acc: 0.96000 val_loss: 0.20559, val_acc: 0.96000
Epoch [3790/10000], loss: 0.20709 acc: 0.96000 val_loss: 0.20535, val_acc: 0.96000
Epoch [3800/10000], loss: 0.20681 acc: 0.96000 val_loss: 0.20510, val_acc: 0.96000
Epoch [3810/10000], loss: 0.20654 acc: 0.96000 val_loss: 0.20485, val_acc: 0.96000
Epoch [3820/10000], loss: 0.20626 acc: 0.96000 val_loss: 0.20461, val_acc: 0.96000
Epoch [3830/10000], loss: 0.20599 acc: 0.96000 val_loss: 0.20437, val_acc: 0.96000
Epoch [3840/10000], loss: 0.20572 acc: 0.96000 val_loss: 0.20412, val_acc: 0.96000
Epoch [3850/10000], loss: 0.20545 acc: 0.96000 val_loss: 0.20388, val_acc: 0.96000
Epoch [3860/10000], loss: 0.20518 acc: 0.96000 val_loss: 0.20364, val_acc: 0.96000
Epoch [3870/10000], loss: 0.20491 acc: 0.96000 val_loss: 0.20340, val_acc: 0.96000
Epoch [3880/10000], loss: 0.20464 acc: 0.96000 val_loss: 0.20316, val_acc: 0.96000
Epoch [3890/10000], loss: 0.20437 acc: 0.96000 val_loss: 0.20292, val_acc: 0.96000
Epoch [3900/10000], loss: 0.20411 acc: 0.96000 val_loss: 0.20269, val_acc: 0.96000
Epoch [3910/10000], loss: 0.20384 acc: 0.96000 val_loss: 0.20245, val_acc: 0.96000
Epoch [3920/10000], loss: 0.20358 acc: 0.96000 val_loss: 0.20222, val_acc: 0.96000
Epoch [3930/10000], loss: 0.20332 acc: 0.96000 val_loss: 0.20198, val_acc: 0.96000
Epoch [3940/10000], loss: 0.20305 acc: 0.96000 val_loss: 0.20175, val_acc: 0.96000
Epoch [3950/10000], loss: 0.20279 acc: 0.96000 val_loss: 0.20152, val_acc: 0.96000
Epoch [3960/10000], loss: 0.20253 acc: 0.96000 val_loss: 0.20128, val_acc: 0.96000
Epoch [3970/10000], loss: 0.20227 acc: 0.96000 val_loss: 0.20105, val_acc: 0.96000
Epoch [3980/10000], loss: 0.20202 acc: 0.96000 val_loss: 0.20082, val_acc: 0.96000
Epoch [3990/10000], loss: 0.20176 acc: 0.96000 val_loss: 0.20059, val_acc: 0.96000
Epoch [4000/10000], loss: 0.20150 acc: 0.96000 val_loss: 0.20037, val_acc: 0.96000
Epoch [4010/10000], loss: 0.20125 acc: 0.96000 val_loss: 0.20014, val_acc: 0.96000
Epoch [4020/10000], loss: 0.20099 acc: 0.97333 val_loss: 0.19991, val_acc: 0.96000
Epoch [4030/10000], loss: 0.20074 acc: 0.97333 val_loss: 0.19969, val_acc: 0.96000
Epoch [4040/10000], loss: 0.20049 acc: 0.97333 val_loss: 0.19946, val_acc: 0.96000
Epoch [4050/10000], loss: 0.20024 acc: 0.97333 val_loss: 0.19924, val_acc: 0.96000
Epoch [4060/10000], loss: 0.19999 acc: 0.97333 val_loss: 0.19902, val_acc: 0.96000
Epoch [4070/10000], loss: 0.19974 acc: 0.97333 val_loss: 0.19880, val_acc: 0.96000
Epoch [4080/10000], loss: 0.19949 acc: 0.97333 val_loss: 0.19858, val_acc: 0.96000
Epoch [4090/10000], loss: 0.19924 acc: 0.97333 val_loss: 0.19835, val_acc: 0.96000
Epoch [4100/10000], loss: 0.19899 acc: 0.97333 val_loss: 0.19814, val_acc: 0.96000
Epoch [4110/10000], loss: 0.19875 acc: 0.97333 val_loss: 0.19792, val_acc: 0.96000
Epoch [4120/10000], loss: 0.19850 acc: 0.97333 val_loss: 0.19770, val_acc: 0.96000
Epoch [4130/10000], loss: 0.19826 acc: 0.97333 val_loss: 0.19748, val_acc: 0.96000
Epoch [4140/10000], loss: 0.19802 acc: 0.97333 val_loss: 0.19727, val_acc: 0.96000
Epoch [4150/10000], loss: 0.19777 acc: 0.97333 val_loss: 0.19705, val_acc: 0.96000
Epoch [4160/10000], loss: 0.19753 acc: 0.97333 val_loss: 0.19684, val_acc: 0.96000
Epoch [4170/10000], loss: 0.19729 acc: 0.97333 val_loss: 0.19662, val_acc: 0.96000
Epoch [4180/10000], loss: 0.19705 acc: 0.97333 val_loss: 0.19641, val_acc: 0.96000
Epoch [4190/10000], loss: 0.19681 acc: 0.97333 val_loss: 0.19620, val_acc: 0.96000
Epoch [4200/10000], loss: 0.19658 acc: 0.97333 val_loss: 0.19599, val_acc: 0.96000
Epoch [4210/10000], loss: 0.19634 acc: 0.97333 val_loss: 0.19578, val_acc: 0.96000
Epoch [4220/10000], loss: 0.19610 acc: 0.97333 val_loss: 0.19557, val_acc: 0.96000
Epoch [4230/10000], loss: 0.19587 acc: 0.97333 val_loss: 0.19536, val_acc: 0.96000
Epoch [4240/10000], loss: 0.19563 acc: 0.97333 val_loss: 0.19515, val_acc: 0.96000
Epoch [4250/10000], loss: 0.19540 acc: 0.97333 val_loss: 0.19494, val_acc: 0.96000
Epoch [4260/10000], loss: 0.19517 acc: 0.97333 val_loss: 0.19474, val_acc: 0.96000
Epoch [4270/10000], loss: 0.19493 acc: 0.97333 val_loss: 0.19453, val_acc: 0.96000
Epoch [4280/10000], loss: 0.19470 acc: 0.97333 val_loss: 0.19433, val_acc: 0.96000
Epoch [4290/10000], loss: 0.19447 acc: 0.97333 val_loss: 0.19412, val_acc: 0.96000
Epoch [4300/10000], loss: 0.19424 acc: 0.97333 val_loss: 0.19392, val_acc: 0.96000
Epoch [4310/10000], loss: 0.19401 acc: 0.97333 val_loss: 0.19372, val_acc: 0.96000
Epoch [4320/10000], loss: 0.19379 acc: 0.97333 val_loss: 0.19351, val_acc: 0.96000
Epoch [4330/10000], loss: 0.19356 acc: 0.97333 val_loss: 0.19331, val_acc: 0.96000
Epoch [4340/10000], loss: 0.19333 acc: 0.97333 val_loss: 0.19311, val_acc: 0.96000
Epoch [4350/10000], loss: 0.19311 acc: 0.97333 val_loss: 0.19291, val_acc: 0.96000
Epoch [4360/10000], loss: 0.19288 acc: 0.97333 val_loss: 0.19271, val_acc: 0.96000
Epoch [4370/10000], loss: 0.19266 acc: 0.97333 val_loss: 0.19252, val_acc: 0.96000
Epoch [4380/10000], loss: 0.19243 acc: 0.97333 val_loss: 0.19232, val_acc: 0.96000
Epoch [4390/10000], loss: 0.19221 acc: 0.97333 val_loss: 0.19212, val_acc: 0.96000
Epoch [4400/10000], loss: 0.19199 acc: 0.97333 val_loss: 0.19192, val_acc: 0.96000
Epoch [4410/10000], loss: 0.19177 acc: 0.97333 val_loss: 0.19173, val_acc: 0.96000
Epoch [4420/10000], loss: 0.19155 acc: 0.97333 val_loss: 0.19153, val_acc: 0.96000
Epoch [4430/10000], loss: 0.19133 acc: 0.97333 val_loss: 0.19134, val_acc: 0.96000
Epoch [4440/10000], loss: 0.19111 acc: 0.97333 val_loss: 0.19115, val_acc: 0.96000
Epoch [4450/10000], loss: 0.19089 acc: 0.97333 val_loss: 0.19095, val_acc: 0.96000
Epoch [4460/10000], loss: 0.19068 acc: 0.97333 val_loss: 0.19076, val_acc: 0.96000
Epoch [4470/10000], loss: 0.19046 acc: 0.97333 val_loss: 0.19057, val_acc: 0.96000
Epoch [4480/10000], loss: 0.19024 acc: 0.97333 val_loss: 0.19038, val_acc: 0.96000
Epoch [4490/10000], loss: 0.19003 acc: 0.97333 val_loss: 0.19019, val_acc: 0.96000
Epoch [4500/10000], loss: 0.18981 acc: 0.97333 val_loss: 0.19000, val_acc: 0.96000
Epoch [4510/10000], loss: 0.18960 acc: 0.97333 val_loss: 0.18981, val_acc: 0.96000
Epoch [4520/10000], loss: 0.18939 acc: 0.97333 val_loss: 0.18962, val_acc: 0.96000
Epoch [4530/10000], loss: 0.18918 acc: 0.97333 val_loss: 0.18944, val_acc: 0.96000
Epoch [4540/10000], loss: 0.18896 acc: 0.97333 val_loss: 0.18925, val_acc: 0.96000
Epoch [4550/10000], loss: 0.18875 acc: 0.97333 val_loss: 0.18906, val_acc: 0.96000
Epoch [4560/10000], loss: 0.18854 acc: 0.97333 val_loss: 0.18888, val_acc: 0.96000
Epoch [4570/10000], loss: 0.18833 acc: 0.97333 val_loss: 0.18869, val_acc: 0.96000
Epoch [4580/10000], loss: 0.18813 acc: 0.97333 val_loss: 0.18851, val_acc: 0.96000
Epoch [4590/10000], loss: 0.18792 acc: 0.97333 val_loss: 0.18833, val_acc: 0.96000
Epoch [4600/10000], loss: 0.18771 acc: 0.97333 val_loss: 0.18814, val_acc: 0.96000
Epoch [4610/10000], loss: 0.18750 acc: 0.97333 val_loss: 0.18796, val_acc: 0.96000
Epoch [4620/10000], loss: 0.18730 acc: 0.97333 val_loss: 0.18778, val_acc: 0.96000
Epoch [4630/10000], loss: 0.18709 acc: 0.97333 val_loss: 0.18760, val_acc: 0.96000
Epoch [4640/10000], loss: 0.18689 acc: 0.97333 val_loss: 0.18742, val_acc: 0.96000
Epoch [4650/10000], loss: 0.18669 acc: 0.97333 val_loss: 0.18724, val_acc: 0.96000
Epoch [4660/10000], loss: 0.18648 acc: 0.97333 val_loss: 0.18706, val_acc: 0.96000
Epoch [4670/10000], loss: 0.18628 acc: 0.97333 val_loss: 0.18688, val_acc: 0.96000
Epoch [4680/10000], loss: 0.18608 acc: 0.97333 val_loss: 0.18670, val_acc: 0.96000
Epoch [4690/10000], loss: 0.18588 acc: 0.97333 val_loss: 0.18653, val_acc: 0.96000
Epoch [4700/10000], loss: 0.18568 acc: 0.97333 val_loss: 0.18635, val_acc: 0.96000
Epoch [4710/10000], loss: 0.18548 acc: 0.97333 val_loss: 0.18617, val_acc: 0.96000
Epoch [4720/10000], loss: 0.18528 acc: 0.97333 val_loss: 0.18600, val_acc: 0.96000
Epoch [4730/10000], loss: 0.18508 acc: 0.97333 val_loss: 0.18582, val_acc: 0.96000
Epoch [4740/10000], loss: 0.18488 acc: 0.97333 val_loss: 0.18565, val_acc: 0.96000
Epoch [4750/10000], loss: 0.18468 acc: 0.97333 val_loss: 0.18548, val_acc: 0.96000
Epoch [4760/10000], loss: 0.18449 acc: 0.97333 val_loss: 0.18530, val_acc: 0.96000
Epoch [4770/10000], loss: 0.18429 acc: 0.97333 val_loss: 0.18513, val_acc: 0.96000
Epoch [4780/10000], loss: 0.18410 acc: 0.97333 val_loss: 0.18496, val_acc: 0.96000
Epoch [4790/10000], loss: 0.18390 acc: 0.97333 val_loss: 0.18479, val_acc: 0.96000
Epoch [4800/10000], loss: 0.18371 acc: 0.97333 val_loss: 0.18462, val_acc: 0.96000
Epoch [4810/10000], loss: 0.18352 acc: 0.97333 val_loss: 0.18445, val_acc: 0.96000
Epoch [4820/10000], loss: 0.18332 acc: 0.97333 val_loss: 0.18428, val_acc: 0.96000
Epoch [4830/10000], loss: 0.18313 acc: 0.97333 val_loss: 0.18411, val_acc: 0.96000
Epoch [4840/10000], loss: 0.18294 acc: 0.97333 val_loss: 0.18394, val_acc: 0.96000
Epoch [4850/10000], loss: 0.18275 acc: 0.97333 val_loss: 0.18377, val_acc: 0.96000
Epoch [4860/10000], loss: 0.18256 acc: 0.97333 val_loss: 0.18361, val_acc: 0.96000
Epoch [4870/10000], loss: 0.18237 acc: 0.97333 val_loss: 0.18344, val_acc: 0.96000
Epoch [4880/10000], loss: 0.18218 acc: 0.97333 val_loss: 0.18327, val_acc: 0.96000
Epoch [4890/10000], loss: 0.18199 acc: 0.97333 val_loss: 0.18311, val_acc: 0.96000
Epoch [4900/10000], loss: 0.18180 acc: 0.97333 val_loss: 0.18294, val_acc: 0.96000
Epoch [4910/10000], loss: 0.18162 acc: 0.97333 val_loss: 0.18278, val_acc: 0.96000
Epoch [4920/10000], loss: 0.18143 acc: 0.97333 val_loss: 0.18261, val_acc: 0.96000
Epoch [4930/10000], loss: 0.18124 acc: 0.97333 val_loss: 0.18245, val_acc: 0.96000
Epoch [4940/10000], loss: 0.18106 acc: 0.97333 val_loss: 0.18229, val_acc: 0.96000
Epoch [4950/10000], loss: 0.18087 acc: 0.97333 val_loss: 0.18212, val_acc: 0.96000
Epoch [4960/10000], loss: 0.18069 acc: 0.97333 val_loss: 0.18196, val_acc: 0.96000
Epoch [4970/10000], loss: 0.18050 acc: 0.97333 val_loss: 0.18180, val_acc: 0.96000
Epoch [4980/10000], loss: 0.18032 acc: 0.97333 val_loss: 0.18164, val_acc: 0.96000
Epoch [4990/10000], loss: 0.18014 acc: 0.97333 val_loss: 0.18148, val_acc: 0.96000
Epoch [5000/10000], loss: 0.17996 acc: 0.97333 val_loss: 0.18132, val_acc: 0.96000
Epoch [5010/10000], loss: 0.17978 acc: 0.97333 val_loss: 0.18116, val_acc: 0.96000
Epoch [5020/10000], loss: 0.17959 acc: 0.97333 val_loss: 0.18100, val_acc: 0.96000
Epoch [5030/10000], loss: 0.17941 acc: 0.97333 val_loss: 0.18084, val_acc: 0.96000
Epoch [5040/10000], loss: 0.17923 acc: 0.97333 val_loss: 0.18069, val_acc: 0.96000
Epoch [5050/10000], loss: 0.17906 acc: 0.97333 val_loss: 0.18053, val_acc: 0.96000
Epoch [5060/10000], loss: 0.17888 acc: 0.97333 val_loss: 0.18037, val_acc: 0.96000
Epoch [5070/10000], loss: 0.17870 acc: 0.97333 val_loss: 0.18022, val_acc: 0.96000
Epoch [5080/10000], loss: 0.17852 acc: 0.97333 val_loss: 0.18006, val_acc: 0.96000
Epoch [5090/10000], loss: 0.17834 acc: 0.97333 val_loss: 0.17991, val_acc: 0.96000
Epoch [5100/10000], loss: 0.17817 acc: 0.97333 val_loss: 0.17975, val_acc: 0.96000
Epoch [5110/10000], loss: 0.17799 acc: 0.97333 val_loss: 0.17960, val_acc: 0.96000
Epoch [5120/10000], loss: 0.17782 acc: 0.97333 val_loss: 0.17944, val_acc: 0.96000
Epoch [5130/10000], loss: 0.17764 acc: 0.97333 val_loss: 0.17929, val_acc: 0.96000
Epoch [5140/10000], loss: 0.17747 acc: 0.97333 val_loss: 0.17914, val_acc: 0.96000
Epoch [5150/10000], loss: 0.17729 acc: 0.97333 val_loss: 0.17899, val_acc: 0.96000
Epoch [5160/10000], loss: 0.17712 acc: 0.97333 val_loss: 0.17883, val_acc: 0.96000
Epoch [5170/10000], loss: 0.17695 acc: 0.97333 val_loss: 0.17868, val_acc: 0.96000
Epoch [5180/10000], loss: 0.17677 acc: 0.97333 val_loss: 0.17853, val_acc: 0.96000
Epoch [5190/10000], loss: 0.17660 acc: 0.97333 val_loss: 0.17838, val_acc: 0.96000
Epoch [5200/10000], loss: 0.17643 acc: 0.97333 val_loss: 0.17823, val_acc: 0.96000
Epoch [5210/10000], loss: 0.17626 acc: 0.97333 val_loss: 0.17808, val_acc: 0.96000
Epoch [5220/10000], loss: 0.17609 acc: 0.97333 val_loss: 0.17793, val_acc: 0.96000
Epoch [5230/10000], loss: 0.17592 acc: 0.97333 val_loss: 0.17779, val_acc: 0.96000
Epoch [5240/10000], loss: 0.17575 acc: 0.97333 val_loss: 0.17764, val_acc: 0.96000
Epoch [5250/10000], loss: 0.17558 acc: 0.97333 val_loss: 0.17749, val_acc: 0.96000
Epoch [5260/10000], loss: 0.17542 acc: 0.97333 val_loss: 0.17734, val_acc: 0.96000
Epoch [5270/10000], loss: 0.17525 acc: 0.97333 val_loss: 0.17720, val_acc: 0.96000
Epoch [5280/10000], loss: 0.17508 acc: 0.97333 val_loss: 0.17705, val_acc: 0.96000
Epoch [5290/10000], loss: 0.17491 acc: 0.97333 val_loss: 0.17690, val_acc: 0.96000
Epoch [5300/10000], loss: 0.17475 acc: 0.97333 val_loss: 0.17676, val_acc: 0.96000
Epoch [5310/10000], loss: 0.17458 acc: 0.97333 val_loss: 0.17661, val_acc: 0.96000
Epoch [5320/10000], loss: 0.17442 acc: 0.97333 val_loss: 0.17647, val_acc: 0.96000
Epoch [5330/10000], loss: 0.17425 acc: 0.97333 val_loss: 0.17633, val_acc: 0.96000
Epoch [5340/10000], loss: 0.17409 acc: 0.97333 val_loss: 0.17618, val_acc: 0.96000
Epoch [5350/10000], loss: 0.17392 acc: 0.97333 val_loss: 0.17604, val_acc: 0.96000
Epoch [5360/10000], loss: 0.17376 acc: 0.97333 val_loss: 0.17590, val_acc: 0.96000
Epoch [5370/10000], loss: 0.17360 acc: 0.97333 val_loss: 0.17576, val_acc: 0.96000
Epoch [5380/10000], loss: 0.17344 acc: 0.97333 val_loss: 0.17561, val_acc: 0.96000
Epoch [5390/10000], loss: 0.17327 acc: 0.97333 val_loss: 0.17547, val_acc: 0.96000
Epoch [5400/10000], loss: 0.17311 acc: 0.97333 val_loss: 0.17533, val_acc: 0.96000
Epoch [5410/10000], loss: 0.17295 acc: 0.97333 val_loss: 0.17519, val_acc: 0.96000
Epoch [5420/10000], loss: 0.17279 acc: 0.97333 val_loss: 0.17505, val_acc: 0.96000
Epoch [5430/10000], loss: 0.17263 acc: 0.97333 val_loss: 0.17491, val_acc: 0.96000
Epoch [5440/10000], loss: 0.17247 acc: 0.97333 val_loss: 0.17477, val_acc: 0.96000
Epoch [5450/10000], loss: 0.17231 acc: 0.97333 val_loss: 0.17463, val_acc: 0.96000
Epoch [5460/10000], loss: 0.17216 acc: 0.97333 val_loss: 0.17450, val_acc: 0.96000
Epoch [5470/10000], loss: 0.17200 acc: 0.97333 val_loss: 0.17436, val_acc: 0.96000
Epoch [5480/10000], loss: 0.17184 acc: 0.97333 val_loss: 0.17422, val_acc: 0.96000
Epoch [5490/10000], loss: 0.17168 acc: 0.97333 val_loss: 0.17408, val_acc: 0.96000
Epoch [5500/10000], loss: 0.17153 acc: 0.97333 val_loss: 0.17395, val_acc: 0.96000
Epoch [5510/10000], loss: 0.17137 acc: 0.97333 val_loss: 0.17381, val_acc: 0.96000
Epoch [5520/10000], loss: 0.17121 acc: 0.97333 val_loss: 0.17368, val_acc: 0.96000
Epoch [5530/10000], loss: 0.17106 acc: 0.97333 val_loss: 0.17354, val_acc: 0.96000
Epoch [5540/10000], loss: 0.17090 acc: 0.97333 val_loss: 0.17340, val_acc: 0.96000
Epoch [5550/10000], loss: 0.17075 acc: 0.97333 val_loss: 0.17327, val_acc: 0.96000
Epoch [5560/10000], loss: 0.17059 acc: 0.97333 val_loss: 0.17314, val_acc: 0.96000
Epoch [5570/10000], loss: 0.17044 acc: 0.97333 val_loss: 0.17300, val_acc: 0.96000
Epoch [5580/10000], loss: 0.17029 acc: 0.98667 val_loss: 0.17287, val_acc: 0.96000
Epoch [5590/10000], loss: 0.17014 acc: 0.98667 val_loss: 0.17274, val_acc: 0.96000
Epoch [5600/10000], loss: 0.16998 acc: 0.98667 val_loss: 0.17260, val_acc: 0.96000
Epoch [5610/10000], loss: 0.16983 acc: 0.98667 val_loss: 0.17247, val_acc: 0.96000
Epoch [5620/10000], loss: 0.16968 acc: 0.98667 val_loss: 0.17234, val_acc: 0.96000
Epoch [5630/10000], loss: 0.16953 acc: 0.98667 val_loss: 0.17221, val_acc: 0.96000
Epoch [5640/10000], loss: 0.16938 acc: 0.98667 val_loss: 0.17208, val_acc: 0.96000
Epoch [5650/10000], loss: 0.16923 acc: 0.98667 val_loss: 0.17195, val_acc: 0.96000
Epoch [5660/10000], loss: 0.16908 acc: 0.98667 val_loss: 0.17182, val_acc: 0.96000
Epoch [5670/10000], loss: 0.16893 acc: 0.98667 val_loss: 0.17169, val_acc: 0.96000
Epoch [5680/10000], loss: 0.16878 acc: 0.98667 val_loss: 0.17156, val_acc: 0.96000
Epoch [5690/10000], loss: 0.16863 acc: 0.98667 val_loss: 0.17143, val_acc: 0.96000
Epoch [5700/10000], loss: 0.16848 acc: 0.98667 val_loss: 0.17130, val_acc: 0.96000
Epoch [5710/10000], loss: 0.16834 acc: 0.98667 val_loss: 0.17117, val_acc: 0.96000
Epoch [5720/10000], loss: 0.16819 acc: 0.98667 val_loss: 0.17104, val_acc: 0.96000
Epoch [5730/10000], loss: 0.16804 acc: 0.98667 val_loss: 0.17092, val_acc: 0.96000
Epoch [5740/10000], loss: 0.16790 acc: 0.98667 val_loss: 0.17079, val_acc: 0.96000
Epoch [5750/10000], loss: 0.16775 acc: 0.98667 val_loss: 0.17066, val_acc: 0.96000
Epoch [5760/10000], loss: 0.16760 acc: 0.98667 val_loss: 0.17054, val_acc: 0.96000
Epoch [5770/10000], loss: 0.16746 acc: 0.98667 val_loss: 0.17041, val_acc: 0.96000
Epoch [5780/10000], loss: 0.16731 acc: 0.98667 val_loss: 0.17028, val_acc: 0.96000
Epoch [5790/10000], loss: 0.16717 acc: 0.98667 val_loss: 0.17016, val_acc: 0.96000
Epoch [5800/10000], loss: 0.16703 acc: 0.98667 val_loss: 0.17003, val_acc: 0.96000
Epoch [5810/10000], loss: 0.16688 acc: 0.98667 val_loss: 0.16991, val_acc: 0.96000
Epoch [5820/10000], loss: 0.16674 acc: 0.98667 val_loss: 0.16978, val_acc: 0.96000
Epoch [5830/10000], loss: 0.16660 acc: 0.98667 val_loss: 0.16966, val_acc: 0.96000
Epoch [5840/10000], loss: 0.16645 acc: 0.98667 val_loss: 0.16954, val_acc: 0.96000
Epoch [5850/10000], loss: 0.16631 acc: 0.98667 val_loss: 0.16941, val_acc: 0.96000
Epoch [5860/10000], loss: 0.16617 acc: 0.98667 val_loss: 0.16929, val_acc: 0.96000
Epoch [5870/10000], loss: 0.16603 acc: 0.98667 val_loss: 0.16917, val_acc: 0.96000
Epoch [5880/10000], loss: 0.16589 acc: 0.98667 val_loss: 0.16905, val_acc: 0.96000
Epoch [5890/10000], loss: 0.16575 acc: 0.98667 val_loss: 0.16892, val_acc: 0.96000
Epoch [5900/10000], loss: 0.16561 acc: 0.98667 val_loss: 0.16880, val_acc: 0.96000
Epoch [5910/10000], loss: 0.16547 acc: 0.98667 val_loss: 0.16868, val_acc: 0.96000
Epoch [5920/10000], loss: 0.16533 acc: 0.98667 val_loss: 0.16856, val_acc: 0.96000
Epoch [5930/10000], loss: 0.16519 acc: 0.98667 val_loss: 0.16844, val_acc: 0.96000
Epoch [5940/10000], loss: 0.16505 acc: 0.98667 val_loss: 0.16832, val_acc: 0.96000
Epoch [5950/10000], loss: 0.16491 acc: 0.98667 val_loss: 0.16820, val_acc: 0.96000
Epoch [5960/10000], loss: 0.16477 acc: 0.98667 val_loss: 0.16808, val_acc: 0.96000
Epoch [5970/10000], loss: 0.16464 acc: 0.98667 val_loss: 0.16796, val_acc: 0.96000
Epoch [5980/10000], loss: 0.16450 acc: 0.98667 val_loss: 0.16784, val_acc: 0.96000
Epoch [5990/10000], loss: 0.16436 acc: 0.98667 val_loss: 0.16772, val_acc: 0.96000
Epoch [6000/10000], loss: 0.16423 acc: 0.98667 val_loss: 0.16761, val_acc: 0.96000
Epoch [6010/10000], loss: 0.16409 acc: 0.98667 val_loss: 0.16749, val_acc: 0.96000
Epoch [6020/10000], loss: 0.16396 acc: 0.98667 val_loss: 0.16737, val_acc: 0.96000
Epoch [6030/10000], loss: 0.16382 acc: 0.98667 val_loss: 0.16725, val_acc: 0.96000
Epoch [6040/10000], loss: 0.16368 acc: 0.98667 val_loss: 0.16714, val_acc: 0.96000
Epoch [6050/10000], loss: 0.16355 acc: 0.98667 val_loss: 0.16702, val_acc: 0.96000
Epoch [6060/10000], loss: 0.16342 acc: 0.98667 val_loss: 0.16690, val_acc: 0.96000
Epoch [6070/10000], loss: 0.16328 acc: 0.98667 val_loss: 0.16679, val_acc: 0.96000
Epoch [6080/10000], loss: 0.16315 acc: 0.98667 val_loss: 0.16667, val_acc: 0.96000
Epoch [6090/10000], loss: 0.16302 acc: 0.98667 val_loss: 0.16656, val_acc: 0.96000
Epoch [6100/10000], loss: 0.16288 acc: 0.98667 val_loss: 0.16644, val_acc: 0.96000
Epoch [6110/10000], loss: 0.16275 acc: 0.98667 val_loss: 0.16633, val_acc: 0.96000
Epoch [6120/10000], loss: 0.16262 acc: 0.98667 val_loss: 0.16621, val_acc: 0.96000
Epoch [6130/10000], loss: 0.16249 acc: 0.98667 val_loss: 0.16610, val_acc: 0.96000
Epoch [6140/10000], loss: 0.16235 acc: 0.98667 val_loss: 0.16599, val_acc: 0.96000
Epoch [6150/10000], loss: 0.16222 acc: 0.98667 val_loss: 0.16587, val_acc: 0.96000
Epoch [6160/10000], loss: 0.16209 acc: 0.98667 val_loss: 0.16576, val_acc: 0.96000
Epoch [6170/10000], loss: 0.16196 acc: 0.98667 val_loss: 0.16565, val_acc: 0.96000
Epoch [6180/10000], loss: 0.16183 acc: 0.98667 val_loss: 0.16554, val_acc: 0.96000
Epoch [6190/10000], loss: 0.16170 acc: 0.98667 val_loss: 0.16542, val_acc: 0.96000
Epoch [6200/10000], loss: 0.16157 acc: 0.98667 val_loss: 0.16531, val_acc: 0.96000
Epoch [6210/10000], loss: 0.16144 acc: 0.98667 val_loss: 0.16520, val_acc: 0.96000
Epoch [6220/10000], loss: 0.16132 acc: 0.98667 val_loss: 0.16509, val_acc: 0.96000
Epoch [6230/10000], loss: 0.16119 acc: 0.98667 val_loss: 0.16498, val_acc: 0.96000
Epoch [6240/10000], loss: 0.16106 acc: 0.98667 val_loss: 0.16487, val_acc: 0.96000
Epoch [6250/10000], loss: 0.16093 acc: 0.98667 val_loss: 0.16476, val_acc: 0.96000
Epoch [6260/10000], loss: 0.16080 acc: 0.98667 val_loss: 0.16465, val_acc: 0.96000
Epoch [6270/10000], loss: 0.16068 acc: 0.98667 val_loss: 0.16454, val_acc: 0.96000
Epoch [6280/10000], loss: 0.16055 acc: 0.98667 val_loss: 0.16443, val_acc: 0.96000
Epoch [6290/10000], loss: 0.16042 acc: 0.98667 val_loss: 0.16432, val_acc: 0.96000
Epoch [6300/10000], loss: 0.16030 acc: 0.98667 val_loss: 0.16421, val_acc: 0.96000
Epoch [6310/10000], loss: 0.16017 acc: 0.98667 val_loss: 0.16410, val_acc: 0.96000
Epoch [6320/10000], loss: 0.16005 acc: 0.98667 val_loss: 0.16399, val_acc: 0.96000
Epoch [6330/10000], loss: 0.15992 acc: 0.98667 val_loss: 0.16389, val_acc: 0.96000
Epoch [6340/10000], loss: 0.15980 acc: 0.98667 val_loss: 0.16378, val_acc: 0.96000
Epoch [6350/10000], loss: 0.15967 acc: 0.98667 val_loss: 0.16367, val_acc: 0.96000
Epoch [6360/10000], loss: 0.15955 acc: 0.98667 val_loss: 0.16356, val_acc: 0.96000
Epoch [6370/10000], loss: 0.15942 acc: 0.98667 val_loss: 0.16346, val_acc: 0.96000
Epoch [6380/10000], loss: 0.15930 acc: 0.98667 val_loss: 0.16335, val_acc: 0.96000
Epoch [6390/10000], loss: 0.15918 acc: 0.98667 val_loss: 0.16325, val_acc: 0.96000
Epoch [6400/10000], loss: 0.15905 acc: 0.98667 val_loss: 0.16314, val_acc: 0.96000
Epoch [6410/10000], loss: 0.15893 acc: 0.98667 val_loss: 0.16303, val_acc: 0.96000
Epoch [6420/10000], loss: 0.15881 acc: 0.98667 val_loss: 0.16293, val_acc: 0.96000
Epoch [6430/10000], loss: 0.15869 acc: 0.98667 val_loss: 0.16282, val_acc: 0.96000
Epoch [6440/10000], loss: 0.15857 acc: 0.98667 val_loss: 0.16272, val_acc: 0.96000
Epoch [6450/10000], loss: 0.15844 acc: 0.98667 val_loss: 0.16261, val_acc: 0.96000
Epoch [6460/10000], loss: 0.15832 acc: 0.98667 val_loss: 0.16251, val_acc: 0.96000
Epoch [6470/10000], loss: 0.15820 acc: 0.98667 val_loss: 0.16241, val_acc: 0.96000
Epoch [6480/10000], loss: 0.15808 acc: 0.98667 val_loss: 0.16230, val_acc: 0.96000
Epoch [6490/10000], loss: 0.15796 acc: 0.98667 val_loss: 0.16220, val_acc: 0.96000
Epoch [6500/10000], loss: 0.15784 acc: 0.98667 val_loss: 0.16210, val_acc: 0.96000
Epoch [6510/10000], loss: 0.15772 acc: 0.98667 val_loss: 0.16199, val_acc: 0.96000
Epoch [6520/10000], loss: 0.15760 acc: 0.98667 val_loss: 0.16189, val_acc: 0.96000
Epoch [6530/10000], loss: 0.15748 acc: 0.98667 val_loss: 0.16179, val_acc: 0.96000
Epoch [6540/10000], loss: 0.15736 acc: 0.98667 val_loss: 0.16169, val_acc: 0.96000
Epoch [6550/10000], loss: 0.15725 acc: 0.98667 val_loss: 0.16158, val_acc: 0.96000
Epoch [6560/10000], loss: 0.15713 acc: 0.98667 val_loss: 0.16148, val_acc: 0.96000
Epoch [6570/10000], loss: 0.15701 acc: 0.98667 val_loss: 0.16138, val_acc: 0.96000
Epoch [6580/10000], loss: 0.15689 acc: 0.98667 val_loss: 0.16128, val_acc: 0.96000
Epoch [6590/10000], loss: 0.15678 acc: 0.98667 val_loss: 0.16118, val_acc: 0.96000
Epoch [6600/10000], loss: 0.15666 acc: 0.98667 val_loss: 0.16108, val_acc: 0.96000
Epoch [6610/10000], loss: 0.15654 acc: 0.98667 val_loss: 0.16098, val_acc: 0.96000
Epoch [6620/10000], loss: 0.15643 acc: 0.98667 val_loss: 0.16088, val_acc: 0.96000
Epoch [6630/10000], loss: 0.15631 acc: 0.98667 val_loss: 0.16078, val_acc: 0.96000
Epoch [6640/10000], loss: 0.15619 acc: 0.98667 val_loss: 0.16068, val_acc: 0.96000
Epoch [6650/10000], loss: 0.15608 acc: 0.98667 val_loss: 0.16058, val_acc: 0.96000
Epoch [6660/10000], loss: 0.15596 acc: 0.98667 val_loss: 0.16048, val_acc: 0.96000
Epoch [6670/10000], loss: 0.15585 acc: 0.98667 val_loss: 0.16038, val_acc: 0.96000
Epoch [6680/10000], loss: 0.15573 acc: 0.98667 val_loss: 0.16028, val_acc: 0.96000
Epoch [6690/10000], loss: 0.15562 acc: 0.98667 val_loss: 0.16019, val_acc: 0.96000
Epoch [6700/10000], loss: 0.15550 acc: 0.98667 val_loss: 0.16009, val_acc: 0.96000
Epoch [6710/10000], loss: 0.15539 acc: 0.98667 val_loss: 0.15999, val_acc: 0.96000
Epoch [6720/10000], loss: 0.15528 acc: 0.98667 val_loss: 0.15989, val_acc: 0.96000
Epoch [6730/10000], loss: 0.15516 acc: 0.98667 val_loss: 0.15980, val_acc: 0.96000
Epoch [6740/10000], loss: 0.15505 acc: 0.98667 val_loss: 0.15970, val_acc: 0.96000
Epoch [6750/10000], loss: 0.15494 acc: 0.98667 val_loss: 0.15960, val_acc: 0.96000
Epoch [6760/10000], loss: 0.15483 acc: 0.98667 val_loss: 0.15951, val_acc: 0.96000
Epoch [6770/10000], loss: 0.15471 acc: 0.98667 val_loss: 0.15941, val_acc: 0.96000
Epoch [6780/10000], loss: 0.15460 acc: 0.98667 val_loss: 0.15931, val_acc: 0.96000
Epoch [6790/10000], loss: 0.15449 acc: 0.98667 val_loss: 0.15922, val_acc: 0.96000
Epoch [6800/10000], loss: 0.15438 acc: 0.98667 val_loss: 0.15912, val_acc: 0.96000
Epoch [6810/10000], loss: 0.15427 acc: 0.98667 val_loss: 0.15903, val_acc: 0.96000
Epoch [6820/10000], loss: 0.15416 acc: 0.98667 val_loss: 0.15893, val_acc: 0.96000
Epoch [6830/10000], loss: 0.15405 acc: 0.98667 val_loss: 0.15884, val_acc: 0.96000
Epoch [6840/10000], loss: 0.15394 acc: 0.98667 val_loss: 0.15874, val_acc: 0.96000
Epoch [6850/10000], loss: 0.15383 acc: 0.98667 val_loss: 0.15865, val_acc: 0.96000
Epoch [6860/10000], loss: 0.15372 acc: 0.98667 val_loss: 0.15855, val_acc: 0.96000
Epoch [6870/10000], loss: 0.15361 acc: 0.98667 val_loss: 0.15846, val_acc: 0.96000
Epoch [6880/10000], loss: 0.15350 acc: 0.98667 val_loss: 0.15837, val_acc: 0.96000
Epoch [6890/10000], loss: 0.15339 acc: 0.98667 val_loss: 0.15827, val_acc: 0.96000
Epoch [6900/10000], loss: 0.15328 acc: 0.98667 val_loss: 0.15818, val_acc: 0.96000
Epoch [6910/10000], loss: 0.15317 acc: 0.98667 val_loss: 0.15809, val_acc: 0.96000
Epoch [6920/10000], loss: 0.15306 acc: 0.98667 val_loss: 0.15800, val_acc: 0.96000
Epoch [6930/10000], loss: 0.15295 acc: 0.98667 val_loss: 0.15790, val_acc: 0.96000
Epoch [6940/10000], loss: 0.15285 acc: 0.98667 val_loss: 0.15781, val_acc: 0.96000
Epoch [6950/10000], loss: 0.15274 acc: 0.98667 val_loss: 0.15772, val_acc: 0.96000
Epoch [6960/10000], loss: 0.15263 acc: 0.98667 val_loss: 0.15763, val_acc: 0.96000
Epoch [6970/10000], loss: 0.15252 acc: 0.98667 val_loss: 0.15754, val_acc: 0.96000
Epoch [6980/10000], loss: 0.15242 acc: 0.98667 val_loss: 0.15744, val_acc: 0.96000
Epoch [6990/10000], loss: 0.15231 acc: 0.98667 val_loss: 0.15735, val_acc: 0.96000
Epoch [7000/10000], loss: 0.15220 acc: 0.98667 val_loss: 0.15726, val_acc: 0.96000
Epoch [7010/10000], loss: 0.15210 acc: 0.98667 val_loss: 0.15717, val_acc: 0.96000
Epoch [7020/10000], loss: 0.15199 acc: 0.98667 val_loss: 0.15708, val_acc: 0.96000
Epoch [7030/10000], loss: 0.15189 acc: 0.98667 val_loss: 0.15699, val_acc: 0.96000
Epoch [7040/10000], loss: 0.15178 acc: 0.98667 val_loss: 0.15690, val_acc: 0.96000
Epoch [7050/10000], loss: 0.15168 acc: 0.98667 val_loss: 0.15681, val_acc: 0.96000
Epoch [7060/10000], loss: 0.15157 acc: 0.98667 val_loss: 0.15672, val_acc: 0.96000
Epoch [7070/10000], loss: 0.15147 acc: 0.98667 val_loss: 0.15663, val_acc: 0.96000
Epoch [7080/10000], loss: 0.15136 acc: 0.98667 val_loss: 0.15654, val_acc: 0.96000
Epoch [7090/10000], loss: 0.15126 acc: 0.98667 val_loss: 0.15645, val_acc: 0.96000
Epoch [7100/10000], loss: 0.15116 acc: 0.98667 val_loss: 0.15637, val_acc: 0.96000
Epoch [7110/10000], loss: 0.15105 acc: 0.98667 val_loss: 0.15628, val_acc: 0.96000
Epoch [7120/10000], loss: 0.15095 acc: 0.98667 val_loss: 0.15619, val_acc: 0.96000
Epoch [7130/10000], loss: 0.15085 acc: 0.98667 val_loss: 0.15610, val_acc: 0.96000
Epoch [7140/10000], loss: 0.15074 acc: 0.98667 val_loss: 0.15601, val_acc: 0.96000
Epoch [7150/10000], loss: 0.15064 acc: 0.98667 val_loss: 0.15593, val_acc: 0.96000
Epoch [7160/10000], loss: 0.15054 acc: 0.98667 val_loss: 0.15584, val_acc: 0.96000
Epoch [7170/10000], loss: 0.15044 acc: 0.98667 val_loss: 0.15575, val_acc: 0.96000
Epoch [7180/10000], loss: 0.15033 acc: 0.98667 val_loss: 0.15566, val_acc: 0.96000
Epoch [7190/10000], loss: 0.15023 acc: 0.98667 val_loss: 0.15558, val_acc: 0.96000
Epoch [7200/10000], loss: 0.15013 acc: 0.98667 val_loss: 0.15549, val_acc: 0.96000
Epoch [7210/10000], loss: 0.15003 acc: 0.98667 val_loss: 0.15541, val_acc: 0.96000
Epoch [7220/10000], loss: 0.14993 acc: 0.98667 val_loss: 0.15532, val_acc: 0.96000
Epoch [7230/10000], loss: 0.14983 acc: 0.98667 val_loss: 0.15523, val_acc: 0.96000
Epoch [7240/10000], loss: 0.14973 acc: 0.98667 val_loss: 0.15515, val_acc: 0.96000
Epoch [7250/10000], loss: 0.14963 acc: 0.98667 val_loss: 0.15506, val_acc: 0.96000
Epoch [7260/10000], loss: 0.14953 acc: 0.98667 val_loss: 0.15498, val_acc: 0.96000
Epoch [7270/10000], loss: 0.14943 acc: 0.98667 val_loss: 0.15489, val_acc: 0.96000
Epoch [7280/10000], loss: 0.14933 acc: 0.98667 val_loss: 0.15481, val_acc: 0.96000
Epoch [7290/10000], loss: 0.14923 acc: 0.98667 val_loss: 0.15472, val_acc: 0.96000
Epoch [7300/10000], loss: 0.14913 acc: 0.98667 val_loss: 0.15464, val_acc: 0.96000
Epoch [7310/10000], loss: 0.14903 acc: 0.98667 val_loss: 0.15455, val_acc: 0.96000
Epoch [7320/10000], loss: 0.14893 acc: 0.98667 val_loss: 0.15447, val_acc: 0.96000
Epoch [7330/10000], loss: 0.14883 acc: 0.98667 val_loss: 0.15439, val_acc: 0.96000
Epoch [7340/10000], loss: 0.14873 acc: 0.98667 val_loss: 0.15430, val_acc: 0.96000
Epoch [7350/10000], loss: 0.14863 acc: 0.98667 val_loss: 0.15422, val_acc: 0.96000
Epoch [7360/10000], loss: 0.14854 acc: 0.98667 val_loss: 0.15413, val_acc: 0.96000
Epoch [7370/10000], loss: 0.14844 acc: 0.98667 val_loss: 0.15405, val_acc: 0.96000
Epoch [7380/10000], loss: 0.14834 acc: 0.98667 val_loss: 0.15397, val_acc: 0.96000
Epoch [7390/10000], loss: 0.14824 acc: 0.98667 val_loss: 0.15389, val_acc: 0.96000
Epoch [7400/10000], loss: 0.14815 acc: 0.98667 val_loss: 0.15380, val_acc: 0.96000
Epoch [7410/10000], loss: 0.14805 acc: 0.98667 val_loss: 0.15372, val_acc: 0.96000
Epoch [7420/10000], loss: 0.14795 acc: 0.98667 val_loss: 0.15364, val_acc: 0.96000
Epoch [7430/10000], loss: 0.14786 acc: 0.98667 val_loss: 0.15356, val_acc: 0.96000
Epoch [7440/10000], loss: 0.14776 acc: 0.98667 val_loss: 0.15348, val_acc: 0.96000
Epoch [7450/10000], loss: 0.14767 acc: 0.98667 val_loss: 0.15339, val_acc: 0.96000
Epoch [7460/10000], loss: 0.14757 acc: 0.98667 val_loss: 0.15331, val_acc: 0.96000
Epoch [7470/10000], loss: 0.14747 acc: 0.98667 val_loss: 0.15323, val_acc: 0.96000
Epoch [7480/10000], loss: 0.14738 acc: 0.98667 val_loss: 0.15315, val_acc: 0.96000
Epoch [7490/10000], loss: 0.14728 acc: 0.98667 val_loss: 0.15307, val_acc: 0.96000
Epoch [7500/10000], loss: 0.14719 acc: 0.98667 val_loss: 0.15299, val_acc: 0.96000
Epoch [7510/10000], loss: 0.14709 acc: 0.98667 val_loss: 0.15291, val_acc: 0.96000
Epoch [7520/10000], loss: 0.14700 acc: 0.98667 val_loss: 0.15283, val_acc: 0.96000
Epoch [7530/10000], loss: 0.14691 acc: 0.98667 val_loss: 0.15275, val_acc: 0.96000
Epoch [7540/10000], loss: 0.14681 acc: 0.98667 val_loss: 0.15267, val_acc: 0.96000
Epoch [7550/10000], loss: 0.14672 acc: 0.98667 val_loss: 0.15259, val_acc: 0.96000
Epoch [7560/10000], loss: 0.14662 acc: 0.98667 val_loss: 0.15251, val_acc: 0.96000
Epoch [7570/10000], loss: 0.14653 acc: 0.98667 val_loss: 0.15243, val_acc: 0.96000
Epoch [7580/10000], loss: 0.14644 acc: 0.98667 val_loss: 0.15235, val_acc: 0.96000
Epoch [7590/10000], loss: 0.14634 acc: 0.98667 val_loss: 0.15227, val_acc: 0.96000
Epoch [7600/10000], loss: 0.14625 acc: 0.98667 val_loss: 0.15219, val_acc: 0.96000
Epoch [7610/10000], loss: 0.14616 acc: 0.98667 val_loss: 0.15211, val_acc: 0.96000
Epoch [7620/10000], loss: 0.14607 acc: 0.98667 val_loss: 0.15204, val_acc: 0.96000
Epoch [7630/10000], loss: 0.14597 acc: 0.98667 val_loss: 0.15196, val_acc: 0.96000
Epoch [7640/10000], loss: 0.14588 acc: 0.98667 val_loss: 0.15188, val_acc: 0.96000
Epoch [7650/10000], loss: 0.14579 acc: 0.98667 val_loss: 0.15180, val_acc: 0.96000
Epoch [7660/10000], loss: 0.14570 acc: 0.98667 val_loss: 0.15172, val_acc: 0.96000
Epoch [7670/10000], loss: 0.14561 acc: 0.98667 val_loss: 0.15165, val_acc: 0.96000
Epoch [7680/10000], loss: 0.14552 acc: 0.98667 val_loss: 0.15157, val_acc: 0.96000
Epoch [7690/10000], loss: 0.14542 acc: 0.98667 val_loss: 0.15149, val_acc: 0.96000
Epoch [7700/10000], loss: 0.14533 acc: 0.98667 val_loss: 0.15142, val_acc: 0.96000
Epoch [7710/10000], loss: 0.14524 acc: 0.98667 val_loss: 0.15134, val_acc: 0.96000
Epoch [7720/10000], loss: 0.14515 acc: 0.98667 val_loss: 0.15126, val_acc: 0.96000
Epoch [7730/10000], loss: 0.14506 acc: 0.98667 val_loss: 0.15119, val_acc: 0.96000
Epoch [7740/10000], loss: 0.14497 acc: 0.98667 val_loss: 0.15111, val_acc: 0.96000
Epoch [7750/10000], loss: 0.14488 acc: 0.98667 val_loss: 0.15103, val_acc: 0.96000
Epoch [7760/10000], loss: 0.14479 acc: 0.98667 val_loss: 0.15096, val_acc: 0.96000
Epoch [7770/10000], loss: 0.14470 acc: 0.98667 val_loss: 0.15088, val_acc: 0.96000
Epoch [7780/10000], loss: 0.14461 acc: 0.98667 val_loss: 0.15081, val_acc: 0.96000
Epoch [7790/10000], loss: 0.14452 acc: 0.98667 val_loss: 0.15073, val_acc: 0.96000
Epoch [7800/10000], loss: 0.14444 acc: 0.98667 val_loss: 0.15066, val_acc: 0.96000
Epoch [7810/10000], loss: 0.14435 acc: 0.98667 val_loss: 0.15058, val_acc: 0.96000
Epoch [7820/10000], loss: 0.14426 acc: 0.98667 val_loss: 0.15051, val_acc: 0.96000
Epoch [7830/10000], loss: 0.14417 acc: 0.98667 val_loss: 0.15043, val_acc: 0.96000
Epoch [7840/10000], loss: 0.14408 acc: 0.98667 val_loss: 0.15036, val_acc: 0.96000
Epoch [7850/10000], loss: 0.14399 acc: 0.98667 val_loss: 0.15028, val_acc: 0.96000
Epoch [7860/10000], loss: 0.14391 acc: 0.98667 val_loss: 0.15021, val_acc: 0.96000
Epoch [7870/10000], loss: 0.14382 acc: 0.98667 val_loss: 0.15013, val_acc: 0.96000
Epoch [7880/10000], loss: 0.14373 acc: 0.98667 val_loss: 0.15006, val_acc: 0.96000
Epoch [7890/10000], loss: 0.14364 acc: 0.98667 val_loss: 0.14999, val_acc: 0.96000
Epoch [7900/10000], loss: 0.14356 acc: 0.98667 val_loss: 0.14991, val_acc: 0.96000
Epoch [7910/10000], loss: 0.14347 acc: 0.98667 val_loss: 0.14984, val_acc: 0.96000
Epoch [7920/10000], loss: 0.14338 acc: 0.98667 val_loss: 0.14976, val_acc: 0.96000
Epoch [7930/10000], loss: 0.14330 acc: 0.98667 val_loss: 0.14969, val_acc: 0.96000
Epoch [7940/10000], loss: 0.14321 acc: 0.98667 val_loss: 0.14962, val_acc: 0.96000
Epoch [7950/10000], loss: 0.14312 acc: 0.98667 val_loss: 0.14955, val_acc: 0.96000
Epoch [7960/10000], loss: 0.14304 acc: 0.98667 val_loss: 0.14947, val_acc: 0.96000
Epoch [7970/10000], loss: 0.14295 acc: 0.98667 val_loss: 0.14940, val_acc: 0.96000
Epoch [7980/10000], loss: 0.14287 acc: 0.98667 val_loss: 0.14933, val_acc: 0.96000
Epoch [7990/10000], loss: 0.14278 acc: 0.98667 val_loss: 0.14926, val_acc: 0.96000
Epoch [8000/10000], loss: 0.14269 acc: 0.98667 val_loss: 0.14918, val_acc: 0.96000
Epoch [8010/10000], loss: 0.14261 acc: 0.98667 val_loss: 0.14911, val_acc: 0.96000
Epoch [8020/10000], loss: 0.14252 acc: 0.98667 val_loss: 0.14904, val_acc: 0.96000
Epoch [8030/10000], loss: 0.14244 acc: 0.98667 val_loss: 0.14897, val_acc: 0.96000
Epoch [8040/10000], loss: 0.14235 acc: 0.98667 val_loss: 0.14890, val_acc: 0.96000
Epoch [8050/10000], loss: 0.14227 acc: 0.98667 val_loss: 0.14883, val_acc: 0.96000
Epoch [8060/10000], loss: 0.14219 acc: 0.98667 val_loss: 0.14876, val_acc: 0.96000
Epoch [8070/10000], loss: 0.14210 acc: 0.98667 val_loss: 0.14868, val_acc: 0.96000
Epoch [8080/10000], loss: 0.14202 acc: 0.98667 val_loss: 0.14861, val_acc: 0.96000
Epoch [8090/10000], loss: 0.14193 acc: 0.98667 val_loss: 0.14854, val_acc: 0.96000
Epoch [8100/10000], loss: 0.14185 acc: 0.98667 val_loss: 0.14847, val_acc: 0.96000
Epoch [8110/10000], loss: 0.14177 acc: 0.98667 val_loss: 0.14840, val_acc: 0.96000
Epoch [8120/10000], loss: 0.14168 acc: 0.98667 val_loss: 0.14833, val_acc: 0.96000
Epoch [8130/10000], loss: 0.14160 acc: 0.98667 val_loss: 0.14826, val_acc: 0.96000
Epoch [8140/10000], loss: 0.14152 acc: 0.98667 val_loss: 0.14819, val_acc: 0.96000
Epoch [8150/10000], loss: 0.14144 acc: 0.98667 val_loss: 0.14812, val_acc: 0.96000
Epoch [8160/10000], loss: 0.14135 acc: 0.98667 val_loss: 0.14805, val_acc: 0.96000
Epoch [8170/10000], loss: 0.14127 acc: 0.98667 val_loss: 0.14798, val_acc: 0.96000
Epoch [8180/10000], loss: 0.14119 acc: 0.98667 val_loss: 0.14791, val_acc: 0.96000
Epoch [8190/10000], loss: 0.14111 acc: 0.98667 val_loss: 0.14785, val_acc: 0.96000
Epoch [8200/10000], loss: 0.14102 acc: 0.98667 val_loss: 0.14778, val_acc: 0.96000
Epoch [8210/10000], loss: 0.14094 acc: 0.98667 val_loss: 0.14771, val_acc: 0.96000
Epoch [8220/10000], loss: 0.14086 acc: 0.98667 val_loss: 0.14764, val_acc: 0.96000
Epoch [8230/10000], loss: 0.14078 acc: 0.98667 val_loss: 0.14757, val_acc: 0.96000
Epoch [8240/10000], loss: 0.14070 acc: 0.98667 val_loss: 0.14750, val_acc: 0.96000
Epoch [8250/10000], loss: 0.14062 acc: 0.98667 val_loss: 0.14743, val_acc: 0.96000
Epoch [8260/10000], loss: 0.14054 acc: 0.98667 val_loss: 0.14737, val_acc: 0.96000
Epoch [8270/10000], loss: 0.14046 acc: 0.98667 val_loss: 0.14730, val_acc: 0.96000
Epoch [8280/10000], loss: 0.14037 acc: 0.98667 val_loss: 0.14723, val_acc: 0.96000
Epoch [8290/10000], loss: 0.14029 acc: 0.98667 val_loss: 0.14716, val_acc: 0.96000
Epoch [8300/10000], loss: 0.14021 acc: 0.98667 val_loss: 0.14710, val_acc: 0.96000
Epoch [8310/10000], loss: 0.14013 acc: 0.98667 val_loss: 0.14703, val_acc: 0.96000
Epoch [8320/10000], loss: 0.14005 acc: 0.98667 val_loss: 0.14696, val_acc: 0.96000
Epoch [8330/10000], loss: 0.13997 acc: 0.98667 val_loss: 0.14689, val_acc: 0.96000
Epoch [8340/10000], loss: 0.13989 acc: 0.98667 val_loss: 0.14683, val_acc: 0.96000
Epoch [8350/10000], loss: 0.13981 acc: 0.98667 val_loss: 0.14676, val_acc: 0.96000
Epoch [8360/10000], loss: 0.13974 acc: 0.98667 val_loss: 0.14669, val_acc: 0.96000
Epoch [8370/10000], loss: 0.13966 acc: 0.98667 val_loss: 0.14663, val_acc: 0.96000
Epoch [8380/10000], loss: 0.13958 acc: 0.98667 val_loss: 0.14656, val_acc: 0.96000
Epoch [8390/10000], loss: 0.13950 acc: 0.98667 val_loss: 0.14649, val_acc: 0.96000
Epoch [8400/10000], loss: 0.13942 acc: 0.98667 val_loss: 0.14643, val_acc: 0.96000
Epoch [8410/10000], loss: 0.13934 acc: 0.98667 val_loss: 0.14636, val_acc: 0.96000
Epoch [8420/10000], loss: 0.13926 acc: 0.98667 val_loss: 0.14630, val_acc: 0.96000
Epoch [8430/10000], loss: 0.13918 acc: 0.98667 val_loss: 0.14623, val_acc: 0.96000
Epoch [8440/10000], loss: 0.13911 acc: 0.98667 val_loss: 0.14617, val_acc: 0.96000
Epoch [8450/10000], loss: 0.13903 acc: 0.98667 val_loss: 0.14610, val_acc: 0.96000
Epoch [8460/10000], loss: 0.13895 acc: 0.98667 val_loss: 0.14603, val_acc: 0.96000
Epoch [8470/10000], loss: 0.13887 acc: 0.98667 val_loss: 0.14597, val_acc: 0.96000
Epoch [8480/10000], loss: 0.13880 acc: 0.98667 val_loss: 0.14590, val_acc: 0.96000
Epoch [8490/10000], loss: 0.13872 acc: 0.98667 val_loss: 0.14584, val_acc: 0.96000
Epoch [8500/10000], loss: 0.13864 acc: 0.98667 val_loss: 0.14578, val_acc: 0.96000
Epoch [8510/10000], loss: 0.13856 acc: 0.98667 val_loss: 0.14571, val_acc: 0.96000
Epoch [8520/10000], loss: 0.13849 acc: 0.98667 val_loss: 0.14565, val_acc: 0.96000
Epoch [8530/10000], loss: 0.13841 acc: 0.98667 val_loss: 0.14558, val_acc: 0.96000
Epoch [8540/10000], loss: 0.13833 acc: 0.98667 val_loss: 0.14552, val_acc: 0.96000
Epoch [8550/10000], loss: 0.13826 acc: 0.98667 val_loss: 0.14545, val_acc: 0.96000
Epoch [8560/10000], loss: 0.13818 acc: 0.98667 val_loss: 0.14539, val_acc: 0.96000
Epoch [8570/10000], loss: 0.13811 acc: 0.98667 val_loss: 0.14533, val_acc: 0.96000
Epoch [8580/10000], loss: 0.13803 acc: 0.98667 val_loss: 0.14526, val_acc: 0.96000
Epoch [8590/10000], loss: 0.13795 acc: 0.98667 val_loss: 0.14520, val_acc: 0.96000
Epoch [8600/10000], loss: 0.13788 acc: 0.98667 val_loss: 0.14514, val_acc: 0.96000
Epoch [8610/10000], loss: 0.13780 acc: 0.98667 val_loss: 0.14507, val_acc: 0.96000
Epoch [8620/10000], loss: 0.13773 acc: 0.98667 val_loss: 0.14501, val_acc: 0.96000
Epoch [8630/10000], loss: 0.13765 acc: 0.98667 val_loss: 0.14495, val_acc: 0.96000
Epoch [8640/10000], loss: 0.13758 acc: 0.98667 val_loss: 0.14488, val_acc: 0.96000
Epoch [8650/10000], loss: 0.13750 acc: 0.98667 val_loss: 0.14482, val_acc: 0.96000
Epoch [8660/10000], loss: 0.13743 acc: 0.98667 val_loss: 0.14476, val_acc: 0.96000
Epoch [8670/10000], loss: 0.13735 acc: 0.98667 val_loss: 0.14470, val_acc: 0.96000
Epoch [8680/10000], loss: 0.13728 acc: 0.98667 val_loss: 0.14463, val_acc: 0.96000
Epoch [8690/10000], loss: 0.13720 acc: 0.98667 val_loss: 0.14457, val_acc: 0.96000
Epoch [8700/10000], loss: 0.13713 acc: 0.98667 val_loss: 0.14451, val_acc: 0.96000
Epoch [8710/10000], loss: 0.13705 acc: 0.98667 val_loss: 0.14445, val_acc: 0.96000
Epoch [8720/10000], loss: 0.13698 acc: 0.98667 val_loss: 0.14438, val_acc: 0.96000
Epoch [8730/10000], loss: 0.13691 acc: 0.98667 val_loss: 0.14432, val_acc: 0.96000
Epoch [8740/10000], loss: 0.13683 acc: 0.98667 val_loss: 0.14426, val_acc: 0.96000
Epoch [8750/10000], loss: 0.13676 acc: 0.98667 val_loss: 0.14420, val_acc: 0.96000
Epoch [8760/10000], loss: 0.13669 acc: 0.98667 val_loss: 0.14414, val_acc: 0.96000
Epoch [8770/10000], loss: 0.13661 acc: 0.98667 val_loss: 0.14408, val_acc: 0.96000
Epoch [8780/10000], loss: 0.13654 acc: 0.98667 val_loss: 0.14402, val_acc: 0.96000
Epoch [8790/10000], loss: 0.13647 acc: 0.98667 val_loss: 0.14396, val_acc: 0.96000
Epoch [8800/10000], loss: 0.13639 acc: 0.98667 val_loss: 0.14389, val_acc: 0.96000
Epoch [8810/10000], loss: 0.13632 acc: 0.98667 val_loss: 0.14383, val_acc: 0.96000
Epoch [8820/10000], loss: 0.13625 acc: 0.98667 val_loss: 0.14377, val_acc: 0.96000
Epoch [8830/10000], loss: 0.13618 acc: 0.98667 val_loss: 0.14371, val_acc: 0.96000
Epoch [8840/10000], loss: 0.13610 acc: 0.98667 val_loss: 0.14365, val_acc: 0.96000
Epoch [8850/10000], loss: 0.13603 acc: 0.98667 val_loss: 0.14359, val_acc: 0.96000
Epoch [8860/10000], loss: 0.13596 acc: 0.98667 val_loss: 0.14353, val_acc: 0.96000
Epoch [8870/10000], loss: 0.13589 acc: 0.98667 val_loss: 0.14347, val_acc: 0.96000
Epoch [8880/10000], loss: 0.13582 acc: 0.98667 val_loss: 0.14341, val_acc: 0.96000
Epoch [8890/10000], loss: 0.13574 acc: 0.98667 val_loss: 0.14335, val_acc: 0.96000
Epoch [8900/10000], loss: 0.13567 acc: 0.98667 val_loss: 0.14329, val_acc: 0.96000
Epoch [8910/10000], loss: 0.13560 acc: 0.98667 val_loss: 0.14323, val_acc: 0.96000
Epoch [8920/10000], loss: 0.13553 acc: 0.98667 val_loss: 0.14317, val_acc: 0.96000
Epoch [8930/10000], loss: 0.13546 acc: 0.98667 val_loss: 0.14311, val_acc: 0.96000
Epoch [8940/10000], loss: 0.13539 acc: 0.98667 val_loss: 0.14306, val_acc: 0.96000
Epoch [8950/10000], loss: 0.13532 acc: 0.98667 val_loss: 0.14300, val_acc: 0.96000
Epoch [8960/10000], loss: 0.13525 acc: 0.98667 val_loss: 0.14294, val_acc: 0.96000
Epoch [8970/10000], loss: 0.13518 acc: 0.98667 val_loss: 0.14288, val_acc: 0.96000
Epoch [8980/10000], loss: 0.13511 acc: 0.98667 val_loss: 0.14282, val_acc: 0.96000
Epoch [8990/10000], loss: 0.13504 acc: 0.98667 val_loss: 0.14276, val_acc: 0.96000
Epoch [9000/10000], loss: 0.13497 acc: 0.98667 val_loss: 0.14270, val_acc: 0.96000
Epoch [9010/10000], loss: 0.13490 acc: 0.98667 val_loss: 0.14264, val_acc: 0.96000
Epoch [9020/10000], loss: 0.13483 acc: 0.98667 val_loss: 0.14259, val_acc: 0.96000
Epoch [9030/10000], loss: 0.13476 acc: 0.98667 val_loss: 0.14253, val_acc: 0.96000
Epoch [9040/10000], loss: 0.13469 acc: 0.98667 val_loss: 0.14247, val_acc: 0.96000
Epoch [9050/10000], loss: 0.13462 acc: 0.98667 val_loss: 0.14241, val_acc: 0.96000
Epoch [9060/10000], loss: 0.13455 acc: 0.98667 val_loss: 0.14236, val_acc: 0.96000
Epoch [9070/10000], loss: 0.13448 acc: 0.98667 val_loss: 0.14230, val_acc: 0.96000
Epoch [9080/10000], loss: 0.13441 acc: 0.98667 val_loss: 0.14224, val_acc: 0.96000
Epoch [9090/10000], loss: 0.13434 acc: 0.98667 val_loss: 0.14218, val_acc: 0.96000
Epoch [9100/10000], loss: 0.13427 acc: 0.98667 val_loss: 0.14213, val_acc: 0.96000
Epoch [9110/10000], loss: 0.13420 acc: 0.98667 val_loss: 0.14207, val_acc: 0.96000
Epoch [9120/10000], loss: 0.13413 acc: 0.98667 val_loss: 0.14201, val_acc: 0.96000
Epoch [9130/10000], loss: 0.13407 acc: 0.98667 val_loss: 0.14195, val_acc: 0.96000
Epoch [9140/10000], loss: 0.13400 acc: 0.98667 val_loss: 0.14190, val_acc: 0.96000
Epoch [9150/10000], loss: 0.13393 acc: 0.98667 val_loss: 0.14184, val_acc: 0.96000
Epoch [9160/10000], loss: 0.13386 acc: 0.98667 val_loss: 0.14178, val_acc: 0.96000
Epoch [9170/10000], loss: 0.13379 acc: 0.98667 val_loss: 0.14173, val_acc: 0.96000
Epoch [9180/10000], loss: 0.13373 acc: 0.98667 val_loss: 0.14167, val_acc: 0.96000
Epoch [9190/10000], loss: 0.13366 acc: 0.98667 val_loss: 0.14161, val_acc: 0.96000
Epoch [9200/10000], loss: 0.13359 acc: 0.98667 val_loss: 0.14156, val_acc: 0.96000
Epoch [9210/10000], loss: 0.13352 acc: 0.98667 val_loss: 0.14150, val_acc: 0.96000
Epoch [9220/10000], loss: 0.13345 acc: 0.98667 val_loss: 0.14145, val_acc: 0.96000
Epoch [9230/10000], loss: 0.13339 acc: 0.98667 val_loss: 0.14139, val_acc: 0.96000
Epoch [9240/10000], loss: 0.13332 acc: 0.98667 val_loss: 0.14133, val_acc: 0.96000
Epoch [9250/10000], loss: 0.13325 acc: 0.98667 val_loss: 0.14128, val_acc: 0.96000
Epoch [9260/10000], loss: 0.13319 acc: 0.98667 val_loss: 0.14122, val_acc: 0.96000
Epoch [9270/10000], loss: 0.13312 acc: 0.98667 val_loss: 0.14117, val_acc: 0.96000
Epoch [9280/10000], loss: 0.13305 acc: 0.98667 val_loss: 0.14111, val_acc: 0.96000
Epoch [9290/10000], loss: 0.13299 acc: 0.98667 val_loss: 0.14106, val_acc: 0.96000
Epoch [9300/10000], loss: 0.13292 acc: 0.98667 val_loss: 0.14100, val_acc: 0.96000
Epoch [9310/10000], loss: 0.13285 acc: 0.98667 val_loss: 0.14095, val_acc: 0.96000
Epoch [9320/10000], loss: 0.13279 acc: 0.98667 val_loss: 0.14089, val_acc: 0.96000
Epoch [9330/10000], loss: 0.13272 acc: 0.98667 val_loss: 0.14084, val_acc: 0.96000
Epoch [9340/10000], loss: 0.13266 acc: 0.98667 val_loss: 0.14078, val_acc: 0.96000
Epoch [9350/10000], loss: 0.13259 acc: 0.98667 val_loss: 0.14073, val_acc: 0.96000
Epoch [9360/10000], loss: 0.13252 acc: 0.98667 val_loss: 0.14067, val_acc: 0.96000
Epoch [9370/10000], loss: 0.13246 acc: 0.98667 val_loss: 0.14062, val_acc: 0.96000
Epoch [9380/10000], loss: 0.13239 acc: 0.98667 val_loss: 0.14056, val_acc: 0.96000
Epoch [9390/10000], loss: 0.13233 acc: 0.98667 val_loss: 0.14051, val_acc: 0.96000
Epoch [9400/10000], loss: 0.13226 acc: 0.98667 val_loss: 0.14046, val_acc: 0.96000
Epoch [9410/10000], loss: 0.13220 acc: 0.98667 val_loss: 0.14040, val_acc: 0.96000
Epoch [9420/10000], loss: 0.13213 acc: 0.98667 val_loss: 0.14035, val_acc: 0.96000
Epoch [9430/10000], loss: 0.13207 acc: 0.98667 val_loss: 0.14029, val_acc: 0.96000
Epoch [9440/10000], loss: 0.13200 acc: 0.98667 val_loss: 0.14024, val_acc: 0.96000
Epoch [9450/10000], loss: 0.13194 acc: 0.98667 val_loss: 0.14019, val_acc: 0.96000
Epoch [9460/10000], loss: 0.13187 acc: 0.98667 val_loss: 0.14013, val_acc: 0.96000
Epoch [9470/10000], loss: 0.13181 acc: 0.98667 val_loss: 0.14008, val_acc: 0.96000
Epoch [9480/10000], loss: 0.13174 acc: 0.98667 val_loss: 0.14003, val_acc: 0.96000
Epoch [9490/10000], loss: 0.13168 acc: 0.98667 val_loss: 0.13997, val_acc: 0.96000
Epoch [9500/10000], loss: 0.13162 acc: 0.98667 val_loss: 0.13992, val_acc: 0.96000
Epoch [9510/10000], loss: 0.13155 acc: 0.98667 val_loss: 0.13987, val_acc: 0.96000
Epoch [9520/10000], loss: 0.13149 acc: 0.98667 val_loss: 0.13982, val_acc: 0.96000
Epoch [9530/10000], loss: 0.13142 acc: 0.98667 val_loss: 0.13976, val_acc: 0.96000
Epoch [9540/10000], loss: 0.13136 acc: 0.98667 val_loss: 0.13971, val_acc: 0.96000
Epoch [9550/10000], loss: 0.13130 acc: 0.98667 val_loss: 0.13966, val_acc: 0.96000
Epoch [9560/10000], loss: 0.13123 acc: 0.98667 val_loss: 0.13960, val_acc: 0.96000
Epoch [9570/10000], loss: 0.13117 acc: 0.98667 val_loss: 0.13955, val_acc: 0.96000
Epoch [9580/10000], loss: 0.13111 acc: 0.98667 val_loss: 0.13950, val_acc: 0.96000
Epoch [9590/10000], loss: 0.13104 acc: 0.98667 val_loss: 0.13945, val_acc: 0.96000
Epoch [9600/10000], loss: 0.13098 acc: 0.98667 val_loss: 0.13940, val_acc: 0.96000
Epoch [9610/10000], loss: 0.13092 acc: 0.98667 val_loss: 0.13934, val_acc: 0.96000
Epoch [9620/10000], loss: 0.13086 acc: 0.98667 val_loss: 0.13929, val_acc: 0.96000
Epoch [9630/10000], loss: 0.13079 acc: 0.98667 val_loss: 0.13924, val_acc: 0.96000
Epoch [9640/10000], loss: 0.13073 acc: 0.98667 val_loss: 0.13919, val_acc: 0.96000
Epoch [9650/10000], loss: 0.13067 acc: 0.98667 val_loss: 0.13914, val_acc: 0.96000
Epoch [9660/10000], loss: 0.13061 acc: 0.98667 val_loss: 0.13909, val_acc: 0.96000
Epoch [9670/10000], loss: 0.13054 acc: 0.98667 val_loss: 0.13903, val_acc: 0.96000
Epoch [9680/10000], loss: 0.13048 acc: 0.98667 val_loss: 0.13898, val_acc: 0.96000
Epoch [9690/10000], loss: 0.13042 acc: 0.98667 val_loss: 0.13893, val_acc: 0.96000
Epoch [9700/10000], loss: 0.13036 acc: 0.98667 val_loss: 0.13888, val_acc: 0.96000
Epoch [9710/10000], loss: 0.13030 acc: 0.98667 val_loss: 0.13883, val_acc: 0.96000
Epoch [9720/10000], loss: 0.13023 acc: 0.98667 val_loss: 0.13878, val_acc: 0.96000
Epoch [9730/10000], loss: 0.13017 acc: 0.98667 val_loss: 0.13873, val_acc: 0.96000
Epoch [9740/10000], loss: 0.13011 acc: 0.98667 val_loss: 0.13868, val_acc: 0.96000
Epoch [9750/10000], loss: 0.13005 acc: 0.98667 val_loss: 0.13863, val_acc: 0.96000
Epoch [9760/10000], loss: 0.12999 acc: 0.98667 val_loss: 0.13858, val_acc: 0.96000
Epoch [9770/10000], loss: 0.12993 acc: 0.98667 val_loss: 0.13853, val_acc: 0.96000
Epoch [9780/10000], loss: 0.12987 acc: 0.98667 val_loss: 0.13847, val_acc: 0.96000
Epoch [9790/10000], loss: 0.12981 acc: 0.98667 val_loss: 0.13842, val_acc: 0.96000
Epoch [9800/10000], loss: 0.12974 acc: 0.98667 val_loss: 0.13837, val_acc: 0.96000
Epoch [9810/10000], loss: 0.12968 acc: 0.98667 val_loss: 0.13832, val_acc: 0.96000
Epoch [9820/10000], loss: 0.12962 acc: 0.98667 val_loss: 0.13827, val_acc: 0.96000
Epoch [9830/10000], loss: 0.12956 acc: 0.98667 val_loss: 0.13822, val_acc: 0.96000
Epoch [9840/10000], loss: 0.12950 acc: 0.98667 val_loss: 0.13817, val_acc: 0.96000
Epoch [9850/10000], loss: 0.12944 acc: 0.98667 val_loss: 0.13812, val_acc: 0.96000
Epoch [9860/10000], loss: 0.12938 acc: 0.98667 val_loss: 0.13808, val_acc: 0.96000
Epoch [9870/10000], loss: 0.12932 acc: 0.98667 val_loss: 0.13803, val_acc: 0.96000
Epoch [9880/10000], loss: 0.12926 acc: 0.98667 val_loss: 0.13798, val_acc: 0.96000
Epoch [9890/10000], loss: 0.12920 acc: 0.98667 val_loss: 0.13793, val_acc: 0.96000
Epoch [9900/10000], loss: 0.12914 acc: 0.98667 val_loss: 0.13788, val_acc: 0.96000
Epoch [9910/10000], loss: 0.12908 acc: 0.98667 val_loss: 0.13783, val_acc: 0.96000
Epoch [9920/10000], loss: 0.12902 acc: 0.98667 val_loss: 0.13778, val_acc: 0.96000
Epoch [9930/10000], loss: 0.12896 acc: 0.98667 val_loss: 0.13773, val_acc: 0.96000
Epoch [9940/10000], loss: 0.12890 acc: 0.98667 val_loss: 0.13768, val_acc: 0.96000
Epoch [9950/10000], loss: 0.12884 acc: 0.98667 val_loss: 0.13763, val_acc: 0.96000
Epoch [9960/10000], loss: 0.12879 acc: 0.98667 val_loss: 0.13758, val_acc: 0.96000
Epoch [9970/10000], loss: 0.12873 acc: 0.98667 val_loss: 0.13754, val_acc: 0.96000
Epoch [9980/10000], loss: 0.12867 acc: 0.98667 val_loss: 0.13749, val_acc: 0.96000
Epoch [9990/10000], loss: 0.12861 acc: 0.98667 val_loss: 0.13744, val_acc: 0.96000

# 손실과 정확도 확인
 
print(f'초기상태 : 손실 : {history[0,3]:.5f}  정확도 : {history[0,4]:.5f}' )
print(f'최종상태 : 손실 : {history[-1,3]:.5f}  정확도 : {history[-1,4]:.5f}' )

초기상태 : 손실 : 3.70707  정확도 : 0.36000
최종상태 : 손실 : 0.13744  정확도 : 0.96000

# 패턴 2 모델의 출력 결과
w = outputs[:5,:].data
print(w.numpy())
 
# 확률값을 얻고 싶은 경우
print(torch.exp(w).numpy())

[[ -5.0138  -0.1021  -2.403 ]
 [ -5.0423  -0.0205  -4.2822]
 [ -0.0609  -2.8283 -16.1468]
 [-11.6712  -3.1905  -0.042 ]
 [ -9.2089  -1.6898  -0.2041]]
[[0.0066 0.9029 0.0905]
 [0.0065 0.9797 0.0138]
 [0.9409 0.0591 0.    ]
 [0.     0.0412 0.9588]
 [0.0001 0.1846 0.8153]]

모델 클래스측에 소프트맥스 함수 만 포함된 경우

# 모델 정의
# 2입력 3출력 로지스틱 회귀 모델
 
class Net(nn.Module):
    def __init__(self, n_input, n_output):
        super().__init__()
        self.l1 = nn.Linear(n_input, n_output)
        # 소프트맥스 함수 정의
        self.softmax = nn.Softmax(dim=1)
 
        # 초깃값을 모두 1로 함
        # "딥러닝을 위한 수학"과 조건을 맞추기 위한 목적
        self.l1.weight.data.fill_(1.0)
        self.l1.bias.data.fill_(1.0)
 
    def forward(self, x):
        x1 = self.l1(x)
        x2 = self.softmax(x1)
        return x2

# 학습률
lr = 0.01
 
# 초기화
net = Net(n_input, n_output)
 
# 손실 함수： NLLLoss 함수
criterion = nn.NLLLoss()
 
# 최적화 함수: 경사 하강법
optimizer = optim.SGD(net.parameters(), lr=lr)
 
# 반복 횟수
num_epochs = 10000
 
# 평가 결과 기록
history = np.zeros((0,5))

for epoch in range(num_epochs):
 
    # 훈련 페이즈
 
    # 경사 초기화
    optimizer.zero_grad()
 
    # 예측 계산
    outputs = net(inputs)
 
    # 여기서 로그 함수를 적용함
    outputs2 = torch.log(outputs)
 
    # 손실 계산
    loss = criterion(outputs2, labels)
 
    # 경사 계산
    loss.backward()
 
    # 파라미터 수정
    optimizer.step()
 
    # 예측 라벨 산출
    predicted = torch.max(outputs, 1)[1]
 
    # 손실과 정확도 계산
    train_loss = loss.item()
    train_acc = (predicted == labels).sum()  / len(labels)
 
    # 예측 페이즈
 
    # 예측 계산
    outputs_test = net(inputs_test)
 
    # 여기서 로그 함수를 적용함
    outputs2_test = torch.log(outputs_test)
 
    # 손실 계산
    loss_test = criterion(outputs2_test, labels_test)
 
    # 예측 라벨 산출
    predicted_test = torch.max(outputs_test, 1)[1]
 
    # 손실과 정확도 계산
    val_loss =  loss_test.item()
    val_acc =  (predicted_test == labels_test).sum() / len(labels_test)
 
    if ( epoch % 10 == 0):
        print (f'Epoch [{epoch}/{num_epochs}], loss: {train_loss:.5f} acc: {train_acc:.5f} val_loss: {val_loss:.5f}, val_acc: {val_acc:.5f}')
        item = np.array([epoch , train_loss, train_acc, val_loss, val_acc])
        history = np.vstack((history, item))

Epoch [0/10000], loss: 1.09861 acc: 0.30667 val_loss: 1.09158, val_acc: 0.26667
Epoch [10/10000], loss: 1.01848 acc: 0.40000 val_loss: 1.04171, val_acc: 0.26667
Epoch [20/10000], loss: 0.96854 acc: 0.40000 val_loss: 0.98850, val_acc: 0.26667
Epoch [30/10000], loss: 0.92459 acc: 0.65333 val_loss: 0.93996, val_acc: 0.57333
Epoch [40/10000], loss: 0.88568 acc: 0.70667 val_loss: 0.89704, val_acc: 0.62667
Epoch [50/10000], loss: 0.85120 acc: 0.70667 val_loss: 0.85918, val_acc: 0.62667
Epoch [60/10000], loss: 0.82059 acc: 0.70667 val_loss: 0.82572, val_acc: 0.62667
Epoch [70/10000], loss: 0.79335 acc: 0.72000 val_loss: 0.79607, val_acc: 0.62667
Epoch [80/10000], loss: 0.76900 acc: 0.72000 val_loss: 0.76968, val_acc: 0.65333
Epoch [90/10000], loss: 0.74717 acc: 0.72000 val_loss: 0.74610, val_acc: 0.65333
Epoch [100/10000], loss: 0.72750 acc: 0.76000 val_loss: 0.72494, val_acc: 0.69333
Epoch [110/10000], loss: 0.70970 acc: 0.77333 val_loss: 0.70585, val_acc: 0.74667
Epoch [120/10000], loss: 0.69354 acc: 0.81333 val_loss: 0.68856, val_acc: 0.76000
Epoch [130/10000], loss: 0.67878 acc: 0.84000 val_loss: 0.67283, val_acc: 0.76000
Epoch [140/10000], loss: 0.66526 acc: 0.84000 val_loss: 0.65846, val_acc: 0.78667
Epoch [150/10000], loss: 0.65283 acc: 0.86667 val_loss: 0.64528, val_acc: 0.78667
Epoch [160/10000], loss: 0.64135 acc: 0.88000 val_loss: 0.63313, val_acc: 0.78667
Epoch [170/10000], loss: 0.63070 acc: 0.89333 val_loss: 0.62190, val_acc: 0.81333
Epoch [180/10000], loss: 0.62080 acc: 0.90667 val_loss: 0.61149, val_acc: 0.81333
Epoch [190/10000], loss: 0.61157 acc: 0.90667 val_loss: 0.60179, val_acc: 0.84000
Epoch [200/10000], loss: 0.60292 acc: 0.90667 val_loss: 0.59273, val_acc: 0.84000
Epoch [210/10000], loss: 0.59481 acc: 0.90667 val_loss: 0.58425, val_acc: 0.88000
Epoch [220/10000], loss: 0.58717 acc: 0.93333 val_loss: 0.57628, val_acc: 0.88000
Epoch [230/10000], loss: 0.57996 acc: 0.93333 val_loss: 0.56877, val_acc: 0.89333
Epoch [240/10000], loss: 0.57313 acc: 0.93333 val_loss: 0.56169, val_acc: 0.90667
Epoch [250/10000], loss: 0.56666 acc: 0.93333 val_loss: 0.55498, val_acc: 0.90667
Epoch [260/10000], loss: 0.56051 acc: 0.92000 val_loss: 0.54862, val_acc: 0.90667
Epoch [270/10000], loss: 0.55465 acc: 0.92000 val_loss: 0.54257, val_acc: 0.90667
Epoch [280/10000], loss: 0.54906 acc: 0.92000 val_loss: 0.53681, val_acc: 0.90667
Epoch [290/10000], loss: 0.54371 acc: 0.92000 val_loss: 0.53131, val_acc: 0.90667
Epoch [300/10000], loss: 0.53859 acc: 0.93333 val_loss: 0.52605, val_acc: 0.90667
Epoch [310/10000], loss: 0.53368 acc: 0.93333 val_loss: 0.52102, val_acc: 0.90667
Epoch [320/10000], loss: 0.52896 acc: 0.93333 val_loss: 0.51619, val_acc: 0.90667
Epoch [330/10000], loss: 0.52442 acc: 0.93333 val_loss: 0.51155, val_acc: 0.90667
Epoch [340/10000], loss: 0.52004 acc: 0.93333 val_loss: 0.50709, val_acc: 0.90667
Epoch [350/10000], loss: 0.51582 acc: 0.93333 val_loss: 0.50280, val_acc: 0.90667
Epoch [360/10000], loss: 0.51173 acc: 0.93333 val_loss: 0.49865, val_acc: 0.90667
Epoch [370/10000], loss: 0.50779 acc: 0.93333 val_loss: 0.49465, val_acc: 0.90667
Epoch [380/10000], loss: 0.50397 acc: 0.93333 val_loss: 0.49078, val_acc: 0.90667
Epoch [390/10000], loss: 0.50026 acc: 0.93333 val_loss: 0.48703, val_acc: 0.90667
Epoch [400/10000], loss: 0.49666 acc: 0.94667 val_loss: 0.48340, val_acc: 0.90667
Epoch [410/10000], loss: 0.49317 acc: 0.94667 val_loss: 0.47988, val_acc: 0.90667
Epoch [420/10000], loss: 0.48978 acc: 0.94667 val_loss: 0.47647, val_acc: 0.90667
Epoch [430/10000], loss: 0.48647 acc: 0.96000 val_loss: 0.47315, val_acc: 0.90667
Epoch [440/10000], loss: 0.48326 acc: 0.96000 val_loss: 0.46992, val_acc: 0.90667
Epoch [450/10000], loss: 0.48012 acc: 0.96000 val_loss: 0.46678, val_acc: 0.90667
Epoch [460/10000], loss: 0.47706 acc: 0.96000 val_loss: 0.46372, val_acc: 0.90667
Epoch [470/10000], loss: 0.47408 acc: 0.96000 val_loss: 0.46073, val_acc: 0.90667
Epoch [480/10000], loss: 0.47116 acc: 0.96000 val_loss: 0.45783, val_acc: 0.90667
Epoch [490/10000], loss: 0.46831 acc: 0.96000 val_loss: 0.45499, val_acc: 0.90667
Epoch [500/10000], loss: 0.46553 acc: 0.96000 val_loss: 0.45221, val_acc: 0.90667
Epoch [510/10000], loss: 0.46280 acc: 0.96000 val_loss: 0.44951, val_acc: 0.90667
Epoch [520/10000], loss: 0.46013 acc: 0.96000 val_loss: 0.44686, val_acc: 0.90667
Epoch [530/10000], loss: 0.45752 acc: 0.96000 val_loss: 0.44426, val_acc: 0.90667
Epoch [540/10000], loss: 0.45496 acc: 0.96000 val_loss: 0.44173, val_acc: 0.90667
Epoch [550/10000], loss: 0.45245 acc: 0.96000 val_loss: 0.43924, val_acc: 0.90667
Epoch [560/10000], loss: 0.44998 acc: 0.96000 val_loss: 0.43681, val_acc: 0.90667
Epoch [570/10000], loss: 0.44757 acc: 0.96000 val_loss: 0.43442, val_acc: 0.90667
Epoch [580/10000], loss: 0.44519 acc: 0.96000 val_loss: 0.43208, val_acc: 0.90667
Epoch [590/10000], loss: 0.44286 acc: 0.96000 val_loss: 0.42979, val_acc: 0.92000
Epoch [600/10000], loss: 0.44057 acc: 0.96000 val_loss: 0.42753, val_acc: 0.92000
Epoch [610/10000], loss: 0.43832 acc: 0.96000 val_loss: 0.42532, val_acc: 0.92000
Epoch [620/10000], loss: 0.43611 acc: 0.96000 val_loss: 0.42315, val_acc: 0.92000
Epoch [630/10000], loss: 0.43393 acc: 0.96000 val_loss: 0.42101, val_acc: 0.92000
Epoch [640/10000], loss: 0.43179 acc: 0.96000 val_loss: 0.41891, val_acc: 0.92000
Epoch [650/10000], loss: 0.42968 acc: 0.96000 val_loss: 0.41685, val_acc: 0.92000
Epoch [660/10000], loss: 0.42761 acc: 0.96000 val_loss: 0.41482, val_acc: 0.92000
Epoch [670/10000], loss: 0.42556 acc: 0.96000 val_loss: 0.41282, val_acc: 0.92000
Epoch [680/10000], loss: 0.42355 acc: 0.96000 val_loss: 0.41085, val_acc: 0.92000
Epoch [690/10000], loss: 0.42157 acc: 0.96000 val_loss: 0.40892, val_acc: 0.92000
Epoch [700/10000], loss: 0.41961 acc: 0.96000 val_loss: 0.40701, val_acc: 0.92000
Epoch [710/10000], loss: 0.41768 acc: 0.96000 val_loss: 0.40513, val_acc: 0.92000
Epoch [720/10000], loss: 0.41578 acc: 0.96000 val_loss: 0.40329, val_acc: 0.92000
Epoch [730/10000], loss: 0.41391 acc: 0.96000 val_loss: 0.40146, val_acc: 0.92000
Epoch [740/10000], loss: 0.41206 acc: 0.96000 val_loss: 0.39967, val_acc: 0.92000
Epoch [750/10000], loss: 0.41024 acc: 0.96000 val_loss: 0.39789, val_acc: 0.92000
Epoch [760/10000], loss: 0.40844 acc: 0.96000 val_loss: 0.39615, val_acc: 0.92000
Epoch [770/10000], loss: 0.40666 acc: 0.96000 val_loss: 0.39443, val_acc: 0.93333
Epoch [780/10000], loss: 0.40491 acc: 0.96000 val_loss: 0.39273, val_acc: 0.93333
Epoch [790/10000], loss: 0.40317 acc: 0.96000 val_loss: 0.39105, val_acc: 0.93333
Epoch [800/10000], loss: 0.40146 acc: 0.96000 val_loss: 0.38939, val_acc: 0.93333
Epoch [810/10000], loss: 0.39977 acc: 0.96000 val_loss: 0.38776, val_acc: 0.93333
Epoch [820/10000], loss: 0.39810 acc: 0.96000 val_loss: 0.38615, val_acc: 0.93333
Epoch [830/10000], loss: 0.39646 acc: 0.96000 val_loss: 0.38456, val_acc: 0.93333
Epoch [840/10000], loss: 0.39483 acc: 0.96000 val_loss: 0.38298, val_acc: 0.93333
Epoch [850/10000], loss: 0.39321 acc: 0.97333 val_loss: 0.38143, val_acc: 0.94667
Epoch [860/10000], loss: 0.39162 acc: 0.97333 val_loss: 0.37990, val_acc: 0.94667
Epoch [870/10000], loss: 0.39005 acc: 0.97333 val_loss: 0.37838, val_acc: 0.94667
Epoch [880/10000], loss: 0.38849 acc: 0.97333 val_loss: 0.37688, val_acc: 0.94667
Epoch [890/10000], loss: 0.38695 acc: 0.97333 val_loss: 0.37540, val_acc: 0.94667
Epoch [900/10000], loss: 0.38543 acc: 0.97333 val_loss: 0.37394, val_acc: 0.94667
Epoch [910/10000], loss: 0.38392 acc: 0.97333 val_loss: 0.37249, val_acc: 0.94667
Epoch [920/10000], loss: 0.38243 acc: 0.97333 val_loss: 0.37106, val_acc: 0.94667
Epoch [930/10000], loss: 0.38096 acc: 0.97333 val_loss: 0.36965, val_acc: 0.94667
Epoch [940/10000], loss: 0.37950 acc: 0.97333 val_loss: 0.36825, val_acc: 0.94667
Epoch [950/10000], loss: 0.37806 acc: 0.97333 val_loss: 0.36686, val_acc: 0.94667
Epoch [960/10000], loss: 0.37663 acc: 0.97333 val_loss: 0.36550, val_acc: 0.96000
Epoch [970/10000], loss: 0.37522 acc: 0.97333 val_loss: 0.36414, val_acc: 0.96000
Epoch [980/10000], loss: 0.37382 acc: 0.97333 val_loss: 0.36280, val_acc: 0.96000
Epoch [990/10000], loss: 0.37243 acc: 0.97333 val_loss: 0.36148, val_acc: 0.96000
Epoch [1000/10000], loss: 0.37106 acc: 0.97333 val_loss: 0.36017, val_acc: 0.96000
Epoch [1010/10000], loss: 0.36970 acc: 0.97333 val_loss: 0.35887, val_acc: 0.96000
Epoch [1020/10000], loss: 0.36836 acc: 0.97333 val_loss: 0.35758, val_acc: 0.96000
Epoch [1030/10000], loss: 0.36703 acc: 0.97333 val_loss: 0.35631, val_acc: 0.96000
Epoch [1040/10000], loss: 0.36571 acc: 0.97333 val_loss: 0.35505, val_acc: 0.96000
Epoch [1050/10000], loss: 0.36440 acc: 0.97333 val_loss: 0.35381, val_acc: 0.96000
Epoch [1060/10000], loss: 0.36311 acc: 0.97333 val_loss: 0.35258, val_acc: 0.96000
Epoch [1070/10000], loss: 0.36183 acc: 0.97333 val_loss: 0.35135, val_acc: 0.96000
Epoch [1080/10000], loss: 0.36056 acc: 0.97333 val_loss: 0.35014, val_acc: 0.96000
Epoch [1090/10000], loss: 0.35930 acc: 0.97333 val_loss: 0.34895, val_acc: 0.96000
Epoch [1100/10000], loss: 0.35805 acc: 0.97333 val_loss: 0.34776, val_acc: 0.96000
Epoch [1110/10000], loss: 0.35682 acc: 0.97333 val_loss: 0.34659, val_acc: 0.96000
Epoch [1120/10000], loss: 0.35559 acc: 0.97333 val_loss: 0.34542, val_acc: 0.96000
Epoch [1130/10000], loss: 0.35438 acc: 0.97333 val_loss: 0.34427, val_acc: 0.96000
Epoch [1140/10000], loss: 0.35318 acc: 0.97333 val_loss: 0.34313, val_acc: 0.96000
Epoch [1150/10000], loss: 0.35199 acc: 0.97333 val_loss: 0.34199, val_acc: 0.96000
Epoch [1160/10000], loss: 0.35081 acc: 0.97333 val_loss: 0.34087, val_acc: 0.96000
Epoch [1170/10000], loss: 0.34964 acc: 0.97333 val_loss: 0.33976, val_acc: 0.96000
Epoch [1180/10000], loss: 0.34848 acc: 0.97333 val_loss: 0.33866, val_acc: 0.96000
Epoch [1190/10000], loss: 0.34732 acc: 0.97333 val_loss: 0.33757, val_acc: 0.96000
Epoch [1200/10000], loss: 0.34618 acc: 0.97333 val_loss: 0.33649, val_acc: 0.96000
Epoch [1210/10000], loss: 0.34505 acc: 0.97333 val_loss: 0.33542, val_acc: 0.96000
Epoch [1220/10000], loss: 0.34393 acc: 0.97333 val_loss: 0.33435, val_acc: 0.96000
Epoch [1230/10000], loss: 0.34282 acc: 0.97333 val_loss: 0.33330, val_acc: 0.96000
Epoch [1240/10000], loss: 0.34172 acc: 0.97333 val_loss: 0.33226, val_acc: 0.96000
Epoch [1250/10000], loss: 0.34062 acc: 0.97333 val_loss: 0.33122, val_acc: 0.96000
Epoch [1260/10000], loss: 0.33954 acc: 0.97333 val_loss: 0.33020, val_acc: 0.96000
Epoch [1270/10000], loss: 0.33846 acc: 0.97333 val_loss: 0.32918, val_acc: 0.96000
Epoch [1280/10000], loss: 0.33740 acc: 0.97333 val_loss: 0.32817, val_acc: 0.96000
Epoch [1290/10000], loss: 0.33634 acc: 0.97333 val_loss: 0.32717, val_acc: 0.96000
Epoch [1300/10000], loss: 0.33529 acc: 0.97333 val_loss: 0.32618, val_acc: 0.96000
Epoch [1310/10000], loss: 0.33425 acc: 0.97333 val_loss: 0.32520, val_acc: 0.96000
Epoch [1320/10000], loss: 0.33321 acc: 0.97333 val_loss: 0.32422, val_acc: 0.96000
Epoch [1330/10000], loss: 0.33219 acc: 0.97333 val_loss: 0.32325, val_acc: 0.96000
Epoch [1340/10000], loss: 0.33117 acc: 0.97333 val_loss: 0.32229, val_acc: 0.96000
Epoch [1350/10000], loss: 0.33016 acc: 0.97333 val_loss: 0.32134, val_acc: 0.96000
Epoch [1360/10000], loss: 0.32916 acc: 0.97333 val_loss: 0.32040, val_acc: 0.96000
Epoch [1370/10000], loss: 0.32817 acc: 0.97333 val_loss: 0.31946, val_acc: 0.96000
Epoch [1380/10000], loss: 0.32719 acc: 0.97333 val_loss: 0.31853, val_acc: 0.96000
Epoch [1390/10000], loss: 0.32621 acc: 0.97333 val_loss: 0.31761, val_acc: 0.96000
Epoch [1400/10000], loss: 0.32524 acc: 0.97333 val_loss: 0.31670, val_acc: 0.96000
Epoch [1410/10000], loss: 0.32428 acc: 0.97333 val_loss: 0.31579, val_acc: 0.96000
Epoch [1420/10000], loss: 0.32332 acc: 0.97333 val_loss: 0.31489, val_acc: 0.96000
Epoch [1430/10000], loss: 0.32237 acc: 0.97333 val_loss: 0.31400, val_acc: 0.96000
Epoch [1440/10000], loss: 0.32143 acc: 0.97333 val_loss: 0.31312, val_acc: 0.96000
Epoch [1450/10000], loss: 0.32050 acc: 0.97333 val_loss: 0.31224, val_acc: 0.96000
Epoch [1460/10000], loss: 0.31957 acc: 0.97333 val_loss: 0.31137, val_acc: 0.96000
Epoch [1470/10000], loss: 0.31865 acc: 0.97333 val_loss: 0.31050, val_acc: 0.96000
Epoch [1480/10000], loss: 0.31774 acc: 0.97333 val_loss: 0.30964, val_acc: 0.96000
Epoch [1490/10000], loss: 0.31683 acc: 0.97333 val_loss: 0.30879, val_acc: 0.96000
Epoch [1500/10000], loss: 0.31593 acc: 0.97333 val_loss: 0.30795, val_acc: 0.96000
Epoch [1510/10000], loss: 0.31504 acc: 0.97333 val_loss: 0.30711, val_acc: 0.96000
Epoch [1520/10000], loss: 0.31415 acc: 0.97333 val_loss: 0.30628, val_acc: 0.96000
Epoch [1530/10000], loss: 0.31327 acc: 0.97333 val_loss: 0.30545, val_acc: 0.96000
Epoch [1540/10000], loss: 0.31240 acc: 0.97333 val_loss: 0.30463, val_acc: 0.96000
Epoch [1550/10000], loss: 0.31153 acc: 0.97333 val_loss: 0.30382, val_acc: 0.96000
Epoch [1560/10000], loss: 0.31067 acc: 0.97333 val_loss: 0.30301, val_acc: 0.96000
Epoch [1570/10000], loss: 0.30981 acc: 0.97333 val_loss: 0.30221, val_acc: 0.96000
Epoch [1580/10000], loss: 0.30896 acc: 0.97333 val_loss: 0.30141, val_acc: 0.96000
Epoch [1590/10000], loss: 0.30812 acc: 0.97333 val_loss: 0.30062, val_acc: 0.96000
Epoch [1600/10000], loss: 0.30728 acc: 0.97333 val_loss: 0.29984, val_acc: 0.96000
Epoch [1610/10000], loss: 0.30645 acc: 0.97333 val_loss: 0.29906, val_acc: 0.96000
Epoch [1620/10000], loss: 0.30562 acc: 0.97333 val_loss: 0.29828, val_acc: 0.96000
Epoch [1630/10000], loss: 0.30480 acc: 0.97333 val_loss: 0.29752, val_acc: 0.96000
Epoch [1640/10000], loss: 0.30399 acc: 0.97333 val_loss: 0.29675, val_acc: 0.96000
Epoch [1650/10000], loss: 0.30318 acc: 0.97333 val_loss: 0.29600, val_acc: 0.96000
Epoch [1660/10000], loss: 0.30237 acc: 0.97333 val_loss: 0.29525, val_acc: 0.96000
Epoch [1670/10000], loss: 0.30158 acc: 0.97333 val_loss: 0.29450, val_acc: 0.96000
Epoch [1680/10000], loss: 0.30078 acc: 0.97333 val_loss: 0.29376, val_acc: 0.96000
Epoch [1690/10000], loss: 0.30000 acc: 0.97333 val_loss: 0.29302, val_acc: 0.96000
Epoch [1700/10000], loss: 0.29922 acc: 0.97333 val_loss: 0.29229, val_acc: 0.96000
Epoch [1710/10000], loss: 0.29844 acc: 0.97333 val_loss: 0.29157, val_acc: 0.96000
Epoch [1720/10000], loss: 0.29767 acc: 0.97333 val_loss: 0.29085, val_acc: 0.96000
Epoch [1730/10000], loss: 0.29690 acc: 0.97333 val_loss: 0.29013, val_acc: 0.96000
Epoch [1740/10000], loss: 0.29614 acc: 0.97333 val_loss: 0.28942, val_acc: 0.96000
Epoch [1750/10000], loss: 0.29538 acc: 0.97333 val_loss: 0.28872, val_acc: 0.96000
Epoch [1760/10000], loss: 0.29463 acc: 0.97333 val_loss: 0.28801, val_acc: 0.96000
Epoch [1770/10000], loss: 0.29389 acc: 0.97333 val_loss: 0.28732, val_acc: 0.96000
Epoch [1780/10000], loss: 0.29315 acc: 0.97333 val_loss: 0.28663, val_acc: 0.96000
Epoch [1790/10000], loss: 0.29241 acc: 0.97333 val_loss: 0.28594, val_acc: 0.96000
Epoch [1800/10000], loss: 0.29168 acc: 0.97333 val_loss: 0.28526, val_acc: 0.96000
Epoch [1810/10000], loss: 0.29095 acc: 0.97333 val_loss: 0.28458, val_acc: 0.96000
Epoch [1820/10000], loss: 0.29023 acc: 0.97333 val_loss: 0.28391, val_acc: 0.96000
Epoch [1830/10000], loss: 0.28951 acc: 0.97333 val_loss: 0.28324, val_acc: 0.96000
Epoch [1840/10000], loss: 0.28880 acc: 0.97333 val_loss: 0.28258, val_acc: 0.96000
Epoch [1850/10000], loss: 0.28809 acc: 0.97333 val_loss: 0.28192, val_acc: 0.96000
Epoch [1860/10000], loss: 0.28739 acc: 0.97333 val_loss: 0.28126, val_acc: 0.96000
Epoch [1870/10000], loss: 0.28669 acc: 0.97333 val_loss: 0.28061, val_acc: 0.96000
Epoch [1880/10000], loss: 0.28599 acc: 0.97333 val_loss: 0.27996, val_acc: 0.96000
Epoch [1890/10000], loss: 0.28530 acc: 0.97333 val_loss: 0.27932, val_acc: 0.96000
Epoch [1900/10000], loss: 0.28462 acc: 0.97333 val_loss: 0.27868, val_acc: 0.96000
Epoch [1910/10000], loss: 0.28394 acc: 0.97333 val_loss: 0.27805, val_acc: 0.96000
Epoch [1920/10000], loss: 0.28326 acc: 0.97333 val_loss: 0.27742, val_acc: 0.96000
Epoch [1930/10000], loss: 0.28258 acc: 0.97333 val_loss: 0.27679, val_acc: 0.96000
Epoch [1940/10000], loss: 0.28192 acc: 0.97333 val_loss: 0.27617, val_acc: 0.96000
Epoch [1950/10000], loss: 0.28125 acc: 0.97333 val_loss: 0.27555, val_acc: 0.96000
Epoch [1960/10000], loss: 0.28059 acc: 0.97333 val_loss: 0.27494, val_acc: 0.96000
Epoch [1970/10000], loss: 0.27993 acc: 0.97333 val_loss: 0.27433, val_acc: 0.96000
Epoch [1980/10000], loss: 0.27928 acc: 0.97333 val_loss: 0.27372, val_acc: 0.96000
Epoch [1990/10000], loss: 0.27863 acc: 0.97333 val_loss: 0.27312, val_acc: 0.96000
Epoch [2000/10000], loss: 0.27799 acc: 0.97333 val_loss: 0.27252, val_acc: 0.96000
Epoch [2010/10000], loss: 0.27735 acc: 0.97333 val_loss: 0.27193, val_acc: 0.96000
Epoch [2020/10000], loss: 0.27671 acc: 0.97333 val_loss: 0.27134, val_acc: 0.96000
Epoch [2030/10000], loss: 0.27608 acc: 0.97333 val_loss: 0.27075, val_acc: 0.96000
Epoch [2040/10000], loss: 0.27545 acc: 0.97333 val_loss: 0.27016, val_acc: 0.96000
Epoch [2050/10000], loss: 0.27482 acc: 0.97333 val_loss: 0.26958, val_acc: 0.96000
Epoch [2060/10000], loss: 0.27420 acc: 0.97333 val_loss: 0.26901, val_acc: 0.96000
Epoch [2070/10000], loss: 0.27358 acc: 0.97333 val_loss: 0.26843, val_acc: 0.96000
Epoch [2080/10000], loss: 0.27297 acc: 0.97333 val_loss: 0.26786, val_acc: 0.96000
Epoch [2090/10000], loss: 0.27236 acc: 0.97333 val_loss: 0.26730, val_acc: 0.96000
Epoch [2100/10000], loss: 0.27175 acc: 0.97333 val_loss: 0.26674, val_acc: 0.96000
Epoch [2110/10000], loss: 0.27115 acc: 0.97333 val_loss: 0.26618, val_acc: 0.96000
Epoch [2120/10000], loss: 0.27055 acc: 0.97333 val_loss: 0.26562, val_acc: 0.96000
Epoch [2130/10000], loss: 0.26995 acc: 0.97333 val_loss: 0.26507, val_acc: 0.96000
Epoch [2140/10000], loss: 0.26936 acc: 0.97333 val_loss: 0.26452, val_acc: 0.96000
Epoch [2150/10000], loss: 0.26877 acc: 0.97333 val_loss: 0.26397, val_acc: 0.96000
Epoch [2160/10000], loss: 0.26818 acc: 0.97333 val_loss: 0.26343, val_acc: 0.96000
Epoch [2170/10000], loss: 0.26760 acc: 0.97333 val_loss: 0.26289, val_acc: 0.96000
Epoch [2180/10000], loss: 0.26702 acc: 0.97333 val_loss: 0.26236, val_acc: 0.96000
Epoch [2190/10000], loss: 0.26644 acc: 0.97333 val_loss: 0.26182, val_acc: 0.96000
Epoch [2200/10000], loss: 0.26587 acc: 0.97333 val_loss: 0.26129, val_acc: 0.96000
Epoch [2210/10000], loss: 0.26530 acc: 0.97333 val_loss: 0.26077, val_acc: 0.96000
Epoch [2220/10000], loss: 0.26473 acc: 0.97333 val_loss: 0.26024, val_acc: 0.96000
Epoch [2230/10000], loss: 0.26417 acc: 0.97333 val_loss: 0.25972, val_acc: 0.96000
Epoch [2240/10000], loss: 0.26361 acc: 0.97333 val_loss: 0.25921, val_acc: 0.96000
Epoch [2250/10000], loss: 0.26305 acc: 0.97333 val_loss: 0.25869, val_acc: 0.96000
Epoch [2260/10000], loss: 0.26250 acc: 0.97333 val_loss: 0.25818, val_acc: 0.96000
Epoch [2270/10000], loss: 0.26195 acc: 0.97333 val_loss: 0.25767, val_acc: 0.96000
Epoch [2280/10000], loss: 0.26140 acc: 0.97333 val_loss: 0.25717, val_acc: 0.96000
Epoch [2290/10000], loss: 0.26086 acc: 0.97333 val_loss: 0.25666, val_acc: 0.96000
Epoch [2300/10000], loss: 0.26032 acc: 0.97333 val_loss: 0.25616, val_acc: 0.96000
Epoch [2310/10000], loss: 0.25978 acc: 0.97333 val_loss: 0.25567, val_acc: 0.96000
Epoch [2320/10000], loss: 0.25924 acc: 0.97333 val_loss: 0.25517, val_acc: 0.96000
Epoch [2330/10000], loss: 0.25871 acc: 0.97333 val_loss: 0.25468, val_acc: 0.96000
Epoch [2340/10000], loss: 0.25818 acc: 0.97333 val_loss: 0.25419, val_acc: 0.96000
Epoch [2350/10000], loss: 0.25766 acc: 0.97333 val_loss: 0.25371, val_acc: 0.96000
Epoch [2360/10000], loss: 0.25713 acc: 0.97333 val_loss: 0.25322, val_acc: 0.96000
Epoch [2370/10000], loss: 0.25661 acc: 0.97333 val_loss: 0.25274, val_acc: 0.96000
Epoch [2380/10000], loss: 0.25609 acc: 0.97333 val_loss: 0.25227, val_acc: 0.96000
Epoch [2390/10000], loss: 0.25558 acc: 0.97333 val_loss: 0.25179, val_acc: 0.96000
Epoch [2400/10000], loss: 0.25507 acc: 0.97333 val_loss: 0.25132, val_acc: 0.96000
Epoch [2410/10000], loss: 0.25456 acc: 0.97333 val_loss: 0.25085, val_acc: 0.96000
Epoch [2420/10000], loss: 0.25405 acc: 0.97333 val_loss: 0.25038, val_acc: 0.96000
Epoch [2430/10000], loss: 0.25355 acc: 0.97333 val_loss: 0.24992, val_acc: 0.96000
Epoch [2440/10000], loss: 0.25304 acc: 0.97333 val_loss: 0.24946, val_acc: 0.96000
Epoch [2450/10000], loss: 0.25255 acc: 0.97333 val_loss: 0.24900, val_acc: 0.96000
Epoch [2460/10000], loss: 0.25205 acc: 0.97333 val_loss: 0.24854, val_acc: 0.96000
Epoch [2470/10000], loss: 0.25156 acc: 0.97333 val_loss: 0.24809, val_acc: 0.96000
Epoch [2480/10000], loss: 0.25107 acc: 0.97333 val_loss: 0.24764, val_acc: 0.96000
Epoch [2490/10000], loss: 0.25058 acc: 0.97333 val_loss: 0.24719, val_acc: 0.96000
Epoch [2500/10000], loss: 0.25009 acc: 0.97333 val_loss: 0.24674, val_acc: 0.96000
Epoch [2510/10000], loss: 0.24961 acc: 0.97333 val_loss: 0.24630, val_acc: 0.96000
Epoch [2520/10000], loss: 0.24913 acc: 0.97333 val_loss: 0.24585, val_acc: 0.96000
Epoch [2530/10000], loss: 0.24865 acc: 0.97333 val_loss: 0.24541, val_acc: 0.96000
Epoch [2540/10000], loss: 0.24818 acc: 0.97333 val_loss: 0.24498, val_acc: 0.96000
Epoch [2550/10000], loss: 0.24770 acc: 0.97333 val_loss: 0.24454, val_acc: 0.96000
Epoch [2560/10000], loss: 0.24723 acc: 0.97333 val_loss: 0.24411, val_acc: 0.96000
Epoch [2570/10000], loss: 0.24676 acc: 0.97333 val_loss: 0.24368, val_acc: 0.96000
Epoch [2580/10000], loss: 0.24630 acc: 0.98667 val_loss: 0.24325, val_acc: 0.96000
Epoch [2590/10000], loss: 0.24584 acc: 0.98667 val_loss: 0.24283, val_acc: 0.96000
Epoch [2600/10000], loss: 0.24537 acc: 0.98667 val_loss: 0.24240, val_acc: 0.96000
Epoch [2610/10000], loss: 0.24492 acc: 0.98667 val_loss: 0.24198, val_acc: 0.96000
Epoch [2620/10000], loss: 0.24446 acc: 0.98667 val_loss: 0.24156, val_acc: 0.96000
Epoch [2630/10000], loss: 0.24401 acc: 0.98667 val_loss: 0.24115, val_acc: 0.96000
Epoch [2640/10000], loss: 0.24355 acc: 0.98667 val_loss: 0.24073, val_acc: 0.96000
Epoch [2650/10000], loss: 0.24311 acc: 0.98667 val_loss: 0.24032, val_acc: 0.96000
Epoch [2660/10000], loss: 0.24266 acc: 0.98667 val_loss: 0.23991, val_acc: 0.96000
Epoch [2670/10000], loss: 0.24221 acc: 0.98667 val_loss: 0.23950, val_acc: 0.96000
Epoch [2680/10000], loss: 0.24177 acc: 0.98667 val_loss: 0.23909, val_acc: 0.96000
Epoch [2690/10000], loss: 0.24133 acc: 0.98667 val_loss: 0.23869, val_acc: 0.96000
Epoch [2700/10000], loss: 0.24089 acc: 0.98667 val_loss: 0.23829, val_acc: 0.96000
Epoch [2710/10000], loss: 0.24046 acc: 0.98667 val_loss: 0.23789, val_acc: 0.96000
Epoch [2720/10000], loss: 0.24002 acc: 0.98667 val_loss: 0.23749, val_acc: 0.96000
Epoch [2730/10000], loss: 0.23959 acc: 0.98667 val_loss: 0.23710, val_acc: 0.96000
Epoch [2740/10000], loss: 0.23916 acc: 0.98667 val_loss: 0.23670, val_acc: 0.96000
Epoch [2750/10000], loss: 0.23874 acc: 0.98667 val_loss: 0.23631, val_acc: 0.96000
Epoch [2760/10000], loss: 0.23831 acc: 0.98667 val_loss: 0.23592, val_acc: 0.96000
Epoch [2770/10000], loss: 0.23789 acc: 0.98667 val_loss: 0.23553, val_acc: 0.96000
Epoch [2780/10000], loss: 0.23747 acc: 0.98667 val_loss: 0.23515, val_acc: 0.96000
Epoch [2790/10000], loss: 0.23705 acc: 0.98667 val_loss: 0.23476, val_acc: 0.96000
Epoch [2800/10000], loss: 0.23663 acc: 0.98667 val_loss: 0.23438, val_acc: 0.96000
Epoch [2810/10000], loss: 0.23622 acc: 0.98667 val_loss: 0.23400, val_acc: 0.96000
Epoch [2820/10000], loss: 0.23580 acc: 0.98667 val_loss: 0.23363, val_acc: 0.96000
Epoch [2830/10000], loss: 0.23539 acc: 0.98667 val_loss: 0.23325, val_acc: 0.96000
Epoch [2840/10000], loss: 0.23498 acc: 0.98667 val_loss: 0.23287, val_acc: 0.96000
Epoch [2850/10000], loss: 0.23458 acc: 0.98667 val_loss: 0.23250, val_acc: 0.96000
Epoch [2860/10000], loss: 0.23417 acc: 0.98667 val_loss: 0.23213, val_acc: 0.96000
Epoch [2870/10000], loss: 0.23377 acc: 0.98667 val_loss: 0.23176, val_acc: 0.96000
Epoch [2880/10000], loss: 0.23337 acc: 0.98667 val_loss: 0.23140, val_acc: 0.96000
Epoch [2890/10000], loss: 0.23297 acc: 0.98667 val_loss: 0.23103, val_acc: 0.96000
Epoch [2900/10000], loss: 0.23257 acc: 0.98667 val_loss: 0.23067, val_acc: 0.96000
Epoch [2910/10000], loss: 0.23218 acc: 0.98667 val_loss: 0.23031, val_acc: 0.96000
Epoch [2920/10000], loss: 0.23178 acc: 0.98667 val_loss: 0.22995, val_acc: 0.96000
Epoch [2930/10000], loss: 0.23139 acc: 0.98667 val_loss: 0.22959, val_acc: 0.96000
Epoch [2940/10000], loss: 0.23100 acc: 0.98667 val_loss: 0.22923, val_acc: 0.96000
Epoch [2950/10000], loss: 0.23061 acc: 0.98667 val_loss: 0.22888, val_acc: 0.96000
Epoch [2960/10000], loss: 0.23023 acc: 0.98667 val_loss: 0.22853, val_acc: 0.96000
Epoch [2970/10000], loss: 0.22984 acc: 0.98667 val_loss: 0.22818, val_acc: 0.96000
Epoch [2980/10000], loss: 0.22946 acc: 0.98667 val_loss: 0.22783, val_acc: 0.96000
Epoch [2990/10000], loss: 0.22908 acc: 0.98667 val_loss: 0.22748, val_acc: 0.96000
Epoch [3000/10000], loss: 0.22870 acc: 0.98667 val_loss: 0.22713, val_acc: 0.96000
Epoch [3010/10000], loss: 0.22832 acc: 0.98667 val_loss: 0.22679, val_acc: 0.96000
Epoch [3020/10000], loss: 0.22795 acc: 0.98667 val_loss: 0.22645, val_acc: 0.96000
Epoch [3030/10000], loss: 0.22757 acc: 0.98667 val_loss: 0.22610, val_acc: 0.96000
Epoch [3040/10000], loss: 0.22720 acc: 0.98667 val_loss: 0.22577, val_acc: 0.96000
Epoch [3050/10000], loss: 0.22683 acc: 0.98667 val_loss: 0.22543, val_acc: 0.96000
Epoch [3060/10000], loss: 0.22646 acc: 0.98667 val_loss: 0.22509, val_acc: 0.96000
Epoch [3070/10000], loss: 0.22610 acc: 0.98667 val_loss: 0.22476, val_acc: 0.96000
Epoch [3080/10000], loss: 0.22573 acc: 0.98667 val_loss: 0.22442, val_acc: 0.96000
Epoch [3090/10000], loss: 0.22537 acc: 0.98667 val_loss: 0.22409, val_acc: 0.96000
Epoch [3100/10000], loss: 0.22501 acc: 0.98667 val_loss: 0.22376, val_acc: 0.96000
Epoch [3110/10000], loss: 0.22465 acc: 0.98667 val_loss: 0.22343, val_acc: 0.96000
Epoch [3120/10000], loss: 0.22429 acc: 0.98667 val_loss: 0.22311, val_acc: 0.96000
Epoch [3130/10000], loss: 0.22393 acc: 0.98667 val_loss: 0.22278, val_acc: 0.96000
Epoch [3140/10000], loss: 0.22357 acc: 0.98667 val_loss: 0.22246, val_acc: 0.96000
Epoch [3150/10000], loss: 0.22322 acc: 0.98667 val_loss: 0.22214, val_acc: 0.96000
Epoch [3160/10000], loss: 0.22287 acc: 0.98667 val_loss: 0.22181, val_acc: 0.96000
Epoch [3170/10000], loss: 0.22252 acc: 0.98667 val_loss: 0.22150, val_acc: 0.96000
Epoch [3180/10000], loss: 0.22217 acc: 0.98667 val_loss: 0.22118, val_acc: 0.96000
Epoch [3190/10000], loss: 0.22182 acc: 0.98667 val_loss: 0.22086, val_acc: 0.96000
Epoch [3200/10000], loss: 0.22148 acc: 0.98667 val_loss: 0.22055, val_acc: 0.96000
Epoch [3210/10000], loss: 0.22113 acc: 0.98667 val_loss: 0.22023, val_acc: 0.96000
Epoch [3220/10000], loss: 0.22079 acc: 0.98667 val_loss: 0.21992, val_acc: 0.96000
Epoch [3230/10000], loss: 0.22045 acc: 0.98667 val_loss: 0.21961, val_acc: 0.96000
Epoch [3240/10000], loss: 0.22011 acc: 0.98667 val_loss: 0.21930, val_acc: 0.96000
Epoch [3250/10000], loss: 0.21977 acc: 0.98667 val_loss: 0.21899, val_acc: 0.96000
Epoch [3260/10000], loss: 0.21943 acc: 0.98667 val_loss: 0.21869, val_acc: 0.96000
Epoch [3270/10000], loss: 0.21910 acc: 0.98667 val_loss: 0.21838, val_acc: 0.96000
Epoch [3280/10000], loss: 0.21876 acc: 0.98667 val_loss: 0.21808, val_acc: 0.96000
Epoch [3290/10000], loss: 0.21843 acc: 0.98667 val_loss: 0.21778, val_acc: 0.96000
Epoch [3300/10000], loss: 0.21810 acc: 0.98667 val_loss: 0.21747, val_acc: 0.96000
Epoch [3310/10000], loss: 0.21777 acc: 0.98667 val_loss: 0.21717, val_acc: 0.96000
Epoch [3320/10000], loss: 0.21744 acc: 0.98667 val_loss: 0.21688, val_acc: 0.96000
Epoch [3330/10000], loss: 0.21711 acc: 0.98667 val_loss: 0.21658, val_acc: 0.96000
Epoch [3340/10000], loss: 0.21679 acc: 0.98667 val_loss: 0.21628, val_acc: 0.96000
Epoch [3350/10000], loss: 0.21646 acc: 0.98667 val_loss: 0.21599, val_acc: 0.96000
Epoch [3360/10000], loss: 0.21614 acc: 0.98667 val_loss: 0.21570, val_acc: 0.96000
Epoch [3370/10000], loss: 0.21582 acc: 0.98667 val_loss: 0.21540, val_acc: 0.96000
Epoch [3380/10000], loss: 0.21550 acc: 0.98667 val_loss: 0.21511, val_acc: 0.96000
Epoch [3390/10000], loss: 0.21518 acc: 0.98667 val_loss: 0.21483, val_acc: 0.96000
Epoch [3400/10000], loss: 0.21487 acc: 0.98667 val_loss: 0.21454, val_acc: 0.96000
Epoch [3410/10000], loss: 0.21455 acc: 0.98667 val_loss: 0.21425, val_acc: 0.96000
Epoch [3420/10000], loss: 0.21424 acc: 0.98667 val_loss: 0.21396, val_acc: 0.96000
Epoch [3430/10000], loss: 0.21392 acc: 0.98667 val_loss: 0.21368, val_acc: 0.96000
Epoch [3440/10000], loss: 0.21361 acc: 0.98667 val_loss: 0.21340, val_acc: 0.96000
Epoch [3450/10000], loss: 0.21330 acc: 0.98667 val_loss: 0.21312, val_acc: 0.96000
Epoch [3460/10000], loss: 0.21299 acc: 0.98667 val_loss: 0.21284, val_acc: 0.96000
Epoch [3470/10000], loss: 0.21268 acc: 0.98667 val_loss: 0.21256, val_acc: 0.96000
Epoch [3480/10000], loss: 0.21238 acc: 0.98667 val_loss: 0.21228, val_acc: 0.96000
Epoch [3490/10000], loss: 0.21207 acc: 0.98667 val_loss: 0.21200, val_acc: 0.96000
Epoch [3500/10000], loss: 0.21177 acc: 0.98667 val_loss: 0.21173, val_acc: 0.96000
Epoch [3510/10000], loss: 0.21146 acc: 0.98667 val_loss: 0.21145, val_acc: 0.96000
Epoch [3520/10000], loss: 0.21116 acc: 0.98667 val_loss: 0.21118, val_acc: 0.96000
Epoch [3530/10000], loss: 0.21086 acc: 0.98667 val_loss: 0.21091, val_acc: 0.96000
Epoch [3540/10000], loss: 0.21056 acc: 0.98667 val_loss: 0.21064, val_acc: 0.96000
Epoch [3550/10000], loss: 0.21026 acc: 0.98667 val_loss: 0.21037, val_acc: 0.96000
Epoch [3560/10000], loss: 0.20997 acc: 0.98667 val_loss: 0.21010, val_acc: 0.96000
Epoch [3570/10000], loss: 0.20967 acc: 0.98667 val_loss: 0.20983, val_acc: 0.96000
Epoch [3580/10000], loss: 0.20938 acc: 0.98667 val_loss: 0.20956, val_acc: 0.96000
Epoch [3590/10000], loss: 0.20909 acc: 0.98667 val_loss: 0.20930, val_acc: 0.96000
Epoch [3600/10000], loss: 0.20879 acc: 0.98667 val_loss: 0.20903, val_acc: 0.96000
Epoch [3610/10000], loss: 0.20850 acc: 0.98667 val_loss: 0.20877, val_acc: 0.96000
Epoch [3620/10000], loss: 0.20821 acc: 0.98667 val_loss: 0.20851, val_acc: 0.96000
Epoch [3630/10000], loss: 0.20793 acc: 0.98667 val_loss: 0.20825, val_acc: 0.96000
Epoch [3640/10000], loss: 0.20764 acc: 0.98667 val_loss: 0.20799, val_acc: 0.96000
Epoch [3650/10000], loss: 0.20735 acc: 0.98667 val_loss: 0.20773, val_acc: 0.96000
Epoch [3660/10000], loss: 0.20707 acc: 0.98667 val_loss: 0.20747, val_acc: 0.96000
Epoch [3670/10000], loss: 0.20678 acc: 0.98667 val_loss: 0.20721, val_acc: 0.96000
Epoch [3680/10000], loss: 0.20650 acc: 0.98667 val_loss: 0.20696, val_acc: 0.96000
Epoch [3690/10000], loss: 0.20622 acc: 0.98667 val_loss: 0.20670, val_acc: 0.96000
Epoch [3700/10000], loss: 0.20594 acc: 0.98667 val_loss: 0.20645, val_acc: 0.96000
Epoch [3710/10000], loss: 0.20566 acc: 0.98667 val_loss: 0.20620, val_acc: 0.96000
Epoch [3720/10000], loss: 0.20538 acc: 0.98667 val_loss: 0.20595, val_acc: 0.96000
Epoch [3730/10000], loss: 0.20511 acc: 0.98667 val_loss: 0.20570, val_acc: 0.96000
Epoch [3740/10000], loss: 0.20483 acc: 0.98667 val_loss: 0.20545, val_acc: 0.96000
Epoch [3750/10000], loss: 0.20455 acc: 0.98667 val_loss: 0.20520, val_acc: 0.96000
Epoch [3760/10000], loss: 0.20428 acc: 0.98667 val_loss: 0.20495, val_acc: 0.96000
Epoch [3770/10000], loss: 0.20401 acc: 0.98667 val_loss: 0.20471, val_acc: 0.96000
Epoch [3780/10000], loss: 0.20374 acc: 0.98667 val_loss: 0.20446, val_acc: 0.96000
Epoch [3790/10000], loss: 0.20347 acc: 0.98667 val_loss: 0.20422, val_acc: 0.96000
Epoch [3800/10000], loss: 0.20320 acc: 0.98667 val_loss: 0.20397, val_acc: 0.96000
Epoch [3810/10000], loss: 0.20293 acc: 0.98667 val_loss: 0.20373, val_acc: 0.96000
Epoch [3820/10000], loss: 0.20266 acc: 0.98667 val_loss: 0.20349, val_acc: 0.96000
Epoch [3830/10000], loss: 0.20239 acc: 0.98667 val_loss: 0.20325, val_acc: 0.96000
Epoch [3840/10000], loss: 0.20213 acc: 0.98667 val_loss: 0.20301, val_acc: 0.96000
Epoch [3850/10000], loss: 0.20186 acc: 0.98667 val_loss: 0.20277, val_acc: 0.96000
Epoch [3860/10000], loss: 0.20160 acc: 0.98667 val_loss: 0.20253, val_acc: 0.96000
Epoch [3870/10000], loss: 0.20134 acc: 0.98667 val_loss: 0.20230, val_acc: 0.96000
Epoch [3880/10000], loss: 0.20108 acc: 0.98667 val_loss: 0.20206, val_acc: 0.96000
Epoch [3890/10000], loss: 0.20082 acc: 0.98667 val_loss: 0.20183, val_acc: 0.96000
Epoch [3900/10000], loss: 0.20056 acc: 0.98667 val_loss: 0.20159, val_acc: 0.96000
Epoch [3910/10000], loss: 0.20030 acc: 0.98667 val_loss: 0.20136, val_acc: 0.96000
Epoch [3920/10000], loss: 0.20004 acc: 0.98667 val_loss: 0.20113, val_acc: 0.96000
Epoch [3930/10000], loss: 0.19979 acc: 0.98667 val_loss: 0.20090, val_acc: 0.96000
Epoch [3940/10000], loss: 0.19953 acc: 0.98667 val_loss: 0.20067, val_acc: 0.96000
Epoch [3950/10000], loss: 0.19928 acc: 0.98667 val_loss: 0.20044, val_acc: 0.96000
Epoch [3960/10000], loss: 0.19902 acc: 0.98667 val_loss: 0.20021, val_acc: 0.96000
Epoch [3970/10000], loss: 0.19877 acc: 0.98667 val_loss: 0.19998, val_acc: 0.96000
Epoch [3980/10000], loss: 0.19852 acc: 0.98667 val_loss: 0.19976, val_acc: 0.96000
Epoch [3990/10000], loss: 0.19827 acc: 0.98667 val_loss: 0.19953, val_acc: 0.96000
Epoch [4000/10000], loss: 0.19802 acc: 0.98667 val_loss: 0.19931, val_acc: 0.96000
Epoch [4010/10000], loss: 0.19777 acc: 0.98667 val_loss: 0.19908, val_acc: 0.96000
Epoch [4020/10000], loss: 0.19752 acc: 0.98667 val_loss: 0.19886, val_acc: 0.96000
Epoch [4030/10000], loss: 0.19728 acc: 0.98667 val_loss: 0.19864, val_acc: 0.96000
Epoch [4040/10000], loss: 0.19703 acc: 0.98667 val_loss: 0.19842, val_acc: 0.96000
Epoch [4050/10000], loss: 0.19679 acc: 0.98667 val_loss: 0.19820, val_acc: 0.96000
Epoch [4060/10000], loss: 0.19654 acc: 0.98667 val_loss: 0.19798, val_acc: 0.96000
Epoch [4070/10000], loss: 0.19630 acc: 0.98667 val_loss: 0.19776, val_acc: 0.96000
Epoch [4080/10000], loss: 0.19606 acc: 0.98667 val_loss: 0.19754, val_acc: 0.96000
Epoch [4090/10000], loss: 0.19582 acc: 0.98667 val_loss: 0.19732, val_acc: 0.96000
Epoch [4100/10000], loss: 0.19557 acc: 0.98667 val_loss: 0.19711, val_acc: 0.96000
Epoch [4110/10000], loss: 0.19534 acc: 0.98667 val_loss: 0.19689, val_acc: 0.96000
Epoch [4120/10000], loss: 0.19510 acc: 0.98667 val_loss: 0.19668, val_acc: 0.96000
Epoch [4130/10000], loss: 0.19486 acc: 0.98667 val_loss: 0.19646, val_acc: 0.96000
Epoch [4140/10000], loss: 0.19462 acc: 0.98667 val_loss: 0.19625, val_acc: 0.96000
Epoch [4150/10000], loss: 0.19439 acc: 0.98667 val_loss: 0.19604, val_acc: 0.96000
Epoch [4160/10000], loss: 0.19415 acc: 0.98667 val_loss: 0.19583, val_acc: 0.96000
Epoch [4170/10000], loss: 0.19392 acc: 0.98667 val_loss: 0.19562, val_acc: 0.96000
Epoch [4180/10000], loss: 0.19368 acc: 0.98667 val_loss: 0.19541, val_acc: 0.96000
Epoch [4190/10000], loss: 0.19345 acc: 0.98667 val_loss: 0.19520, val_acc: 0.96000
Epoch [4200/10000], loss: 0.19322 acc: 0.98667 val_loss: 0.19499, val_acc: 0.96000
Epoch [4210/10000], loss: 0.19299 acc: 0.98667 val_loss: 0.19478, val_acc: 0.96000
Epoch [4220/10000], loss: 0.19276 acc: 0.98667 val_loss: 0.19457, val_acc: 0.96000
Epoch [4230/10000], loss: 0.19253 acc: 0.98667 val_loss: 0.19437, val_acc: 0.96000
Epoch [4240/10000], loss: 0.19230 acc: 0.98667 val_loss: 0.19416, val_acc: 0.96000
Epoch [4250/10000], loss: 0.19207 acc: 0.98667 val_loss: 0.19396, val_acc: 0.96000
Epoch [4260/10000], loss: 0.19184 acc: 0.98667 val_loss: 0.19376, val_acc: 0.96000
Epoch [4270/10000], loss: 0.19162 acc: 0.98667 val_loss: 0.19355, val_acc: 0.96000
Epoch [4280/10000], loss: 0.19139 acc: 0.98667 val_loss: 0.19335, val_acc: 0.96000
Epoch [4290/10000], loss: 0.19117 acc: 0.98667 val_loss: 0.19315, val_acc: 0.96000
Epoch [4300/10000], loss: 0.19094 acc: 0.98667 val_loss: 0.19295, val_acc: 0.96000
Epoch [4310/10000], loss: 0.19072 acc: 0.98667 val_loss: 0.19275, val_acc: 0.96000
Epoch [4320/10000], loss: 0.19050 acc: 0.98667 val_loss: 0.19255, val_acc: 0.96000
Epoch [4330/10000], loss: 0.19028 acc: 0.98667 val_loss: 0.19235, val_acc: 0.96000
Epoch [4340/10000], loss: 0.19006 acc: 0.98667 val_loss: 0.19215, val_acc: 0.96000
Epoch [4350/10000], loss: 0.18984 acc: 0.98667 val_loss: 0.19196, val_acc: 0.96000
Epoch [4360/10000], loss: 0.18962 acc: 0.98667 val_loss: 0.19176, val_acc: 0.96000
Epoch [4370/10000], loss: 0.18940 acc: 0.98667 val_loss: 0.19156, val_acc: 0.96000
Epoch [4380/10000], loss: 0.18918 acc: 0.98667 val_loss: 0.19137, val_acc: 0.96000
Epoch [4390/10000], loss: 0.18897 acc: 0.98667 val_loss: 0.19118, val_acc: 0.96000
Epoch [4400/10000], loss: 0.18875 acc: 0.98667 val_loss: 0.19098, val_acc: 0.96000
Epoch [4410/10000], loss: 0.18853 acc: 0.98667 val_loss: 0.19079, val_acc: 0.96000
Epoch [4420/10000], loss: 0.18832 acc: 0.98667 val_loss: 0.19060, val_acc: 0.96000
Epoch [4430/10000], loss: 0.18811 acc: 0.98667 val_loss: 0.19041, val_acc: 0.96000
Epoch [4440/10000], loss: 0.18789 acc: 0.98667 val_loss: 0.19021, val_acc: 0.96000
Epoch [4450/10000], loss: 0.18768 acc: 0.98667 val_loss: 0.19002, val_acc: 0.96000
Epoch [4460/10000], loss: 0.18747 acc: 0.98667 val_loss: 0.18984, val_acc: 0.96000
Epoch [4470/10000], loss: 0.18726 acc: 0.98667 val_loss: 0.18965, val_acc: 0.96000
Epoch [4480/10000], loss: 0.18705 acc: 0.98667 val_loss: 0.18946, val_acc: 0.96000
Epoch [4490/10000], loss: 0.18684 acc: 0.98667 val_loss: 0.18927, val_acc: 0.96000
Epoch [4500/10000], loss: 0.18663 acc: 0.98667 val_loss: 0.18908, val_acc: 0.96000
Epoch [4510/10000], loss: 0.18642 acc: 0.98667 val_loss: 0.18890, val_acc: 0.96000
Epoch [4520/10000], loss: 0.18622 acc: 0.98667 val_loss: 0.18871, val_acc: 0.96000
Epoch [4530/10000], loss: 0.18601 acc: 0.98667 val_loss: 0.18853, val_acc: 0.96000
Epoch [4540/10000], loss: 0.18580 acc: 0.98667 val_loss: 0.18834, val_acc: 0.96000
Epoch [4550/10000], loss: 0.18560 acc: 0.98667 val_loss: 0.18816, val_acc: 0.96000
Epoch [4560/10000], loss: 0.18539 acc: 0.98667 val_loss: 0.18798, val_acc: 0.96000
Epoch [4570/10000], loss: 0.18519 acc: 0.98667 val_loss: 0.18780, val_acc: 0.96000
Epoch [4580/10000], loss: 0.18499 acc: 0.98667 val_loss: 0.18762, val_acc: 0.96000
Epoch [4590/10000], loss: 0.18478 acc: 0.98667 val_loss: 0.18743, val_acc: 0.96000
Epoch [4600/10000], loss: 0.18458 acc: 0.98667 val_loss: 0.18725, val_acc: 0.96000
Epoch [4610/10000], loss: 0.18438 acc: 0.98667 val_loss: 0.18707, val_acc: 0.96000
Epoch [4620/10000], loss: 0.18418 acc: 0.98667 val_loss: 0.18690, val_acc: 0.96000
Epoch [4630/10000], loss: 0.18398 acc: 0.98667 val_loss: 0.18672, val_acc: 0.96000
Epoch [4640/10000], loss: 0.18378 acc: 0.98667 val_loss: 0.18654, val_acc: 0.96000
Epoch [4650/10000], loss: 0.18358 acc: 0.98667 val_loss: 0.18636, val_acc: 0.96000
Epoch [4660/10000], loss: 0.18339 acc: 0.98667 val_loss: 0.18619, val_acc: 0.96000
Epoch [4670/10000], loss: 0.18319 acc: 0.98667 val_loss: 0.18601, val_acc: 0.96000
Epoch [4680/10000], loss: 0.18299 acc: 0.98667 val_loss: 0.18583, val_acc: 0.96000
Epoch [4690/10000], loss: 0.18280 acc: 0.98667 val_loss: 0.18566, val_acc: 0.96000
Epoch [4700/10000], loss: 0.18260 acc: 0.98667 val_loss: 0.18549, val_acc: 0.96000
Epoch [4710/10000], loss: 0.18241 acc: 0.98667 val_loss: 0.18531, val_acc: 0.96000
Epoch [4720/10000], loss: 0.18221 acc: 0.98667 val_loss: 0.18514, val_acc: 0.96000
Epoch [4730/10000], loss: 0.18202 acc: 0.98667 val_loss: 0.18497, val_acc: 0.96000
Epoch [4740/10000], loss: 0.18183 acc: 0.98667 val_loss: 0.18479, val_acc: 0.96000
Epoch [4750/10000], loss: 0.18164 acc: 0.98667 val_loss: 0.18462, val_acc: 0.96000
Epoch [4760/10000], loss: 0.18144 acc: 0.98667 val_loss: 0.18445, val_acc: 0.96000
Epoch [4770/10000], loss: 0.18125 acc: 0.98667 val_loss: 0.18428, val_acc: 0.96000
Epoch [4780/10000], loss: 0.18106 acc: 0.98667 val_loss: 0.18411, val_acc: 0.96000
Epoch [4790/10000], loss: 0.18087 acc: 0.98667 val_loss: 0.18395, val_acc: 0.96000
Epoch [4800/10000], loss: 0.18068 acc: 0.98667 val_loss: 0.18378, val_acc: 0.96000
Epoch [4810/10000], loss: 0.18050 acc: 0.98667 val_loss: 0.18361, val_acc: 0.96000
Epoch [4820/10000], loss: 0.18031 acc: 0.98667 val_loss: 0.18344, val_acc: 0.96000
Epoch [4830/10000], loss: 0.18012 acc: 0.98667 val_loss: 0.18328, val_acc: 0.96000
Epoch [4840/10000], loss: 0.17994 acc: 0.98667 val_loss: 0.18311, val_acc: 0.96000
Epoch [4850/10000], loss: 0.17975 acc: 0.98667 val_loss: 0.18294, val_acc: 0.96000
Epoch [4860/10000], loss: 0.17956 acc: 0.98667 val_loss: 0.18278, val_acc: 0.96000
Epoch [4870/10000], loss: 0.17938 acc: 0.98667 val_loss: 0.18261, val_acc: 0.96000
Epoch [4880/10000], loss: 0.17920 acc: 0.98667 val_loss: 0.18245, val_acc: 0.96000
Epoch [4890/10000], loss: 0.17901 acc: 0.98667 val_loss: 0.18229, val_acc: 0.96000
Epoch [4900/10000], loss: 0.17883 acc: 0.98667 val_loss: 0.18212, val_acc: 0.96000
Epoch [4910/10000], loss: 0.17865 acc: 0.98667 val_loss: 0.18196, val_acc: 0.96000
Epoch [4920/10000], loss: 0.17846 acc: 0.98667 val_loss: 0.18180, val_acc: 0.96000
Epoch [4930/10000], loss: 0.17828 acc: 0.98667 val_loss: 0.18164, val_acc: 0.96000
Epoch [4940/10000], loss: 0.17810 acc: 0.98667 val_loss: 0.18148, val_acc: 0.96000
Epoch [4950/10000], loss: 0.17792 acc: 0.98667 val_loss: 0.18132, val_acc: 0.96000
Epoch [4960/10000], loss: 0.17774 acc: 0.98667 val_loss: 0.18116, val_acc: 0.96000
Epoch [4970/10000], loss: 0.17756 acc: 0.98667 val_loss: 0.18100, val_acc: 0.96000
Epoch [4980/10000], loss: 0.17739 acc: 0.98667 val_loss: 0.18084, val_acc: 0.96000
Epoch [4990/10000], loss: 0.17721 acc: 0.98667 val_loss: 0.18068, val_acc: 0.96000
Epoch [5000/10000], loss: 0.17703 acc: 0.98667 val_loss: 0.18053, val_acc: 0.96000
Epoch [5010/10000], loss: 0.17685 acc: 0.98667 val_loss: 0.18037, val_acc: 0.96000
Epoch [5020/10000], loss: 0.17668 acc: 0.98667 val_loss: 0.18021, val_acc: 0.96000
Epoch [5030/10000], loss: 0.17650 acc: 0.98667 val_loss: 0.18006, val_acc: 0.96000
Epoch [5040/10000], loss: 0.17633 acc: 0.98667 val_loss: 0.17990, val_acc: 0.96000
Epoch [5050/10000], loss: 0.17615 acc: 0.98667 val_loss: 0.17975, val_acc: 0.96000
Epoch [5060/10000], loss: 0.17598 acc: 0.98667 val_loss: 0.17959, val_acc: 0.96000
Epoch [5070/10000], loss: 0.17581 acc: 0.98667 val_loss: 0.17944, val_acc: 0.96000
Epoch [5080/10000], loss: 0.17563 acc: 0.98667 val_loss: 0.17928, val_acc: 0.96000
Epoch [5090/10000], loss: 0.17546 acc: 0.98667 val_loss: 0.17913, val_acc: 0.96000
Epoch [5100/10000], loss: 0.17529 acc: 0.98667 val_loss: 0.17898, val_acc: 0.96000
Epoch [5110/10000], loss: 0.17512 acc: 0.98667 val_loss: 0.17883, val_acc: 0.96000
Epoch [5120/10000], loss: 0.17495 acc: 0.98667 val_loss: 0.17867, val_acc: 0.96000
Epoch [5130/10000], loss: 0.17478 acc: 0.98667 val_loss: 0.17852, val_acc: 0.96000
Epoch [5140/10000], loss: 0.17461 acc: 0.98667 val_loss: 0.17837, val_acc: 0.96000
Epoch [5150/10000], loss: 0.17444 acc: 0.98667 val_loss: 0.17822, val_acc: 0.96000
Epoch [5160/10000], loss: 0.17427 acc: 0.98667 val_loss: 0.17807, val_acc: 0.96000
Epoch [5170/10000], loss: 0.17410 acc: 0.98667 val_loss: 0.17792, val_acc: 0.96000
Epoch [5180/10000], loss: 0.17393 acc: 0.98667 val_loss: 0.17778, val_acc: 0.96000
Epoch [5190/10000], loss: 0.17377 acc: 0.98667 val_loss: 0.17763, val_acc: 0.96000
Epoch [5200/10000], loss: 0.17360 acc: 0.98667 val_loss: 0.17748, val_acc: 0.96000
Epoch [5210/10000], loss: 0.17343 acc: 0.98667 val_loss: 0.17733, val_acc: 0.96000
Epoch [5220/10000], loss: 0.17327 acc: 0.98667 val_loss: 0.17719, val_acc: 0.96000
Epoch [5230/10000], loss: 0.17310 acc: 0.98667 val_loss: 0.17704, val_acc: 0.96000
Epoch [5240/10000], loss: 0.17294 acc: 0.98667 val_loss: 0.17689, val_acc: 0.96000
Epoch [5250/10000], loss: 0.17277 acc: 0.98667 val_loss: 0.17675, val_acc: 0.96000
Epoch [5260/10000], loss: 0.17261 acc: 0.98667 val_loss: 0.17660, val_acc: 0.96000
Epoch [5270/10000], loss: 0.17245 acc: 0.98667 val_loss: 0.17646, val_acc: 0.96000
Epoch [5280/10000], loss: 0.17229 acc: 0.98667 val_loss: 0.17631, val_acc: 0.96000
Epoch [5290/10000], loss: 0.17212 acc: 0.98667 val_loss: 0.17617, val_acc: 0.96000
Epoch [5300/10000], loss: 0.17196 acc: 0.98667 val_loss: 0.17603, val_acc: 0.96000
Epoch [5310/10000], loss: 0.17180 acc: 0.98667 val_loss: 0.17589, val_acc: 0.96000
Epoch [5320/10000], loss: 0.17164 acc: 0.98667 val_loss: 0.17574, val_acc: 0.96000
Epoch [5330/10000], loss: 0.17148 acc: 0.98667 val_loss: 0.17560, val_acc: 0.96000
Epoch [5340/10000], loss: 0.17132 acc: 0.98667 val_loss: 0.17546, val_acc: 0.96000
Epoch [5350/10000], loss: 0.17116 acc: 0.98667 val_loss: 0.17532, val_acc: 0.96000
Epoch [5360/10000], loss: 0.17100 acc: 0.98667 val_loss: 0.17518, val_acc: 0.96000
Epoch [5370/10000], loss: 0.17084 acc: 0.98667 val_loss: 0.17504, val_acc: 0.96000
Epoch [5380/10000], loss: 0.17068 acc: 0.98667 val_loss: 0.17490, val_acc: 0.96000
Epoch [5390/10000], loss: 0.17053 acc: 0.98667 val_loss: 0.17476, val_acc: 0.96000
Epoch [5400/10000], loss: 0.17037 acc: 0.98667 val_loss: 0.17462, val_acc: 0.96000
Epoch [5410/10000], loss: 0.17021 acc: 0.98667 val_loss: 0.17448, val_acc: 0.96000
Epoch [5420/10000], loss: 0.17006 acc: 0.98667 val_loss: 0.17434, val_acc: 0.96000
Epoch [5430/10000], loss: 0.16990 acc: 0.98667 val_loss: 0.17421, val_acc: 0.96000
Epoch [5440/10000], loss: 0.16975 acc: 0.98667 val_loss: 0.17407, val_acc: 0.96000
Epoch [5450/10000], loss: 0.16959 acc: 0.98667 val_loss: 0.17393, val_acc: 0.96000
Epoch [5460/10000], loss: 0.16944 acc: 0.98667 val_loss: 0.17380, val_acc: 0.96000
Epoch [5470/10000], loss: 0.16928 acc: 0.98667 val_loss: 0.17366, val_acc: 0.96000
Epoch [5480/10000], loss: 0.16913 acc: 0.98667 val_loss: 0.17352, val_acc: 0.96000
Epoch [5490/10000], loss: 0.16898 acc: 0.98667 val_loss: 0.17339, val_acc: 0.96000
Epoch [5500/10000], loss: 0.16883 acc: 0.98667 val_loss: 0.17325, val_acc: 0.96000
Epoch [5510/10000], loss: 0.16867 acc: 0.98667 val_loss: 0.17312, val_acc: 0.96000
Epoch [5520/10000], loss: 0.16852 acc: 0.98667 val_loss: 0.17299, val_acc: 0.96000
Epoch [5530/10000], loss: 0.16837 acc: 0.98667 val_loss: 0.17285, val_acc: 0.96000
Epoch [5540/10000], loss: 0.16822 acc: 0.98667 val_loss: 0.17272, val_acc: 0.96000
Epoch [5550/10000], loss: 0.16807 acc: 0.98667 val_loss: 0.17259, val_acc: 0.96000
Epoch [5560/10000], loss: 0.16792 acc: 0.98667 val_loss: 0.17246, val_acc: 0.96000
Epoch [5570/10000], loss: 0.16777 acc: 0.98667 val_loss: 0.17232, val_acc: 0.96000
Epoch [5580/10000], loss: 0.16762 acc: 0.98667 val_loss: 0.17219, val_acc: 0.96000
Epoch [5590/10000], loss: 0.16747 acc: 0.98667 val_loss: 0.17206, val_acc: 0.96000
Epoch [5600/10000], loss: 0.16732 acc: 0.98667 val_loss: 0.17193, val_acc: 0.96000
Epoch [5610/10000], loss: 0.16718 acc: 0.98667 val_loss: 0.17180, val_acc: 0.96000
Epoch [5620/10000], loss: 0.16703 acc: 0.98667 val_loss: 0.17167, val_acc: 0.96000
Epoch [5630/10000], loss: 0.16688 acc: 0.98667 val_loss: 0.17154, val_acc: 0.96000
Epoch [5640/10000], loss: 0.16674 acc: 0.98667 val_loss: 0.17141, val_acc: 0.96000
Epoch [5650/10000], loss: 0.16659 acc: 0.98667 val_loss: 0.17128, val_acc: 0.96000
Epoch [5660/10000], loss: 0.16644 acc: 0.98667 val_loss: 0.17115, val_acc: 0.96000
Epoch [5670/10000], loss: 0.16630 acc: 0.98667 val_loss: 0.17103, val_acc: 0.96000
Epoch [5680/10000], loss: 0.16615 acc: 0.98667 val_loss: 0.17090, val_acc: 0.96000
Epoch [5690/10000], loss: 0.16601 acc: 0.98667 val_loss: 0.17077, val_acc: 0.96000
Epoch [5700/10000], loss: 0.16587 acc: 0.98667 val_loss: 0.17064, val_acc: 0.96000
Epoch [5710/10000], loss: 0.16572 acc: 0.98667 val_loss: 0.17052, val_acc: 0.96000
Epoch [5720/10000], loss: 0.16558 acc: 0.98667 val_loss: 0.17039, val_acc: 0.96000
Epoch [5730/10000], loss: 0.16544 acc: 0.98667 val_loss: 0.17027, val_acc: 0.96000
Epoch [5740/10000], loss: 0.16529 acc: 0.98667 val_loss: 0.17014, val_acc: 0.96000
Epoch [5750/10000], loss: 0.16515 acc: 0.98667 val_loss: 0.17001, val_acc: 0.96000
Epoch [5760/10000], loss: 0.16501 acc: 0.98667 val_loss: 0.16989, val_acc: 0.96000
Epoch [5770/10000], loss: 0.16487 acc: 0.98667 val_loss: 0.16977, val_acc: 0.96000
Epoch [5780/10000], loss: 0.16473 acc: 0.98667 val_loss: 0.16964, val_acc: 0.96000
Epoch [5790/10000], loss: 0.16459 acc: 0.98667 val_loss: 0.16952, val_acc: 0.96000
Epoch [5800/10000], loss: 0.16445 acc: 0.98667 val_loss: 0.16939, val_acc: 0.96000
Epoch [5810/10000], loss: 0.16431 acc: 0.98667 val_loss: 0.16927, val_acc: 0.96000
Epoch [5820/10000], loss: 0.16417 acc: 0.98667 val_loss: 0.16915, val_acc: 0.96000
Epoch [5830/10000], loss: 0.16403 acc: 0.98667 val_loss: 0.16903, val_acc: 0.96000
Epoch [5840/10000], loss: 0.16389 acc: 0.98667 val_loss: 0.16891, val_acc: 0.96000
Epoch [5850/10000], loss: 0.16375 acc: 0.98667 val_loss: 0.16878, val_acc: 0.96000
Epoch [5860/10000], loss: 0.16361 acc: 0.98667 val_loss: 0.16866, val_acc: 0.96000
Epoch [5870/10000], loss: 0.16348 acc: 0.98667 val_loss: 0.16854, val_acc: 0.96000
Epoch [5880/10000], loss: 0.16334 acc: 0.98667 val_loss: 0.16842, val_acc: 0.96000
Epoch [5890/10000], loss: 0.16320 acc: 0.98667 val_loss: 0.16830, val_acc: 0.96000
Epoch [5900/10000], loss: 0.16307 acc: 0.98667 val_loss: 0.16818, val_acc: 0.96000
Epoch [5910/10000], loss: 0.16293 acc: 0.98667 val_loss: 0.16806, val_acc: 0.96000
Epoch [5920/10000], loss: 0.16280 acc: 0.98667 val_loss: 0.16794, val_acc: 0.96000
Epoch [5930/10000], loss: 0.16266 acc: 0.98667 val_loss: 0.16782, val_acc: 0.96000
Epoch [5940/10000], loss: 0.16253 acc: 0.98667 val_loss: 0.16771, val_acc: 0.96000
Epoch [5950/10000], loss: 0.16239 acc: 0.98667 val_loss: 0.16759, val_acc: 0.96000
Epoch [5960/10000], loss: 0.16226 acc: 0.98667 val_loss: 0.16747, val_acc: 0.96000
Epoch [5970/10000], loss: 0.16212 acc: 0.98667 val_loss: 0.16735, val_acc: 0.96000
Epoch [5980/10000], loss: 0.16199 acc: 0.98667 val_loss: 0.16724, val_acc: 0.96000
Epoch [5990/10000], loss: 0.16186 acc: 0.98667 val_loss: 0.16712, val_acc: 0.96000
Epoch [6000/10000], loss: 0.16172 acc: 0.98667 val_loss: 0.16700, val_acc: 0.96000
Epoch [6010/10000], loss: 0.16159 acc: 0.98667 val_loss: 0.16689, val_acc: 0.96000
Epoch [6020/10000], loss: 0.16146 acc: 0.98667 val_loss: 0.16677, val_acc: 0.96000
Epoch [6030/10000], loss: 0.16133 acc: 0.98667 val_loss: 0.16665, val_acc: 0.96000
Epoch [6040/10000], loss: 0.16120 acc: 0.98667 val_loss: 0.16654, val_acc: 0.96000
Epoch [6050/10000], loss: 0.16107 acc: 0.98667 val_loss: 0.16642, val_acc: 0.96000
Epoch [6060/10000], loss: 0.16093 acc: 0.98667 val_loss: 0.16631, val_acc: 0.96000
Epoch [6070/10000], loss: 0.16080 acc: 0.98667 val_loss: 0.16620, val_acc: 0.96000
Epoch [6080/10000], loss: 0.16067 acc: 0.98667 val_loss: 0.16608, val_acc: 0.96000
Epoch [6090/10000], loss: 0.16055 acc: 0.98667 val_loss: 0.16597, val_acc: 0.96000
Epoch [6100/10000], loss: 0.16042 acc: 0.98667 val_loss: 0.16585, val_acc: 0.96000
Epoch [6110/10000], loss: 0.16029 acc: 0.98667 val_loss: 0.16574, val_acc: 0.96000
Epoch [6120/10000], loss: 0.16016 acc: 0.98667 val_loss: 0.16563, val_acc: 0.96000
Epoch [6130/10000], loss: 0.16003 acc: 0.98667 val_loss: 0.16552, val_acc: 0.96000
Epoch [6140/10000], loss: 0.15990 acc: 0.98667 val_loss: 0.16540, val_acc: 0.96000
Epoch [6150/10000], loss: 0.15978 acc: 0.98667 val_loss: 0.16529, val_acc: 0.96000
Epoch [6160/10000], loss: 0.15965 acc: 0.98667 val_loss: 0.16518, val_acc: 0.96000
Epoch [6170/10000], loss: 0.15952 acc: 0.98667 val_loss: 0.16507, val_acc: 0.96000
Epoch [6180/10000], loss: 0.15939 acc: 0.98667 val_loss: 0.16496, val_acc: 0.96000
Epoch [6190/10000], loss: 0.15927 acc: 0.98667 val_loss: 0.16485, val_acc: 0.96000
Epoch [6200/10000], loss: 0.15914 acc: 0.98667 val_loss: 0.16474, val_acc: 0.96000
Epoch [6210/10000], loss: 0.15902 acc: 0.98667 val_loss: 0.16463, val_acc: 0.96000
Epoch [6220/10000], loss: 0.15889 acc: 0.98667 val_loss: 0.16452, val_acc: 0.96000
Epoch [6230/10000], loss: 0.15877 acc: 0.98667 val_loss: 0.16441, val_acc: 0.96000
Epoch [6240/10000], loss: 0.15864 acc: 0.98667 val_loss: 0.16430, val_acc: 0.96000
Epoch [6250/10000], loss: 0.15852 acc: 0.98667 val_loss: 0.16419, val_acc: 0.96000
Epoch [6260/10000], loss: 0.15839 acc: 0.98667 val_loss: 0.16408, val_acc: 0.96000
Epoch [6270/10000], loss: 0.15827 acc: 0.98667 val_loss: 0.16398, val_acc: 0.96000
Epoch [6280/10000], loss: 0.15815 acc: 0.98667 val_loss: 0.16387, val_acc: 0.96000
Epoch [6290/10000], loss: 0.15802 acc: 0.98667 val_loss: 0.16376, val_acc: 0.96000
Epoch [6300/10000], loss: 0.15790 acc: 0.98667 val_loss: 0.16365, val_acc: 0.96000
Epoch [6310/10000], loss: 0.15778 acc: 0.98667 val_loss: 0.16355, val_acc: 0.96000
Epoch [6320/10000], loss: 0.15766 acc: 0.98667 val_loss: 0.16344, val_acc: 0.96000
Epoch [6330/10000], loss: 0.15754 acc: 0.98667 val_loss: 0.16333, val_acc: 0.96000
Epoch [6340/10000], loss: 0.15741 acc: 0.98667 val_loss: 0.16323, val_acc: 0.96000
Epoch [6350/10000], loss: 0.15729 acc: 0.98667 val_loss: 0.16312, val_acc: 0.96000
Epoch [6360/10000], loss: 0.15717 acc: 0.98667 val_loss: 0.16302, val_acc: 0.96000
Epoch [6370/10000], loss: 0.15705 acc: 0.98667 val_loss: 0.16291, val_acc: 0.96000
Epoch [6380/10000], loss: 0.15693 acc: 0.98667 val_loss: 0.16281, val_acc: 0.96000
Epoch [6390/10000], loss: 0.15681 acc: 0.98667 val_loss: 0.16270, val_acc: 0.96000
Epoch [6400/10000], loss: 0.15669 acc: 0.98667 val_loss: 0.16260, val_acc: 0.96000
Epoch [6410/10000], loss: 0.15657 acc: 0.98667 val_loss: 0.16249, val_acc: 0.96000
Epoch [6420/10000], loss: 0.15645 acc: 0.98667 val_loss: 0.16239, val_acc: 0.96000
Epoch [6430/10000], loss: 0.15634 acc: 0.98667 val_loss: 0.16228, val_acc: 0.96000
Epoch [6440/10000], loss: 0.15622 acc: 0.98667 val_loss: 0.16218, val_acc: 0.96000
Epoch [6450/10000], loss: 0.15610 acc: 0.98667 val_loss: 0.16208, val_acc: 0.96000
Epoch [6460/10000], loss: 0.15598 acc: 0.98667 val_loss: 0.16198, val_acc: 0.96000
Epoch [6470/10000], loss: 0.15586 acc: 0.98667 val_loss: 0.16187, val_acc: 0.96000
Epoch [6480/10000], loss: 0.15575 acc: 0.98667 val_loss: 0.16177, val_acc: 0.96000
Epoch [6490/10000], loss: 0.15563 acc: 0.98667 val_loss: 0.16167, val_acc: 0.96000
Epoch [6500/10000], loss: 0.15551 acc: 0.98667 val_loss: 0.16157, val_acc: 0.96000
Epoch [6510/10000], loss: 0.15540 acc: 0.98667 val_loss: 0.16147, val_acc: 0.96000
Epoch [6520/10000], loss: 0.15528 acc: 0.98667 val_loss: 0.16136, val_acc: 0.96000
Epoch [6530/10000], loss: 0.15516 acc: 0.98667 val_loss: 0.16126, val_acc: 0.96000
Epoch [6540/10000], loss: 0.15505 acc: 0.98667 val_loss: 0.16116, val_acc: 0.96000
Epoch [6550/10000], loss: 0.15493 acc: 0.98667 val_loss: 0.16106, val_acc: 0.96000
Epoch [6560/10000], loss: 0.15482 acc: 0.98667 val_loss: 0.16096, val_acc: 0.96000
Epoch [6570/10000], loss: 0.15470 acc: 0.98667 val_loss: 0.16086, val_acc: 0.96000
Epoch [6580/10000], loss: 0.15459 acc: 0.98667 val_loss: 0.16076, val_acc: 0.96000
Epoch [6590/10000], loss: 0.15448 acc: 0.98667 val_loss: 0.16066, val_acc: 0.96000
Epoch [6600/10000], loss: 0.15436 acc: 0.98667 val_loss: 0.16056, val_acc: 0.96000
Epoch [6610/10000], loss: 0.15425 acc: 0.98667 val_loss: 0.16047, val_acc: 0.96000
Epoch [6620/10000], loss: 0.15414 acc: 0.98667 val_loss: 0.16037, val_acc: 0.96000
Epoch [6630/10000], loss: 0.15402 acc: 0.98667 val_loss: 0.16027, val_acc: 0.96000
Epoch [6640/10000], loss: 0.15391 acc: 0.98667 val_loss: 0.16017, val_acc: 0.96000
Epoch [6650/10000], loss: 0.15380 acc: 0.98667 val_loss: 0.16007, val_acc: 0.96000
Epoch [6660/10000], loss: 0.15369 acc: 0.98667 val_loss: 0.15997, val_acc: 0.96000
Epoch [6670/10000], loss: 0.15357 acc: 0.98667 val_loss: 0.15988, val_acc: 0.96000
Epoch [6680/10000], loss: 0.15346 acc: 0.98667 val_loss: 0.15978, val_acc: 0.96000
Epoch [6690/10000], loss: 0.15335 acc: 0.98667 val_loss: 0.15968, val_acc: 0.96000
Epoch [6700/10000], loss: 0.15324 acc: 0.98667 val_loss: 0.15959, val_acc: 0.96000
Epoch [6710/10000], loss: 0.15313 acc: 0.98667 val_loss: 0.15949, val_acc: 0.96000
Epoch [6720/10000], loss: 0.15302 acc: 0.98667 val_loss: 0.15939, val_acc: 0.96000
Epoch [6730/10000], loss: 0.15291 acc: 0.98667 val_loss: 0.15930, val_acc: 0.96000
Epoch [6740/10000], loss: 0.15280 acc: 0.98667 val_loss: 0.15920, val_acc: 0.96000
Epoch [6750/10000], loss: 0.15269 acc: 0.98667 val_loss: 0.15911, val_acc: 0.96000
Epoch [6760/10000], loss: 0.15258 acc: 0.98667 val_loss: 0.15901, val_acc: 0.96000
Epoch [6770/10000], loss: 0.15247 acc: 0.98667 val_loss: 0.15892, val_acc: 0.96000
Epoch [6780/10000], loss: 0.15236 acc: 0.98667 val_loss: 0.15882, val_acc: 0.96000
Epoch [6790/10000], loss: 0.15225 acc: 0.98667 val_loss: 0.15873, val_acc: 0.96000
Epoch [6800/10000], loss: 0.15214 acc: 0.98667 val_loss: 0.15863, val_acc: 0.96000
Epoch [6810/10000], loss: 0.15204 acc: 0.98667 val_loss: 0.15854, val_acc: 0.96000
Epoch [6820/10000], loss: 0.15193 acc: 0.98667 val_loss: 0.15845, val_acc: 0.96000
Epoch [6830/10000], loss: 0.15182 acc: 0.98667 val_loss: 0.15835, val_acc: 0.96000
Epoch [6840/10000], loss: 0.15171 acc: 0.98667 val_loss: 0.15826, val_acc: 0.96000
Epoch [6850/10000], loss: 0.15161 acc: 0.98667 val_loss: 0.15817, val_acc: 0.96000
Epoch [6860/10000], loss: 0.15150 acc: 0.98667 val_loss: 0.15807, val_acc: 0.96000
Epoch [6870/10000], loss: 0.15139 acc: 0.98667 val_loss: 0.15798, val_acc: 0.96000
Epoch [6880/10000], loss: 0.15129 acc: 0.98667 val_loss: 0.15789, val_acc: 0.96000
Epoch [6890/10000], loss: 0.15118 acc: 0.98667 val_loss: 0.15780, val_acc: 0.96000
Epoch [6900/10000], loss: 0.15108 acc: 0.98667 val_loss: 0.15771, val_acc: 0.96000
Epoch [6910/10000], loss: 0.15097 acc: 0.98667 val_loss: 0.15761, val_acc: 0.96000
Epoch [6920/10000], loss: 0.15086 acc: 0.98667 val_loss: 0.15752, val_acc: 0.96000
Epoch [6930/10000], loss: 0.15076 acc: 0.98667 val_loss: 0.15743, val_acc: 0.96000
Epoch [6940/10000], loss: 0.15065 acc: 0.98667 val_loss: 0.15734, val_acc: 0.96000
Epoch [6950/10000], loss: 0.15055 acc: 0.98667 val_loss: 0.15725, val_acc: 0.96000
Epoch [6960/10000], loss: 0.15045 acc: 0.98667 val_loss: 0.15716, val_acc: 0.96000
Epoch [6970/10000], loss: 0.15034 acc: 0.98667 val_loss: 0.15707, val_acc: 0.96000
Epoch [6980/10000], loss: 0.15024 acc: 0.98667 val_loss: 0.15698, val_acc: 0.96000
Epoch [6990/10000], loss: 0.15013 acc: 0.98667 val_loss: 0.15689, val_acc: 0.96000
Epoch [7000/10000], loss: 0.15003 acc: 0.98667 val_loss: 0.15680, val_acc: 0.96000
Epoch [7010/10000], loss: 0.14993 acc: 0.98667 val_loss: 0.15671, val_acc: 0.96000
Epoch [7020/10000], loss: 0.14983 acc: 0.98667 val_loss: 0.15662, val_acc: 0.96000
Epoch [7030/10000], loss: 0.14972 acc: 0.98667 val_loss: 0.15653, val_acc: 0.96000
Epoch [7040/10000], loss: 0.14962 acc: 0.98667 val_loss: 0.15644, val_acc: 0.96000
Epoch [7050/10000], loss: 0.14952 acc: 0.98667 val_loss: 0.15636, val_acc: 0.96000
Epoch [7060/10000], loss: 0.14942 acc: 0.98667 val_loss: 0.15627, val_acc: 0.96000
Epoch [7070/10000], loss: 0.14931 acc: 0.98667 val_loss: 0.15618, val_acc: 0.96000
Epoch [7080/10000], loss: 0.14921 acc: 0.98667 val_loss: 0.15609, val_acc: 0.96000
Epoch [7090/10000], loss: 0.14911 acc: 0.98667 val_loss: 0.15600, val_acc: 0.96000
Epoch [7100/10000], loss: 0.14901 acc: 0.98667 val_loss: 0.15592, val_acc: 0.96000
Epoch [7110/10000], loss: 0.14891 acc: 0.98667 val_loss: 0.15583, val_acc: 0.96000
Epoch [7120/10000], loss: 0.14881 acc: 0.98667 val_loss: 0.15574, val_acc: 0.96000
Epoch [7130/10000], loss: 0.14871 acc: 0.98667 val_loss: 0.15565, val_acc: 0.96000
Epoch [7140/10000], loss: 0.14861 acc: 0.98667 val_loss: 0.15557, val_acc: 0.96000
Epoch [7150/10000], loss: 0.14851 acc: 0.98667 val_loss: 0.15548, val_acc: 0.96000
Epoch [7160/10000], loss: 0.14841 acc: 0.98667 val_loss: 0.15540, val_acc: 0.96000
Epoch [7170/10000], loss: 0.14831 acc: 0.98667 val_loss: 0.15531, val_acc: 0.96000
Epoch [7180/10000], loss: 0.14821 acc: 0.98667 val_loss: 0.15522, val_acc: 0.96000
Epoch [7190/10000], loss: 0.14811 acc: 0.98667 val_loss: 0.15514, val_acc: 0.96000
Epoch [7200/10000], loss: 0.14801 acc: 0.98667 val_loss: 0.15505, val_acc: 0.96000
Epoch [7210/10000], loss: 0.14792 acc: 0.98667 val_loss: 0.15497, val_acc: 0.96000
Epoch [7220/10000], loss: 0.14782 acc: 0.98667 val_loss: 0.15488, val_acc: 0.96000
Epoch [7230/10000], loss: 0.14772 acc: 0.98667 val_loss: 0.15480, val_acc: 0.96000
Epoch [7240/10000], loss: 0.14762 acc: 0.98667 val_loss: 0.15471, val_acc: 0.96000
Epoch [7250/10000], loss: 0.14752 acc: 0.98667 val_loss: 0.15463, val_acc: 0.96000
Epoch [7260/10000], loss: 0.14743 acc: 0.98667 val_loss: 0.15455, val_acc: 0.96000
Epoch [7270/10000], loss: 0.14733 acc: 0.98667 val_loss: 0.15446, val_acc: 0.96000
Epoch [7280/10000], loss: 0.14723 acc: 0.98667 val_loss: 0.15438, val_acc: 0.96000
Epoch [7290/10000], loss: 0.14714 acc: 0.98667 val_loss: 0.15429, val_acc: 0.96000
Epoch [7300/10000], loss: 0.14704 acc: 0.98667 val_loss: 0.15421, val_acc: 0.96000
Epoch [7310/10000], loss: 0.14694 acc: 0.98667 val_loss: 0.15413, val_acc: 0.96000
Epoch [7320/10000], loss: 0.14685 acc: 0.98667 val_loss: 0.15404, val_acc: 0.96000
Epoch [7330/10000], loss: 0.14675 acc: 0.98667 val_loss: 0.15396, val_acc: 0.96000
Epoch [7340/10000], loss: 0.14666 acc: 0.98667 val_loss: 0.15388, val_acc: 0.96000
Epoch [7350/10000], loss: 0.14656 acc: 0.98667 val_loss: 0.15380, val_acc: 0.96000
Epoch [7360/10000], loss: 0.14646 acc: 0.98667 val_loss: 0.15371, val_acc: 0.96000
Epoch [7370/10000], loss: 0.14637 acc: 0.98667 val_loss: 0.15363, val_acc: 0.96000
Epoch [7380/10000], loss: 0.14627 acc: 0.98667 val_loss: 0.15355, val_acc: 0.96000
Epoch [7390/10000], loss: 0.14618 acc: 0.98667 val_loss: 0.15347, val_acc: 0.96000
Epoch [7400/10000], loss: 0.14609 acc: 0.98667 val_loss: 0.15339, val_acc: 0.96000
Epoch [7410/10000], loss: 0.14599 acc: 0.98667 val_loss: 0.15331, val_acc: 0.96000
Epoch [7420/10000], loss: 0.14590 acc: 0.98667 val_loss: 0.15323, val_acc: 0.96000
Epoch [7430/10000], loss: 0.14580 acc: 0.98667 val_loss: 0.15314, val_acc: 0.96000
Epoch [7440/10000], loss: 0.14571 acc: 0.98667 val_loss: 0.15306, val_acc: 0.96000
Epoch [7450/10000], loss: 0.14562 acc: 0.98667 val_loss: 0.15298, val_acc: 0.96000
Epoch [7460/10000], loss: 0.14552 acc: 0.98667 val_loss: 0.15290, val_acc: 0.96000
Epoch [7470/10000], loss: 0.14543 acc: 0.98667 val_loss: 0.15282, val_acc: 0.96000
Epoch [7480/10000], loss: 0.14534 acc: 0.98667 val_loss: 0.15274, val_acc: 0.96000
Epoch [7490/10000], loss: 0.14525 acc: 0.98667 val_loss: 0.15266, val_acc: 0.96000
Epoch [7500/10000], loss: 0.14515 acc: 0.98667 val_loss: 0.15258, val_acc: 0.96000
Epoch [7510/10000], loss: 0.14506 acc: 0.98667 val_loss: 0.15250, val_acc: 0.96000
Epoch [7520/10000], loss: 0.14497 acc: 0.98667 val_loss: 0.15243, val_acc: 0.96000
Epoch [7530/10000], loss: 0.14488 acc: 0.98667 val_loss: 0.15235, val_acc: 0.96000
Epoch [7540/10000], loss: 0.14479 acc: 0.98667 val_loss: 0.15227, val_acc: 0.96000
Epoch [7550/10000], loss: 0.14470 acc: 0.98667 val_loss: 0.15219, val_acc: 0.96000
Epoch [7560/10000], loss: 0.14460 acc: 0.98667 val_loss: 0.15211, val_acc: 0.96000
Epoch [7570/10000], loss: 0.14451 acc: 0.98667 val_loss: 0.15203, val_acc: 0.96000
Epoch [7580/10000], loss: 0.14442 acc: 0.98667 val_loss: 0.15195, val_acc: 0.96000
Epoch [7590/10000], loss: 0.14433 acc: 0.98667 val_loss: 0.15188, val_acc: 0.96000
Epoch [7600/10000], loss: 0.14424 acc: 0.98667 val_loss: 0.15180, val_acc: 0.96000
Epoch [7610/10000], loss: 0.14415 acc: 0.98667 val_loss: 0.15172, val_acc: 0.96000
Epoch [7620/10000], loss: 0.14406 acc: 0.98667 val_loss: 0.15164, val_acc: 0.96000
Epoch [7630/10000], loss: 0.14397 acc: 0.98667 val_loss: 0.15157, val_acc: 0.96000
Epoch [7640/10000], loss: 0.14388 acc: 0.98667 val_loss: 0.15149, val_acc: 0.96000
Epoch [7650/10000], loss: 0.14379 acc: 0.98667 val_loss: 0.15141, val_acc: 0.96000
Epoch [7660/10000], loss: 0.14370 acc: 0.98667 val_loss: 0.15134, val_acc: 0.96000
Epoch [7670/10000], loss: 0.14362 acc: 0.98667 val_loss: 0.15126, val_acc: 0.96000
Epoch [7680/10000], loss: 0.14353 acc: 0.98667 val_loss: 0.15118, val_acc: 0.96000
Epoch [7690/10000], loss: 0.14344 acc: 0.98667 val_loss: 0.15111, val_acc: 0.96000
Epoch [7700/10000], loss: 0.14335 acc: 0.98667 val_loss: 0.15103, val_acc: 0.96000
Epoch [7710/10000], loss: 0.14326 acc: 0.98667 val_loss: 0.15096, val_acc: 0.96000
Epoch [7720/10000], loss: 0.14317 acc: 0.98667 val_loss: 0.15088, val_acc: 0.96000
Epoch [7730/10000], loss: 0.14309 acc: 0.98667 val_loss: 0.15080, val_acc: 0.96000
Epoch [7740/10000], loss: 0.14300 acc: 0.98667 val_loss: 0.15073, val_acc: 0.96000
Epoch [7750/10000], loss: 0.14291 acc: 0.98667 val_loss: 0.15065, val_acc: 0.96000
Epoch [7760/10000], loss: 0.14282 acc: 0.98667 val_loss: 0.15058, val_acc: 0.96000
Epoch [7770/10000], loss: 0.14274 acc: 0.98667 val_loss: 0.15050, val_acc: 0.96000
Epoch [7780/10000], loss: 0.14265 acc: 0.98667 val_loss: 0.15043, val_acc: 0.96000
Epoch [7790/10000], loss: 0.14256 acc: 0.98667 val_loss: 0.15036, val_acc: 0.96000
Epoch [7800/10000], loss: 0.14248 acc: 0.98667 val_loss: 0.15028, val_acc: 0.96000
Epoch [7810/10000], loss: 0.14239 acc: 0.98667 val_loss: 0.15021, val_acc: 0.96000
Epoch [7820/10000], loss: 0.14230 acc: 0.98667 val_loss: 0.15013, val_acc: 0.96000
Epoch [7830/10000], loss: 0.14222 acc: 0.98667 val_loss: 0.15006, val_acc: 0.96000
Epoch [7840/10000], loss: 0.14213 acc: 0.98667 val_loss: 0.14999, val_acc: 0.96000
Epoch [7850/10000], loss: 0.14205 acc: 0.98667 val_loss: 0.14991, val_acc: 0.96000
Epoch [7860/10000], loss: 0.14196 acc: 0.98667 val_loss: 0.14984, val_acc: 0.96000
Epoch [7870/10000], loss: 0.14188 acc: 0.98667 val_loss: 0.14977, val_acc: 0.96000
Epoch [7880/10000], loss: 0.14179 acc: 0.98667 val_loss: 0.14969, val_acc: 0.96000
Epoch [7890/10000], loss: 0.14171 acc: 0.98667 val_loss: 0.14962, val_acc: 0.96000
Epoch [7900/10000], loss: 0.14162 acc: 0.98667 val_loss: 0.14955, val_acc: 0.96000
Epoch [7910/10000], loss: 0.14154 acc: 0.98667 val_loss: 0.14948, val_acc: 0.96000
Epoch [7920/10000], loss: 0.14145 acc: 0.98667 val_loss: 0.14940, val_acc: 0.96000
Epoch [7930/10000], loss: 0.14137 acc: 0.98667 val_loss: 0.14933, val_acc: 0.96000
Epoch [7940/10000], loss: 0.14128 acc: 0.98667 val_loss: 0.14926, val_acc: 0.96000
Epoch [7950/10000], loss: 0.14120 acc: 0.98667 val_loss: 0.14919, val_acc: 0.96000
Epoch [7960/10000], loss: 0.14112 acc: 0.98667 val_loss: 0.14912, val_acc: 0.96000
Epoch [7970/10000], loss: 0.14103 acc: 0.98667 val_loss: 0.14904, val_acc: 0.96000
Epoch [7980/10000], loss: 0.14095 acc: 0.98667 val_loss: 0.14897, val_acc: 0.96000
Epoch [7990/10000], loss: 0.14087 acc: 0.98667 val_loss: 0.14890, val_acc: 0.96000
Epoch [8000/10000], loss: 0.14078 acc: 0.98667 val_loss: 0.14883, val_acc: 0.96000
Epoch [8010/10000], loss: 0.14070 acc: 0.98667 val_loss: 0.14876, val_acc: 0.96000
Epoch [8020/10000], loss: 0.14062 acc: 0.98667 val_loss: 0.14869, val_acc: 0.96000
Epoch [8030/10000], loss: 0.14054 acc: 0.98667 val_loss: 0.14862, val_acc: 0.96000
Epoch [8040/10000], loss: 0.14045 acc: 0.98667 val_loss: 0.14855, val_acc: 0.96000
Epoch [8050/10000], loss: 0.14037 acc: 0.98667 val_loss: 0.14848, val_acc: 0.96000
Epoch [8060/10000], loss: 0.14029 acc: 0.98667 val_loss: 0.14841, val_acc: 0.96000
Epoch [8070/10000], loss: 0.14021 acc: 0.98667 val_loss: 0.14834, val_acc: 0.96000
Epoch [8080/10000], loss: 0.14013 acc: 0.98667 val_loss: 0.14827, val_acc: 0.96000
Epoch [8090/10000], loss: 0.14004 acc: 0.98667 val_loss: 0.14820, val_acc: 0.96000
Epoch [8100/10000], loss: 0.13996 acc: 0.98667 val_loss: 0.14813, val_acc: 0.96000
Epoch [8110/10000], loss: 0.13988 acc: 0.98667 val_loss: 0.14806, val_acc: 0.96000
Epoch [8120/10000], loss: 0.13980 acc: 0.98667 val_loss: 0.14799, val_acc: 0.96000
Epoch [8130/10000], loss: 0.13972 acc: 0.98667 val_loss: 0.14792, val_acc: 0.96000
Epoch [8140/10000], loss: 0.13964 acc: 0.98667 val_loss: 0.14785, val_acc: 0.96000
Epoch [8150/10000], loss: 0.13956 acc: 0.98667 val_loss: 0.14778, val_acc: 0.96000
Epoch [8160/10000], loss: 0.13948 acc: 0.98667 val_loss: 0.14771, val_acc: 0.96000
Epoch [8170/10000], loss: 0.13940 acc: 0.98667 val_loss: 0.14765, val_acc: 0.96000
Epoch [8180/10000], loss: 0.13932 acc: 0.98667 val_loss: 0.14758, val_acc: 0.96000
Epoch [8190/10000], loss: 0.13924 acc: 0.98667 val_loss: 0.14751, val_acc: 0.96000
Epoch [8200/10000], loss: 0.13916 acc: 0.98667 val_loss: 0.14744, val_acc: 0.96000
Epoch [8210/10000], loss: 0.13908 acc: 0.98667 val_loss: 0.14737, val_acc: 0.96000
Epoch [8220/10000], loss: 0.13900 acc: 0.98667 val_loss: 0.14731, val_acc: 0.96000
Epoch [8230/10000], loss: 0.13892 acc: 0.98667 val_loss: 0.14724, val_acc: 0.96000
Epoch [8240/10000], loss: 0.13884 acc: 0.98667 val_loss: 0.14717, val_acc: 0.96000
Epoch [8250/10000], loss: 0.13876 acc: 0.98667 val_loss: 0.14710, val_acc: 0.96000
Epoch [8260/10000], loss: 0.13869 acc: 0.98667 val_loss: 0.14704, val_acc: 0.96000
Epoch [8270/10000], loss: 0.13861 acc: 0.98667 val_loss: 0.14697, val_acc: 0.96000
Epoch [8280/10000], loss: 0.13853 acc: 0.98667 val_loss: 0.14690, val_acc: 0.96000
Epoch [8290/10000], loss: 0.13845 acc: 0.98667 val_loss: 0.14684, val_acc: 0.96000
Epoch [8300/10000], loss: 0.13837 acc: 0.98667 val_loss: 0.14677, val_acc: 0.96000
Epoch [8310/10000], loss: 0.13829 acc: 0.98667 val_loss: 0.14670, val_acc: 0.96000
Epoch [8320/10000], loss: 0.13822 acc: 0.98667 val_loss: 0.14664, val_acc: 0.96000
Epoch [8330/10000], loss: 0.13814 acc: 0.98667 val_loss: 0.14657, val_acc: 0.96000
Epoch [8340/10000], loss: 0.13806 acc: 0.98667 val_loss: 0.14650, val_acc: 0.96000
Epoch [8350/10000], loss: 0.13798 acc: 0.98667 val_loss: 0.14644, val_acc: 0.96000
Epoch [8360/10000], loss: 0.13791 acc: 0.98667 val_loss: 0.14637, val_acc: 0.96000
Epoch [8370/10000], loss: 0.13783 acc: 0.98667 val_loss: 0.14631, val_acc: 0.96000
Epoch [8380/10000], loss: 0.13775 acc: 0.98667 val_loss: 0.14624, val_acc: 0.96000
Epoch [8390/10000], loss: 0.13768 acc: 0.98667 val_loss: 0.14618, val_acc: 0.96000
Epoch [8400/10000], loss: 0.13760 acc: 0.98667 val_loss: 0.14611, val_acc: 0.96000
Epoch [8410/10000], loss: 0.13752 acc: 0.98667 val_loss: 0.14605, val_acc: 0.96000
Epoch [8420/10000], loss: 0.13745 acc: 0.98667 val_loss: 0.14598, val_acc: 0.96000
Epoch [8430/10000], loss: 0.13737 acc: 0.98667 val_loss: 0.14592, val_acc: 0.96000
Epoch [8440/10000], loss: 0.13730 acc: 0.98667 val_loss: 0.14585, val_acc: 0.96000
Epoch [8450/10000], loss: 0.13722 acc: 0.98667 val_loss: 0.14579, val_acc: 0.96000
Epoch [8460/10000], loss: 0.13714 acc: 0.98667 val_loss: 0.14572, val_acc: 0.96000
Epoch [8470/10000], loss: 0.13707 acc: 0.98667 val_loss: 0.14566, val_acc: 0.96000
Epoch [8480/10000], loss: 0.13699 acc: 0.98667 val_loss: 0.14559, val_acc: 0.96000
Epoch [8490/10000], loss: 0.13692 acc: 0.98667 val_loss: 0.14553, val_acc: 0.96000
Epoch [8500/10000], loss: 0.13684 acc: 0.98667 val_loss: 0.14547, val_acc: 0.96000
Epoch [8510/10000], loss: 0.13677 acc: 0.98667 val_loss: 0.14540, val_acc: 0.96000
Epoch [8520/10000], loss: 0.13669 acc: 0.98667 val_loss: 0.14534, val_acc: 0.96000
Epoch [8530/10000], loss: 0.13662 acc: 0.98667 val_loss: 0.14528, val_acc: 0.96000
Epoch [8540/10000], loss: 0.13654 acc: 0.98667 val_loss: 0.14521, val_acc: 0.96000
Epoch [8550/10000], loss: 0.13647 acc: 0.98667 val_loss: 0.14515, val_acc: 0.96000
Epoch [8560/10000], loss: 0.13640 acc: 0.98667 val_loss: 0.14509, val_acc: 0.96000
Epoch [8570/10000], loss: 0.13632 acc: 0.98667 val_loss: 0.14502, val_acc: 0.96000
Epoch [8580/10000], loss: 0.13625 acc: 0.98667 val_loss: 0.14496, val_acc: 0.96000
Epoch [8590/10000], loss: 0.13617 acc: 0.98667 val_loss: 0.14490, val_acc: 0.96000
Epoch [8600/10000], loss: 0.13610 acc: 0.98667 val_loss: 0.14483, val_acc: 0.96000
Epoch [8610/10000], loss: 0.13603 acc: 0.98667 val_loss: 0.14477, val_acc: 0.96000
Epoch [8620/10000], loss: 0.13595 acc: 0.98667 val_loss: 0.14471, val_acc: 0.96000
Epoch [8630/10000], loss: 0.13588 acc: 0.98667 val_loss: 0.14465, val_acc: 0.96000
Epoch [8640/10000], loss: 0.13581 acc: 0.98667 val_loss: 0.14459, val_acc: 0.96000
Epoch [8650/10000], loss: 0.13574 acc: 0.98667 val_loss: 0.14452, val_acc: 0.96000
Epoch [8660/10000], loss: 0.13566 acc: 0.98667 val_loss: 0.14446, val_acc: 0.96000
Epoch [8670/10000], loss: 0.13559 acc: 0.98667 val_loss: 0.14440, val_acc: 0.96000
Epoch [8680/10000], loss: 0.13552 acc: 0.98667 val_loss: 0.14434, val_acc: 0.96000
Epoch [8690/10000], loss: 0.13545 acc: 0.98667 val_loss: 0.14428, val_acc: 0.96000
Epoch [8700/10000], loss: 0.13537 acc: 0.98667 val_loss: 0.14422, val_acc: 0.96000
Epoch [8710/10000], loss: 0.13530 acc: 0.98667 val_loss: 0.14415, val_acc: 0.96000
Epoch [8720/10000], loss: 0.13523 acc: 0.98667 val_loss: 0.14409, val_acc: 0.96000
Epoch [8730/10000], loss: 0.13516 acc: 0.98667 val_loss: 0.14403, val_acc: 0.96000
Epoch [8740/10000], loss: 0.13509 acc: 0.98667 val_loss: 0.14397, val_acc: 0.96000
Epoch [8750/10000], loss: 0.13501 acc: 0.98667 val_loss: 0.14391, val_acc: 0.96000
Epoch [8760/10000], loss: 0.13494 acc: 0.98667 val_loss: 0.14385, val_acc: 0.96000
Epoch [8770/10000], loss: 0.13487 acc: 0.98667 val_loss: 0.14379, val_acc: 0.96000
Epoch [8780/10000], loss: 0.13480 acc: 0.98667 val_loss: 0.14373, val_acc: 0.96000
Epoch [8790/10000], loss: 0.13473 acc: 0.98667 val_loss: 0.14367, val_acc: 0.96000
Epoch [8800/10000], loss: 0.13466 acc: 0.98667 val_loss: 0.14361, val_acc: 0.96000
Epoch [8810/10000], loss: 0.13459 acc: 0.98667 val_loss: 0.14355, val_acc: 0.96000
Epoch [8820/10000], loss: 0.13452 acc: 0.98667 val_loss: 0.14349, val_acc: 0.96000
Epoch [8830/10000], loss: 0.13445 acc: 0.98667 val_loss: 0.14343, val_acc: 0.96000
Epoch [8840/10000], loss: 0.13438 acc: 0.98667 val_loss: 0.14337, val_acc: 0.96000
Epoch [8850/10000], loss: 0.13431 acc: 0.98667 val_loss: 0.14331, val_acc: 0.96000
Epoch [8860/10000], loss: 0.13424 acc: 0.98667 val_loss: 0.14325, val_acc: 0.96000
Epoch [8870/10000], loss: 0.13417 acc: 0.98667 val_loss: 0.14319, val_acc: 0.96000
Epoch [8880/10000], loss: 0.13410 acc: 0.98667 val_loss: 0.14313, val_acc: 0.96000
Epoch [8890/10000], loss: 0.13403 acc: 0.98667 val_loss: 0.14308, val_acc: 0.96000
Epoch [8900/10000], loss: 0.13396 acc: 0.98667 val_loss: 0.14302, val_acc: 0.96000
Epoch [8910/10000], loss: 0.13389 acc: 0.98667 val_loss: 0.14296, val_acc: 0.96000
Epoch [8920/10000], loss: 0.13382 acc: 0.98667 val_loss: 0.14290, val_acc: 0.96000
Epoch [8930/10000], loss: 0.13375 acc: 0.98667 val_loss: 0.14284, val_acc: 0.96000
Epoch [8940/10000], loss: 0.13368 acc: 0.98667 val_loss: 0.14278, val_acc: 0.96000
Epoch [8950/10000], loss: 0.13361 acc: 0.98667 val_loss: 0.14272, val_acc: 0.96000
Epoch [8960/10000], loss: 0.13354 acc: 0.98667 val_loss: 0.14266, val_acc: 0.96000
Epoch [8970/10000], loss: 0.13347 acc: 0.98667 val_loss: 0.14261, val_acc: 0.96000
Epoch [8980/10000], loss: 0.13341 acc: 0.98667 val_loss: 0.14255, val_acc: 0.96000
Epoch [8990/10000], loss: 0.13334 acc: 0.98667 val_loss: 0.14249, val_acc: 0.96000
Epoch [9000/10000], loss: 0.13327 acc: 0.98667 val_loss: 0.14243, val_acc: 0.96000
Epoch [9010/10000], loss: 0.13320 acc: 0.98667 val_loss: 0.14238, val_acc: 0.96000
Epoch [9020/10000], loss: 0.13313 acc: 0.98667 val_loss: 0.14232, val_acc: 0.96000
Epoch [9030/10000], loss: 0.13307 acc: 0.98667 val_loss: 0.14226, val_acc: 0.96000
Epoch [9040/10000], loss: 0.13300 acc: 0.98667 val_loss: 0.14220, val_acc: 0.96000
Epoch [9050/10000], loss: 0.13293 acc: 0.98667 val_loss: 0.14215, val_acc: 0.96000
Epoch [9060/10000], loss: 0.13286 acc: 0.98667 val_loss: 0.14209, val_acc: 0.96000
Epoch [9070/10000], loss: 0.13280 acc: 0.98667 val_loss: 0.14203, val_acc: 0.96000
Epoch [9080/10000], loss: 0.13273 acc: 0.98667 val_loss: 0.14198, val_acc: 0.96000
Epoch [9090/10000], loss: 0.13266 acc: 0.98667 val_loss: 0.14192, val_acc: 0.96000
Epoch [9100/10000], loss: 0.13259 acc: 0.98667 val_loss: 0.14186, val_acc: 0.96000
Epoch [9110/10000], loss: 0.13253 acc: 0.98667 val_loss: 0.14181, val_acc: 0.96000
Epoch [9120/10000], loss: 0.13246 acc: 0.98667 val_loss: 0.14175, val_acc: 0.96000
Epoch [9130/10000], loss: 0.13239 acc: 0.98667 val_loss: 0.14169, val_acc: 0.96000
Epoch [9140/10000], loss: 0.13233 acc: 0.98667 val_loss: 0.14164, val_acc: 0.96000
Epoch [9150/10000], loss: 0.13226 acc: 0.98667 val_loss: 0.14158, val_acc: 0.96000
Epoch [9160/10000], loss: 0.13220 acc: 0.98667 val_loss: 0.14153, val_acc: 0.96000
Epoch [9170/10000], loss: 0.13213 acc: 0.98667 val_loss: 0.14147, val_acc: 0.96000
Epoch [9180/10000], loss: 0.13206 acc: 0.98667 val_loss: 0.14141, val_acc: 0.96000
Epoch [9190/10000], loss: 0.13200 acc: 0.98667 val_loss: 0.14136, val_acc: 0.96000
Epoch [9200/10000], loss: 0.13193 acc: 0.98667 val_loss: 0.14130, val_acc: 0.96000
Epoch [9210/10000], loss: 0.13187 acc: 0.98667 val_loss: 0.14125, val_acc: 0.96000
Epoch [9220/10000], loss: 0.13180 acc: 0.98667 val_loss: 0.14119, val_acc: 0.96000
Epoch [9230/10000], loss: 0.13174 acc: 0.98667 val_loss: 0.14114, val_acc: 0.96000
Epoch [9240/10000], loss: 0.13167 acc: 0.98667 val_loss: 0.14108, val_acc: 0.96000
Epoch [9250/10000], loss: 0.13160 acc: 0.98667 val_loss: 0.14103, val_acc: 0.96000
Epoch [9260/10000], loss: 0.13154 acc: 0.98667 val_loss: 0.14097, val_acc: 0.96000
Epoch [9270/10000], loss: 0.13147 acc: 0.98667 val_loss: 0.14092, val_acc: 0.96000
Epoch [9280/10000], loss: 0.13141 acc: 0.98667 val_loss: 0.14086, val_acc: 0.96000
Epoch [9290/10000], loss: 0.13135 acc: 0.98667 val_loss: 0.14081, val_acc: 0.96000
Epoch [9300/10000], loss: 0.13128 acc: 0.98667 val_loss: 0.14075, val_acc: 0.96000
Epoch [9310/10000], loss: 0.13122 acc: 0.98667 val_loss: 0.14070, val_acc: 0.96000
Epoch [9320/10000], loss: 0.13115 acc: 0.98667 val_loss: 0.14065, val_acc: 0.96000
Epoch [9330/10000], loss: 0.13109 acc: 0.98667 val_loss: 0.14059, val_acc: 0.96000
Epoch [9340/10000], loss: 0.13102 acc: 0.98667 val_loss: 0.14054, val_acc: 0.96000
Epoch [9350/10000], loss: 0.13096 acc: 0.98667 val_loss: 0.14048, val_acc: 0.96000
Epoch [9360/10000], loss: 0.13090 acc: 0.98667 val_loss: 0.14043, val_acc: 0.96000
Epoch [9370/10000], loss: 0.13083 acc: 0.98667 val_loss: 0.14038, val_acc: 0.96000
Epoch [9380/10000], loss: 0.13077 acc: 0.98667 val_loss: 0.14032, val_acc: 0.96000
Epoch [9390/10000], loss: 0.13070 acc: 0.98667 val_loss: 0.14027, val_acc: 0.96000
Epoch [9400/10000], loss: 0.13064 acc: 0.98667 val_loss: 0.14022, val_acc: 0.96000
Epoch [9410/10000], loss: 0.13058 acc: 0.98667 val_loss: 0.14016, val_acc: 0.96000
Epoch [9420/10000], loss: 0.13051 acc: 0.98667 val_loss: 0.14011, val_acc: 0.96000
Epoch [9430/10000], loss: 0.13045 acc: 0.98667 val_loss: 0.14006, val_acc: 0.96000
Epoch [9440/10000], loss: 0.13039 acc: 0.98667 val_loss: 0.14000, val_acc: 0.96000
Epoch [9450/10000], loss: 0.13033 acc: 0.98667 val_loss: 0.13995, val_acc: 0.96000
Epoch [9460/10000], loss: 0.13026 acc: 0.98667 val_loss: 0.13990, val_acc: 0.96000
Epoch [9470/10000], loss: 0.13020 acc: 0.98667 val_loss: 0.13984, val_acc: 0.96000
Epoch [9480/10000], loss: 0.13014 acc: 0.98667 val_loss: 0.13979, val_acc: 0.96000
Epoch [9490/10000], loss: 0.13008 acc: 0.98667 val_loss: 0.13974, val_acc: 0.96000
Epoch [9500/10000], loss: 0.13001 acc: 0.98667 val_loss: 0.13969, val_acc: 0.96000
Epoch [9510/10000], loss: 0.12995 acc: 0.98667 val_loss: 0.13963, val_acc: 0.96000
Epoch [9520/10000], loss: 0.12989 acc: 0.98667 val_loss: 0.13958, val_acc: 0.96000
Epoch [9530/10000], loss: 0.12983 acc: 0.98667 val_loss: 0.13953, val_acc: 0.96000
Epoch [9540/10000], loss: 0.12976 acc: 0.98667 val_loss: 0.13948, val_acc: 0.96000
Epoch [9550/10000], loss: 0.12970 acc: 0.98667 val_loss: 0.13943, val_acc: 0.96000
Epoch [9560/10000], loss: 0.12964 acc: 0.98667 val_loss: 0.13937, val_acc: 0.96000
Epoch [9570/10000], loss: 0.12958 acc: 0.98667 val_loss: 0.13932, val_acc: 0.96000
Epoch [9580/10000], loss: 0.12952 acc: 0.98667 val_loss: 0.13927, val_acc: 0.96000
Epoch [9590/10000], loss: 0.12946 acc: 0.98667 val_loss: 0.13922, val_acc: 0.96000
Epoch [9600/10000], loss: 0.12940 acc: 0.98667 val_loss: 0.13917, val_acc: 0.96000
Epoch [9610/10000], loss: 0.12933 acc: 0.98667 val_loss: 0.13912, val_acc: 0.96000
Epoch [9620/10000], loss: 0.12927 acc: 0.98667 val_loss: 0.13907, val_acc: 0.96000
Epoch [9630/10000], loss: 0.12921 acc: 0.98667 val_loss: 0.13901, val_acc: 0.96000
Epoch [9640/10000], loss: 0.12915 acc: 0.98667 val_loss: 0.13896, val_acc: 0.96000
Epoch [9650/10000], loss: 0.12909 acc: 0.98667 val_loss: 0.13891, val_acc: 0.96000
Epoch [9660/10000], loss: 0.12903 acc: 0.98667 val_loss: 0.13886, val_acc: 0.96000
Epoch [9670/10000], loss: 0.12897 acc: 0.98667 val_loss: 0.13881, val_acc: 0.96000
Epoch [9680/10000], loss: 0.12891 acc: 0.98667 val_loss: 0.13876, val_acc: 0.96000
Epoch [9690/10000], loss: 0.12885 acc: 0.98667 val_loss: 0.13871, val_acc: 0.96000
Epoch [9700/10000], loss: 0.12879 acc: 0.98667 val_loss: 0.13866, val_acc: 0.96000
Epoch [9710/10000], loss: 0.12873 acc: 0.98667 val_loss: 0.13861, val_acc: 0.96000
Epoch [9720/10000], loss: 0.12867 acc: 0.98667 val_loss: 0.13856, val_acc: 0.96000
Epoch [9730/10000], loss: 0.12861 acc: 0.98667 val_loss: 0.13851, val_acc: 0.96000
Epoch [9740/10000], loss: 0.12855 acc: 0.98667 val_loss: 0.13846, val_acc: 0.96000
Epoch [9750/10000], loss: 0.12849 acc: 0.98667 val_loss: 0.13841, val_acc: 0.96000
Epoch [9760/10000], loss: 0.12843 acc: 0.98667 val_loss: 0.13836, val_acc: 0.96000
Epoch [9770/10000], loss: 0.12837 acc: 0.98667 val_loss: 0.13831, val_acc: 0.96000
Epoch [9780/10000], loss: 0.12831 acc: 0.98667 val_loss: 0.13826, val_acc: 0.96000
Epoch [9790/10000], loss: 0.12825 acc: 0.98667 val_loss: 0.13821, val_acc: 0.96000
Epoch [9800/10000], loss: 0.12819 acc: 0.98667 val_loss: 0.13816, val_acc: 0.96000
Epoch [9810/10000], loss: 0.12813 acc: 0.98667 val_loss: 0.13811, val_acc: 0.96000
Epoch [9820/10000], loss: 0.12808 acc: 0.98667 val_loss: 0.13806, val_acc: 0.96000
Epoch [9830/10000], loss: 0.12802 acc: 0.98667 val_loss: 0.13801, val_acc: 0.96000
Epoch [9840/10000], loss: 0.12796 acc: 0.98667 val_loss: 0.13796, val_acc: 0.96000
Epoch [9850/10000], loss: 0.12790 acc: 0.98667 val_loss: 0.13791, val_acc: 0.96000
Epoch [9860/10000], loss: 0.12784 acc: 0.98667 val_loss: 0.13786, val_acc: 0.96000
Epoch [9870/10000], loss: 0.12778 acc: 0.98667 val_loss: 0.13782, val_acc: 0.96000
Epoch [9880/10000], loss: 0.12772 acc: 0.98667 val_loss: 0.13777, val_acc: 0.96000
Epoch [9890/10000], loss: 0.12767 acc: 0.98667 val_loss: 0.13772, val_acc: 0.96000
Epoch [9900/10000], loss: 0.12761 acc: 0.98667 val_loss: 0.13767, val_acc: 0.96000
Epoch [9910/10000], loss: 0.12755 acc: 0.98667 val_loss: 0.13762, val_acc: 0.96000
Epoch [9920/10000], loss: 0.12749 acc: 0.98667 val_loss: 0.13757, val_acc: 0.96000
Epoch [9930/10000], loss: 0.12743 acc: 0.98667 val_loss: 0.13752, val_acc: 0.96000
Epoch [9940/10000], loss: 0.12738 acc: 0.98667 val_loss: 0.13748, val_acc: 0.96000
Epoch [9950/10000], loss: 0.12732 acc: 0.98667 val_loss: 0.13743, val_acc: 0.96000
Epoch [9960/10000], loss: 0.12726 acc: 0.98667 val_loss: 0.13738, val_acc: 0.96000
Epoch [9970/10000], loss: 0.12720 acc: 0.98667 val_loss: 0.13733, val_acc: 0.96000
Epoch [9980/10000], loss: 0.12715 acc: 0.98667 val_loss: 0.13728, val_acc: 0.96000
Epoch [9990/10000], loss: 0.12709 acc: 0.98667 val_loss: 0.13724, val_acc: 0.96000

# 손실과 정확도 확인
 
print(f'초기상태 : 손실 : {history[0,3]:.5f}  정확도 : {history[0,4]:.5f}' )
print(f'최종상태 : 손실 : {history[-1,3]:.5f}  정확도 : {history[-1,4]:.5f}' )

초기상태 : 손실 : 1.09158  정확도 : 0.26667
최종상태 : 손실 : 0.13724  정확도 : 0.96000

# 패턴 3 모델의 출력값
w = outputs[:5,:].data.numpy()
print(w)

[[0.0059 0.9056 0.0885]
 [0.0069 0.9792 0.0139]
 [0.9452 0.0548 0.    ]
 [0.     0.0404 0.9596]
 [0.0001 0.1743 0.8256]]