원본코드를 colab에서 실행한 관계로 vscode나 로컬 쥬피터환경에서 실행할 경우 코드일부와 파일경로 변경이 필요하니 참고용으로만 봐야합니다.
목차
[깃허브 레포지토리]
[참고]
1.데이터셋과 프로젝트의 목적¶
1.
데이터셋
•
본인이 직접 "날리면"과 "바이든", "알 수 없는" 음성 데이터를
각각20개씩 녹음
•
총 60개의 데이터
•
음성 녹음의 정교성을 올리기 위해 골드웨이브 사용
•
샘플링 비율 8000, 스테레오 채널 설정
•
실제 테스트 데이터는 "굥 각하의 음성 중 ‘바이든은’ or 자칭
‘날리면’ 부분을 컷팅한 오디오 파일"
2.
추가 데이터
•
TTS기능을 사용하여 만든 음성데이터 12개
•
추가 데이터 확보를 위해 "데이터 증강(Augmentation) 사용"하여 또
(60+12)*5개 =360개 확보
3.
전체 데이터
•
갯수: 432개(72 + 360개)
•
본인 녹음: 60개(클래스당 20개)
•
TTS기능 활용: 12개(클래스당 4개)
•
데이터 증강(5가지 기법): 72*5 = 288개(클래스당 144개)
4.골드웨이브 작업 모습
2.이론적 배경¶
1.
CNN이용
•
CNN은 데이터에서 패턴을 추출하는데 유용한 알고리즘으로 주로 이미지
분야에 특화되어 있음
•
이미지가 벡터라는 점을 감안한다면 오디오 샘플 또한 CNN 알고리즘을
적용할수 있음
1.
오디오 데이터 전처리
•
CNN을 사용하기 위해서는 stft나 melspectrogram 피쳐맵 데이터 생성을
위한 전처리 과정이 필요함
1.
오디오 전처리도 귀찮다면?
•
1차원 CNN을 적용해볼 수 있음
1.
음성 데이터 증강(Augmentation)은 어떻게?
•
화이트 노이즈 추가
•
shiftinf 추가
•
Stretching 추가
•
피치 변경(음의 높이 또는 높낮이)
3.데이터 셋 확인¶
일단 시각화 먼저
총 3개의 시각화
1.
원본 음성파일 시각화
2.
STFT결과 시각화
3.
멜스펙트럼 결과 시각화
In [ ]:
#라이브러리 import
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import glob #glob는 파일들의 리스트를 뽑을 때 사용하는 라이브러리
import pickle #데이터를 저장하고 불러올때 매우 유용한 라이브러리
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Conv2D, MaxPool2D, Input, Dense, Flatten,Dropout, GlobalAvgPool2D
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from IPython.display import Audio, display
import librosa.display, librosa
In [ ]:
#1개의 오디오 파일만 시각화
# 실제 test해볼 파일을 지정해서 들어보기
file_path = '/content/drive/MyDrive/Colab Notebooks/8.음향관련/sj_sound_data/train/biden/biden1.wav'
y, sr = librosa.load(file_path)
#일단 들어보기
display(Audio(data=y,rate=sr))
Your browser does not support the audio element.
주의-음성데이터 최대길이 확인¶
음성 데이터의 길이가 제각각인 경우
음성 데이터 길이가 제일 긴 것을 기준으로 짧은 부분은 padding처리 해야
함
In [ ]:
# 데이터 타입과 shape을 잘 기억하기
# 제일 긴 음성데이터의 길이를 max_len변수에 저장
max_len = len(y)
max_len
Out[ ]:
33075
In [ ]:
#데이터 살펴보기
print(sr, y.shape)
print(y)
#숫자는 아래 해석 참고
22050 (33075,)
[-1.8256820e-04 -1.4008603e-04 4.7545291e-05 ... -1.0961011e-02
-7.8880982e-03 -3.9390200e-03]
음성 데이터 해석¶
sr값은 22050: 1초에 22050개의 샘플이 기록되어있고(22050Hz)
음성길이가 약 1.x초 이므로
총 33148개의 샘플이 들어있는 것임
3.1.음성데이터 시각화(FFT전)
In [ ]:
# 푸리에 변환전 x축은 타임, y축은 진폭의 형태라고 생각하자!
plt.figure(figsize = (12, 3))
plt.plot(y, lw = 1) #lw는 line width옵션을 의미
plt.xlabel("time")
plt.ylabel("data")
plt.xlim(0, len(y))
plt.show()
3.2.학습에 사용될 음성데이터 확인¶
•
train 데이터
•
총 3개의 class 존재함
1.주의점 학습에 사용될 음성데이터의 길이가 모두 동일해야하니 전처리
필요
In [ ]:
#glob 라이브러리 사용
import glob
short = str('/content/drive/MyDrive/Colab Notebooks/8.음향관련/sj_sound_data/train/')
unknown_dir = glob.glob(short + "/unknown/*")
biden_dir = glob.glob(short +"/biden/*")
nali_dir = glob.glob(short +"/nali/*")
print("unknown_dir의 파일갯수:", len(unknown_dir))
print("biden_dir 파일갯수:", len(biden_dir))
print("nali_dir 파일갯수:", len(nali_dir))
unknown_dir의 파일갯수: 24
biden_dir 파일갯수: 24
nali_dir 파일갯수: 24
In [ ]:
##각각의 file_path를 리스트로
unknown_file_path = []
for i in range(len(unknown_dir)):
unknown_file_path.append(unknown_dir[i])
biden_file_path = []
for i in range(len(biden_dir)):
biden_file_path.append(biden_dir[i])
nali_file_path = []
for i in range(len(nali_dir)):
nali_file_path.append(nali_dir[i])
#잘되었는지 1개의 nali_file_path 출력
print(len(nali_file_path))
24
In [ ]:
#하나만 출력해보기
unknown_file_path[23]
Out[ ]:
'/content/drive/MyDrive/Colab Notebooks/8.음향관련/sj_sound_data/train/unknown/clova_unknown (3).wav'
In [ ]:
#해당 파일을 librosa.load로 모두 불러와서 배열로 저장
#각각 3개의 클래스에 해당되는 데이터를 담을 배열 선언
unknown_array = []
#여기서 33075의 의미는 음성파일의 최대 길이
for i in range(len(unknown_file_path)):
y, sr = librosa.load(unknown_file_path[i])
if y.shape != max_len:
zero_padding = tf.zeros(max_len- tf.shape(y), dtype=tf.float32)
# zero_padding
y = tf.concat([y, zero_padding],0)
unknown_array.append(y)
unknown_array = np.array(unknown_array)
biden_array = []
#여기서 33075의 의미는 음성파일의 최대 길이
for i in range(len(biden_file_path)):
y, sr = librosa.load(biden_file_path[i])
if y.shape != max_len:
zero_padding = tf.zeros(max_len- tf.shape(y), dtype=tf.float32)
# zero_padding
y = tf.concat([y, zero_padding],0)
biden_array.append(y)
biden_array = np.array(biden_array)
nali_array = []
#여기서 33075의 의미는 음성파일의 최대 길이
for i in range(len(nali_file_path)):
y, sr = librosa.load(nali_file_path[i])
if y.shape != max_len:
zero_padding = tf.zeros(max_len- tf.shape(y), dtype=tf.float32)
# zero_padding
y = tf.concat([y, zero_padding],0)
nali_array.append(y)
nali_array = np.array(nali_array)
In [ ]:
# 넘파이 배열로 잘 저장되었는지 확인
# np.random.randint(6) # 0 or 1 or ~ or 0부터 5까지 랜덤한 숫자 1개
random_i = np.random.randint(len(nali_file_path))
display(Audio(data=nali_array[random_i],rate=sr))
Your browser does not support the audio element.
In [ ]:
#각각의 배열에 저장된 값 확인
print("배열shape:", nali_array.shape)
배열shape: (24, 33075)
3.3.학습데이터 시각화
In [ ]:
#시각화를 위해 배열 차원 변경
nali_array_visual = nali_array.reshape(8,3, -1)
biden_array_visual = biden_array.reshape(8,3, -1)
unknown_array_visual = unknown_array.reshape(8,3, -1)
In [ ]:
#날리면 음성 데이터 시각화
#데이터가 많으니 앞 15개만 시각화
fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(20, 10))
for i in range(5):
for j in range(3):
axes[i][j].plot(nali_array_visual[i][j], lw = 2) #lw는 line width옵션을 의미
# axes[i][j].set_title("sound_wave")
axes[i][j].set_xlabel('time')
axes[i][j].set_ylabel('amp')
fig.tight_layout(pad=2)
fig.suptitle("This is nali_sounde_wave", fontsize=16)
plt.show()
In [ ]:
#바이든 음성 데이터 시각화
#데이터가 많으니 앞 10개만 시각화
fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(20, 10))
for i in range(5):
for j in range(3):
axes[i][j].plot(biden_array_visual[i][j], color='red',lw = 2) #lw는 line width옵션을 의미
# axes[i][j].set_title("sound_wave")
axes[i][j].set_xlabel('time')
axes[i][j].set_ylabel('amp')
fig.tight_layout(pad=2)
fig.suptitle("This is biden_sounde_wave", fontsize=16, y = -0.01)
plt.show()
In [ ]:
#unknown 음성 데이터 시각화
#데이터가 많으니 앞 10개만 시각화
fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(20, 10))
for i in range(5):
for j in range(3):
axes[i][j].plot(unknown_array_visual[i][j], color='orange',lw = 2) #lw는 line width옵션을 의미
# axes[i][j].set_title("sound_wave")
axes[i][j].set_xlabel('time')
axes[i][j].set_ylabel('amp')
fig.tight_layout(pad=2)
fig.suptitle("This is biden_sounde_wave", fontsize=16, y = -0.01)
plt.show()
In [ ]:
#3개 클래스에서 하나씩 뽑아서 시각화 비교
fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(20, 10))
for i in range(5):
for j in range(3):
if j==0:
axes[i][j].plot(unknown_array_visual[i][j], color='orange',lw = 2) #lw는 line width옵션을 의미
axes[i][j].set_title("unknowm")
axes[i][j].set_xlabel('time')
axes[i][j].set_ylabel('amp')
elif j==1:
axes[i][j].plot(biden_array_visual[i][j], color='red',lw = 2) #lw는 line width옵션을 의미
axes[i][j].set_title("biden")
axes[i][j].set_xlabel('time')
axes[i][j].set_ylabel('amp')
elif j==2:
axes[i][j].plot(nali_array_visual[i][j], color='blue',lw = 2) #lw는 line width옵션을 의미
axes[i][j].set_title("nali")
axes[i][j].set_xlabel('time')
axes[i][j].set_ylabel('amp')
fig.tight_layout(pad=2)
fig.suptitle("This is 3case_sounde_wave", fontsize=16, y = -0.01)
plt.show()
3.4.스펙트럼 분석¶
•
시간에 따른 진폭파형으로는 분석이 불가능
•
STFT(Short-Time Fourier Transform)을 실시
•
STFT의 값은 너무 미세해서 차이를 파악하고 관찰하기 쉽지
않음
•
따라서 데시벨로 변환한 다음 시각화하는 것이 일반적
In [ ]:
# window는 STFT에 사용할 윈도우의 종류
# n_fft는 윈도우의 길이, 그리고 hop length는 얼마나 겹칠 것인지를 설정
nali_sample = librosa.stft(nali_array[1], n_fft=2048, window='hann', hop_length=512)
nali_sample.shape
Out[ ]:
(1025, 65)
In [ ]:
nali_array[1].shape
Out[ ]:
(33075,)
In [ ]:
# 데시벨 값으로 변환하지 않으면
#스펙트럼 변환 후 시각화(librosa라이브러리 활용)
nali_spec = librosa.amplitude_to_db(np.abs(nali_sample), ref=np.max)
librosa.display.specshow(nali_spec, x_axis='time', y_axis='log')
plt.colorbar(format='%+2.0f dB')
plt.show()
In [ ]:
#이제 전체 데이터를 STFT한 다음 시각화 해보기
#STFT값과 데시벨로 변환한 spec값을 각각 저장
nali_stft = [] #SFT값을 저장할 리스트
nali_spec = [] #SFT값을 데시벨로 바꾸어 저장할 리스트
for i in range(len(nali_array)):
temp1 = librosa.stft(nali_array[i], n_fft=2048, window='hann', hop_length=512)
nali_stft.append(temp1)
nali_spec.append(librosa.amplitude_to_db(np.abs(temp1), ref=np.max))
#리스트를 넘파이 배열로
nali_stft = np.array(nali_stft)
nali_spec = np.array(nali_spec)
biden_stft = []
biden_spec = []
for i in range(len(biden_array)):
temp2 = librosa.stft(biden_array[i], n_fft=2048, window='hann', hop_length=512)
biden_stft.append(temp2)
biden_spec.append(librosa.amplitude_to_db(np.abs(temp2), ref=np.max))
#리스트를 넘파이 배열로
biden_stft = np.array(biden_stft)
biden_spec = np.array(biden_spec)
unknown_stft = []
unknown_spec = []
for i in range(len(unknown_array)):
temp3 = librosa.stft(unknown_array[i], n_fft=2048, window='hann', hop_length=512)
unknown_stft.append(temp3)
unknown_spec.append(librosa.amplitude_to_db(np.abs(temp3), ref=np.max))
#리스트를 넘파이 배열로
unknown_stft = np.array(unknown_stft)
unknown_spec = np.array(unknown_spec)
In [ ]:
#각 배열의 shape확인
print("nali_stft 의 shape:", nali_stft.shape)
print("nali_spec 의 shape:", nali_spec.shape)
print("biden_stft 의 shape:", biden_stft.shape)
print("biden_spec 의 shape:", biden_spec.shape)
print("unknown_stft 의 shape:", unknown_stft.shape)
print("unknown_spec 의 shape:", unknown_spec.shape)
nali_stft 의 shape: (24, 1025, 65)
nali_spec 의 shape: (24, 1025, 65)
biden_stft 의 shape: (24, 1025, 65)
biden_spec 의 shape: (24, 1025, 65)
unknown_stft 의 shape: (24, 1025, 65)
unknown_spec 의 shape: (24, 1025, 65)
3.5.스펙트럼 분석결과 시각화
In [ ]:
unknown_spec_visual = unknown_spec
biden_spec_visual = biden_spec
nali_spec_visual = nali_spec
In [ ]:
librosa.display.specshow(nali_spec_visual[7], x_axis='time', y_axis='log')
plt.colorbar(format='%+2.0f dB')
Out[ ]:
<matplotlib.colorbar.Colorbar at 0x7fe4a9b36dd0>
In [ ]:
#3개 클래스에서 각각 10개씩 총 30개 STFT 시각화 비교
nali_num=0
biden_num=0
unknown_num=0
plt.figure(figsize=(20,40))
for i in range(1,31):
plt.subplot(10,3,i)
if i%3 == 0:
librosa.display.specshow(unknown_spec[unknown_num], x_axis='time', y_axis='log')
plt.xlabel("unknown")
unknown_num = unknown_num +1
elif i%3 == 1:
librosa.display.specshow(biden_spec[biden_num], x_axis='time', y_axis='log')
plt.xlabel("biden")
biden_num = biden_num +1
elif i%3 == 2:
librosa.display.specshow(nali_spec[nali_num], x_axis='time', y_axis='log')
plt.xlabel("nali")
nali_num = nali_num +1
3.6.멜스펙트럼으로 변환 후 시각화
In [ ]:
#하나만 멜 스펙트럼으로 변환해보기
S = librosa.feature.melspectrogram(nali_array[1], sr=sr, n_mels=128, fmax=8000)
# db(데시벨)로 변환하기 위해 power_to_db()메서드 사용
mel_spec = librosa.display.specshow(librosa.power_to_db(S, ref=np.max), sr=sr, x_axis='time', y_axis='mel' )
plt.colorbar(format='%+2.0f dB')
plt.show()
In [ ]:
#STFT와 멜 스펙트럼 변환 차이 살펴보기
plt.subplot(2,1,1)
plt.xlabel("STFT")
nali_stft1 = librosa.stft(nali_array[1], n_fft=2048, window='hann', hop_length=512)
nali_spec = librosa.amplitude_to_db(np.abs(nali_stft1), ref=np.max)
librosa.display.specshow(nali_spec, x_axis='time', y_axis='log')
plt.colorbar(format='%+2.0f dB')
plt.subplot(2,1,2)
plt.xlabel("melspec")
nali_mel_spec1 = librosa.feature.melspectrogram(nali_array[1], sr=sr, n_mels=128, fmax=8000)
nali_mel_spec1_db = librosa.power_to_db(nali_mel_spec1, ref=np.max)
librosa.display.specshow(nali_mel_spec1_db, x_axis='time', y_axis='mel')
plt.colorbar(format='%+2.0f dB')
plt.show()
In [ ]:
# 전체 데이터 멜 스펙트럼으로 변환
#Mel Scale Spectrum값과 데시벨로 변환한 mel_db값을 각각 저장
nali_mel_spec = [] #Mel Scale Spectrum값을 저장할 리스트
nali_mel_db = [] #mMel Scale Spectrum값을 데시벨로 바꾸어 저장할 리스트
for i in range(len(nali_array)):
temp1 = librosa.feature.melspectrogram(nali_array[i], sr=sr, n_mels=128, fmax=8000)
nali_mel_spec.append(temp1)
nali_mel_db.append(librosa.amplitude_to_db(np.abs(temp1), ref=np.max))
#리스트를 넘파이 배열로
nali_mel_spec = np.array(nali_mel_spec)
nali_mel_db = np.array(nali_mel_db)
biden_mel_spec = [] #Mel Scale Spectrum값을 저장할 리스트
biden_mel_db = [] #mMel Scale Spectrum값을 데시벨로 바꾸어 저장할 리스트
for i in range(len(biden_array)):
temp2 = librosa.feature.melspectrogram(biden_array[i], sr=sr, n_mels=128, fmax=8000)
biden_mel_spec.append(temp2)
biden_mel_db.append(librosa.amplitude_to_db(np.abs(temp2), ref=np.max))
#리스트를 넘파이 배열로
biden_mel_spec = np.array(biden_mel_spec)
biden_mel_db = np.array(biden_mel_db)
unknown_mel_spec = [] #Mel Scale Spectrum값을 저장할 리스트
unknown_mel_db = [] #mMel Scale Spectrum값을 데시벨로 바꾸어 저장할 리스트
for i in range(len(unknown_array)):
temp3 = librosa.feature.melspectrogram(unknown_array[i], sr=sr, n_mels=128, fmax=8000)
unknown_mel_spec.append(temp3)
unknown_mel_db.append(librosa.amplitude_to_db(np.abs(temp3), ref=np.max))
#리스트를 넘파이 배열로
unknown_mel_spec = np.array(unknown_mel_spec)
unknown_mel_db = np.array(unknown_mel_db)
In [ ]:
#각 배열의 shape확인
print("nali_stft 의 shape:", nali_mel_spec.shape)
print("nali_spec 의 shape:", nali_mel_db.shape)
print("biden_stft 의 shape:", biden_mel_spec.shape)
print("biden_spec 의 shape:", biden_mel_db.shape)
print("unknown_stft 의 shape:", unknown_mel_spec.shape)
print("unknown_spec 의 shape:", unknown_mel_db.shape)
nali_stft 의 shape: (24, 128, 65)
nali_spec 의 shape: (24, 128, 65)
biden_stft 의 shape: (24, 128, 65)
biden_spec 의 shape: (24, 128, 65)
unknown_stft 의 shape: (24, 128, 65)
unknown_spec 의 shape: (24, 128, 65)
In [ ]:
#3개 클래스에서 각각 10개씩 총 30개 STFT 시각화 비교
nali_num=0
biden_num=0
unknown_num=0
plt.figure(figsize=(20,40))
for i in range(1,31):
plt.subplot(10,3,i)
if i%3 == 0:
librosa.display.specshow(unknown_mel_db[unknown_num], x_axis='time', y_axis='mel')
plt.xlabel("unknown")
unknown_num = unknown_num +1
elif i%3 == 1:
librosa.display.specshow(biden_mel_db[biden_num], x_axis='time', y_axis='mel')
plt.xlabel("biden")
biden_num = biden_num +1
elif i%3 == 2:
librosa.display.specshow(nali_mel_db[nali_num], x_axis='time', y_axis='mel')
plt.xlabel("nali")
nali_num = nali_num +1
3.7.데이터 분석의 결과¶
1.
STFT서는 잘 드러나지 않았지만 Mel spectogram으로 만들어 시각화해보면 바이든 음성과 날리면 음성의 차이를 볼 수 있음
•
'바이든'은 전체 주파수의 색상이 좀 더 고르게 분포되어
•
바이든의 경우 사람의 음성주파수가 저-고까지 골고루 사용되면서 특히 512 - 1024HZ대역의 색상이 도드라 보임
•
'날리면'은 512 이하의 저주파수의 색상이 도드라 보임을 알수있음
1.
이는 반면 '날리면'의 경우 음성주파수의 저주파 영역이 좀 더 강조되서 사용된다는 결론
2.
또한, 바이든의 경우 주파수가 시간대비 비대칭 적인 모습이 강한 반면, 날리면의 경우 주파수가 시간대비 좌우 대칭적인 모습이 좀 더 강하게 나타남을 볼 수 있음
3.
물론 여기까지는 실험자의 음성과 네이버 클로버 음성만 가지고 한 결과~
3.8.테스트 데이터 분석해보기
In [ ]:
# 실제 test해볼 파일을 지정해서 들어보기
test_file_path = '/content/drive/MyDrive/Colab Notebooks/8.음향관련/sj_sound_data/test/test1.wav'
y_test, sr = librosa.load(test_file_path)
#일단 들어보기
display(Audio(data=y_test,rate=sr))
Your browser does not support the audio element.
In [ ]:
#멜스펙트럼으로 변환
test_mel = librosa.feature.melspectrogram(y_test, sr=sr, n_mels=128, fmax=8000)
test_mel_db = librosa.amplitude_to_db(np.abs(test_mel), ref=np.max)
librosa.display.specshow(test_mel_db, x_axis='time', y_axis='mel')
plt.show()
4.음성 데이터 증강¶
•
화이트 노이즈 추가
•
shiftinf 추가
•
Stretching 추가(주의: 늘어지더라도 음성데이터 길이는 통일 시켜야
함)
•
Minus추가
•
피치 변경(음의 높이 또는 높낮이)
4.1.음성데이터 증강을 위한 함수 생성하기
In [ ]:
#1.white Noise 만드는 함수
def add_white_noise(data, sr=22050, noise_rate=np.random.uniform(0,0.3)):
wn = np.random.randn(len(data))
data_wn = data + noise_rate*wn
data_wn = data_wn.astype(type(data[0])) #노이즈 작업전 datatype으로 데이터 형태 변환
return data_wn
In [ ]:
#2.Shifting 만드는 함수
def shifting_sound(data, sr=22050, roll_rate=0.1):
# 그냥 [1, 2, 3, 4] 를 [4, 1, 2, 3]으로 만들어주는 방식
data_roll = np.roll(data, int(len(data) * roll_rate))
data_roll = data_roll.astype(type(data[0]))
return data_roll
In [ ]:
#3. stretch_sound 만드는 함수
def stretch_sound(data, sr=22050, roll_rate=0.8):
# 그냥 [1, 2, 3, 4] 를 [4, 1, 2, 3]으로 만들어주는 방식
stretch_data = librosa.effects.time_stretch(data, roll_rate)
stretch_data = stretch_data.astype(type(data[0]))
#늘어나더라도 원본 데이터의 길이와 동일하게 잘라줌
stretch_data = stretch_data[:len(data)]
return stretch_data
In [ ]:
#4. 위상뒤집기
def minus_sound(data, sr=22050):
# 위상을 뒤집는 것으로서 원래 소리와 똑같이 들린다.
temp_numpy = (-1.3)*data
return temp_numpy
In [ ]:
#5. 피치 조절
def change_pitch(data, sr=22050, pitch_factor=np.random.randint(-5,5)):
ch_pi = librosa.effects.pitch_shift(data, sr, pitch_factor)
return ch_pi
4.2.전체 데이터에 대한 데이터 증강 실시¶
해당 배열과 동일한 zero배열을 만든 다음 음성 데이터 증강 함수를
사용해서 나온 결과(넘파일 배열)을 저장하는 방식
In [ ]:
#날리면 전체 넘파이 배열에 대해서 실시
#음성데이터 증강을 위해 넘파일 배열생성(shape은 동일하게 )
nali_array_ad = np.zeros_like(nali_array)
nali_array_sh = np.zeros_like(nali_array)
nali_array_st = np.zeros_like(nali_array)
nali_array_mi = np.zeros_like(nali_array)
nali_array_ch = np.zeros_like(nali_array)
#실제 데이터 증강 실시1
for i in range(len(nali_array)):
nali_array_ad[i] = add_white_noise(nali_array[i])
nali_array_sh[i] = shifting_sound(nali_array[i])
nali_array_st[i] = stretch_sound(nali_array[i])
nali_array_mi[i] = minus_sound(nali_array[i])
nali_array_ch[i] = change_pitch(nali_array[i])
In [ ]:
#바이든 전체 넘파이 배열에 대해서 실시
#음성데이터 증강을 위해 넘파일 배열생성(shape은 동일하게 )
biden_array_ad = np.zeros_like(biden_array)
biden_array_sh = np.zeros_like(biden_array)
biden_array_st = np.zeros_like(biden_array)
biden_array_mi = np.zeros_like(biden_array)
biden_array_ch = np.zeros_like(biden_array)
#실제 데이터 증강 실시2
for i in range(len(biden_array)):
biden_array_ad[i] = add_white_noise(biden_array[i])
biden_array_sh[i] = shifting_sound(biden_array[i])
biden_array_st[i] = stretch_sound(biden_array[i])
biden_array_mi[i] = minus_sound(biden_array[i])
biden_array_ch[i] = change_pitch(biden_array[i])
In [ ]:
#unknown 전체 넘파이 배열에 대해서 실시
#음성데이터 증강을 위해 넘파일 배열생성(shape은 동일하게 )
unknown_array_ad = np.zeros_like(unknown_array)
unknown_array_sh = np.zeros_like(unknown_array)
unknown_array_st = np.zeros_like(unknown_array)
unknown_array_mi = np.zeros_like(unknown_array)
unknown_array_ch = np.zeros_like(unknown_array)
#실제 데이터 증강 실시3
for i in range(len(unknown_array)):
unknown_array_ad[i] = add_white_noise(unknown_array[i])
unknown_array_sh[i] = shifting_sound(unknown_array[i])
unknown_array_st[i] = stretch_sound(unknown_array[i])
unknown_array_mi[i] = minus_sound(unknown_array[i])
unknown_array_ch[i] = change_pitch(unknown_array[i])
4.3. 만들어진 음성데이터와 원본데이터 합치기¶
•
원본 데이터 배열: biden_array, nali_array,
unknown_biden_array
•
만들어진 데이터 배열: 각각 5 묶음씩
unknown_array_ad unknown_array_sh unknown_array_st unknown_array_mi
unknown_array_ch
In [ ]:
unknown_array_ad.shape
Out[ ]:
(24, 33075)
In [ ]:
nali_hap = np.concatenate([nali_array, nali_array_ad, nali_array_sh, nali_array_st,nali_array_mi, nali_array_ch])
biden_hap = np.concatenate([biden_array, biden_array_ad, biden_array_sh, biden_array_st,biden_array_mi, biden_array_ch])
unknown_hap = np.concatenate([unknown_array, unknown_array_ad, unknown_array_sh, unknown_array_st,unknown_array_mi, unknown_array_ch])
In [ ]:
nali_hap.shape
Out[ ]:
(144, 33075)
In [ ]:
#아무거나 하나 끄집어 내서 들어보자
display(Audio(data=biden_hap[55],rate=sr))
Your browser does not support the audio element.
5.음성데이터 특성 추출(MFCC활용)¶
librosa라이브러리의 mfcc메서드 활용
•
librosa.feature.mfcc
1.
MFCC(Mel-frequency cepstral coefficients)이란?
•
Mel Scale Spectrum을 40개의 주파수 구역(band)으로 묶은뒤에 이를 다시
푸리에 변환하여 얻은 계수
•
스펙트럼이 어떤 모양으로 되어 있는지를 나타내는 특성값이라고 생각할
수 있음
1.
스펙트로그램과 비교하여 압축된 정보를 담고 있다고 생각할 수
있음
•
-> 오디오 신호 처리 분야에서 많이 사용되는 소리 데이터의
특징값(Feature)
1.
압축하는 과정에서 손실이 발생, 노이즈가 제거되는 효과가 있음
•
이 때문에 MFCC값이 고유의 음성 정보를 담고 있다고 보기도 함
5.0.왜 mfcc를 쓰는가?¶
MFCC는 실제로 화자 검증에 사용되기도 함
1.
화자 검증이란?
•
화자 인식(Speaker Recognition)의 세부 분류로서 말하는 사람이 그
사람이 맞는지를 확인하는 기술
•
시스템에 등록된 음성에만 반응하는 아이폰의 Siri를 예로 들 수
있음
MFCC는 등록된 음성과 현재 입력된 음성의 유사도를 판별하는 근거의
일부로 쓰임
1.
음악 장르 분류
•
MFCC는 음성뿐만 아니라 음악 신호에서도 사용됨
•
화자 인식에서 화자의 특징을 표현할 수 있는 것처럼, 음악의 특징도
MFCC로 표현할 수 있음
5.1.전체 데이터에 대해 mfcc값 추출
In [ ]:
#일단 1개 음성데이터만 mfcc값 추출
nali_mfccs_sample = librosa.feature.mfcc(y=nali_hap[1], sr=sr, n_mfcc=40)
nali_mfccs_sample.shape
Out[ ]:
(40, 65)
In [ ]:
# 위와 비슷한 방식으로 진행
# 빈 리스트 생성 후 값을 저장한 다음 넘파이 배열로 전환
nali_mfccs = []
biden_mfccs = []
unknown_mfccs = []
for i in range(len(nali_hap)):
nali_mfccs_temp = librosa.feature.mfcc(y=nali_hap[i], sr=sr, n_mfcc=40)
nali_mfccs.append(nali_mfccs_temp)
for i in range(len(biden_hap)):
biden_mfccs_temp = librosa.feature.mfcc(y=biden_hap[i], sr=sr, n_mfcc=40)
biden_mfccs.append(biden_mfccs_temp)
for i in range(len(unknown_hap)):
unknown_mfccs_temp = librosa.feature.mfcc(y=unknown_hap[i], sr=sr, n_mfcc=40)
unknown_mfccs.append(unknown_mfccs_temp)
#넘파이 배열로
nali_mfccs = np.array(nali_mfccs)
biden_mfccs = np.array(biden_mfccs)
unknown_mfccs = np.array(unknown_mfccs)
In [ ]:
#하나의 값 살펴보기
#주파수 밴드값 40개에 따른 값이 순차적으로 들어가 있음을 확인할 수 있음
print("shape살펴보기:", nali_mfccs[1].shape)
nali_mfccs[1]
shape살펴보기: (40, 65)
Out[ ]:
array([[-4.9913110e+02, -4.9564725e+02, -4.4911636e+02, ...,
-4.5145660e+02, -4.6994110e+02, -4.5325845e+02],
[ 1.4472638e+02, 1.4387582e+02, 1.4847940e+02, ...,
1.9463066e+02, 1.7764575e+02, 1.3753247e+02],
[-1.8510242e+01, -2.3417852e+01, -6.9817963e+01, ...,
3.1735716e+00, 8.2744229e-01, -4.1717410e+00],
...,
[ 4.9690681e+00, -1.7284521e-01, -4.4170566e+00, ...,
-5.6388378e+00, -5.0381408e+00, -2.0909131e+00],
[ 1.7101536e+00, -1.2693942e+00, -4.0835123e+00, ...,
-5.2187519e+00, -3.9217777e+00, -1.8445796e+00],
[ 2.1824310e+00, -1.0145936e+00, -6.0215049e+00, ...,
-3.6354413e+00, -4.1027637e+00, -7.1901979e+00]], dtype=float32)
In [ ]:
#3개의 배열을 합하여 train_data 셋 만들기
train = np.concatenate([nali_mfccs, biden_mfccs, unknown_mfccs])
train.shape
Out[ ]:
(432, 40, 65)
In [ ]:
len(train)
Out[ ]:
432
5.2.원핫인코딩을 통해 라벨링
In [ ]:
# 라벨 딕셔너리 생성
labels_dic = { 0:'nali', 1:'biden', 2:"unknown"}
In [ ]:
# train_hap의 길이와 동일한 1차원 배열 선언
labels_hap = np.array(range(len(train)))
labels_hap[:len(nali_mfccs)] = 0
labels_hap[len(nali_mfccs): len(nali_mfccs) + len(biden_mfccs) ] = 1
labels_hap[len(nali_mfccs) + len(biden_mfccs):] = 2
In [ ]:
#전체 라벨 살펴보기
labels_hap
Out[ ]:
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, 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, 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, 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, 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, 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, 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])
In [ ]:
#원핫 인코딩
labels = tf.keras.utils.to_categorical(labels_hap)
print(labels)
[[1. 0. 0.]
[1. 0. 0.]
[1. 0. 0.]
...
[0. 0. 1.]
[0. 0. 1.]
[0. 0. 1.]]
5.3.넘파이값 중간저장¶
여기까지 생성된 넘파이 배열값을 중간저장해보기
쭉이어서 실습하는 경우 생략 가능
In [ ]:
#넘파이 배열값 저장하기
np.savez('/content/drive/MyDrive/Colab Notebooks/8.음향관련/np_biden_nali/biden_nali.npz', x=train, y=labels) #각각 이름 붙여줘야 불러오기 가능
In [ ]:
#저장된 넘파이 값 불러오기
data = np.load('/content/drive/MyDrive/Colab Notebooks/8.음향관련/np_biden_nali/biden_nali.npz')
In [ ]:
# 불러온 넘파이 배열 객체의 요소들 살펴보기
list(data)
Out[ ]:
['x', 'y']
In [ ]:
# 저장된 값 확인
# 이때 data안의 x인자와 y인자로 다시 train과 labels를 지정하면 됨
train = data['x']
labels = data['y']
#잘 불러와졌는지 확인
train.shape
Out[ ]:
(432, 40, 65)
In [ ]:
# 1개 데이터 살펴보기
train[55]
Out[ ]:
array([[-4.8383432e+02, -4.8399628e+02, -4.8448148e+02, ...,
-4.8263776e+02, -4.8268063e+02, -4.8698911e+02],
[ 1.2132401e+02, 1.2063139e+02, 1.2019527e+02, ...,
1.2439251e+02, 1.2386364e+02, 1.2079936e+02],
[-1.0269537e+01, -1.3739555e+01, -1.6760509e+01, ...,
-9.4014015e+00, -9.7376108e+00, -7.8921719e+00],
...,
[-3.8066702e+00, -6.0530100e+00, -4.7379303e+00, ...,
-2.1580973e+00, -8.4639680e-01, -2.1941042e+00],
[-5.6251526e+00, -4.9300432e+00, -3.5507956e+00, ...,
-5.2756071e-03, -9.7138762e-01, -6.1927958e+00],
[-1.3666394e+00, -2.7397871e-03, 2.1868539e-01, ...,
-2.2799945e-01, -4.4908357e-01, -5.0657244e+00]], dtype=float32)
5.4.훈련 데이터 셋, 검증 데이터 셋을 만들기
In [ ]:
#사이킷런 라이브러리 활용
from sklearn.model_selection import train_test_split
train_sound, test_sound, train_labels, test_labels = train_test_split(train, labels, test_size=0.2, random_state = 42)
#훈련 데이터셋 살펴보기
train_sound.shape
Out[ ]:
(345, 40, 65)
In [ ]:
#훈련 데이터 라벨 살펴보기
train_labels
Out[ ]:
array([[1., 0., 0.],
[0., 1., 0.],
[1., 0., 0.],
...,
[0., 1., 0.],
[0., 0., 1.],
[1., 0., 0.]], dtype=float32)
In [ ]:
#훈련데이터 하나를 음성으로 다시 변환해서 들어보고
temp = librosa.feature.inverse. mfcc_to_audio(train_sound[130])
display(Audio(data=temp,rate=sr))
Your browser does not support the audio element.
In [ ]:
#라벨링이 맞게 되어 있는지 확인
train_labels[130]
# labels_dic = { 0:'nali', 1:'biden', 2:"unknown"}
Out[ ]:
array([1., 0., 0.], dtype=float32)
6.CNN모델 만들기(3개 클래스)
6.1.관련 메서드 불러오기
In [ ]:
from tensorflow.keras import layers
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Conv2D, MaxPool2D, Input, Dense, Flatten,Dropout, GlobalAvgPool2D
6.2.모델 만들기¶
다중분류로 진행하므로 손실함수는 바이너리 크로스 엔트로피가 아닌
categorical_cross_entropy 사용
In [ ]:
#함수형 API사용
# input데이터의 shape는 (배치, 40, 65)
# output데이터의 shape는 (배치,3) 여기서3은 바이든, 날리면, 모르겠다
# 1번 모델은 풀링없이
def biden_nali_model1():
inputs = tf.keras.Input(shape=(40, 65,1))
x1 = Conv2D(16,2,activation= 'relu', padding='same')(inputs)
x1 = Dropout(0.2)(x1)
x2 = Conv2D(32,2,activation= 'relu', padding='same')(x1)
x2 = MaxPool2D(2)(x2)
x2 = Dropout(0.2)(x2)
x3 = Conv2D(32,2,activation= 'relu', padding='same')(x2)
x3 = Dropout(0.2)(x3)
x4 = Conv2D(64,2,activation= 'relu', padding='same')(x3)
x4 = Dropout(0.2)(x4)
x5 = Conv2D(128,2,activation= 'relu')(x4)
x5 = MaxPool2D(2)(x5)
#마지막 단계에서는 dropout생략
x6 = GlobalAvgPool2D()(x5)
outputs = Dense(3, activation='softmax')(x6)
model = Model(inputs,outputs)
return model
In [ ]:
def biden_nali_model2():
inputs = tf.keras.Input(shape=(40, 65,1))
x1 = Conv2D(16,2,activation= 'relu')(inputs)
x1 = MaxPool2D(2)(x1)
x1 = Dropout(0.2)(x1)
x2 = Conv2D(32,2,activation= 'relu')(x1)
x2 = MaxPool2D(2)(x2)
x2 = Dropout(0.2)(x2)
x3 = Conv2D(32,2,activation= 'relu')(x2)
x3 = MaxPool2D(2)(x3)
x3 = Dropout(0.2)(x3)
x4 = Conv2D(64,2,activation= 'relu')(x3)
x4 = MaxPool2D(2)(x4)
x4 = Dropout(0.2)(x4)
#마지막 단계에서는 dropout생략
x5 = Flatten()(x4)
outputs = Dense(3, activation='softmax')(x5)
model = Model(inputs,outputs)
return model
In [ ]:
# 모델 만들고 모델 구조 확인
model1 = biden_nali_model1()
model2 = biden_nali_model2()
In [ ]:
# 1번 모델의 구조
model1.summary()
Model: "model_8"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_10 (InputLayer) [(None, 40, 65, 1)] 0
conv2d_37 (Conv2D) (None, 40, 65, 16) 80
dropout_32 (Dropout) (None, 40, 65, 16) 0
conv2d_38 (Conv2D) (None, 40, 65, 32) 2080
max_pooling2d_27 (MaxPoolin (None, 20, 32, 32) 0
g2D)
dropout_33 (Dropout) (None, 20, 32, 32) 0
conv2d_39 (Conv2D) (None, 20, 32, 32) 4128
dropout_34 (Dropout) (None, 20, 32, 32) 0
conv2d_40 (Conv2D) (None, 20, 32, 64) 8256
dropout_35 (Dropout) (None, 20, 32, 64) 0
conv2d_41 (Conv2D) (None, 19, 31, 128) 32896
max_pooling2d_28 (MaxPoolin (None, 9, 15, 128) 0
g2D)
global_average_pooling2d_4 (None, 128) 0
(GlobalAveragePooling2D)
dense_8 (Dense) (None, 3) 387
=================================================================
Total params: 47,827
Trainable params: 47,827
Non-trainable params: 0
_________________________________________________________________
In [ ]:
# 2번 모델의 구조
model2.summary()
Model: "model_9"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_11 (InputLayer) [(None, 40, 65, 1)] 0
conv2d_42 (Conv2D) (None, 39, 64, 16) 80
max_pooling2d_29 (MaxPoolin (None, 19, 32, 16) 0
g2D)
dropout_36 (Dropout) (None, 19, 32, 16) 0
conv2d_43 (Conv2D) (None, 18, 31, 32) 2080
max_pooling2d_30 (MaxPoolin (None, 9, 15, 32) 0
g2D)
dropout_37 (Dropout) (None, 9, 15, 32) 0
conv2d_44 (Conv2D) (None, 8, 14, 32) 4128
max_pooling2d_31 (MaxPoolin (None, 4, 7, 32) 0
g2D)
dropout_38 (Dropout) (None, 4, 7, 32) 0
conv2d_45 (Conv2D) (None, 3, 6, 64) 8256
max_pooling2d_32 (MaxPoolin (None, 1, 3, 64) 0
g2D)
dropout_39 (Dropout) (None, 1, 3, 64) 0
flatten_4 (Flatten) (None, 192) 0
dense_9 (Dense) (None, 3) 579
=================================================================
Total params: 15,123
Trainable params: 15,123
Non-trainable params: 0
_________________________________________________________________
In [ ]:
# 옵티마이져 설정
opt1 = tf.keras.optimizers.Adam(learning_rate=0.001)
opt2 = tf.keras.optimizers.Adam(learning_rate=0.0005)
model1.compile(loss = 'categorical_crossentropy', optimizer=opt1, metrics='accuracy')
model2.compile(loss = 'categorical_crossentropy', optimizer=opt2, metrics='accuracy')
In [ ]:
callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=15)]
6.3.학습시키기
In [ ]:
# train_expand_images, train_labels을 학습시키고 15 epochs을 돌리고 그 진행 사항을 hist1에 저장
# train_expand_images의 shape와 model의 input데이터의 shape이 다르기 때문에 reshape필요
hist1 = model1.fit(train_sound.reshape(-1,40,65,1), train_labels,
validation_data = (test_sound.reshape((-1,40,65,1)), test_labels),
batch_size = 128, epochs=500, verbose=1, callbacks=callbacks)
Epoch 1/500
3/3 [==============================] - 0s 66ms/step - loss: 0.2373 - accuracy: 0.9043 - val_loss: 0.3829 - val_accuracy: 0.8966
Epoch 2/500
3/3 [==============================] - 0s 47ms/step - loss: 0.1825 - accuracy: 0.9333 - val_loss: 0.3802 - val_accuracy: 0.9080
Epoch 3/500
3/3 [==============================] - 0s 42ms/step - loss: 0.1749 - accuracy: 0.9333 - val_loss: 0.4042 - val_accuracy: 0.8966
Epoch 4/500
3/3 [==============================] - 0s 42ms/step - loss: 0.1879 - accuracy: 0.9246 - val_loss: 0.3670 - val_accuracy: 0.9080
Epoch 5/500
3/3 [==============================] - 0s 45ms/step - loss: 0.1785 - accuracy: 0.9275 - val_loss: 0.3627 - val_accuracy: 0.9195
Epoch 6/500
3/3 [==============================] - 0s 40ms/step - loss: 0.1655 - accuracy: 0.9304 - val_loss: 0.3590 - val_accuracy: 0.9080
Epoch 7/500
3/3 [==============================] - 0s 40ms/step - loss: 0.1855 - accuracy: 0.9304 - val_loss: 0.3578 - val_accuracy: 0.9310
Epoch 8/500
3/3 [==============================] - 0s 41ms/step - loss: 0.1741 - accuracy: 0.9362 - val_loss: 0.3635 - val_accuracy: 0.9310
Epoch 9/500
3/3 [==============================] - 0s 38ms/step - loss: 0.1700 - accuracy: 0.9391 - val_loss: 0.3733 - val_accuracy: 0.8966
Epoch 10/500
3/3 [==============================] - 0s 42ms/step - loss: 0.1712 - accuracy: 0.9478 - val_loss: 0.3781 - val_accuracy: 0.9195
Epoch 11/500
3/3 [==============================] - 0s 39ms/step - loss: 0.1800 - accuracy: 0.9333 - val_loss: 0.3786 - val_accuracy: 0.9080
Epoch 12/500
3/3 [==============================] - 0s 40ms/step - loss: 0.1967 - accuracy: 0.9217 - val_loss: 0.4012 - val_accuracy: 0.9195
Epoch 13/500
3/3 [==============================] - 0s 39ms/step - loss: 0.2125 - accuracy: 0.9130 - val_loss: 0.3809 - val_accuracy: 0.8966
Epoch 14/500
3/3 [==============================] - 0s 39ms/step - loss: 0.2407 - accuracy: 0.9101 - val_loss: 0.3935 - val_accuracy: 0.8851
Epoch 15/500
3/3 [==============================] - 0s 39ms/step - loss: 0.2333 - accuracy: 0.9043 - val_loss: 0.3600 - val_accuracy: 0.9195
Epoch 16/500
3/3 [==============================] - 0s 39ms/step - loss: 0.2164 - accuracy: 0.9101 - val_loss: 0.3697 - val_accuracy: 0.9195
Epoch 17/500
3/3 [==============================] - 0s 39ms/step - loss: 0.2206 - accuracy: 0.9101 - val_loss: 0.3755 - val_accuracy: 0.9195
Epoch 18/500
3/3 [==============================] - 0s 56ms/step - loss: 0.2315 - accuracy: 0.9101 - val_loss: 0.3928 - val_accuracy: 0.8966
Epoch 19/500
3/3 [==============================] - 0s 70ms/step - loss: 0.2094 - accuracy: 0.9188 - val_loss: 0.4336 - val_accuracy: 0.8966
Epoch 20/500
3/3 [==============================] - 0s 58ms/step - loss: 0.2261 - accuracy: 0.9043 - val_loss: 0.4493 - val_accuracy: 0.7931
Epoch 21/500
3/3 [==============================] - 0s 64ms/step - loss: 0.2484 - accuracy: 0.9101 - val_loss: 0.4418 - val_accuracy: 0.8621
Epoch 22/500
3/3 [==============================] - 0s 59ms/step - loss: 0.2004 - accuracy: 0.9043 - val_loss: 0.3654 - val_accuracy: 0.9195
In [ ]:
#학습 진행사항을 plt로 출력
# hist1의 accuracy plt의 plot을 이용하여 출력
plt.plot(hist1.history['accuracy'], label='accuracy')
plt.plot(hist1.history['loss'], label='loss')
plt.plot(hist1.history['val_accuracy'], label='val_accuracy')
plt.plot(hist1.history['val_loss'], label='val_loss')
plt.ylim(0.0, 1.0)
plt.legend(loc='upper left')
plt.show()
In [ ]:
# model2는 조기 종료 없이 진행
hist2 = model2.fit(train_sound.reshape(-1,40,65,1), train_labels,
validation_data = (test_sound.reshape((-1,40,65,1)), test_labels),
batch_size=128, epochs=1000, verbose=1)
Epoch 1/1000
3/3 [==============================] - 1s 207ms/step - loss: 10.6634 - accuracy: 0.3333 - val_loss: 1.8911 - val_accuracy: 0.4138
Epoch 2/1000
3/3 [==============================] - 0s 19ms/step - loss: 8.8385 - accuracy: 0.3594 - val_loss: 1.6298 - val_accuracy: 0.4253
Epoch 3/1000
3/3 [==============================] - 0s 20ms/step - loss: 8.1504 - accuracy: 0.2667 - val_loss: 1.3170 - val_accuracy: 0.4253
Epoch 4/1000
3/3 [==============================] - 0s 19ms/step - loss: 6.3507 - accuracy: 0.3797 - val_loss: 1.2881 - val_accuracy: 0.4483
Epoch 5/1000
3/3 [==============================] - 0s 22ms/step - loss: 6.8481 - accuracy: 0.2928 - val_loss: 1.1463 - val_accuracy: 0.4368
Epoch 6/1000
3/3 [==============================] - 0s 23ms/step - loss: 5.4418 - accuracy: 0.3188 - val_loss: 1.1798 - val_accuracy: 0.4023
Epoch 7/1000
3/3 [==============================] - 0s 21ms/step - loss: 5.6249 - accuracy: 0.3391 - val_loss: 1.1693 - val_accuracy: 0.4253
Epoch 8/1000
3/3 [==============================] - 0s 21ms/step - loss: 4.4611 - accuracy: 0.3449 - val_loss: 1.1246 - val_accuracy: 0.4483
Epoch 9/1000
3/3 [==============================] - 0s 21ms/step - loss: 4.2979 - accuracy: 0.3652 - val_loss: 1.0989 - val_accuracy: 0.4368
Epoch 10/1000
3/3 [==============================] - 0s 22ms/step - loss: 3.7040 - accuracy: 0.3565 - val_loss: 1.0631 - val_accuracy: 0.5402
Epoch 11/1000
3/3 [==============================] - 0s 21ms/step - loss: 3.5407 - accuracy: 0.3768 - val_loss: 1.0686 - val_accuracy: 0.5402
Epoch 12/1000
3/3 [==============================] - 0s 19ms/step - loss: 2.9471 - accuracy: 0.3971 - val_loss: 1.0719 - val_accuracy: 0.4943
Epoch 13/1000
3/3 [==============================] - 0s 20ms/step - loss: 3.1691 - accuracy: 0.3565 - val_loss: 1.0618 - val_accuracy: 0.5057
Epoch 14/1000
3/3 [==============================] - 0s 19ms/step - loss: 2.9005 - accuracy: 0.3623 - val_loss: 1.0645 - val_accuracy: 0.5172
Epoch 15/1000
3/3 [==============================] - 0s 21ms/step - loss: 2.7357 - accuracy: 0.3797 - val_loss: 1.0689 - val_accuracy: 0.4943
Epoch 16/1000
3/3 [==============================] - 0s 23ms/step - loss: 2.2361 - accuracy: 0.4290 - val_loss: 1.0668 - val_accuracy: 0.5057
Epoch 17/1000
3/3 [==============================] - 0s 22ms/step - loss: 2.5358 - accuracy: 0.3739 - val_loss: 1.0600 - val_accuracy: 0.4828
Epoch 18/1000
3/3 [==============================] - 0s 25ms/step - loss: 2.1070 - accuracy: 0.3710 - val_loss: 1.0611 - val_accuracy: 0.4943
Epoch 19/1000
3/3 [==============================] - 0s 22ms/step - loss: 2.2899 - accuracy: 0.3536 - val_loss: 1.0623 - val_accuracy: 0.4828
Epoch 20/1000
3/3 [==============================] - 0s 21ms/step - loss: 2.0500 - accuracy: 0.4174 - val_loss: 1.0592 - val_accuracy: 0.4828
Epoch 21/1000
3/3 [==============================] - 0s 26ms/step - loss: 2.0326 - accuracy: 0.3536 - val_loss: 1.0466 - val_accuracy: 0.4828
Epoch 22/1000
3/3 [==============================] - 0s 21ms/step - loss: 1.9686 - accuracy: 0.3855 - val_loss: 1.0371 - val_accuracy: 0.4368
Epoch 23/1000
3/3 [==============================] - 0s 21ms/step - loss: 1.7986 - accuracy: 0.3913 - val_loss: 1.0335 - val_accuracy: 0.4598
Epoch 24/1000
3/3 [==============================] - 0s 24ms/step - loss: 1.6429 - accuracy: 0.4203 - val_loss: 1.0315 - val_accuracy: 0.4598
Epoch 25/1000
3/3 [==============================] - 0s 21ms/step - loss: 1.6483 - accuracy: 0.4290 - val_loss: 1.0270 - val_accuracy: 0.4483
Epoch 26/1000
3/3 [==============================] - 0s 22ms/step - loss: 1.5953 - accuracy: 0.4290 - val_loss: 1.0219 - val_accuracy: 0.4828
Epoch 27/1000
3/3 [==============================] - 0s 22ms/step - loss: 1.6324 - accuracy: 0.4058 - val_loss: 1.0179 - val_accuracy: 0.4598
Epoch 28/1000
3/3 [==============================] - 0s 20ms/step - loss: 1.4463 - accuracy: 0.4377 - val_loss: 1.0183 - val_accuracy: 0.4483
Epoch 29/1000
3/3 [==============================] - 0s 20ms/step - loss: 1.4832 - accuracy: 0.4290 - val_loss: 1.0197 - val_accuracy: 0.4483
Epoch 30/1000
3/3 [==============================] - 0s 22ms/step - loss: 1.4317 - accuracy: 0.4580 - val_loss: 1.0203 - val_accuracy: 0.4483
Epoch 31/1000
3/3 [==============================] - 0s 22ms/step - loss: 1.3526 - accuracy: 0.4493 - val_loss: 1.0217 - val_accuracy: 0.4598
Epoch 32/1000
3/3 [==============================] - 0s 22ms/step - loss: 1.2652 - accuracy: 0.4609 - val_loss: 1.0185 - val_accuracy: 0.4483
Epoch 33/1000
3/3 [==============================] - 0s 22ms/step - loss: 1.3732 - accuracy: 0.4435 - val_loss: 1.0106 - val_accuracy: 0.4713
Epoch 34/1000
3/3 [==============================] - 0s 22ms/step - loss: 1.3832 - accuracy: 0.4029 - val_loss: 1.0063 - val_accuracy: 0.4713
Epoch 35/1000
3/3 [==============================] - 0s 21ms/step - loss: 1.3429 - accuracy: 0.4174 - val_loss: 0.9999 - val_accuracy: 0.4828
Epoch 36/1000
3/3 [==============================] - 0s 22ms/step - loss: 1.3910 - accuracy: 0.4116 - val_loss: 0.9896 - val_accuracy: 0.5172
Epoch 37/1000
3/3 [==============================] - 0s 30ms/step - loss: 1.2503 - accuracy: 0.4812 - val_loss: 0.9810 - val_accuracy: 0.5172
Epoch 38/1000
3/3 [==============================] - 0s 24ms/step - loss: 1.2076 - accuracy: 0.4812 - val_loss: 0.9746 - val_accuracy: 0.5172
Epoch 39/1000
3/3 [==============================] - 0s 24ms/step - loss: 1.1483 - accuracy: 0.5217 - val_loss: 0.9684 - val_accuracy: 0.5287
Epoch 40/1000
3/3 [==============================] - 0s 20ms/step - loss: 1.0728 - accuracy: 0.5188 - val_loss: 0.9617 - val_accuracy: 0.5172
Epoch 41/1000
3/3 [==============================] - 0s 24ms/step - loss: 1.2473 - accuracy: 0.4406 - val_loss: 0.9545 - val_accuracy: 0.5172
Epoch 42/1000
3/3 [==============================] - 0s 23ms/step - loss: 1.1225 - accuracy: 0.4899 - val_loss: 0.9455 - val_accuracy: 0.5747
Epoch 43/1000
3/3 [==============================] - 0s 20ms/step - loss: 1.1531 - accuracy: 0.4841 - val_loss: 0.9386 - val_accuracy: 0.5862
Epoch 44/1000
3/3 [==============================] - 0s 23ms/step - loss: 1.1298 - accuracy: 0.5014 - val_loss: 0.9325 - val_accuracy: 0.5632
Epoch 45/1000
3/3 [==============================] - 0s 21ms/step - loss: 1.1001 - accuracy: 0.5130 - val_loss: 0.9268 - val_accuracy: 0.5977
Epoch 46/1000
3/3 [==============================] - 0s 20ms/step - loss: 1.1131 - accuracy: 0.4986 - val_loss: 0.9235 - val_accuracy: 0.5747
Epoch 47/1000
3/3 [==============================] - 0s 23ms/step - loss: 1.0870 - accuracy: 0.5072 - val_loss: 0.9213 - val_accuracy: 0.5632
Epoch 48/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.9990 - accuracy: 0.5246 - val_loss: 0.9126 - val_accuracy: 0.5977
Epoch 49/1000
3/3 [==============================] - 0s 21ms/step - loss: 1.0783 - accuracy: 0.5101 - val_loss: 0.9041 - val_accuracy: 0.6207
Epoch 50/1000
3/3 [==============================] - 0s 22ms/step - loss: 1.0289 - accuracy: 0.5014 - val_loss: 0.8930 - val_accuracy: 0.6207
Epoch 51/1000
3/3 [==============================] - 0s 21ms/step - loss: 1.1638 - accuracy: 0.4522 - val_loss: 0.8864 - val_accuracy: 0.6207
Epoch 52/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.9578 - accuracy: 0.5768 - val_loss: 0.8824 - val_accuracy: 0.6207
Epoch 53/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.9682 - accuracy: 0.5333 - val_loss: 0.8799 - val_accuracy: 0.6092
Epoch 54/1000
3/3 [==============================] - 0s 24ms/step - loss: 1.0474 - accuracy: 0.4957 - val_loss: 0.8767 - val_accuracy: 0.6322
Epoch 55/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.9907 - accuracy: 0.5478 - val_loss: 0.8736 - val_accuracy: 0.6322
Epoch 56/1000
3/3 [==============================] - 0s 20ms/step - loss: 1.0076 - accuracy: 0.5362 - val_loss: 0.8672 - val_accuracy: 0.6207
Epoch 57/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.9306 - accuracy: 0.5855 - val_loss: 0.8562 - val_accuracy: 0.6207
Epoch 58/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.9223 - accuracy: 0.5797 - val_loss: 0.8452 - val_accuracy: 0.5862
Epoch 59/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.9418 - accuracy: 0.5739 - val_loss: 0.8378 - val_accuracy: 0.5977
Epoch 60/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.9686 - accuracy: 0.5681 - val_loss: 0.8311 - val_accuracy: 0.5977
Epoch 61/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.8923 - accuracy: 0.5942 - val_loss: 0.8259 - val_accuracy: 0.6092
Epoch 62/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.9205 - accuracy: 0.5623 - val_loss: 0.8239 - val_accuracy: 0.6322
Epoch 63/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.8963 - accuracy: 0.5768 - val_loss: 0.8204 - val_accuracy: 0.6552
Epoch 64/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.8693 - accuracy: 0.5913 - val_loss: 0.8162 - val_accuracy: 0.6437
Epoch 65/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.8755 - accuracy: 0.6145 - val_loss: 0.8088 - val_accuracy: 0.6322
Epoch 66/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.8538 - accuracy: 0.6116 - val_loss: 0.8021 - val_accuracy: 0.6437
Epoch 67/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.8525 - accuracy: 0.6377 - val_loss: 0.7988 - val_accuracy: 0.6437
Epoch 68/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.8650 - accuracy: 0.6174 - val_loss: 0.7977 - val_accuracy: 0.6322
Epoch 69/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.8717 - accuracy: 0.5739 - val_loss: 0.7976 - val_accuracy: 0.6437
Epoch 70/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.8164 - accuracy: 0.6261 - val_loss: 0.7960 - val_accuracy: 0.6437
Epoch 71/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.7734 - accuracy: 0.6493 - val_loss: 0.7943 - val_accuracy: 0.6552
Epoch 72/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.7318 - accuracy: 0.6841 - val_loss: 0.7917 - val_accuracy: 0.6667
Epoch 73/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.8014 - accuracy: 0.6435 - val_loss: 0.7850 - val_accuracy: 0.6667
Epoch 74/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.8070 - accuracy: 0.6145 - val_loss: 0.7775 - val_accuracy: 0.6437
Epoch 75/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.7520 - accuracy: 0.6464 - val_loss: 0.7700 - val_accuracy: 0.6322
Epoch 76/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.8137 - accuracy: 0.6145 - val_loss: 0.7668 - val_accuracy: 0.6322
Epoch 77/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.7864 - accuracy: 0.6203 - val_loss: 0.7630 - val_accuracy: 0.6322
Epoch 78/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.8474 - accuracy: 0.6058 - val_loss: 0.7626 - val_accuracy: 0.6322
Epoch 79/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.6994 - accuracy: 0.6754 - val_loss: 0.7644 - val_accuracy: 0.6667
Epoch 80/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.7504 - accuracy: 0.6493 - val_loss: 0.7645 - val_accuracy: 0.6437
Epoch 81/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.7911 - accuracy: 0.6348 - val_loss: 0.7655 - val_accuracy: 0.6322
Epoch 82/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.7188 - accuracy: 0.6667 - val_loss: 0.7686 - val_accuracy: 0.6552
Epoch 83/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.6837 - accuracy: 0.6319 - val_loss: 0.7668 - val_accuracy: 0.6552
Epoch 84/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.8353 - accuracy: 0.6319 - val_loss: 0.7590 - val_accuracy: 0.6782
Epoch 85/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.8061 - accuracy: 0.6261 - val_loss: 0.7507 - val_accuracy: 0.6782
Epoch 86/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.7088 - accuracy: 0.6638 - val_loss: 0.7423 - val_accuracy: 0.6667
Epoch 87/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.6890 - accuracy: 0.6899 - val_loss: 0.7355 - val_accuracy: 0.6897
Epoch 88/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.6991 - accuracy: 0.6841 - val_loss: 0.7324 - val_accuracy: 0.6667
Epoch 89/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.7543 - accuracy: 0.6377 - val_loss: 0.7323 - val_accuracy: 0.6552
Epoch 90/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.6988 - accuracy: 0.7130 - val_loss: 0.7341 - val_accuracy: 0.6667
Epoch 91/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.7298 - accuracy: 0.6435 - val_loss: 0.7355 - val_accuracy: 0.6782
Epoch 92/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.6881 - accuracy: 0.6928 - val_loss: 0.7338 - val_accuracy: 0.6782
Epoch 93/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.7214 - accuracy: 0.6609 - val_loss: 0.7291 - val_accuracy: 0.6782
Epoch 94/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.6980 - accuracy: 0.6783 - val_loss: 0.7250 - val_accuracy: 0.6782
Epoch 95/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.6822 - accuracy: 0.7217 - val_loss: 0.7190 - val_accuracy: 0.6782
Epoch 96/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.6573 - accuracy: 0.7072 - val_loss: 0.7144 - val_accuracy: 0.6782
Epoch 97/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.6897 - accuracy: 0.6696 - val_loss: 0.7157 - val_accuracy: 0.6897
Epoch 98/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.7413 - accuracy: 0.6551 - val_loss: 0.7228 - val_accuracy: 0.6897
Epoch 99/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.6791 - accuracy: 0.6812 - val_loss: 0.7263 - val_accuracy: 0.6897
Epoch 100/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.6502 - accuracy: 0.6841 - val_loss: 0.7231 - val_accuracy: 0.6782
Epoch 101/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.6717 - accuracy: 0.7159 - val_loss: 0.7147 - val_accuracy: 0.6897
Epoch 102/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.6477 - accuracy: 0.6928 - val_loss: 0.7070 - val_accuracy: 0.6897
Epoch 103/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.6259 - accuracy: 0.6899 - val_loss: 0.7023 - val_accuracy: 0.6782
Epoch 104/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.6310 - accuracy: 0.7304 - val_loss: 0.7002 - val_accuracy: 0.6782
Epoch 105/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.6320 - accuracy: 0.7449 - val_loss: 0.7012 - val_accuracy: 0.6782
Epoch 106/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.6554 - accuracy: 0.6870 - val_loss: 0.7056 - val_accuracy: 0.6897
Epoch 107/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.6473 - accuracy: 0.6725 - val_loss: 0.7027 - val_accuracy: 0.7011
Epoch 108/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.5790 - accuracy: 0.7478 - val_loss: 0.6932 - val_accuracy: 0.6782
Epoch 109/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.6006 - accuracy: 0.7507 - val_loss: 0.6820 - val_accuracy: 0.6667
Epoch 110/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.5994 - accuracy: 0.7449 - val_loss: 0.6755 - val_accuracy: 0.6667
Epoch 111/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.6475 - accuracy: 0.7014 - val_loss: 0.6756 - val_accuracy: 0.6667
Epoch 112/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.5550 - accuracy: 0.7507 - val_loss: 0.6755 - val_accuracy: 0.6667
Epoch 113/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.5740 - accuracy: 0.7362 - val_loss: 0.6739 - val_accuracy: 0.6782
Epoch 114/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.6435 - accuracy: 0.7275 - val_loss: 0.6729 - val_accuracy: 0.7011
Epoch 115/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.5138 - accuracy: 0.7739 - val_loss: 0.6684 - val_accuracy: 0.7011
Epoch 116/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.5880 - accuracy: 0.7275 - val_loss: 0.6625 - val_accuracy: 0.6782
Epoch 117/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.5936 - accuracy: 0.7188 - val_loss: 0.6571 - val_accuracy: 0.6667
Epoch 118/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.6005 - accuracy: 0.7391 - val_loss: 0.6542 - val_accuracy: 0.6897
Epoch 119/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.5852 - accuracy: 0.7246 - val_loss: 0.6513 - val_accuracy: 0.7126
Epoch 120/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.5554 - accuracy: 0.7478 - val_loss: 0.6477 - val_accuracy: 0.7241
Epoch 121/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.6032 - accuracy: 0.7362 - val_loss: 0.6415 - val_accuracy: 0.7126
Epoch 122/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.5656 - accuracy: 0.7333 - val_loss: 0.6338 - val_accuracy: 0.7126
Epoch 123/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.5282 - accuracy: 0.7710 - val_loss: 0.6298 - val_accuracy: 0.7126
Epoch 124/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.5553 - accuracy: 0.7565 - val_loss: 0.6280 - val_accuracy: 0.7241
Epoch 125/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.5147 - accuracy: 0.7710 - val_loss: 0.6283 - val_accuracy: 0.7586
Epoch 126/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.6067 - accuracy: 0.7188 - val_loss: 0.6280 - val_accuracy: 0.7471
Epoch 127/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.5945 - accuracy: 0.7391 - val_loss: 0.6266 - val_accuracy: 0.7471
Epoch 128/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.5596 - accuracy: 0.7478 - val_loss: 0.6231 - val_accuracy: 0.7471
Epoch 129/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.5909 - accuracy: 0.7101 - val_loss: 0.6113 - val_accuracy: 0.7471
Epoch 130/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.5266 - accuracy: 0.7652 - val_loss: 0.6035 - val_accuracy: 0.7471
Epoch 131/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.5120 - accuracy: 0.7507 - val_loss: 0.5998 - val_accuracy: 0.7471
Epoch 132/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.5868 - accuracy: 0.7449 - val_loss: 0.6001 - val_accuracy: 0.7356
Epoch 133/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.4503 - accuracy: 0.8145 - val_loss: 0.6000 - val_accuracy: 0.7701
Epoch 134/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.5166 - accuracy: 0.7797 - val_loss: 0.5985 - val_accuracy: 0.7816
Epoch 135/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4810 - accuracy: 0.7826 - val_loss: 0.5932 - val_accuracy: 0.7586
Epoch 136/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.5250 - accuracy: 0.7623 - val_loss: 0.5827 - val_accuracy: 0.7701
Epoch 137/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4685 - accuracy: 0.7913 - val_loss: 0.5755 - val_accuracy: 0.7586
Epoch 138/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.5359 - accuracy: 0.7652 - val_loss: 0.5733 - val_accuracy: 0.7701
Epoch 139/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.5138 - accuracy: 0.7739 - val_loss: 0.5706 - val_accuracy: 0.7701
Epoch 140/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4812 - accuracy: 0.7826 - val_loss: 0.5702 - val_accuracy: 0.7816
Epoch 141/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.4626 - accuracy: 0.7942 - val_loss: 0.5700 - val_accuracy: 0.7816
Epoch 142/1000
3/3 [==============================] - 0s 20ms/step - loss: 0.4829 - accuracy: 0.7971 - val_loss: 0.5653 - val_accuracy: 0.7816
Epoch 143/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.4846 - accuracy: 0.7884 - val_loss: 0.5628 - val_accuracy: 0.7816
Epoch 144/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.4974 - accuracy: 0.7913 - val_loss: 0.5622 - val_accuracy: 0.7701
Epoch 145/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.5493 - accuracy: 0.7710 - val_loss: 0.5614 - val_accuracy: 0.7586
Epoch 146/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4729 - accuracy: 0.8000 - val_loss: 0.5600 - val_accuracy: 0.7471
Epoch 147/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4673 - accuracy: 0.7884 - val_loss: 0.5567 - val_accuracy: 0.7701
Epoch 148/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.5370 - accuracy: 0.7681 - val_loss: 0.5505 - val_accuracy: 0.7816
Epoch 149/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.4811 - accuracy: 0.7768 - val_loss: 0.5439 - val_accuracy: 0.7701
Epoch 150/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.4691 - accuracy: 0.7739 - val_loss: 0.5398 - val_accuracy: 0.7931
Epoch 151/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.4816 - accuracy: 0.7855 - val_loss: 0.5367 - val_accuracy: 0.7931
Epoch 152/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.4327 - accuracy: 0.8000 - val_loss: 0.5358 - val_accuracy: 0.7931
Epoch 153/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4730 - accuracy: 0.7739 - val_loss: 0.5347 - val_accuracy: 0.8046
Epoch 154/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.4601 - accuracy: 0.7884 - val_loss: 0.5359 - val_accuracy: 0.8046
Epoch 155/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.4184 - accuracy: 0.8348 - val_loss: 0.5325 - val_accuracy: 0.8046
Epoch 156/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.4549 - accuracy: 0.7971 - val_loss: 0.5246 - val_accuracy: 0.7931
Epoch 157/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4583 - accuracy: 0.7971 - val_loss: 0.5215 - val_accuracy: 0.7931
Epoch 158/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.4283 - accuracy: 0.8319 - val_loss: 0.5221 - val_accuracy: 0.7931
Epoch 159/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.4256 - accuracy: 0.8174 - val_loss: 0.5224 - val_accuracy: 0.7931
Epoch 160/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.4271 - accuracy: 0.8029 - val_loss: 0.5216 - val_accuracy: 0.7931
Epoch 161/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4418 - accuracy: 0.8058 - val_loss: 0.5175 - val_accuracy: 0.7816
Epoch 162/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.4537 - accuracy: 0.7971 - val_loss: 0.5110 - val_accuracy: 0.8046
Epoch 163/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.4504 - accuracy: 0.8116 - val_loss: 0.5051 - val_accuracy: 0.7816
Epoch 164/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.4026 - accuracy: 0.8319 - val_loss: 0.5032 - val_accuracy: 0.7816
Epoch 165/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.4345 - accuracy: 0.8116 - val_loss: 0.5057 - val_accuracy: 0.8046
Epoch 166/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.4475 - accuracy: 0.7942 - val_loss: 0.5086 - val_accuracy: 0.8046
Epoch 167/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3994 - accuracy: 0.8203 - val_loss: 0.5084 - val_accuracy: 0.8046
Epoch 168/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.4062 - accuracy: 0.8261 - val_loss: 0.5057 - val_accuracy: 0.8046
Epoch 169/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.4188 - accuracy: 0.8174 - val_loss: 0.5006 - val_accuracy: 0.8046
Epoch 170/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.4172 - accuracy: 0.8203 - val_loss: 0.4962 - val_accuracy: 0.7701
Epoch 171/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.4288 - accuracy: 0.8174 - val_loss: 0.4942 - val_accuracy: 0.7701
Epoch 172/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3906 - accuracy: 0.8348 - val_loss: 0.4964 - val_accuracy: 0.7586
Epoch 173/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3916 - accuracy: 0.8203 - val_loss: 0.4918 - val_accuracy: 0.7701
Epoch 174/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.4544 - accuracy: 0.7884 - val_loss: 0.4887 - val_accuracy: 0.7816
Epoch 175/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3987 - accuracy: 0.8203 - val_loss: 0.4887 - val_accuracy: 0.7931
Epoch 176/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.3684 - accuracy: 0.8406 - val_loss: 0.4833 - val_accuracy: 0.7701
Epoch 177/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3816 - accuracy: 0.8232 - val_loss: 0.4787 - val_accuracy: 0.7816
Epoch 178/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4159 - accuracy: 0.7942 - val_loss: 0.4773 - val_accuracy: 0.7701
Epoch 179/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.3642 - accuracy: 0.8377 - val_loss: 0.4774 - val_accuracy: 0.7701
Epoch 180/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3769 - accuracy: 0.8319 - val_loss: 0.4818 - val_accuracy: 0.7701
Epoch 181/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.4005 - accuracy: 0.8261 - val_loss: 0.4841 - val_accuracy: 0.7701
Epoch 182/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.4162 - accuracy: 0.8232 - val_loss: 0.4859 - val_accuracy: 0.7931
Epoch 183/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.3827 - accuracy: 0.8406 - val_loss: 0.4801 - val_accuracy: 0.7701
Epoch 184/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.3680 - accuracy: 0.8435 - val_loss: 0.4762 - val_accuracy: 0.7816
Epoch 185/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3728 - accuracy: 0.8435 - val_loss: 0.4760 - val_accuracy: 0.7816
Epoch 186/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.3891 - accuracy: 0.8261 - val_loss: 0.4770 - val_accuracy: 0.7816
Epoch 187/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3538 - accuracy: 0.8667 - val_loss: 0.4821 - val_accuracy: 0.7816
Epoch 188/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.3732 - accuracy: 0.8464 - val_loss: 0.4846 - val_accuracy: 0.7931
Epoch 189/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3478 - accuracy: 0.8493 - val_loss: 0.4783 - val_accuracy: 0.8046
Epoch 190/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3337 - accuracy: 0.8406 - val_loss: 0.4708 - val_accuracy: 0.7931
Epoch 191/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3921 - accuracy: 0.8232 - val_loss: 0.4632 - val_accuracy: 0.7816
Epoch 192/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3796 - accuracy: 0.8435 - val_loss: 0.4594 - val_accuracy: 0.7701
Epoch 193/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3748 - accuracy: 0.8261 - val_loss: 0.4581 - val_accuracy: 0.7701
Epoch 194/1000
3/3 [==============================] - 0s 31ms/step - loss: 0.3500 - accuracy: 0.8406 - val_loss: 0.4572 - val_accuracy: 0.7816
Epoch 195/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.3077 - accuracy: 0.8609 - val_loss: 0.4594 - val_accuracy: 0.8046
Epoch 196/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3934 - accuracy: 0.8232 - val_loss: 0.4601 - val_accuracy: 0.8046
Epoch 197/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3402 - accuracy: 0.8377 - val_loss: 0.4608 - val_accuracy: 0.8161
Epoch 198/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3490 - accuracy: 0.8522 - val_loss: 0.4569 - val_accuracy: 0.8161
Epoch 199/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.4035 - accuracy: 0.8232 - val_loss: 0.4519 - val_accuracy: 0.8046
Epoch 200/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3578 - accuracy: 0.8406 - val_loss: 0.4494 - val_accuracy: 0.8161
Epoch 201/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3613 - accuracy: 0.8406 - val_loss: 0.4487 - val_accuracy: 0.8161
Epoch 202/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.3577 - accuracy: 0.8464 - val_loss: 0.4505 - val_accuracy: 0.7931
Epoch 203/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3363 - accuracy: 0.8638 - val_loss: 0.4519 - val_accuracy: 0.8046
Epoch 204/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3476 - accuracy: 0.8435 - val_loss: 0.4498 - val_accuracy: 0.8046
Epoch 205/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.3474 - accuracy: 0.8522 - val_loss: 0.4484 - val_accuracy: 0.8046
Epoch 206/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3539 - accuracy: 0.8261 - val_loss: 0.4457 - val_accuracy: 0.8046
Epoch 207/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.3057 - accuracy: 0.8638 - val_loss: 0.4431 - val_accuracy: 0.8046
Epoch 208/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3206 - accuracy: 0.8493 - val_loss: 0.4407 - val_accuracy: 0.7816
Epoch 209/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3322 - accuracy: 0.8609 - val_loss: 0.4387 - val_accuracy: 0.8046
Epoch 210/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3323 - accuracy: 0.8725 - val_loss: 0.4386 - val_accuracy: 0.7816
Epoch 211/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3190 - accuracy: 0.8696 - val_loss: 0.4388 - val_accuracy: 0.7816
Epoch 212/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3375 - accuracy: 0.8522 - val_loss: 0.4383 - val_accuracy: 0.8161
Epoch 213/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3139 - accuracy: 0.8841 - val_loss: 0.4381 - val_accuracy: 0.8046
Epoch 214/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3432 - accuracy: 0.8580 - val_loss: 0.4366 - val_accuracy: 0.8046
Epoch 215/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.3142 - accuracy: 0.8522 - val_loss: 0.4326 - val_accuracy: 0.7931
Epoch 216/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3169 - accuracy: 0.8609 - val_loss: 0.4307 - val_accuracy: 0.8276
Epoch 217/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2985 - accuracy: 0.8493 - val_loss: 0.4305 - val_accuracy: 0.8161
Epoch 218/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3501 - accuracy: 0.8464 - val_loss: 0.4297 - val_accuracy: 0.8046
Epoch 219/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.3200 - accuracy: 0.8580 - val_loss: 0.4288 - val_accuracy: 0.8161
Epoch 220/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2669 - accuracy: 0.8899 - val_loss: 0.4286 - val_accuracy: 0.7931
Epoch 221/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.2997 - accuracy: 0.8522 - val_loss: 0.4257 - val_accuracy: 0.8046
Epoch 222/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.3323 - accuracy: 0.8609 - val_loss: 0.4232 - val_accuracy: 0.8046
Epoch 223/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3064 - accuracy: 0.8464 - val_loss: 0.4233 - val_accuracy: 0.8046
Epoch 224/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.2942 - accuracy: 0.8754 - val_loss: 0.4248 - val_accuracy: 0.8276
Epoch 225/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3554 - accuracy: 0.8406 - val_loss: 0.4274 - val_accuracy: 0.8161
Epoch 226/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3192 - accuracy: 0.8638 - val_loss: 0.4275 - val_accuracy: 0.8161
Epoch 227/1000
3/3 [==============================] - 0s 31ms/step - loss: 0.2902 - accuracy: 0.8812 - val_loss: 0.4271 - val_accuracy: 0.8046
Epoch 228/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2832 - accuracy: 0.8725 - val_loss: 0.4259 - val_accuracy: 0.8046
Epoch 229/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2750 - accuracy: 0.8783 - val_loss: 0.4269 - val_accuracy: 0.8046
Epoch 230/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3171 - accuracy: 0.8551 - val_loss: 0.4275 - val_accuracy: 0.8046
Epoch 231/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.2617 - accuracy: 0.8928 - val_loss: 0.4253 - val_accuracy: 0.7931
Epoch 232/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3045 - accuracy: 0.8609 - val_loss: 0.4220 - val_accuracy: 0.8161
Epoch 233/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2591 - accuracy: 0.9043 - val_loss: 0.4224 - val_accuracy: 0.7931
Epoch 234/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2877 - accuracy: 0.8667 - val_loss: 0.4216 - val_accuracy: 0.7931
Epoch 235/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.3016 - accuracy: 0.8754 - val_loss: 0.4207 - val_accuracy: 0.8046
Epoch 236/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.2654 - accuracy: 0.8928 - val_loss: 0.4199 - val_accuracy: 0.7931
Epoch 237/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2768 - accuracy: 0.8696 - val_loss: 0.4212 - val_accuracy: 0.7931
Epoch 238/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2624 - accuracy: 0.8899 - val_loss: 0.4199 - val_accuracy: 0.7931
Epoch 239/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2556 - accuracy: 0.9159 - val_loss: 0.4170 - val_accuracy: 0.7931
Epoch 240/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.2574 - accuracy: 0.8899 - val_loss: 0.4156 - val_accuracy: 0.8161
Epoch 241/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.2668 - accuracy: 0.9043 - val_loss: 0.4149 - val_accuracy: 0.8046
Epoch 242/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3429 - accuracy: 0.8696 - val_loss: 0.4153 - val_accuracy: 0.8046
Epoch 243/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2792 - accuracy: 0.8957 - val_loss: 0.4158 - val_accuracy: 0.7931
Epoch 244/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.3142 - accuracy: 0.8754 - val_loss: 0.4177 - val_accuracy: 0.8046
Epoch 245/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2529 - accuracy: 0.8841 - val_loss: 0.4191 - val_accuracy: 0.7931
Epoch 246/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.3454 - accuracy: 0.8493 - val_loss: 0.4193 - val_accuracy: 0.7931
Epoch 247/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2802 - accuracy: 0.8841 - val_loss: 0.4179 - val_accuracy: 0.7931
Epoch 248/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2646 - accuracy: 0.8928 - val_loss: 0.4142 - val_accuracy: 0.8046
Epoch 249/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2950 - accuracy: 0.8812 - val_loss: 0.4092 - val_accuracy: 0.8046
Epoch 250/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.2944 - accuracy: 0.8667 - val_loss: 0.4053 - val_accuracy: 0.8161
Epoch 251/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2635 - accuracy: 0.8957 - val_loss: 0.4030 - val_accuracy: 0.8161
Epoch 252/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2535 - accuracy: 0.8725 - val_loss: 0.4022 - val_accuracy: 0.8161
Epoch 253/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2731 - accuracy: 0.8580 - val_loss: 0.4031 - val_accuracy: 0.8046
Epoch 254/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.3048 - accuracy: 0.8754 - val_loss: 0.4072 - val_accuracy: 0.8046
Epoch 255/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.3064 - accuracy: 0.8696 - val_loss: 0.4117 - val_accuracy: 0.7931
Epoch 256/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2640 - accuracy: 0.8957 - val_loss: 0.4119 - val_accuracy: 0.7931
Epoch 257/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.2950 - accuracy: 0.8725 - val_loss: 0.4119 - val_accuracy: 0.8046
Epoch 258/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2544 - accuracy: 0.8928 - val_loss: 0.4084 - val_accuracy: 0.8046
Epoch 259/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2768 - accuracy: 0.8841 - val_loss: 0.4053 - val_accuracy: 0.8276
Epoch 260/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2777 - accuracy: 0.8841 - val_loss: 0.4044 - val_accuracy: 0.8161
Epoch 261/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2672 - accuracy: 0.8812 - val_loss: 0.4066 - val_accuracy: 0.8046
Epoch 262/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2631 - accuracy: 0.8986 - val_loss: 0.4080 - val_accuracy: 0.8046
Epoch 263/1000
3/3 [==============================] - 0s 44ms/step - loss: 0.2276 - accuracy: 0.8986 - val_loss: 0.4088 - val_accuracy: 0.8046
Epoch 264/1000
3/3 [==============================] - 0s 65ms/step - loss: 0.2116 - accuracy: 0.9043 - val_loss: 0.4104 - val_accuracy: 0.8046
Epoch 265/1000
3/3 [==============================] - 0s 58ms/step - loss: 0.2481 - accuracy: 0.8899 - val_loss: 0.4123 - val_accuracy: 0.8161
Epoch 266/1000
3/3 [==============================] - 0s 64ms/step - loss: 0.2655 - accuracy: 0.8957 - val_loss: 0.4131 - val_accuracy: 0.8046
Epoch 267/1000
3/3 [==============================] - 0s 82ms/step - loss: 0.2500 - accuracy: 0.8870 - val_loss: 0.4100 - val_accuracy: 0.7931
Epoch 268/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.2584 - accuracy: 0.8841 - val_loss: 0.4046 - val_accuracy: 0.8046
Epoch 269/1000
3/3 [==============================] - 0s 48ms/step - loss: 0.2784 - accuracy: 0.8928 - val_loss: 0.4010 - val_accuracy: 0.7931
Epoch 270/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2484 - accuracy: 0.8899 - val_loss: 0.3987 - val_accuracy: 0.8046
Epoch 271/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2964 - accuracy: 0.8783 - val_loss: 0.3994 - val_accuracy: 0.8046
Epoch 272/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2735 - accuracy: 0.8754 - val_loss: 0.4018 - val_accuracy: 0.8161
Epoch 273/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2825 - accuracy: 0.8870 - val_loss: 0.4016 - val_accuracy: 0.8046
Epoch 274/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2550 - accuracy: 0.9043 - val_loss: 0.4026 - val_accuracy: 0.8046
Epoch 275/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2908 - accuracy: 0.8696 - val_loss: 0.4041 - val_accuracy: 0.7931
Epoch 276/1000
3/3 [==============================] - 0s 56ms/step - loss: 0.2562 - accuracy: 0.9043 - val_loss: 0.4035 - val_accuracy: 0.7931
Epoch 277/1000
3/3 [==============================] - 0s 56ms/step - loss: 0.2806 - accuracy: 0.8754 - val_loss: 0.4010 - val_accuracy: 0.7931
Epoch 278/1000
3/3 [==============================] - 0s 57ms/step - loss: 0.2389 - accuracy: 0.9101 - val_loss: 0.3996 - val_accuracy: 0.7931
Epoch 279/1000
3/3 [==============================] - 0s 60ms/step - loss: 0.2188 - accuracy: 0.9043 - val_loss: 0.3984 - val_accuracy: 0.7931
Epoch 280/1000
3/3 [==============================] - 0s 52ms/step - loss: 0.2532 - accuracy: 0.8957 - val_loss: 0.3996 - val_accuracy: 0.8161
Epoch 281/1000
3/3 [==============================] - 0s 46ms/step - loss: 0.2563 - accuracy: 0.8899 - val_loss: 0.4002 - val_accuracy: 0.8161
Epoch 282/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.2439 - accuracy: 0.9014 - val_loss: 0.4008 - val_accuracy: 0.8046
Epoch 283/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2494 - accuracy: 0.8957 - val_loss: 0.4024 - val_accuracy: 0.8046
Epoch 284/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2200 - accuracy: 0.9246 - val_loss: 0.4044 - val_accuracy: 0.8046
Epoch 285/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.2588 - accuracy: 0.8783 - val_loss: 0.4027 - val_accuracy: 0.8046
Epoch 286/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2399 - accuracy: 0.8957 - val_loss: 0.4013 - val_accuracy: 0.8046
Epoch 287/1000
3/3 [==============================] - 0s 31ms/step - loss: 0.2813 - accuracy: 0.8812 - val_loss: 0.4016 - val_accuracy: 0.7931
Epoch 288/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2643 - accuracy: 0.8754 - val_loss: 0.4024 - val_accuracy: 0.7931
Epoch 289/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.2429 - accuracy: 0.9014 - val_loss: 0.4025 - val_accuracy: 0.7931
Epoch 290/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2309 - accuracy: 0.8957 - val_loss: 0.4029 - val_accuracy: 0.7931
Epoch 291/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2649 - accuracy: 0.8667 - val_loss: 0.4049 - val_accuracy: 0.7931
Epoch 292/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2218 - accuracy: 0.8986 - val_loss: 0.4045 - val_accuracy: 0.7931
Epoch 293/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2078 - accuracy: 0.9014 - val_loss: 0.4025 - val_accuracy: 0.7931
Epoch 294/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2359 - accuracy: 0.8986 - val_loss: 0.4035 - val_accuracy: 0.7931
Epoch 295/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2383 - accuracy: 0.9072 - val_loss: 0.4072 - val_accuracy: 0.7931
Epoch 296/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2404 - accuracy: 0.9043 - val_loss: 0.4089 - val_accuracy: 0.7931
Epoch 297/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.2145 - accuracy: 0.9130 - val_loss: 0.4081 - val_accuracy: 0.8046
Epoch 298/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2552 - accuracy: 0.9159 - val_loss: 0.4053 - val_accuracy: 0.8046
Epoch 299/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2235 - accuracy: 0.9130 - val_loss: 0.4027 - val_accuracy: 0.7931
Epoch 300/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1984 - accuracy: 0.9275 - val_loss: 0.4020 - val_accuracy: 0.8046
Epoch 301/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2222 - accuracy: 0.9072 - val_loss: 0.4002 - val_accuracy: 0.8046
Epoch 302/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2405 - accuracy: 0.9043 - val_loss: 0.3970 - val_accuracy: 0.8161
Epoch 303/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2521 - accuracy: 0.8812 - val_loss: 0.3971 - val_accuracy: 0.8161
Epoch 304/1000
3/3 [==============================] - 0s 34ms/step - loss: 0.2292 - accuracy: 0.9072 - val_loss: 0.3989 - val_accuracy: 0.8046
Epoch 305/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.2560 - accuracy: 0.8899 - val_loss: 0.3980 - val_accuracy: 0.8046
Epoch 306/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2390 - accuracy: 0.8899 - val_loss: 0.3985 - val_accuracy: 0.8046
Epoch 307/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2567 - accuracy: 0.9014 - val_loss: 0.3997 - val_accuracy: 0.7931
Epoch 308/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2174 - accuracy: 0.9246 - val_loss: 0.4001 - val_accuracy: 0.7931
Epoch 309/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2395 - accuracy: 0.8870 - val_loss: 0.3977 - val_accuracy: 0.8046
Epoch 310/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1994 - accuracy: 0.9304 - val_loss: 0.3955 - val_accuracy: 0.8046
Epoch 311/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2316 - accuracy: 0.9188 - val_loss: 0.3922 - val_accuracy: 0.8161
Epoch 312/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2122 - accuracy: 0.9072 - val_loss: 0.3902 - val_accuracy: 0.8161
Epoch 313/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.2619 - accuracy: 0.9101 - val_loss: 0.3892 - val_accuracy: 0.8046
Epoch 314/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2070 - accuracy: 0.9159 - val_loss: 0.3892 - val_accuracy: 0.8161
Epoch 315/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1922 - accuracy: 0.9159 - val_loss: 0.3923 - val_accuracy: 0.8046
Epoch 316/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2095 - accuracy: 0.9130 - val_loss: 0.3941 - val_accuracy: 0.8161
Epoch 317/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2340 - accuracy: 0.9014 - val_loss: 0.3905 - val_accuracy: 0.8046
Epoch 318/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2073 - accuracy: 0.9217 - val_loss: 0.3864 - val_accuracy: 0.8161
Epoch 319/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2015 - accuracy: 0.9159 - val_loss: 0.3865 - val_accuracy: 0.7931
Epoch 320/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1863 - accuracy: 0.9159 - val_loss: 0.3866 - val_accuracy: 0.7931
Epoch 321/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1921 - accuracy: 0.9246 - val_loss: 0.3884 - val_accuracy: 0.7931
Epoch 322/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2059 - accuracy: 0.9072 - val_loss: 0.3918 - val_accuracy: 0.8276
Epoch 323/1000
3/3 [==============================] - 0s 38ms/step - loss: 0.2426 - accuracy: 0.9014 - val_loss: 0.3909 - val_accuracy: 0.8161
Epoch 324/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1882 - accuracy: 0.9159 - val_loss: 0.3886 - val_accuracy: 0.8161
Epoch 325/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2359 - accuracy: 0.9014 - val_loss: 0.3885 - val_accuracy: 0.8161
Epoch 326/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2072 - accuracy: 0.9130 - val_loss: 0.3878 - val_accuracy: 0.8161
Epoch 327/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2004 - accuracy: 0.9275 - val_loss: 0.3863 - val_accuracy: 0.8161
Epoch 328/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2009 - accuracy: 0.9101 - val_loss: 0.3847 - val_accuracy: 0.8161
Epoch 329/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2114 - accuracy: 0.9043 - val_loss: 0.3851 - val_accuracy: 0.8161
Epoch 330/1000
3/3 [==============================] - 0s 35ms/step - loss: 0.2406 - accuracy: 0.9043 - val_loss: 0.3859 - val_accuracy: 0.8161
Epoch 331/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2045 - accuracy: 0.9130 - val_loss: 0.3877 - val_accuracy: 0.8161
Epoch 332/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1911 - accuracy: 0.9217 - val_loss: 0.3871 - val_accuracy: 0.8161
Epoch 333/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1663 - accuracy: 0.9333 - val_loss: 0.3858 - val_accuracy: 0.8161
Epoch 334/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2162 - accuracy: 0.9072 - val_loss: 0.3864 - val_accuracy: 0.8161
Epoch 335/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2125 - accuracy: 0.9246 - val_loss: 0.3862 - val_accuracy: 0.8161
Epoch 336/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1926 - accuracy: 0.9304 - val_loss: 0.3856 - val_accuracy: 0.8161
Epoch 337/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1903 - accuracy: 0.9217 - val_loss: 0.3862 - val_accuracy: 0.8161
Epoch 338/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1940 - accuracy: 0.9217 - val_loss: 0.3869 - val_accuracy: 0.8161
Epoch 339/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2088 - accuracy: 0.9130 - val_loss: 0.3857 - val_accuracy: 0.8161
Epoch 340/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2326 - accuracy: 0.9101 - val_loss: 0.3853 - val_accuracy: 0.8391
Epoch 341/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2136 - accuracy: 0.9072 - val_loss: 0.3818 - val_accuracy: 0.8391
Epoch 342/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2368 - accuracy: 0.9101 - val_loss: 0.3799 - val_accuracy: 0.8391
Epoch 343/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1859 - accuracy: 0.9014 - val_loss: 0.3803 - val_accuracy: 0.8391
Epoch 344/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.1822 - accuracy: 0.9391 - val_loss: 0.3791 - val_accuracy: 0.8391
Epoch 345/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1768 - accuracy: 0.9275 - val_loss: 0.3780 - val_accuracy: 0.8391
Epoch 346/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2052 - accuracy: 0.9072 - val_loss: 0.3796 - val_accuracy: 0.8391
Epoch 347/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.2135 - accuracy: 0.9188 - val_loss: 0.3797 - val_accuracy: 0.8161
Epoch 348/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1829 - accuracy: 0.9217 - val_loss: 0.3778 - val_accuracy: 0.8161
Epoch 349/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2084 - accuracy: 0.9014 - val_loss: 0.3786 - val_accuracy: 0.8161
Epoch 350/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.2287 - accuracy: 0.9130 - val_loss: 0.3811 - val_accuracy: 0.8276
Epoch 351/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2024 - accuracy: 0.9014 - val_loss: 0.3831 - val_accuracy: 0.8276
Epoch 352/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1990 - accuracy: 0.9217 - val_loss: 0.3860 - val_accuracy: 0.8276
Epoch 353/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1955 - accuracy: 0.9217 - val_loss: 0.3843 - val_accuracy: 0.8276
Epoch 354/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1990 - accuracy: 0.9101 - val_loss: 0.3801 - val_accuracy: 0.8276
Epoch 355/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1759 - accuracy: 0.9362 - val_loss: 0.3767 - val_accuracy: 0.8506
Epoch 356/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1826 - accuracy: 0.9333 - val_loss: 0.3747 - val_accuracy: 0.8621
Epoch 357/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1618 - accuracy: 0.9478 - val_loss: 0.3730 - val_accuracy: 0.8621
Epoch 358/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1850 - accuracy: 0.9420 - val_loss: 0.3749 - val_accuracy: 0.8736
Epoch 359/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1827 - accuracy: 0.9304 - val_loss: 0.3761 - val_accuracy: 0.8736
Epoch 360/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.1704 - accuracy: 0.9391 - val_loss: 0.3733 - val_accuracy: 0.8621
Epoch 361/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1993 - accuracy: 0.9275 - val_loss: 0.3690 - val_accuracy: 0.8506
Epoch 362/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1990 - accuracy: 0.9159 - val_loss: 0.3697 - val_accuracy: 0.8391
Epoch 363/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1669 - accuracy: 0.9333 - val_loss: 0.3716 - val_accuracy: 0.8391
Epoch 364/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1780 - accuracy: 0.9304 - val_loss: 0.3719 - val_accuracy: 0.8391
Epoch 365/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1829 - accuracy: 0.9304 - val_loss: 0.3744 - val_accuracy: 0.8276
Epoch 366/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1836 - accuracy: 0.9246 - val_loss: 0.3765 - val_accuracy: 0.8621
Epoch 367/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2229 - accuracy: 0.8928 - val_loss: 0.3703 - val_accuracy: 0.8506
Epoch 368/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.1541 - accuracy: 0.9391 - val_loss: 0.3650 - val_accuracy: 0.8621
Epoch 369/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1996 - accuracy: 0.9159 - val_loss: 0.3664 - val_accuracy: 0.8506
Epoch 370/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.2020 - accuracy: 0.9217 - val_loss: 0.3706 - val_accuracy: 0.8391
Epoch 371/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1695 - accuracy: 0.9449 - val_loss: 0.3736 - val_accuracy: 0.8276
Epoch 372/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1745 - accuracy: 0.9246 - val_loss: 0.3733 - val_accuracy: 0.8391
Epoch 373/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.1736 - accuracy: 0.9333 - val_loss: 0.3717 - val_accuracy: 0.8506
Epoch 374/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2084 - accuracy: 0.9159 - val_loss: 0.3726 - val_accuracy: 0.8391
Epoch 375/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.1883 - accuracy: 0.9217 - val_loss: 0.3729 - val_accuracy: 0.8506
Epoch 376/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1839 - accuracy: 0.9304 - val_loss: 0.3722 - val_accuracy: 0.8506
Epoch 377/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1962 - accuracy: 0.9304 - val_loss: 0.3694 - val_accuracy: 0.8506
Epoch 378/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1866 - accuracy: 0.9333 - val_loss: 0.3686 - val_accuracy: 0.8276
Epoch 379/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.1980 - accuracy: 0.9246 - val_loss: 0.3691 - val_accuracy: 0.8276
Epoch 380/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2146 - accuracy: 0.9246 - val_loss: 0.3713 - val_accuracy: 0.8276
Epoch 381/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1948 - accuracy: 0.9101 - val_loss: 0.3742 - val_accuracy: 0.8391
Epoch 382/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1790 - accuracy: 0.9217 - val_loss: 0.3749 - val_accuracy: 0.8391
Epoch 383/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1615 - accuracy: 0.9420 - val_loss: 0.3741 - val_accuracy: 0.8506
Epoch 384/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1485 - accuracy: 0.9391 - val_loss: 0.3729 - val_accuracy: 0.8736
Epoch 385/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1929 - accuracy: 0.9246 - val_loss: 0.3738 - val_accuracy: 0.8621
Epoch 386/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1754 - accuracy: 0.9333 - val_loss: 0.3742 - val_accuracy: 0.8621
Epoch 387/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1758 - accuracy: 0.9275 - val_loss: 0.3729 - val_accuracy: 0.8621
Epoch 388/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1767 - accuracy: 0.9217 - val_loss: 0.3713 - val_accuracy: 0.8736
Epoch 389/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1859 - accuracy: 0.9217 - val_loss: 0.3698 - val_accuracy: 0.8736
Epoch 390/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.1597 - accuracy: 0.9362 - val_loss: 0.3674 - val_accuracy: 0.8621
Epoch 391/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1780 - accuracy: 0.9246 - val_loss: 0.3666 - val_accuracy: 0.8391
Epoch 392/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1947 - accuracy: 0.9217 - val_loss: 0.3676 - val_accuracy: 0.8276
Epoch 393/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2110 - accuracy: 0.9159 - val_loss: 0.3676 - val_accuracy: 0.8276
Epoch 394/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1615 - accuracy: 0.9159 - val_loss: 0.3677 - val_accuracy: 0.8506
Epoch 395/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.2139 - accuracy: 0.9101 - val_loss: 0.3605 - val_accuracy: 0.8506
Epoch 396/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1657 - accuracy: 0.9304 - val_loss: 0.3561 - val_accuracy: 0.8506
Epoch 397/1000
3/3 [==============================] - 0s 34ms/step - loss: 0.1532 - accuracy: 0.9449 - val_loss: 0.3532 - val_accuracy: 0.8506
Epoch 398/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1798 - accuracy: 0.9159 - val_loss: 0.3547 - val_accuracy: 0.8506
Epoch 399/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1540 - accuracy: 0.9391 - val_loss: 0.3569 - val_accuracy: 0.8391
Epoch 400/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.2216 - accuracy: 0.8986 - val_loss: 0.3596 - val_accuracy: 0.8506
Epoch 401/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1834 - accuracy: 0.9159 - val_loss: 0.3625 - val_accuracy: 0.8391
Epoch 402/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2011 - accuracy: 0.9159 - val_loss: 0.3610 - val_accuracy: 0.8506
Epoch 403/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1778 - accuracy: 0.9246 - val_loss: 0.3615 - val_accuracy: 0.8506
Epoch 404/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.1491 - accuracy: 0.9420 - val_loss: 0.3649 - val_accuracy: 0.8851
Epoch 405/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1972 - accuracy: 0.9217 - val_loss: 0.3667 - val_accuracy: 0.8851
Epoch 406/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.2009 - accuracy: 0.9130 - val_loss: 0.3614 - val_accuracy: 0.8736
Epoch 407/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.1362 - accuracy: 0.9507 - val_loss: 0.3557 - val_accuracy: 0.8736
Epoch 408/1000
3/3 [==============================] - 0s 31ms/step - loss: 0.1436 - accuracy: 0.9449 - val_loss: 0.3531 - val_accuracy: 0.8736
Epoch 409/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1733 - accuracy: 0.9449 - val_loss: 0.3520 - val_accuracy: 0.8621
Epoch 410/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1722 - accuracy: 0.9391 - val_loss: 0.3518 - val_accuracy: 0.8621
Epoch 411/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1557 - accuracy: 0.9362 - val_loss: 0.3543 - val_accuracy: 0.8621
Epoch 412/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1498 - accuracy: 0.9391 - val_loss: 0.3583 - val_accuracy: 0.8621
Epoch 413/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1986 - accuracy: 0.9246 - val_loss: 0.3595 - val_accuracy: 0.8506
Epoch 414/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1444 - accuracy: 0.9478 - val_loss: 0.3576 - val_accuracy: 0.8621
Epoch 415/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1547 - accuracy: 0.9420 - val_loss: 0.3585 - val_accuracy: 0.8621
Epoch 416/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1446 - accuracy: 0.9478 - val_loss: 0.3540 - val_accuracy: 0.8621
Epoch 417/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1463 - accuracy: 0.9420 - val_loss: 0.3466 - val_accuracy: 0.8851
Epoch 418/1000
3/3 [==============================] - 0s 34ms/step - loss: 0.1617 - accuracy: 0.9391 - val_loss: 0.3449 - val_accuracy: 0.8851
Epoch 419/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1624 - accuracy: 0.9333 - val_loss: 0.3484 - val_accuracy: 0.8851
Epoch 420/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1587 - accuracy: 0.9362 - val_loss: 0.3485 - val_accuracy: 0.8851
Epoch 421/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1572 - accuracy: 0.9275 - val_loss: 0.3455 - val_accuracy: 0.8851
Epoch 422/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1669 - accuracy: 0.9246 - val_loss: 0.3438 - val_accuracy: 0.8851
Epoch 423/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1734 - accuracy: 0.9304 - val_loss: 0.3430 - val_accuracy: 0.8736
Epoch 424/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1494 - accuracy: 0.9362 - val_loss: 0.3438 - val_accuracy: 0.8736
Epoch 425/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.1487 - accuracy: 0.9536 - val_loss: 0.3445 - val_accuracy: 0.8851
Epoch 426/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1345 - accuracy: 0.9478 - val_loss: 0.3438 - val_accuracy: 0.8851
Epoch 427/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.1474 - accuracy: 0.9333 - val_loss: 0.3412 - val_accuracy: 0.8966
Epoch 428/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1594 - accuracy: 0.9333 - val_loss: 0.3385 - val_accuracy: 0.8966
Epoch 429/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1774 - accuracy: 0.9217 - val_loss: 0.3354 - val_accuracy: 0.8966
Epoch 430/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1551 - accuracy: 0.9478 - val_loss: 0.3340 - val_accuracy: 0.8851
Epoch 431/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.2005 - accuracy: 0.9101 - val_loss: 0.3372 - val_accuracy: 0.8966
Epoch 432/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1332 - accuracy: 0.9391 - val_loss: 0.3415 - val_accuracy: 0.8851
Epoch 433/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1780 - accuracy: 0.9217 - val_loss: 0.3431 - val_accuracy: 0.8736
Epoch 434/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1735 - accuracy: 0.9333 - val_loss: 0.3397 - val_accuracy: 0.8966
Epoch 435/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1481 - accuracy: 0.9391 - val_loss: 0.3385 - val_accuracy: 0.8966
Epoch 436/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.1583 - accuracy: 0.9507 - val_loss: 0.3396 - val_accuracy: 0.8966
Epoch 437/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1585 - accuracy: 0.9420 - val_loss: 0.3410 - val_accuracy: 0.8966
Epoch 438/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1596 - accuracy: 0.9217 - val_loss: 0.3430 - val_accuracy: 0.8966
Epoch 439/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1591 - accuracy: 0.9449 - val_loss: 0.3454 - val_accuracy: 0.9080
Epoch 440/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1680 - accuracy: 0.9420 - val_loss: 0.3467 - val_accuracy: 0.8966
Epoch 441/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1458 - accuracy: 0.9333 - val_loss: 0.3478 - val_accuracy: 0.8966
Epoch 442/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.1754 - accuracy: 0.9362 - val_loss: 0.3474 - val_accuracy: 0.8851
Epoch 443/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1787 - accuracy: 0.9275 - val_loss: 0.3497 - val_accuracy: 0.8736
Epoch 444/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1636 - accuracy: 0.9420 - val_loss: 0.3517 - val_accuracy: 0.8621
Epoch 445/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1464 - accuracy: 0.9478 - val_loss: 0.3531 - val_accuracy: 0.8621
Epoch 446/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1546 - accuracy: 0.9420 - val_loss: 0.3513 - val_accuracy: 0.8966
Epoch 447/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1590 - accuracy: 0.9304 - val_loss: 0.3480 - val_accuracy: 0.8851
Epoch 448/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1730 - accuracy: 0.9130 - val_loss: 0.3490 - val_accuracy: 0.8966
Epoch 449/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1654 - accuracy: 0.9536 - val_loss: 0.3502 - val_accuracy: 0.9195
Epoch 450/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1279 - accuracy: 0.9536 - val_loss: 0.3460 - val_accuracy: 0.9080
Epoch 451/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.1413 - accuracy: 0.9478 - val_loss: 0.3413 - val_accuracy: 0.8966
Epoch 452/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1453 - accuracy: 0.9507 - val_loss: 0.3400 - val_accuracy: 0.8851
Epoch 453/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1500 - accuracy: 0.9507 - val_loss: 0.3409 - val_accuracy: 0.8851
Epoch 454/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1576 - accuracy: 0.9449 - val_loss: 0.3399 - val_accuracy: 0.8736
Epoch 455/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1327 - accuracy: 0.9594 - val_loss: 0.3388 - val_accuracy: 0.8966
Epoch 456/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1456 - accuracy: 0.9333 - val_loss: 0.3390 - val_accuracy: 0.8966
Epoch 457/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1410 - accuracy: 0.9420 - val_loss: 0.3396 - val_accuracy: 0.8966
Epoch 458/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1383 - accuracy: 0.9536 - val_loss: 0.3437 - val_accuracy: 0.8966
Epoch 459/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.1634 - accuracy: 0.9217 - val_loss: 0.3470 - val_accuracy: 0.8736
Epoch 460/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1418 - accuracy: 0.9536 - val_loss: 0.3484 - val_accuracy: 0.8851
Epoch 461/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1706 - accuracy: 0.9420 - val_loss: 0.3487 - val_accuracy: 0.8966
Epoch 462/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1169 - accuracy: 0.9507 - val_loss: 0.3494 - val_accuracy: 0.8851
Epoch 463/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1079 - accuracy: 0.9594 - val_loss: 0.3510 - val_accuracy: 0.8851
Epoch 464/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1405 - accuracy: 0.9362 - val_loss: 0.3528 - val_accuracy: 0.8851
Epoch 465/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1549 - accuracy: 0.9420 - val_loss: 0.3535 - val_accuracy: 0.8851
Epoch 466/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1484 - accuracy: 0.9449 - val_loss: 0.3555 - val_accuracy: 0.8851
Epoch 467/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1577 - accuracy: 0.9333 - val_loss: 0.3612 - val_accuracy: 0.8966
Epoch 468/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.1444 - accuracy: 0.9507 - val_loss: 0.3663 - val_accuracy: 0.8851
Epoch 469/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1475 - accuracy: 0.9478 - val_loss: 0.3683 - val_accuracy: 0.8851
Epoch 470/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1359 - accuracy: 0.9449 - val_loss: 0.3660 - val_accuracy: 0.8851
Epoch 471/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.1525 - accuracy: 0.9391 - val_loss: 0.3614 - val_accuracy: 0.8966
Epoch 472/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1373 - accuracy: 0.9507 - val_loss: 0.3575 - val_accuracy: 0.8966
Epoch 473/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1219 - accuracy: 0.9681 - val_loss: 0.3547 - val_accuracy: 0.8851
Epoch 474/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1663 - accuracy: 0.9391 - val_loss: 0.3551 - val_accuracy: 0.8851
Epoch 475/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.1226 - accuracy: 0.9536 - val_loss: 0.3560 - val_accuracy: 0.8966
Epoch 476/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1638 - accuracy: 0.9217 - val_loss: 0.3562 - val_accuracy: 0.8966
Epoch 477/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1237 - accuracy: 0.9594 - val_loss: 0.3520 - val_accuracy: 0.8966
Epoch 478/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1510 - accuracy: 0.9594 - val_loss: 0.3467 - val_accuracy: 0.8966
Epoch 479/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1393 - accuracy: 0.9420 - val_loss: 0.3435 - val_accuracy: 0.8966
Epoch 480/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1459 - accuracy: 0.9507 - val_loss: 0.3413 - val_accuracy: 0.8966
Epoch 481/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1188 - accuracy: 0.9594 - val_loss: 0.3402 - val_accuracy: 0.8966
Epoch 482/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.1450 - accuracy: 0.9449 - val_loss: 0.3402 - val_accuracy: 0.8966
Epoch 483/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1391 - accuracy: 0.9449 - val_loss: 0.3416 - val_accuracy: 0.9080
Epoch 484/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1262 - accuracy: 0.9594 - val_loss: 0.3455 - val_accuracy: 0.9080
Epoch 485/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1647 - accuracy: 0.9449 - val_loss: 0.3450 - val_accuracy: 0.9195
Epoch 486/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1269 - accuracy: 0.9652 - val_loss: 0.3462 - val_accuracy: 0.9195
Epoch 487/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.1072 - accuracy: 0.9536 - val_loss: 0.3469 - val_accuracy: 0.9195
Epoch 488/1000
3/3 [==============================] - 0s 31ms/step - loss: 0.1236 - accuracy: 0.9391 - val_loss: 0.3475 - val_accuracy: 0.9195
Epoch 489/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1699 - accuracy: 0.9333 - val_loss: 0.3436 - val_accuracy: 0.8966
Epoch 490/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1270 - accuracy: 0.9362 - val_loss: 0.3424 - val_accuracy: 0.8966
Epoch 491/1000
3/3 [==============================] - 0s 21ms/step - loss: 0.1792 - accuracy: 0.9478 - val_loss: 0.3444 - val_accuracy: 0.8966
Epoch 492/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.1249 - accuracy: 0.9681 - val_loss: 0.3449 - val_accuracy: 0.9080
Epoch 493/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1438 - accuracy: 0.9449 - val_loss: 0.3503 - val_accuracy: 0.9195
Epoch 494/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1218 - accuracy: 0.9478 - val_loss: 0.3593 - val_accuracy: 0.8851
Epoch 495/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1400 - accuracy: 0.9391 - val_loss: 0.3594 - val_accuracy: 0.8851
Epoch 496/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1380 - accuracy: 0.9507 - val_loss: 0.3515 - val_accuracy: 0.9080
Epoch 497/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1235 - accuracy: 0.9391 - val_loss: 0.3418 - val_accuracy: 0.9195
Epoch 498/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1543 - accuracy: 0.9449 - val_loss: 0.3373 - val_accuracy: 0.8966
Epoch 499/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.1555 - accuracy: 0.9449 - val_loss: 0.3383 - val_accuracy: 0.8851
Epoch 500/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1447 - accuracy: 0.9565 - val_loss: 0.3408 - val_accuracy: 0.9080
Epoch 501/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1236 - accuracy: 0.9536 - val_loss: 0.3428 - val_accuracy: 0.9195
Epoch 502/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.1264 - accuracy: 0.9478 - val_loss: 0.3435 - val_accuracy: 0.9195
Epoch 503/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1487 - accuracy: 0.9449 - val_loss: 0.3419 - val_accuracy: 0.9195
Epoch 504/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1447 - accuracy: 0.9420 - val_loss: 0.3364 - val_accuracy: 0.9195
Epoch 505/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1056 - accuracy: 0.9536 - val_loss: 0.3345 - val_accuracy: 0.9080
Epoch 506/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1138 - accuracy: 0.9594 - val_loss: 0.3332 - val_accuracy: 0.9195
Epoch 507/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1129 - accuracy: 0.9478 - val_loss: 0.3336 - val_accuracy: 0.9195
Epoch 508/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1545 - accuracy: 0.9304 - val_loss: 0.3349 - val_accuracy: 0.9195
Epoch 509/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1235 - accuracy: 0.9507 - val_loss: 0.3339 - val_accuracy: 0.9195
Epoch 510/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1293 - accuracy: 0.9362 - val_loss: 0.3369 - val_accuracy: 0.8966
Epoch 511/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1049 - accuracy: 0.9623 - val_loss: 0.3392 - val_accuracy: 0.8966
Epoch 512/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.1050 - accuracy: 0.9652 - val_loss: 0.3420 - val_accuracy: 0.8966
Epoch 513/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1374 - accuracy: 0.9594 - val_loss: 0.3434 - val_accuracy: 0.8966
Epoch 514/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1109 - accuracy: 0.9652 - val_loss: 0.3412 - val_accuracy: 0.9195
Epoch 515/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1264 - accuracy: 0.9420 - val_loss: 0.3388 - val_accuracy: 0.9195
Epoch 516/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1256 - accuracy: 0.9536 - val_loss: 0.3399 - val_accuracy: 0.9195
Epoch 517/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1292 - accuracy: 0.9536 - val_loss: 0.3475 - val_accuracy: 0.8966
Epoch 518/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.1262 - accuracy: 0.9449 - val_loss: 0.3553 - val_accuracy: 0.8851
Epoch 519/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1102 - accuracy: 0.9565 - val_loss: 0.3588 - val_accuracy: 0.8851
Epoch 520/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1417 - accuracy: 0.9507 - val_loss: 0.3546 - val_accuracy: 0.8851
Epoch 521/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1160 - accuracy: 0.9536 - val_loss: 0.3437 - val_accuracy: 0.9080
Epoch 522/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1494 - accuracy: 0.9478 - val_loss: 0.3384 - val_accuracy: 0.9080
Epoch 523/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.1102 - accuracy: 0.9623 - val_loss: 0.3353 - val_accuracy: 0.8966
Epoch 524/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1199 - accuracy: 0.9507 - val_loss: 0.3357 - val_accuracy: 0.9080
Epoch 525/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.1309 - accuracy: 0.9449 - val_loss: 0.3373 - val_accuracy: 0.9195
Epoch 526/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1200 - accuracy: 0.9449 - val_loss: 0.3380 - val_accuracy: 0.9195
Epoch 527/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1362 - accuracy: 0.9594 - val_loss: 0.3347 - val_accuracy: 0.9195
Epoch 528/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.1449 - accuracy: 0.9362 - val_loss: 0.3343 - val_accuracy: 0.9195
Epoch 529/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0904 - accuracy: 0.9768 - val_loss: 0.3352 - val_accuracy: 0.9195
Epoch 530/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1582 - accuracy: 0.9246 - val_loss: 0.3392 - val_accuracy: 0.9195
Epoch 531/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1524 - accuracy: 0.9333 - val_loss: 0.3460 - val_accuracy: 0.8966
Epoch 532/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1147 - accuracy: 0.9623 - val_loss: 0.3507 - val_accuracy: 0.8966
Epoch 533/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1286 - accuracy: 0.9594 - val_loss: 0.3495 - val_accuracy: 0.8966
Epoch 534/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1336 - accuracy: 0.9623 - val_loss: 0.3412 - val_accuracy: 0.8966
Epoch 535/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1526 - accuracy: 0.9420 - val_loss: 0.3335 - val_accuracy: 0.9080
Epoch 536/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1214 - accuracy: 0.9478 - val_loss: 0.3308 - val_accuracy: 0.9080
Epoch 537/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1155 - accuracy: 0.9565 - val_loss: 0.3297 - val_accuracy: 0.9195
Epoch 538/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1244 - accuracy: 0.9507 - val_loss: 0.3309 - val_accuracy: 0.9195
Epoch 539/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1179 - accuracy: 0.9333 - val_loss: 0.3343 - val_accuracy: 0.9195
Epoch 540/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1097 - accuracy: 0.9565 - val_loss: 0.3380 - val_accuracy: 0.8966
Epoch 541/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1121 - accuracy: 0.9565 - val_loss: 0.3343 - val_accuracy: 0.8966
Epoch 542/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0961 - accuracy: 0.9565 - val_loss: 0.3326 - val_accuracy: 0.9195
Epoch 543/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.1089 - accuracy: 0.9594 - val_loss: 0.3303 - val_accuracy: 0.9195
Epoch 544/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1113 - accuracy: 0.9594 - val_loss: 0.3294 - val_accuracy: 0.9195
Epoch 545/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1292 - accuracy: 0.9536 - val_loss: 0.3288 - val_accuracy: 0.9195
Epoch 546/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1119 - accuracy: 0.9449 - val_loss: 0.3348 - val_accuracy: 0.8966
Epoch 547/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1220 - accuracy: 0.9565 - val_loss: 0.3414 - val_accuracy: 0.8966
Epoch 548/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1075 - accuracy: 0.9536 - val_loss: 0.3414 - val_accuracy: 0.8966
Epoch 549/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.0884 - accuracy: 0.9710 - val_loss: 0.3368 - val_accuracy: 0.8966
Epoch 550/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1211 - accuracy: 0.9594 - val_loss: 0.3324 - val_accuracy: 0.8966
Epoch 551/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.1294 - accuracy: 0.9478 - val_loss: 0.3316 - val_accuracy: 0.8966
Epoch 552/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1259 - accuracy: 0.9478 - val_loss: 0.3314 - val_accuracy: 0.8966
Epoch 553/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1147 - accuracy: 0.9536 - val_loss: 0.3337 - val_accuracy: 0.8851
Epoch 554/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1372 - accuracy: 0.9478 - val_loss: 0.3305 - val_accuracy: 0.8851
Epoch 555/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0891 - accuracy: 0.9652 - val_loss: 0.3250 - val_accuracy: 0.8851
Epoch 556/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0966 - accuracy: 0.9623 - val_loss: 0.3198 - val_accuracy: 0.9080
Epoch 557/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1166 - accuracy: 0.9652 - val_loss: 0.3234 - val_accuracy: 0.8851
Epoch 558/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.0948 - accuracy: 0.9710 - val_loss: 0.3321 - val_accuracy: 0.8851
Epoch 559/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1178 - accuracy: 0.9594 - val_loss: 0.3403 - val_accuracy: 0.8851
Epoch 560/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0938 - accuracy: 0.9681 - val_loss: 0.3425 - val_accuracy: 0.8851
Epoch 561/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1106 - accuracy: 0.9536 - val_loss: 0.3345 - val_accuracy: 0.8966
Epoch 562/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1202 - accuracy: 0.9536 - val_loss: 0.3305 - val_accuracy: 0.9195
Epoch 563/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1024 - accuracy: 0.9594 - val_loss: 0.3282 - val_accuracy: 0.9195
Epoch 564/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0798 - accuracy: 0.9681 - val_loss: 0.3295 - val_accuracy: 0.9195
Epoch 565/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.1197 - accuracy: 0.9536 - val_loss: 0.3327 - val_accuracy: 0.9195
Epoch 566/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1259 - accuracy: 0.9420 - val_loss: 0.3369 - val_accuracy: 0.9195
Epoch 567/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0854 - accuracy: 0.9652 - val_loss: 0.3343 - val_accuracy: 0.9195
Epoch 568/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0837 - accuracy: 0.9623 - val_loss: 0.3302 - val_accuracy: 0.9080
Epoch 569/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0816 - accuracy: 0.9739 - val_loss: 0.3324 - val_accuracy: 0.9080
Epoch 570/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1028 - accuracy: 0.9623 - val_loss: 0.3371 - val_accuracy: 0.8966
Epoch 571/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1490 - accuracy: 0.9478 - val_loss: 0.3422 - val_accuracy: 0.8851
Epoch 572/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0991 - accuracy: 0.9623 - val_loss: 0.3457 - val_accuracy: 0.8851
Epoch 573/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1216 - accuracy: 0.9710 - val_loss: 0.3465 - val_accuracy: 0.8966
Epoch 574/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0962 - accuracy: 0.9710 - val_loss: 0.3429 - val_accuracy: 0.8966
Epoch 575/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1106 - accuracy: 0.9623 - val_loss: 0.3371 - val_accuracy: 0.9080
Epoch 576/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1111 - accuracy: 0.9652 - val_loss: 0.3296 - val_accuracy: 0.9195
Epoch 577/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.0994 - accuracy: 0.9594 - val_loss: 0.3261 - val_accuracy: 0.8966
Epoch 578/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.0978 - accuracy: 0.9623 - val_loss: 0.3270 - val_accuracy: 0.9195
Epoch 579/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1140 - accuracy: 0.9652 - val_loss: 0.3307 - val_accuracy: 0.9195
Epoch 580/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.0782 - accuracy: 0.9826 - val_loss: 0.3356 - val_accuracy: 0.9310
Epoch 581/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1024 - accuracy: 0.9768 - val_loss: 0.3377 - val_accuracy: 0.9195
Epoch 582/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1461 - accuracy: 0.9449 - val_loss: 0.3305 - val_accuracy: 0.9310
Epoch 583/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1056 - accuracy: 0.9536 - val_loss: 0.3236 - val_accuracy: 0.9080
Epoch 584/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0783 - accuracy: 0.9681 - val_loss: 0.3214 - val_accuracy: 0.8966
Epoch 585/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0947 - accuracy: 0.9652 - val_loss: 0.3204 - val_accuracy: 0.8966
Epoch 586/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1106 - accuracy: 0.9565 - val_loss: 0.3223 - val_accuracy: 0.9080
Epoch 587/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0886 - accuracy: 0.9710 - val_loss: 0.3258 - val_accuracy: 0.9080
Epoch 588/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1168 - accuracy: 0.9594 - val_loss: 0.3259 - val_accuracy: 0.9080
Epoch 589/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.0971 - accuracy: 0.9652 - val_loss: 0.3285 - val_accuracy: 0.9080
Epoch 590/1000
3/3 [==============================] - 0s 39ms/step - loss: 0.0855 - accuracy: 0.9681 - val_loss: 0.3364 - val_accuracy: 0.9080
Epoch 591/1000
3/3 [==============================] - 0s 30ms/step - loss: 0.1017 - accuracy: 0.9623 - val_loss: 0.3392 - val_accuracy: 0.9080
Epoch 592/1000
3/3 [==============================] - 0s 30ms/step - loss: 0.0977 - accuracy: 0.9507 - val_loss: 0.3375 - val_accuracy: 0.9080
Epoch 593/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.1348 - accuracy: 0.9507 - val_loss: 0.3361 - val_accuracy: 0.9080
Epoch 594/1000
3/3 [==============================] - 0s 31ms/step - loss: 0.1109 - accuracy: 0.9681 - val_loss: 0.3372 - val_accuracy: 0.9310
Epoch 595/1000
3/3 [==============================] - 0s 37ms/step - loss: 0.1093 - accuracy: 0.9478 - val_loss: 0.3389 - val_accuracy: 0.9195
Epoch 596/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.1067 - accuracy: 0.9536 - val_loss: 0.3431 - val_accuracy: 0.8966
Epoch 597/1000
3/3 [==============================] - 0s 30ms/step - loss: 0.1165 - accuracy: 0.9536 - val_loss: 0.3451 - val_accuracy: 0.8966
Epoch 598/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.0782 - accuracy: 0.9710 - val_loss: 0.3449 - val_accuracy: 0.9080
Epoch 599/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.0845 - accuracy: 0.9681 - val_loss: 0.3425 - val_accuracy: 0.9080
Epoch 600/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.0947 - accuracy: 0.9710 - val_loss: 0.3374 - val_accuracy: 0.9080
Epoch 601/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.0971 - accuracy: 0.9594 - val_loss: 0.3331 - val_accuracy: 0.9080
Epoch 602/1000
3/3 [==============================] - 0s 38ms/step - loss: 0.1120 - accuracy: 0.9536 - val_loss: 0.3376 - val_accuracy: 0.9080
Epoch 603/1000
3/3 [==============================] - 0s 34ms/step - loss: 0.0949 - accuracy: 0.9623 - val_loss: 0.3448 - val_accuracy: 0.8966
Epoch 604/1000
3/3 [==============================] - 0s 35ms/step - loss: 0.0985 - accuracy: 0.9594 - val_loss: 0.3519 - val_accuracy: 0.8966
Epoch 605/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.0907 - accuracy: 0.9652 - val_loss: 0.3555 - val_accuracy: 0.9080
Epoch 606/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.0984 - accuracy: 0.9594 - val_loss: 0.3460 - val_accuracy: 0.9080
Epoch 607/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.1111 - accuracy: 0.9594 - val_loss: 0.3405 - val_accuracy: 0.9080
Epoch 608/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.1239 - accuracy: 0.9536 - val_loss: 0.3377 - val_accuracy: 0.9080
Epoch 609/1000
3/3 [==============================] - 0s 32ms/step - loss: 0.0990 - accuracy: 0.9623 - val_loss: 0.3416 - val_accuracy: 0.8966
Epoch 610/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.0762 - accuracy: 0.9739 - val_loss: 0.3470 - val_accuracy: 0.8966
Epoch 611/1000
3/3 [==============================] - 0s 30ms/step - loss: 0.1097 - accuracy: 0.9623 - val_loss: 0.3511 - val_accuracy: 0.8966
Epoch 612/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.1082 - accuracy: 0.9652 - val_loss: 0.3531 - val_accuracy: 0.8966
Epoch 613/1000
3/3 [==============================] - 0s 40ms/step - loss: 0.0975 - accuracy: 0.9594 - val_loss: 0.3492 - val_accuracy: 0.8966
Epoch 614/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.0989 - accuracy: 0.9565 - val_loss: 0.3394 - val_accuracy: 0.9080
Epoch 615/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.1100 - accuracy: 0.9565 - val_loss: 0.3398 - val_accuracy: 0.8851
Epoch 616/1000
3/3 [==============================] - 0s 31ms/step - loss: 0.0888 - accuracy: 0.9710 - val_loss: 0.3461 - val_accuracy: 0.8966
Epoch 617/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.0962 - accuracy: 0.9652 - val_loss: 0.3489 - val_accuracy: 0.8966
Epoch 618/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.0917 - accuracy: 0.9623 - val_loss: 0.3480 - val_accuracy: 0.9080
Epoch 619/1000
3/3 [==============================] - 0s 33ms/step - loss: 0.0878 - accuracy: 0.9710 - val_loss: 0.3486 - val_accuracy: 0.8966
Epoch 620/1000
3/3 [==============================] - 0s 35ms/step - loss: 0.1114 - accuracy: 0.9623 - val_loss: 0.3529 - val_accuracy: 0.8966
Epoch 621/1000
3/3 [==============================] - 0s 44ms/step - loss: 0.1230 - accuracy: 0.9565 - val_loss: 0.3452 - val_accuracy: 0.9080
Epoch 622/1000
3/3 [==============================] - 0s 42ms/step - loss: 0.0756 - accuracy: 0.9768 - val_loss: 0.3366 - val_accuracy: 0.9080
Epoch 623/1000
3/3 [==============================] - 0s 40ms/step - loss: 0.1003 - accuracy: 0.9594 - val_loss: 0.3363 - val_accuracy: 0.8966
Epoch 624/1000
3/3 [==============================] - 0s 38ms/step - loss: 0.1324 - accuracy: 0.9449 - val_loss: 0.3399 - val_accuracy: 0.8966
Epoch 625/1000
3/3 [==============================] - 0s 42ms/step - loss: 0.1137 - accuracy: 0.9623 - val_loss: 0.3418 - val_accuracy: 0.8851
Epoch 626/1000
3/3 [==============================] - 0s 41ms/step - loss: 0.0954 - accuracy: 0.9681 - val_loss: 0.3410 - val_accuracy: 0.8966
Epoch 627/1000
3/3 [==============================] - 0s 39ms/step - loss: 0.1143 - accuracy: 0.9536 - val_loss: 0.3355 - val_accuracy: 0.9080
Epoch 628/1000
3/3 [==============================] - 0s 39ms/step - loss: 0.0919 - accuracy: 0.9565 - val_loss: 0.3333 - val_accuracy: 0.9080
Epoch 629/1000
3/3 [==============================] - 0s 35ms/step - loss: 0.1088 - accuracy: 0.9507 - val_loss: 0.3332 - val_accuracy: 0.9080
Epoch 630/1000
3/3 [==============================] - 0s 44ms/step - loss: 0.0889 - accuracy: 0.9652 - val_loss: 0.3369 - val_accuracy: 0.9080
Epoch 631/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.0780 - accuracy: 0.9797 - val_loss: 0.3430 - val_accuracy: 0.9195
Epoch 632/1000
3/3 [==============================] - 0s 34ms/step - loss: 0.0614 - accuracy: 0.9826 - val_loss: 0.3483 - val_accuracy: 0.9080
Epoch 633/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.0829 - accuracy: 0.9652 - val_loss: 0.3472 - val_accuracy: 0.9195
Epoch 634/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.0733 - accuracy: 0.9768 - val_loss: 0.3455 - val_accuracy: 0.9080
Epoch 635/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.0864 - accuracy: 0.9652 - val_loss: 0.3456 - val_accuracy: 0.8966
Epoch 636/1000
3/3 [==============================] - 0s 37ms/step - loss: 0.0765 - accuracy: 0.9710 - val_loss: 0.3472 - val_accuracy: 0.8851
Epoch 637/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1352 - accuracy: 0.9362 - val_loss: 0.3465 - val_accuracy: 0.8851
Epoch 638/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1019 - accuracy: 0.9594 - val_loss: 0.3426 - val_accuracy: 0.8966
Epoch 639/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.0776 - accuracy: 0.9768 - val_loss: 0.3393 - val_accuracy: 0.9080
Epoch 640/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1153 - accuracy: 0.9565 - val_loss: 0.3411 - val_accuracy: 0.8966
Epoch 641/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.1506 - accuracy: 0.9449 - val_loss: 0.3411 - val_accuracy: 0.8966
Epoch 642/1000
3/3 [==============================] - 0s 30ms/step - loss: 0.0741 - accuracy: 0.9739 - val_loss: 0.3380 - val_accuracy: 0.8966
Epoch 643/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.1119 - accuracy: 0.9565 - val_loss: 0.3342 - val_accuracy: 0.8966
Epoch 644/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1077 - accuracy: 0.9594 - val_loss: 0.3292 - val_accuracy: 0.8966
Epoch 645/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1276 - accuracy: 0.9507 - val_loss: 0.3302 - val_accuracy: 0.8966
Epoch 646/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.1331 - accuracy: 0.9594 - val_loss: 0.3372 - val_accuracy: 0.9080
Epoch 647/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0756 - accuracy: 0.9710 - val_loss: 0.3417 - val_accuracy: 0.9080
Epoch 648/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1145 - accuracy: 0.9623 - val_loss: 0.3419 - val_accuracy: 0.8966
Epoch 649/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1194 - accuracy: 0.9623 - val_loss: 0.3322 - val_accuracy: 0.9080
Epoch 650/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0822 - accuracy: 0.9710 - val_loss: 0.3250 - val_accuracy: 0.9080
Epoch 651/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0679 - accuracy: 0.9710 - val_loss: 0.3211 - val_accuracy: 0.8966
Epoch 652/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.0712 - accuracy: 0.9768 - val_loss: 0.3175 - val_accuracy: 0.9080
Epoch 653/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0711 - accuracy: 0.9739 - val_loss: 0.3151 - val_accuracy: 0.9080
Epoch 654/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.0945 - accuracy: 0.9797 - val_loss: 0.3190 - val_accuracy: 0.8966
Epoch 655/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0707 - accuracy: 0.9768 - val_loss: 0.3233 - val_accuracy: 0.8966
Epoch 656/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.0747 - accuracy: 0.9710 - val_loss: 0.3262 - val_accuracy: 0.9080
Epoch 657/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0764 - accuracy: 0.9768 - val_loss: 0.3290 - val_accuracy: 0.8966
Epoch 658/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0860 - accuracy: 0.9768 - val_loss: 0.3299 - val_accuracy: 0.9080
Epoch 659/1000
3/3 [==============================] - 0s 31ms/step - loss: 0.1093 - accuracy: 0.9449 - val_loss: 0.3236 - val_accuracy: 0.8966
Epoch 660/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0862 - accuracy: 0.9797 - val_loss: 0.3236 - val_accuracy: 0.9080
Epoch 661/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0979 - accuracy: 0.9623 - val_loss: 0.3345 - val_accuracy: 0.9080
Epoch 662/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.0614 - accuracy: 0.9797 - val_loss: 0.3441 - val_accuracy: 0.9080
Epoch 663/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1284 - accuracy: 0.9594 - val_loss: 0.3502 - val_accuracy: 0.9080
Epoch 664/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1082 - accuracy: 0.9594 - val_loss: 0.3510 - val_accuracy: 0.9080
Epoch 665/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.0870 - accuracy: 0.9652 - val_loss: 0.3459 - val_accuracy: 0.9195
Epoch 666/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0878 - accuracy: 0.9768 - val_loss: 0.3457 - val_accuracy: 0.9195
Epoch 667/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.0628 - accuracy: 0.9710 - val_loss: 0.3460 - val_accuracy: 0.9080
Epoch 668/1000
3/3 [==============================] - 0s 22ms/step - loss: 0.1032 - accuracy: 0.9594 - val_loss: 0.3522 - val_accuracy: 0.8966
Epoch 669/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.0835 - accuracy: 0.9681 - val_loss: 0.3633 - val_accuracy: 0.8966
Epoch 670/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.1032 - accuracy: 0.9681 - val_loss: 0.3771 - val_accuracy: 0.8851
Epoch 671/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0899 - accuracy: 0.9565 - val_loss: 0.3805 - val_accuracy: 0.8851
Epoch 672/1000
3/3 [==============================] - 0s 36ms/step - loss: 0.1246 - accuracy: 0.9594 - val_loss: 0.3666 - val_accuracy: 0.9080
Epoch 673/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0892 - accuracy: 0.9594 - val_loss: 0.3560 - val_accuracy: 0.9080
Epoch 674/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.0914 - accuracy: 0.9623 - val_loss: 0.3513 - val_accuracy: 0.9080
Epoch 675/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.0902 - accuracy: 0.9623 - val_loss: 0.3471 - val_accuracy: 0.9195
Epoch 676/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1019 - accuracy: 0.9623 - val_loss: 0.3479 - val_accuracy: 0.9195
Epoch 677/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0739 - accuracy: 0.9768 - val_loss: 0.3508 - val_accuracy: 0.9080
Epoch 678/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.0962 - accuracy: 0.9681 - val_loss: 0.3584 - val_accuracy: 0.8966
Epoch 679/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.0736 - accuracy: 0.9710 - val_loss: 0.3664 - val_accuracy: 0.9080
Epoch 680/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0799 - accuracy: 0.9739 - val_loss: 0.3663 - val_accuracy: 0.9080
Epoch 681/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.0725 - accuracy: 0.9710 - val_loss: 0.3548 - val_accuracy: 0.9080
Epoch 682/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0857 - accuracy: 0.9652 - val_loss: 0.3419 - val_accuracy: 0.9195
Epoch 683/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.1057 - accuracy: 0.9623 - val_loss: 0.3383 - val_accuracy: 0.9080
Epoch 684/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.0824 - accuracy: 0.9681 - val_loss: 0.3409 - val_accuracy: 0.9080
Epoch 685/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0984 - accuracy: 0.9623 - val_loss: 0.3394 - val_accuracy: 0.9080
Epoch 686/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0872 - accuracy: 0.9652 - val_loss: 0.3353 - val_accuracy: 0.9080
Epoch 687/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0778 - accuracy: 0.9681 - val_loss: 0.3294 - val_accuracy: 0.9080
Epoch 688/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0891 - accuracy: 0.9652 - val_loss: 0.3275 - val_accuracy: 0.9080
Epoch 689/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0798 - accuracy: 0.9710 - val_loss: 0.3284 - val_accuracy: 0.9195
Epoch 690/1000
3/3 [==============================] - 0s 24ms/step - loss: 0.0871 - accuracy: 0.9565 - val_loss: 0.3306 - val_accuracy: 0.9195
Epoch 691/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.1253 - accuracy: 0.9594 - val_loss: 0.3377 - val_accuracy: 0.8966
Epoch 692/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0713 - accuracy: 0.9768 - val_loss: 0.3500 - val_accuracy: 0.9080
Epoch 693/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0817 - accuracy: 0.9826 - val_loss: 0.3536 - val_accuracy: 0.9080
Epoch 694/1000
3/3 [==============================] - 0s 27ms/step - loss: 0.1317 - accuracy: 0.9536 - val_loss: 0.3497 - val_accuracy: 0.8966
Epoch 695/1000
3/3 [==============================] - 0s 23ms/step - loss: 0.0713 - accuracy: 0.9768 - val_loss: 0.3535 - val_accuracy: 0.8966
Epoch 696/1000
3/3 [==============================] - 0s 26ms/step - loss: 0.0779 - accuracy: 0.9768 - val_loss: 0.3571 - val_accuracy: 0.8966
Epoch 697/1000
3/3 [==============================] - 0s 25ms/step - loss: 0.0876 - accuracy: 0.9681 - val_loss: 0.3578 - val_accuracy: 0.8966
Epoch 698/1000
3/3 [==============================] - 0s 28ms/step - loss: 0.0868 - accuracy: 0.9710 - val_loss: 0.3561 - val_accuracy: 0.8851
Epoch 699/1000
3/3 [==============================] - 0s 29ms/step - loss: 0.0806 - accuracy: 0.9739 - val_loss: 0.3560 - val_accuracy: 0.8851
Epoch 700/1000
3/3 [==============================] - 0s 34ms/step - loss: 0.0805 - accuracy: 0.9739 - val_loss: 0.3529 - val_accuracy: 0.8966
Epoch
In [ ]:
#학습 진행사항을 plt로 출력
# hist2의 accuracy plt의 plot을 이용하여 출력
plt.plot(hist2.history['accuracy'], label='accuracy')
plt.plot(hist2.history['loss'], label='loss')
plt.plot(hist2.history['val_accuracy'], label='val_accuracy')
plt.plot(hist2.history['val_loss'], label='val_loss')
plt.legend(loc='upper left')
plt.ylim(0.0, 1.0)
plt.show()
6.4.모델 평가하기
In [ ]:
#1번 모델 평가
model1.evaluate(test_sound.reshape(-1,40,65,1), test_labels, batch_size=32)
3/3 [==============================] - 0s 51ms/step - loss: 0.3654 - accuracy: 0.9195
Out[ ]:
[0.36540985107421875, 0.9195402264595032]
In [ ]:
#2번 모델 평가
model2.evaluate(test_sound.reshape(-1,40,65,1), test_labels, batch_size=32)
3/3 [==============================] - 0s 32ms/step - loss: 0.3797 - accuracy: 0.9310
Out[ ]:
[0.3796848952770233, 0.931034505367279]
6.5.테스트¶
해당 음성 데이터를 불러온 다음
mfcc변환한 값으로 테스트 해야 함
In [ ]:
file_path = '/content/drive/MyDrive/Colab Notebooks/8.음향관련/sj_sound_data/test/test1.wav'
test_yun_sound, sr = librosa.load(file_path)
In [ ]:
#테스트 사운드 들어보기
display(Audio(data=test_yun_sound,rate=sr))
Your browser does not support the audio element.
In [ ]:
#mfcc값으로 변환 전 음성데이터 길이 조절
max_len = 33075
zero_padding = tf.zeros(max_len - tf.shape(test_yun_sound), dtype=tf.float32)
test_yun_sound = tf.concat([test_yun_sound, zero_padding],0)
test_yun_sound = np.array(test_yun_sound)
type(test_yun_sound)
Out[ ]:
numpy.ndarray
In [ ]:
#zero패딩된 테스트 음성 다시 들어보기
display(Audio(data=test_yun_sound,rate=sr))
Your browser does not support the audio element.
In [ ]:
#모델 주입전 mfcc값으로 변환
test_yun_mfcc = librosa.feature.mfcc(y=test_yun_sound, sr=sr, n_mfcc=40)
test_yun_mfcc.shape
Out[ ]:
(40, 65)
In [ ]:
#모델에 주입하기 위해 shape 확인후 변환
#우리가 주입해야할 shape는 40,65인데 현재 yun데이터는 40,33
test_yun = test_yun_mfcc.reshape(1, 40, 65, 1)
test_yun.shape
Out[ ]:
(1, 40, 65, 1)
In [ ]:
#test 데이터를 2개의 모델에 주입
yun_result1 = model1.predict(test_yun)
yun_result2 = model2.predict(test_yun)
1/1 [==============================] - 0s 130ms/step
1/1 [==============================] - 0s 64ms/step
In [ ]:
# 결과확인1-숫자로
print("1번 모델의 결과:", yun_result1)
print("2번 모델의 결과:", yun_result2)
1번 모델의 결과: [[8.4320849e-01 2.6454317e-04 1.5652700e-01]]
2번 모델의 결과: [[0.80751425 0.05412688 0.1383589 ]]
In [ ]:
#결과 확인2-그래프로
plt.subplot(2,1,1)
plt.bar(range(3), yun_result1[0])
plt.subplot(2,1,2)
plt.bar(range(3), yun_result2[0])
plt.show()
print("날리면이면 0, 바이든이면1, cnn모델도 모르겠으면 2")
# labels_dic = { 0:'nali', 1:'biden', 2:"unknown"}
날리면이면 0, 바이든이면1, cnn모델도 모르겠으면 2
In [ ]:
#결과가 이상하니 잡음제거 안된 원본 음성으로
file_path2 = '/content/drive/MyDrive/Colab Notebooks/8.음향관련/sj_sound_data/test/test_yun(잡음포함).wav'
test_yun_sound2, sr = librosa.load(file_path2)
display(Audio(data=test_yun_sound2,rate=sr))
Your browser does not support the audio element.
In [ ]:
test_yun_sound2.shape
Out[ ]:
(33075,)
In [ ]:
# 리브로사 라이브러리로 mfcc변환
test_yun_mfcc2 = librosa.feature.mfcc(y=test_yun_sound2, sr=sr, n_mfcc=40)
test_yun2 = test_yun_mfcc2.reshape(1, 40, 65, 1)
In [ ]:
# 학습된 모델에 넣고 예측하기
yun_result3 = model1.predict(test_yun2)
yun_result4 = model2.predict(test_yun2)
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 16ms/step
In [ ]:
#결과 확인2-그래프로
plt.subplot(2,1,1)
plt.bar(range(3), yun_result3[0])
plt.subplot(2,1,2)
plt.bar(range(3), yun_result4[0])
plt.show()
print("날리면이면 0, 바이든이면1, cnn모델도 모르겠으면 2")
# labels_dic = { 0:'nali', 1:'biden', 2:"unknown"}
날리면이면 0, 바이든이면1, cnn모델도 모르겠으면 2
7.클래스를 3개에서 2개로¶
이진분류로 진행하므로 손실함수는 바이너리 크로스 엔트로피 사용
In [ ]:
# 저장된 값 확인
# 이때 data안의 x인자와 y인자로 다시 train과 labels를 지정하면 됨
train = data['x']
labels = data['y']
#잘 불러와졌는지 확인
train.shape
Out[ ]:
(432, 40, 65)
7.1.라벨링 다시
In [ ]:
train2 = train[:288]
In [ ]:
labels_hap2 = np.array(range(len(train[:288])))
labels_hap2[:144] = 0
labels_hap2[144: ] = 1
labels2 = tf.keras.utils.to_categorical(labels_hap2)
labels2
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In [ ]:
train_sound2, test_sound2, train_labels2, test_labels2 = train_test_split(train2, labels2, test_size=0.2, random_state = 42)
7.2.모델 다시
In [ ]:
def biden_nali_model3():
inputs = tf.keras.Input(shape=(40, 65,1))
x1 = Conv2D(16,2,activation= 'relu', padding='same')(inputs)
# x1 = MaxPool2D(2)(x1)
x1 = Dropout(0.2)(x1)
x2 = Conv2D(32,2,activation= 'relu')(x1)
x2 = MaxPool2D(2)(x2)
x2 = Dropout(0.2)(x2)
x3 = Conv2D(32,2,activation= 'relu', padding='same')(x2)
# x3 = MaxPool2D(2)(x3)
x3 = Dropout(0.2)(x3)
x4 = Conv2D(64,2,activation= 'relu')(x3)
x4 = MaxPool2D(2)(x4)
x4 = Dropout(0.2)(x4)
x5 = Conv2D(128,2,activation= 'relu')(x4)
x5 = MaxPool2D(2)(x5)
#마지막 단계에서는 dropout생략
x6 = GlobalAvgPool2D()(x5)
outputs = Dense(2, activation='softmax')(x6)
model = Model(inputs,outputs)
return model
In [ ]:
def biden_nali_model4():
inputs = tf.keras.Input(shape=(40, 65,1))
x1 = Conv2D(16,2,activation= 'relu')(inputs)
x1 = MaxPool2D(2)(x1)
x1 = Dropout(0.2)(x1)
x2 = Conv2D(32,2,activation= 'relu')(x1)
x2 = MaxPool2D(2)(x2)
x2 = Dropout(0.2)(x2)
x3 = Conv2D(32,2,activation= 'relu')(x2)
x3 = MaxPool2D(2)(x3)
x3 = Dropout(0.2)(x3)
x4 = Conv2D(64,2,activation= 'relu')(x3)
x4 = MaxPool2D(2)(x4)
x4 = Dropout(0.2)(x4)
#마지막 단계에서는 dropout생략
x5 = Flatten()(x4)
outputs = Dense(2, activation='softmax')(x5)
model = Model(inputs,outputs)
return model
In [ ]:
# 모델 서머리 확인
model3 = biden_nali_model3()
model4 = biden_nali_model4()
model3.summary()
Model: "model_10"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_12 (InputLayer) [(None, 40, 65, 1)] 0
conv2d_46 (Conv2D) (None, 40, 65, 16) 80
dropout_40 (Dropout) (None, 40, 65, 16) 0
conv2d_47 (Conv2D) (None, 39, 64, 32) 2080
max_pooling2d_33 (MaxPoolin (None, 19, 32, 32) 0
g2D)
dropout_41 (Dropout) (None, 19, 32, 32) 0
conv2d_48 (Conv2D) (None, 19, 32, 32) 4128
dropout_42 (Dropout) (None, 19, 32, 32) 0
conv2d_49 (Conv2D) (None, 18, 31, 64) 8256
max_pooling2d_34 (MaxPoolin (None, 9, 15, 64) 0
g2D)
dropout_43 (Dropout) (None, 9, 15, 64) 0
conv2d_50 (Conv2D) (None, 8, 14, 128) 32896
max_pooling2d_35 (MaxPoolin (None, 4, 7, 128) 0
g2D)
global_average_pooling2d_5 (None, 128) 0
(GlobalAveragePooling2D)
dense_10 (Dense) (None, 2) 258
=================================================================
Total params: 47,698
Trainable params: 47,698
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt1 = tf.keras.optimizers.Adam(learning_rate=0.001)
opt2 = tf.keras.optimizers.Adam(learning_rate=0.0005)
model3.compile(loss = 'binary_crossentropy', optimizer=opt1, metrics='accuracy')
model4.compile(loss = 'binary_crossentropy', optimizer=opt2, metrics='accuracy')
7.3.학습다시
In [ ]:
callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=30)]
In [ ]:
hist3 = model3.fit(train_sound2.reshape(-1,40,65,1), train_labels2,
validation_data = (test_sound2.reshape((-1,40,65,1)), test_labels2),
batch_size = 128, epochs=1000, verbose=1, callbacks=callbacks)
Epoch 1/1000
2/2 [==============================] - 0s 70ms/step - loss: 0.1764 - accuracy: 0.9348 - val_loss: 0.4788 - val_accuracy: 0.8621
Epoch 2/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.1604 - accuracy: 0.9391 - val_loss: 0.3853 - val_accuracy: 0.8966
Epoch 3/1000
2/2 [==============================] - 0s 45ms/step - loss: 0.1690 - accuracy: 0.9435 - val_loss: 0.4133 - val_accuracy: 0.8966
Epoch 4/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.1601 - accuracy: 0.9391 - val_loss: 0.3938 - val_accuracy: 0.8793
Epoch 5/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.1546 - accuracy: 0.9435 - val_loss: 0.3884 - val_accuracy: 0.8793
Epoch 6/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.1446 - accuracy: 0.9652 - val_loss: 0.4432 - val_accuracy: 0.8621
Epoch 7/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1507 - accuracy: 0.9391 - val_loss: 0.3808 - val_accuracy: 0.8966
Epoch 8/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.1405 - accuracy: 0.9522 - val_loss: 0.4124 - val_accuracy: 0.8621
Epoch 9/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1606 - accuracy: 0.9348 - val_loss: 0.3664 - val_accuracy: 0.8793
Epoch 10/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1497 - accuracy: 0.9348 - val_loss: 0.3336 - val_accuracy: 0.8966
Epoch 11/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1556 - accuracy: 0.9348 - val_loss: 0.4342 - val_accuracy: 0.8276
Epoch 12/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.1455 - accuracy: 0.9522 - val_loss: 0.3952 - val_accuracy: 0.8793
Epoch 13/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.1604 - accuracy: 0.9217 - val_loss: 0.3218 - val_accuracy: 0.8966
Epoch 14/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.1321 - accuracy: 0.9565 - val_loss: 0.3116 - val_accuracy: 0.8966
Epoch 15/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1380 - accuracy: 0.9652 - val_loss: 0.4978 - val_accuracy: 0.7759
Epoch 16/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.1705 - accuracy: 0.9391 - val_loss: 0.2845 - val_accuracy: 0.8966
Epoch 17/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1695 - accuracy: 0.9217 - val_loss: 0.4674 - val_accuracy: 0.8103
Epoch 18/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1599 - accuracy: 0.9174 - val_loss: 0.4272 - val_accuracy: 0.8793
Epoch 19/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.1232 - accuracy: 0.9565 - val_loss: 0.2727 - val_accuracy: 0.8621
Epoch 20/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.1593 - accuracy: 0.9391 - val_loss: 0.4939 - val_accuracy: 0.8276
Epoch 21/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.1376 - accuracy: 0.9478 - val_loss: 0.3326 - val_accuracy: 0.8966
Epoch 22/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.1526 - accuracy: 0.9304 - val_loss: 0.4135 - val_accuracy: 0.8966
Epoch 23/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1255 - accuracy: 0.9522 - val_loss: 0.4966 - val_accuracy: 0.8793
Epoch 24/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.1304 - accuracy: 0.9478 - val_loss: 0.2772 - val_accuracy: 0.8621
Epoch 25/1000
2/2 [==============================] - 0s 52ms/step - loss: 0.1348 - accuracy: 0.9609 - val_loss: 0.4573 - val_accuracy: 0.8966
Epoch 26/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1361 - accuracy: 0.9435 - val_loss: 0.4341 - val_accuracy: 0.8966
Epoch 27/1000
2/2 [==============================] - 0s 46ms/step - loss: 0.1157 - accuracy: 0.9696 - val_loss: 0.2887 - val_accuracy: 0.8966
Epoch 28/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.1110 - accuracy: 0.9609 - val_loss: 0.3879 - val_accuracy: 0.9138
Epoch 29/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.1138 - accuracy: 0.9522 - val_loss: 0.4001 - val_accuracy: 0.8966
Epoch 30/1000
2/2 [==============================] - 0s 53ms/step - loss: 0.1133 - accuracy: 0.9609 - val_loss: 0.2816 - val_accuracy: 0.8966
Epoch 31/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1325 - accuracy: 0.9435 - val_loss: 0.3637 - val_accuracy: 0.8966
Epoch 32/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1083 - accuracy: 0.9609 - val_loss: 0.4041 - val_accuracy: 0.9138
Epoch 33/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.1029 - accuracy: 0.9739 - val_loss: 0.2514 - val_accuracy: 0.8621
Epoch 34/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.1275 - accuracy: 0.9652 - val_loss: 0.4176 - val_accuracy: 0.9138
Epoch 35/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1263 - accuracy: 0.9522 - val_loss: 0.3127 - val_accuracy: 0.8966
Epoch 36/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.1106 - accuracy: 0.9609 - val_loss: 0.3014 - val_accuracy: 0.8966
Epoch 37/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0955 - accuracy: 0.9826 - val_loss: 0.3339 - val_accuracy: 0.9138
Epoch 38/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1260 - accuracy: 0.9522 - val_loss: 0.2379 - val_accuracy: 0.8793
Epoch 39/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.1223 - accuracy: 0.9565 - val_loss: 0.2765 - val_accuracy: 0.8793
Epoch 40/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.1249 - accuracy: 0.9565 - val_loss: 0.4741 - val_accuracy: 0.8448
Epoch 41/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1290 - accuracy: 0.9565 - val_loss: 0.1948 - val_accuracy: 0.8966
Epoch 42/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1582 - accuracy: 0.9391 - val_loss: 0.3969 - val_accuracy: 0.9138
Epoch 43/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1736 - accuracy: 0.9261 - val_loss: 0.3302 - val_accuracy: 0.9138
Epoch 44/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1461 - accuracy: 0.9478 - val_loss: 0.1916 - val_accuracy: 0.8966
Epoch 45/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1075 - accuracy: 0.9739 - val_loss: 0.3947 - val_accuracy: 0.8448
Epoch 46/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1210 - accuracy: 0.9391 - val_loss: 0.2729 - val_accuracy: 0.9138
Epoch 47/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.1003 - accuracy: 0.9739 - val_loss: 0.1818 - val_accuracy: 0.8966
Epoch 48/1000
2/2 [==============================] - 0s 53ms/step - loss: 0.1117 - accuracy: 0.9652 - val_loss: 0.3876 - val_accuracy: 0.9138
Epoch 49/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1287 - accuracy: 0.9304 - val_loss: 0.3124 - val_accuracy: 0.9138
Epoch 50/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1059 - accuracy: 0.9696 - val_loss: 0.2093 - val_accuracy: 0.8966
Epoch 51/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1043 - accuracy: 0.9652 - val_loss: 0.3413 - val_accuracy: 0.9138
Epoch 52/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.1071 - accuracy: 0.9522 - val_loss: 0.3088 - val_accuracy: 0.9138
Epoch 53/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0833 - accuracy: 0.9783 - val_loss: 0.1699 - val_accuracy: 0.8966
Epoch 54/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.1082 - accuracy: 0.9609 - val_loss: 0.2642 - val_accuracy: 0.9138
Epoch 55/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1024 - accuracy: 0.9478 - val_loss: 0.3187 - val_accuracy: 0.9138
Epoch 56/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1208 - accuracy: 0.9522 - val_loss: 0.1874 - val_accuracy: 0.8966
Epoch 57/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0887 - accuracy: 0.9826 - val_loss: 0.2602 - val_accuracy: 0.8621
Epoch 58/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0945 - accuracy: 0.9739 - val_loss: 0.3512 - val_accuracy: 0.9138
Epoch 59/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0980 - accuracy: 0.9609 - val_loss: 0.2155 - val_accuracy: 0.8793
Epoch 60/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0990 - accuracy: 0.9696 - val_loss: 0.2136 - val_accuracy: 0.8793
Epoch 61/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0867 - accuracy: 0.9652 - val_loss: 0.2739 - val_accuracy: 0.9138
Epoch 62/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0917 - accuracy: 0.9739 - val_loss: 0.2204 - val_accuracy: 0.8793
Epoch 63/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0953 - accuracy: 0.9696 - val_loss: 0.2207 - val_accuracy: 0.8793
Epoch 64/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0774 - accuracy: 0.9870 - val_loss: 0.2577 - val_accuracy: 0.8793
Epoch 65/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0886 - accuracy: 0.9739 - val_loss: 0.2345 - val_accuracy: 0.8621
Epoch 66/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0706 - accuracy: 0.9870 - val_loss: 0.1817 - val_accuracy: 0.8966
Epoch 67/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0945 - accuracy: 0.9783 - val_loss: 0.2941 - val_accuracy: 0.9138
Epoch 68/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0922 - accuracy: 0.9696 - val_loss: 0.2773 - val_accuracy: 0.9138
Epoch 69/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0733 - accuracy: 0.9870 - val_loss: 0.2004 - val_accuracy: 0.8793
Epoch 70/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0753 - accuracy: 0.9739 - val_loss: 0.2098 - val_accuracy: 0.8966
Epoch 71/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0742 - accuracy: 0.9826 - val_loss: 0.2133 - val_accuracy: 0.9138
Epoch 72/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0804 - accuracy: 0.9739 - val_loss: 0.1760 - val_accuracy: 0.8966
Epoch 73/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0714 - accuracy: 0.9783 - val_loss: 0.1906 - val_accuracy: 0.9138
Epoch 74/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0797 - accuracy: 0.9696 - val_loss: 0.2231 - val_accuracy: 0.9138
Epoch 75/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0948 - accuracy: 0.9652 - val_loss: 0.1474 - val_accuracy: 0.9310
Epoch 76/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.1002 - accuracy: 0.9696 - val_loss: 0.2471 - val_accuracy: 0.8966
Epoch 77/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0994 - accuracy: 0.9565 - val_loss: 0.1795 - val_accuracy: 0.8966
Epoch 78/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0727 - accuracy: 0.9739 - val_loss: 0.1368 - val_accuracy: 0.9483
Epoch 79/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0726 - accuracy: 0.9826 - val_loss: 0.2171 - val_accuracy: 0.9310
Epoch 80/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0939 - accuracy: 0.9652 - val_loss: 0.2313 - val_accuracy: 0.9138
Epoch 81/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0778 - accuracy: 0.9652 - val_loss: 0.1296 - val_accuracy: 0.9310
Epoch 82/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0890 - accuracy: 0.9783 - val_loss: 0.1750 - val_accuracy: 0.9655
Epoch 83/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0882 - accuracy: 0.9696 - val_loss: 0.1815 - val_accuracy: 0.9483
Epoch 84/1000
2/2 [==============================] - 0s 57ms/step - loss: 0.0581 - accuracy: 0.9739 - val_loss: 0.1473 - val_accuracy: 0.9310
Epoch 85/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0724 - accuracy: 0.9826 - val_loss: 0.1623 - val_accuracy: 0.9138
Epoch 86/1000
2/2 [==============================] - 0s 54ms/step - loss: 0.0752 - accuracy: 0.9739 - val_loss: 0.2009 - val_accuracy: 0.9138
Epoch 87/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0669 - accuracy: 0.9826 - val_loss: 0.1485 - val_accuracy: 0.9310
Epoch 88/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0669 - accuracy: 0.9826 - val_loss: 0.1331 - val_accuracy: 0.9310
Epoch 89/1000
2/2 [==============================] - 0s 45ms/step - loss: 0.0688 - accuracy: 0.9826 - val_loss: 0.1495 - val_accuracy: 0.9483
Epoch 90/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0584 - accuracy: 0.9783 - val_loss: 0.1799 - val_accuracy: 0.9310
Epoch 91/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0733 - accuracy: 0.9783 - val_loss: 0.1481 - val_accuracy: 0.9310
Epoch 92/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0596 - accuracy: 0.9870 - val_loss: 0.1562 - val_accuracy: 0.9138
Epoch 93/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0626 - accuracy: 0.9826 - val_loss: 0.2218 - val_accuracy: 0.8966
Epoch 94/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0689 - accuracy: 0.9696 - val_loss: 0.1777 - val_accuracy: 0.9138
Epoch 95/1000
2/2 [==============================] - 0s 62ms/step - loss: 0.0563 - accuracy: 0.9826 - val_loss: 0.1237 - val_accuracy: 0.9483
Epoch 96/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0819 - accuracy: 0.9739 - val_loss: 0.1838 - val_accuracy: 0.9310
Epoch 97/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0537 - accuracy: 0.9870 - val_loss: 0.2070 - val_accuracy: 0.9138
Epoch 98/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0773 - accuracy: 0.9826 - val_loss: 0.1207 - val_accuracy: 0.9483
Epoch 99/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0727 - accuracy: 0.9783 - val_loss: 0.1314 - val_accuracy: 0.9310
Epoch 100/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0517 - accuracy: 0.9913 - val_loss: 0.1766 - val_accuracy: 0.9310
Epoch 101/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0576 - accuracy: 0.9739 - val_loss: 0.1346 - val_accuracy: 0.9655
Epoch 102/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0762 - accuracy: 0.9826 - val_loss: 0.1296 - val_accuracy: 0.9655
Epoch 103/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0663 - accuracy: 0.9826 - val_loss: 0.1670 - val_accuracy: 0.9483
Epoch 104/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0621 - accuracy: 0.9739 - val_loss: 0.1336 - val_accuracy: 0.9655
Epoch 105/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0656 - accuracy: 0.9783 - val_loss: 0.1188 - val_accuracy: 0.9483
Epoch 106/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0538 - accuracy: 0.9783 - val_loss: 0.1200 - val_accuracy: 0.9483
Epoch 107/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0585 - accuracy: 0.9826 - val_loss: 0.1864 - val_accuracy: 0.9310
Epoch 108/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0649 - accuracy: 0.9826 - val_loss: 0.1276 - val_accuracy: 0.9655
Epoch 109/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0606 - accuracy: 0.9870 - val_loss: 0.1191 - val_accuracy: 0.9655
Epoch 110/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0595 - accuracy: 0.9826 - val_loss: 0.1064 - val_accuracy: 0.9483
Epoch 111/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0704 - accuracy: 0.9870 - val_loss: 0.1303 - val_accuracy: 0.9655
Epoch 112/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0558 - accuracy: 0.9739 - val_loss: 0.1055 - val_accuracy: 0.9655
Epoch 113/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0625 - accuracy: 0.9870 - val_loss: 0.1105 - val_accuracy: 0.9655
Epoch 114/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0511 - accuracy: 0.9826 - val_loss: 0.1735 - val_accuracy: 0.9655
Epoch 115/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0676 - accuracy: 0.9696 - val_loss: 0.1051 - val_accuracy: 0.9828
Epoch 116/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0696 - accuracy: 0.9826 - val_loss: 0.1038 - val_accuracy: 0.9483
Epoch 117/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0603 - accuracy: 0.9826 - val_loss: 0.1272 - val_accuracy: 0.9655
Epoch 118/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0517 - accuracy: 0.9826 - val_loss: 0.1700 - val_accuracy: 0.9655
Epoch 119/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0554 - accuracy: 0.9870 - val_loss: 0.1331 - val_accuracy: 0.9483
Epoch 120/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0541 - accuracy: 0.9870 - val_loss: 0.0977 - val_accuracy: 0.9828
Epoch 121/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0446 - accuracy: 0.9913 - val_loss: 0.0946 - val_accuracy: 0.9828
Epoch 122/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0622 - accuracy: 0.9826 - val_loss: 0.1030 - val_accuracy: 0.9828
Epoch 123/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0487 - accuracy: 0.9870 - val_loss: 0.0975 - val_accuracy: 0.9828
Epoch 124/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0519 - accuracy: 0.9826 - val_loss: 0.1195 - val_accuracy: 0.9655
Epoch 125/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0531 - accuracy: 0.9826 - val_loss: 0.1555 - val_accuracy: 0.9483
Epoch 126/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0566 - accuracy: 0.9783 - val_loss: 0.1124 - val_accuracy: 0.9483
Epoch 127/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0589 - accuracy: 0.9783 - val_loss: 0.2973 - val_accuracy: 0.9138
Epoch 128/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0905 - accuracy: 0.9696 - val_loss: 0.1477 - val_accuracy: 0.9483
Epoch 129/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0648 - accuracy: 0.9783 - val_loss: 0.0949 - val_accuracy: 0.9655
Epoch 130/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0539 - accuracy: 0.9870 - val_loss: 0.1863 - val_accuracy: 0.9310
Epoch 131/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0599 - accuracy: 0.9739 - val_loss: 0.1950 - val_accuracy: 0.9310
Epoch 132/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0763 - accuracy: 0.9739 - val_loss: 0.1026 - val_accuracy: 0.9483
Epoch 133/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0877 - accuracy: 0.9696 - val_loss: 0.2717 - val_accuracy: 0.9138
Epoch 134/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1293 - accuracy: 0.9435 - val_loss: 0.1302 - val_accuracy: 0.9483
Epoch 135/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.1012 - accuracy: 0.9696 - val_loss: 0.0922 - val_accuracy: 0.9828
Epoch 136/1000
2/2 [==============================] - 0s 54ms/step - loss: 0.1083 - accuracy: 0.9652 - val_loss: 0.1233 - val_accuracy: 0.9655
Epoch 137/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0479 - accuracy: 0.9870 - val_loss: 0.1191 - val_accuracy: 0.9310
Epoch 138/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0919 - accuracy: 0.9652 - val_loss: 0.1484 - val_accuracy: 0.9483
Epoch 139/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0508 - accuracy: 0.9826 - val_loss: 0.4519 - val_accuracy: 0.8793
Epoch 140/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.1028 - accuracy: 0.9609 - val_loss: 0.1143 - val_accuracy: 0.9483
Epoch 141/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.1131 - accuracy: 0.9565 - val_loss: 0.1076 - val_accuracy: 0.9655
Epoch 142/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0578 - accuracy: 0.9739 - val_loss: 0.3124 - val_accuracy: 0.8448
Epoch 143/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0667 - accuracy: 0.9652 - val_loss: 0.1363 - val_accuracy: 0.9483
Epoch 144/1000
2/2 [==============================] - 0s 45ms/step - loss: 0.0518 - accuracy: 0.9783 - val_loss: 0.1095 - val_accuracy: 0.9310
Epoch 145/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0760 - accuracy: 0.9739 - val_loss: 0.0937 - val_accuracy: 0.9828
Epoch 146/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0484 - accuracy: 0.9783 - val_loss: 0.1921 - val_accuracy: 0.9483
Epoch 147/1000
2/2 [==============================] - 0s 66ms/step - loss: 0.0698 - accuracy: 0.9696 - val_loss: 0.1282 - val_accuracy: 0.9483
Epoch 148/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0500 - accuracy: 0.9826 - val_loss: 0.0981 - val_accuracy: 0.9483
Epoch 149/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0775 - accuracy: 0.9870 - val_loss: 0.0947 - val_accuracy: 0.9483
Epoch 150/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0402 - accuracy: 0.9870 - val_loss: 0.2477 - val_accuracy: 0.9138
Epoch 151/1000
2/2 [==============================] - 0s 45ms/step - loss: 0.1077 - accuracy: 0.9652 - val_loss: 0.1163 - val_accuracy: 0.9483
Epoch 152/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0566 - accuracy: 0.9870 - val_loss: 0.1754 - val_accuracy: 0.9138
Epoch 153/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.1489 - accuracy: 0.9522 - val_loss: 0.2137 - val_accuracy: 0.9483
Epoch 154/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.1452 - accuracy: 0.9391 - val_loss: 0.1790 - val_accuracy: 0.9655
Epoch 155/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0685 - accuracy: 0.9652 - val_loss: 0.1468 - val_accuracy: 0.9310
Epoch 156/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.1586 - accuracy: 0.9304 - val_loss: 0.1551 - val_accuracy: 0.9483
Epoch 157/1000
2/2 [==============================] - 0s 55ms/step - loss: 0.0805 - accuracy: 0.9696 - val_loss: 0.3124 - val_accuracy: 0.8621
Epoch 158/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.1004 - accuracy: 0.9565 - val_loss: 0.0936 - val_accuracy: 0.9483
Epoch 159/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0703 - accuracy: 0.9783 - val_loss: 0.1335 - val_accuracy: 0.9310
Epoch 160/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0847 - accuracy: 0.9652 - val_loss: 0.0908 - val_accuracy: 0.9828
Epoch 161/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0506 - accuracy: 0.9783 - val_loss: 0.1456 - val_accuracy: 0.9483
Epoch 162/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0581 - accuracy: 0.9783 - val_loss: 0.0979 - val_accuracy: 0.9828
Epoch 163/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0584 - accuracy: 0.9826 - val_loss: 0.0940 - val_accuracy: 0.9483
Epoch 164/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0623 - accuracy: 0.9913 - val_loss: 0.0925 - val_accuracy: 0.9483
Epoch 165/1000
2/2 [==============================] - 0s 47ms/step - loss: 0.0450 - accuracy: 0.9913 - val_loss: 0.1224 - val_accuracy: 0.9483
Epoch 166/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0421 - accuracy: 0.9913 - val_loss: 0.1651 - val_accuracy: 0.9483
Epoch 167/1000
2/2 [==============================] - 0s 54ms/step - loss: 0.0520 - accuracy: 0.9826 - val_loss: 0.1048 - val_accuracy: 0.9655
Epoch 168/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0529 - accuracy: 0.9826 - val_loss: 0.0861 - val_accuracy: 0.9655
Epoch 169/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0455 - accuracy: 0.9870 - val_loss: 0.0847 - val_accuracy: 0.9655
Epoch 170/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0349 - accuracy: 0.9870 - val_loss: 0.0863 - val_accuracy: 0.9828
Epoch 171/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0444 - accuracy: 0.9913 - val_loss: 0.0971 - val_accuracy: 0.9655
Epoch 172/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0446 - accuracy: 0.9870 - val_loss: 0.0896 - val_accuracy: 0.9828
Epoch 173/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0315 - accuracy: 0.9957 - val_loss: 0.0810 - val_accuracy: 0.9828
Epoch 174/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0471 - accuracy: 0.9783 - val_loss: 0.0794 - val_accuracy: 0.9828
Epoch 175/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0380 - accuracy: 0.9913 - val_loss: 0.0795 - val_accuracy: 0.9828
Epoch 176/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0395 - accuracy: 0.9826 - val_loss: 0.0844 - val_accuracy: 0.9828
Epoch 177/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0552 - accuracy: 0.9870 - val_loss: 0.0810 - val_accuracy: 0.9828
Epoch 178/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0351 - accuracy: 0.9957 - val_loss: 0.0801 - val_accuracy: 0.9828
Epoch 179/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0399 - accuracy: 0.9913 - val_loss: 0.0806 - val_accuracy: 0.9828
Epoch 180/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0323 - accuracy: 0.9913 - val_loss: 0.0808 - val_accuracy: 0.9828
Epoch 181/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0407 - accuracy: 0.9870 - val_loss: 0.0803 - val_accuracy: 0.9828
Epoch 182/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0469 - accuracy: 0.9870 - val_loss: 0.0782 - val_accuracy: 0.9828
Epoch 183/1000
2/2 [==============================] - 0s 55ms/step - loss: 0.0402 - accuracy: 0.9913 - val_loss: 0.0764 - val_accuracy: 0.9828
Epoch 184/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0429 - accuracy: 0.9870 - val_loss: 0.0749 - val_accuracy: 0.9828
Epoch 185/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0333 - accuracy: 0.9870 - val_loss: 0.0781 - val_accuracy: 0.9828
Epoch 186/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0408 - accuracy: 0.9913 - val_loss: 0.0790 - val_accuracy: 0.9828
Epoch 187/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0295 - accuracy: 0.9957 - val_loss: 0.0789 - val_accuracy: 0.9828
Epoch 188/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0410 - accuracy: 0.9870 - val_loss: 0.0744 - val_accuracy: 0.9828
Epoch 189/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0523 - accuracy: 0.9783 - val_loss: 0.0733 - val_accuracy: 0.9828
Epoch 190/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0309 - accuracy: 0.9913 - val_loss: 0.1234 - val_accuracy: 0.9483
Epoch 191/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0456 - accuracy: 0.9870 - val_loss: 0.1291 - val_accuracy: 0.9655
Epoch 192/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0435 - accuracy: 0.9826 - val_loss: 0.0732 - val_accuracy: 0.9828
Epoch 193/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0462 - accuracy: 0.9826 - val_loss: 0.0781 - val_accuracy: 0.9655
Epoch 194/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0485 - accuracy: 0.9913 - val_loss: 0.0738 - val_accuracy: 0.9828
Epoch 195/1000
2/2 [==============================] - 0s 60ms/step - loss: 0.0351 - accuracy: 0.9913 - val_loss: 0.0737 - val_accuracy: 0.9828
Epoch 196/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0448 - accuracy: 0.9783 - val_loss: 0.0762 - val_accuracy: 0.9828
Epoch 197/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0397 - accuracy: 0.9826 - val_loss: 0.0720 - val_accuracy: 0.9828
Epoch 198/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0337 - accuracy: 0.9913 - val_loss: 0.0786 - val_accuracy: 0.9655
Epoch 199/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0329 - accuracy: 0.9913 - val_loss: 0.0781 - val_accuracy: 0.9655
Epoch 200/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0372 - accuracy: 0.9957 - val_loss: 0.0738 - val_accuracy: 0.9828
Epoch 201/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0327 - accuracy: 0.9826 - val_loss: 0.0861 - val_accuracy: 0.9828
Epoch 202/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0432 - accuracy: 0.9826 - val_loss: 0.0724 - val_accuracy: 0.9828
Epoch 203/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0259 - accuracy: 0.9957 - val_loss: 0.0803 - val_accuracy: 0.9655
Epoch 204/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0519 - accuracy: 0.9783 - val_loss: 0.0703 - val_accuracy: 0.9828
Epoch 205/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0347 - accuracy: 0.9870 - val_loss: 0.0844 - val_accuracy: 0.9828
Epoch 206/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0310 - accuracy: 0.9870 - val_loss: 0.0903 - val_accuracy: 0.9828
Epoch 207/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0401 - accuracy: 0.9826 - val_loss: 0.0717 - val_accuracy: 0.9828
Epoch 208/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0328 - accuracy: 0.9870 - val_loss: 0.0712 - val_accuracy: 0.9828
Epoch 209/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0301 - accuracy: 0.9913 - val_loss: 0.0761 - val_accuracy: 0.9828
Epoch 210/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0218 - accuracy: 0.9957 - val_loss: 0.0866 - val_accuracy: 0.9828
Epoch 211/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0343 - accuracy: 0.9870 - val_loss: 0.0783 - val_accuracy: 0.9828
Epoch 212/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0329 - accuracy: 0.9913 - val_loss: 0.0692 - val_accuracy: 0.9828
Epoch 213/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0299 - accuracy: 0.9913 - val_loss: 0.0689 - val_accuracy: 0.9828
Epoch 214/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0450 - accuracy: 0.9739 - val_loss: 0.0720 - val_accuracy: 0.9828
Epoch 215/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0282 - accuracy: 0.9913 - val_loss: 0.0696 - val_accuracy: 0.9828
Epoch 216/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0334 - accuracy: 0.9870 - val_loss: 0.0858 - val_accuracy: 0.9828
Epoch 217/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0238 - accuracy: 1.0000 - val_loss: 0.0873 - val_accuracy: 0.9828
Epoch 218/1000
2/2 [==============================] - 0s 45ms/step - loss: 0.0312 - accuracy: 0.9870 - val_loss: 0.0710 - val_accuracy: 0.9828
Epoch 219/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0387 - accuracy: 0.9870 - val_loss: 0.0720 - val_accuracy: 0.9655
Epoch 220/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0286 - accuracy: 0.9957 - val_loss: 0.0691 - val_accuracy: 0.9828
Epoch 221/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0313 - accuracy: 0.9913 - val_loss: 0.0695 - val_accuracy: 0.9828
Epoch 222/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0258 - accuracy: 0.9957 - val_loss: 0.0780 - val_accuracy: 0.9828
Epoch 223/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0244 - accuracy: 0.9957 - val_loss: 0.0676 - val_accuracy: 0.9828
Epoch 224/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0338 - accuracy: 0.9913 - val_loss: 0.0665 - val_accuracy: 0.9828
Epoch 225/1000
2/2 [==============================] - 0s 46ms/step - loss: 0.0238 - accuracy: 0.9913 - val_loss: 0.0668 - val_accuracy: 0.9828
Epoch 226/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0375 - accuracy: 0.9826 - val_loss: 0.0645 - val_accuracy: 0.9828
Epoch 227/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0245 - accuracy: 0.9913 - val_loss: 0.0648 - val_accuracy: 0.9828
Epoch 228/1000
2/2 [==============================] - 0s 53ms/step - loss: 0.0254 - accuracy: 0.9913 - val_loss: 0.0641 - val_accuracy: 0.9828
Epoch 229/1000
2/2 [==============================] - 0s 52ms/step - loss: 0.0382 - accuracy: 0.9826 - val_loss: 0.0634 - val_accuracy: 0.9828
Epoch 230/1000
2/2 [==============================] - 0s 53ms/step - loss: 0.0218 - accuracy: 0.9913 - val_loss: 0.0631 - val_accuracy: 0.9828
Epoch 231/1000
2/2 [==============================] - 0s 49ms/step - loss: 0.0287 - accuracy: 0.9913 - val_loss: 0.0657 - val_accuracy: 0.9828
Epoch 232/1000
2/2 [==============================] - 0s 57ms/step - loss: 0.0297 - accuracy: 0.9913 - val_loss: 0.0664 - val_accuracy: 0.9828
Epoch 233/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.0243 - accuracy: 0.9913 - val_loss: 0.0625 - val_accuracy: 0.9828
Epoch 234/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0301 - accuracy: 0.9913 - val_loss: 0.0689 - val_accuracy: 0.9828
Epoch 235/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0384 - accuracy: 0.9870 - val_loss: 0.0649 - val_accuracy: 0.9828
Epoch 236/1000
2/2 [==============================] - 0s 45ms/step - loss: 0.0494 - accuracy: 0.9826 - val_loss: 0.0620 - val_accuracy: 0.9828
Epoch 237/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9828
Epoch 238/1000
2/2 [==============================] - 0s 51ms/step - loss: 0.0279 - accuracy: 0.9957 - val_loss: 0.0677 - val_accuracy: 0.9828
Epoch 239/1000
2/2 [==============================] - 0s 46ms/step - loss: 0.0308 - accuracy: 0.9957 - val_loss: 0.0664 - val_accuracy: 0.9828
Epoch 240/1000
2/2 [==============================] - 0s 45ms/step - loss: 0.0328 - accuracy: 0.9913 - val_loss: 0.0702 - val_accuracy: 0.9828
Epoch 241/1000
2/2 [==============================] - 0s 50ms/step - loss: 0.0266 - accuracy: 0.9957 - val_loss: 0.0636 - val_accuracy: 0.9828
Epoch 242/1000
2/2 [==============================] - 0s 52ms/step - loss: 0.0229 - accuracy: 0.9913 - val_loss: 0.0968 - val_accuracy: 0.9655
Epoch 243/1000
2/2 [==============================] - 0s 51ms/step - loss: 0.0373 - accuracy: 0.9870 - val_loss: 0.0830 - val_accuracy: 0.9655
Epoch 244/1000
2/2 [==============================] - 0s 49ms/step - loss: 0.0326 - accuracy: 0.9913 - val_loss: 0.0710 - val_accuracy: 0.9655
Epoch 245/1000
2/2 [==============================] - 0s 60ms/step - loss: 0.0408 - accuracy: 0.9826 - val_loss: 0.0676 - val_accuracy: 0.9828
Epoch 246/1000
2/2 [==============================] - 0s 46ms/step - loss: 0.0244 - accuracy: 0.9870 - val_loss: 0.1112 - val_accuracy: 0.9483
Epoch 247/1000
2/2 [==============================] - 0s 47ms/step - loss: 0.0281 - accuracy: 0.9913 - val_loss: 0.1257 - val_accuracy: 0.9483
Epoch 248/1000
2/2 [==============================] - 0s 47ms/step - loss: 0.0257 - accuracy: 0.9957 - val_loss: 0.0629 - val_accuracy: 0.9828
Epoch 249/1000
2/2 [==============================] - 0s 52ms/step - loss: 0.0379 - accuracy: 0.9870 - val_loss: 0.0627 - val_accuracy: 0.9828
Epoch 250/1000
2/2 [==============================] - 0s 53ms/step - loss: 0.0269 - accuracy: 0.9957 - val_loss: 0.0614 - val_accuracy: 0.9828
Epoch 251/1000
2/2 [==============================] - 0s 65ms/step - loss: 0.0284 - accuracy: 0.9913 - val_loss: 0.0613 - val_accuracy: 0.9828
Epoch 252/1000
2/2 [==============================] - 0s 47ms/step - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.0611 - val_accuracy: 0.9828
Epoch 253/1000
2/2 [==============================] - 0s 49ms/step - loss: 0.0194 - accuracy: 1.0000 - val_loss: 0.0626 - val_accuracy: 0.9828
Epoch 254/1000
2/2 [==============================] - 0s 47ms/step - loss: 0.0386 - accuracy: 0.9870 - val_loss: 0.0691 - val_accuracy: 0.9828
Epoch 255/1000
2/2 [==============================] - 0s 62ms/step - loss: 0.0227 - accuracy: 0.9913 - val_loss: 0.0800 - val_accuracy: 0.9828
Epoch 256/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.0183 - accuracy: 0.9913 - val_loss: 0.0817 - val_accuracy: 0.9828
Epoch 257/1000
2/2 [==============================] - 0s 45ms/step - loss: 0.0264 - accuracy: 0.9913 - val_loss: 0.0844 - val_accuracy: 0.9655
Epoch 258/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.0315 - accuracy: 0.9913 - val_loss: 0.0718 - val_accuracy: 0.9655
Epoch 259/1000
2/2 [==============================] - 0s 50ms/step - loss: 0.0265 - accuracy: 0.9870 - val_loss: 0.0668 - val_accuracy: 0.9828
Epoch 260/1000
2/2 [==============================] - 0s 53ms/step - loss: 0.0192 - accuracy: 0.9913 - val_loss: 0.0655 - val_accuracy: 0.9828
Epoch 261/1000
2/2 [==============================] - 0s 63ms/step - loss: 0.0366 - accuracy: 0.9913 - val_loss: 0.0615 - val_accuracy: 0.9828
Epoch 262/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0163 - accuracy: 0.9957 - val_loss: 0.0605 - val_accuracy: 0.9828
Epoch 263/1000
2/2 [==============================] - 0s 48ms/step - loss: 0.0251 - accuracy: 0.9913 - val_loss: 0.0588 - val_accuracy: 0.9828
Epoch 264/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0253 - accuracy: 0.9913 - val_loss: 0.0650 - val_accuracy: 0.9828
Epoch 265/1000
2/2 [==============================] - 0s 62ms/step - loss: 0.0324 - accuracy: 0.9870 - val_loss: 0.0661 - val_accuracy: 0.9828
Epoch 266/1000
2/2 [==============================] - 0s 60ms/step - loss: 0.0341 - accuracy: 0.9913 - val_loss: 0.0594 - val_accuracy: 0.9828
Epoch 267/1000
2/2 [==============================] - 0s 65ms/step - loss: 0.0266 - accuracy: 0.9913 - val_loss: 0.0731 - val_accuracy: 0.9655
Epoch 268/1000
2/2 [==============================] - 0s 62ms/step - loss: 0.0395 - accuracy: 0.9870 - val_loss: 0.0594 - val_accuracy: 0.9828
Epoch 269/1000
2/2 [==============================] - 0s 54ms/step - loss: 0.0171 - accuracy: 0.9957 - val_loss: 0.0668 - val_accuracy: 0.9828
Epoch 270/1000
2/2 [==============================] - 0s 46ms/step - loss: 0.0217 - accuracy: 0.9957 - val_loss: 0.0698 - val_accuracy: 0.9828
Epoch 271/1000
2/2 [==============================] - 0s 46ms/step - loss: 0.0283 - accuracy: 0.9913 - val_loss: 0.0683 - val_accuracy: 0.9828
Epoch 272/1000
2/2 [==============================] - 0s 52ms/step - loss: 0.0339 - accuracy: 0.9870 - val_loss: 0.0633 - val_accuracy: 0.9828
Epoch 273/1000
2/2 [==============================] - 0s 50ms/step - loss: 0.0172 - accuracy: 0.9957 - val_loss: 0.0606 - val_accuracy: 0.9828
Epoch 274/1000
2/2 [==============================] - 0s 49ms/step - loss: 0.0219 - accuracy: 0.9913 - val_loss: 0.0654 - val_accuracy: 0.9828
Epoch 275/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0284 - accuracy: 0.9870 - val_loss: 0.0623 - val_accuracy: 0.9828
Epoch 276/1000
2/2 [==============================] - 0s 55ms/step - loss: 0.0145 - accuracy: 0.9957 - val_loss: 0.0608 - val_accuracy: 0.9828
Epoch 277/1000
2/2 [==============================] - 0s 60ms/step - loss: 0.0196 - accuracy: 0.9913 - val_loss: 0.0575 - val_accuracy: 0.9828
Epoch 278/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0165 - accuracy: 0.9957 - val_loss: 0.0717 - val_accuracy: 0.9828
Epoch 279/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0290 - accuracy: 0.9870 - val_loss: 0.0576 - val_accuracy: 0.9828
Epoch 280/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0247 - accuracy: 0.9826 - val_loss: 0.0607 - val_accuracy: 0.9828
Epoch 281/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.0200 - accuracy: 0.9957 - val_loss: 0.0573 - val_accuracy: 0.9828
Epoch 282/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0181 - accuracy: 0.9957 - val_loss: 0.0592 - val_accuracy: 0.9828
Epoch 283/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0146 - accuracy: 0.9957 - val_loss: 0.0587 - val_accuracy: 0.9828
Epoch 284/1000
2/2 [==============================] - 0s 45ms/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 0.0572 - val_accuracy: 0.9828
Epoch 285/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0164 - accuracy: 0.9957 - val_loss: 0.0625 - val_accuracy: 0.9828
Epoch 286/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0172 - accuracy: 0.9913 - val_loss: 0.0618 - val_accuracy: 0.9828
Epoch 287/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0132 - accuracy: 1.0000 - val_loss: 0.0595 - val_accuracy: 0.9828
Epoch 288/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0139 - accuracy: 0.9957 - val_loss: 0.0632 - val_accuracy: 0.9828
Epoch 289/1000
2/2 [==============================] - 0s 51ms/step - loss: 0.0359 - accuracy: 0.9870 - val_loss: 0.0674 - val_accuracy: 0.9828
Epoch 290/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0389 - accuracy: 0.9870 - val_loss: 0.0575 - val_accuracy: 0.9828
Epoch 291/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0409 - accuracy: 0.9870 - val_loss: 0.0925 - val_accuracy: 0.9655
Epoch 292/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0230 - accuracy: 0.9957 - val_loss: 0.0623 - val_accuracy: 0.9828
Epoch 293/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.0374 - accuracy: 0.9870 - val_loss: 0.0642 - val_accuracy: 0.9828
Epoch 294/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0204 - accuracy: 0.9913 - val_loss: 0.0906 - val_accuracy: 0.9655
Epoch 295/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0288 - accuracy: 0.9957 - val_loss: 0.0918 - val_accuracy: 0.9655
Epoch 296/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0207 - accuracy: 0.9957 - val_loss: 0.0590 - val_accuracy: 0.9828
Epoch 297/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0258 - accuracy: 0.9913 - val_loss: 0.0607 - val_accuracy: 0.9828
Epoch 298/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0156 - accuracy: 0.9957 - val_loss: 0.0643 - val_accuracy: 0.9828
Epoch 299/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.0320 - accuracy: 0.9826 - val_loss: 0.0663 - val_accuracy: 0.9828
Epoch 300/1000
2/2 [==============================] - 0s 47ms/step - loss: 0.0249 - accuracy: 0.9913 - val_loss: 0.0676 - val_accuracy: 0.9828
Epoch 301/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0201 - accuracy: 0.9870 - val_loss: 0.0567 - val_accuracy: 0.9828
Epoch 302/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0205 - accuracy: 0.9913 - val_loss: 0.1184 - val_accuracy: 0.9483
Epoch 303/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.1484 - val_accuracy: 0.9310
Epoch 304/1000
2/2 [==============================] - 0s 49ms/step - loss: 0.0229 - accuracy: 0.9913 - val_loss: 0.0573 - val_accuracy: 0.9828
Epoch 305/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0245 - accuracy: 0.9826 - val_loss: 0.0611 - val_accuracy: 0.9828
Epoch 306/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0246 - accuracy: 0.9913 - val_loss: 0.0680 - val_accuracy: 0.9828
Epoch 307/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0246 - accuracy: 0.9913 - val_loss: 0.0606 - val_accuracy: 0.9828
Epoch 308/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0305 - accuracy: 0.9826 - val_loss: 0.0704 - val_accuracy: 0.9828
Epoch 309/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0257 - accuracy: 0.9913 - val_loss: 0.0609 - val_accuracy: 0.9828
Epoch 310/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0217 - accuracy: 0.9913 - val_loss: 0.0994 - val_accuracy: 0.9828
Epoch 311/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0311 - accuracy: 1.0000 - val_loss: 0.0584 - val_accuracy: 0.9828
Epoch 312/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.0264 - accuracy: 0.9913 - val_loss: 0.0657 - val_accuracy: 0.9828
Epoch 313/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0300 - accuracy: 0.9913 - val_loss: 0.0577 - val_accuracy: 0.9828
Epoch 314/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0172 - accuracy: 0.9957 - val_loss: 0.1031 - val_accuracy: 0.9310
Epoch 315/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0417 - accuracy: 0.9870 - val_loss: 0.0536 - val_accuracy: 0.9828
Epoch 316/1000
2/2 [==============================] - 0s 49ms/step - loss: 0.0153 - accuracy: 0.9957 - val_loss: 0.0772 - val_accuracy: 0.9655
Epoch 317/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0387 - accuracy: 0.9870 - val_loss: 0.0552 - val_accuracy: 0.9828
Epoch 318/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0097 - accuracy: 1.0000 - val_loss: 0.1705 - val_accuracy: 0.9138
Epoch 319/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0571 - accuracy: 0.9739 - val_loss: 0.0551 - val_accuracy: 0.9828
Epoch 320/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0272 - accuracy: 0.9826 - val_loss: 0.1014 - val_accuracy: 0.9655
Epoch 321/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0498 - accuracy: 0.9870 - val_loss: 0.0574 - val_accuracy: 0.9828
Epoch 322/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0298 - accuracy: 0.9870 - val_loss: 0.0710 - val_accuracy: 0.9828
Epoch 323/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.0626 - val_accuracy: 0.9828
Epoch 324/1000
2/2 [==============================] - 0s 44ms/step - loss: 0.0160 - accuracy: 0.9957 - val_loss: 0.0713 - val_accuracy: 0.9828
Epoch 325/1000
2/2 [==============================] - 0s 53ms/step - loss: 0.0167 - accuracy: 0.9913 - val_loss: 0.0616 - val_accuracy: 0.9828
Epoch 326/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0133 - accuracy: 1.0000 - val_loss: 0.0603 - val_accuracy: 0.9828
Epoch 327/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0215 - accuracy: 0.9957 - val_loss: 0.0611 - val_accuracy: 0.9828
Epoch 328/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.0618 - val_accuracy: 0.9828
Epoch 329/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0216 - accuracy: 0.9957 - val_loss: 0.0596 - val_accuracy: 0.9828
Epoch 330/1000
2/2 [==============================] - 0s 47ms/step - loss: 0.0250 - accuracy: 0.9957 - val_loss: 0.0530 - val_accuracy: 0.9828
Epoch 331/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0147 - accuracy: 1.0000 - val_loss: 0.0549 - val_accuracy: 0.9828
Epoch 332/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0125 - accuracy: 0.9957 - val_loss: 0.0539 - val_accuracy: 0.9828
Epoch 333/1000
2/2 [==============================] - 0s 46ms/step - loss: 0.0188 - accuracy: 0.9913 - val_loss: 0.0556 - val_accuracy: 0.9828
Epoch 334/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0132 - accuracy: 1.0000 - val_loss: 0.0559 - val_accuracy: 0.9828
Epoch 335/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0147 - accuracy: 0.9957 - val_loss: 0.0645 - val_accuracy: 0.9828
Epoch 336/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0167 - accuracy: 1.0000 - val_loss: 0.0603 - val_accuracy: 0.9828
Epoch 337/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0209 - accuracy: 0.9913 - val_loss: 0.0696 - val_accuracy: 0.9828
Epoch 338/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0180 - accuracy: 0.9957 - val_loss: 0.0786 - val_accuracy: 0.9655
Epoch 339/1000
2/2 [==============================] - 0s 59ms/step - loss: 0.0236 - accuracy: 0.9870 - val_loss: 0.0584 - val_accuracy: 0.9828
Epoch 340/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0242 - accuracy: 0.9957 - val_loss: 0.0620 - val_accuracy: 0.9828
Epoch 341/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.0575 - val_accuracy: 0.9828
Epoch 342/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0146 - accuracy: 1.0000 - val_loss: 0.0595 - val_accuracy: 0.9828
Epoch 343/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.0531 - val_accuracy: 0.9828
Epoch 344/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0264 - accuracy: 0.9913 - val_loss: 0.0531 - val_accuracy: 0.9828
Epoch 345/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0168 - accuracy: 0.9957 - val_loss: 0.0518 - val_accuracy: 0.9828
Epoch 346/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0150 - accuracy: 0.9957 - val_loss: 0.0566 - val_accuracy: 0.9828
Epoch 347/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0108 - accuracy: 1.0000 - val_loss: 0.0589 - val_accuracy: 0.9828
Epoch 348/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0135 - accuracy: 1.0000 - val_loss: 0.0588 - val_accuracy: 0.9828
Epoch 349/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0133 - accuracy: 1.0000 - val_loss: 0.0587 - val_accuracy: 0.9828
Epoch 350/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.0619 - val_accuracy: 0.9828
Epoch 351/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0141 - accuracy: 0.9957 - val_loss: 0.0640 - val_accuracy: 0.9828
Epoch 352/1000
2/2 [==============================] - 0s 46ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.0668 - val_accuracy: 0.9828
Epoch 353/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0145 - accuracy: 0.9957 - val_loss: 0.0588 - val_accuracy: 0.9828
Epoch 354/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0254 - accuracy: 0.9913 - val_loss: 0.0556 - val_accuracy: 0.9828
Epoch 355/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.0664 - val_accuracy: 0.9828
Epoch 356/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0136 - accuracy: 0.9957 - val_loss: 0.0633 - val_accuracy: 0.9828
Epoch 357/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.0586 - val_accuracy: 0.9828
Epoch 358/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9483
Epoch 359/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0161 - accuracy: 1.0000 - val_loss: 0.1588 - val_accuracy: 0.9310
Epoch 360/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0347 - accuracy: 0.9957 - val_loss: 0.0591 - val_accuracy: 0.9828
Epoch 361/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0180 - accuracy: 0.9957 - val_loss: 0.0598 - val_accuracy: 0.9828
Epoch 362/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.0535 - val_accuracy: 0.9828
Epoch 363/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0156 - accuracy: 0.9957 - val_loss: 0.0510 - val_accuracy: 0.9828
Epoch 364/1000
2/2 [==============================] - 0s 54ms/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.0515 - val_accuracy: 0.9828
Epoch 365/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.0525 - val_accuracy: 0.9828
Epoch 366/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0109 - accuracy: 0.9957 - val_loss: 0.0574 - val_accuracy: 0.9828
Epoch 367/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0187 - accuracy: 0.9957 - val_loss: 0.0573 - val_accuracy: 0.9828
Epoch 368/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.0525 - val_accuracy: 0.9828
Epoch 369/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.0522 - val_accuracy: 0.9828
Epoch 370/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.0563 - val_accuracy: 0.9828
Epoch 371/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0182 - accuracy: 0.9957 - val_loss: 0.0569 - val_accuracy: 0.9828
Epoch 372/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0137 - accuracy: 0.9957 - val_loss: 0.0592 - val_accuracy: 0.9828
Epoch 373/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.0595 - val_accuracy: 0.9828
Epoch 374/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.0621 - val_accuracy: 0.9828
Epoch 375/1000
2/2 [==============================] - 0s 61ms/step - loss: 0.0134 - accuracy: 0.9957 - val_loss: 0.0627 - val_accuracy: 0.9828
Epoch 376/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0142 - accuracy: 1.0000 - val_loss: 0.0544 - val_accuracy: 0.9828
Epoch 377/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.0560 - val_accuracy: 0.9828
Epoch 378/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0185 - accuracy: 0.9913 - val_loss: 0.0558 - val_accuracy: 0.9828
Epoch 379/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0102 - accuracy: 1.0000 - val_loss: 0.0694 - val_accuracy: 0.9828
Epoch 380/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0117 - accuracy: 0.9957 - val_loss: 0.0636 - val_accuracy: 0.9828
Epoch 381/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.0549 - val_accuracy: 0.9828
Epoch 382/1000
2/2 [==============================] - 0s 35ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.0508 - val_accuracy: 0.9828
Epoch 383/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0109 - accuracy: 0.9957 - val_loss: 0.0506 - val_accuracy: 0.9828
Epoch 384/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0223 - accuracy: 0.9957 - val_loss: 0.0636 - val_accuracy: 0.9828
Epoch 385/1000
2/2 [==============================] - 0s 47ms/step - loss: 0.0234 - accuracy: 0.9913 - val_loss: 0.0513 - val_accuracy: 0.9828
Epoch 386/1000
2/2 [==============================] - 0s 46ms/step - loss: 0.0112 - accuracy: 0.9957 - val_loss: 0.0523 - val_accuracy: 0.9828
Epoch 387/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.0521 - val_accuracy: 0.9828
Epoch 388/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0114 - accuracy: 0.9957 - val_loss: 0.0646 - val_accuracy: 0.9828
Epoch 389/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0131 - accuracy: 0.9957 - val_loss: 0.0646 - val_accuracy: 0.9828
Epoch 390/1000
2/2 [==============================] - 0s 36ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0568 - val_accuracy: 0.9828
Epoch 391/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0102 - accuracy: 1.0000 - val_loss: 0.0570 - val_accuracy: 0.9828
Epoch 392/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0182 - accuracy: 0.9957 - val_loss: 0.0834 - val_accuracy: 0.9655
Epoch 393/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0210 - accuracy: 0.9913 - val_loss: 0.0774 - val_accuracy: 0.9828
Epoch 394/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0115 - accuracy: 0.9957 - val_loss: 0.0548 - val_accuracy: 0.9828
Epoch 395/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0146 - accuracy: 0.9957 - val_loss: 0.0902 - val_accuracy: 0.9483
Epoch 396/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0276 - accuracy: 0.9826 - val_loss: 0.0611 - val_accuracy: 0.9828
Epoch 397/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0111 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9655
Epoch 398/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0277 - accuracy: 0.9826 - val_loss: 0.0692 - val_accuracy: 0.9828
Epoch 399/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.0556 - val_accuracy: 0.9828
Epoch 400/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.0536 - val_accuracy: 0.9828
Epoch 401/1000
2/2 [==============================] - 0s 39ms/step - loss: 0.0164 - accuracy: 0.9957 - val_loss: 0.0607 - val_accuracy: 0.9828
Epoch 402/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.0632 - val_accuracy: 0.9828
Epoch 403/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.0595 - val_accuracy: 0.9828
Epoch 404/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.0577 - val_accuracy: 0.9828
Epoch 405/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.0621 - val_accuracy: 0.9828
Epoch 406/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.0726 - val_accuracy: 0.9655
Epoch 407/1000
2/2 [==============================] - 0s 40ms/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.0613 - val_accuracy: 0.9828
Epoch 408/1000
2/2 [==============================] - 0s 38ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.0638 - val_accuracy: 0.9828
Epoch 409/1000
2/2 [==============================] - 0s 42ms/step - loss: 0.0172 - accuracy: 0.9957 - val_loss: 0.0588 - val_accuracy: 0.9828
Epoch 410/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0105 - accuracy: 1.0000 - val_loss: 0.0816 - val_accuracy: 0.9655
Epoch 411/1000
2/2 [==============================] - 0s 37ms/step - loss: 0.0138 - accuracy: 0.9957 - val_loss: 0.0578 - val_accuracy: 0.9828
Epoch 412/1000
2/2 [==============================] - 0s 43ms/step - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.0677 - val_accuracy: 0.9828
Epoch 413/1000
2/2 [==============================] - 0s 41ms/step - loss: 0.0140 - accuracy: 0.9957 - val_loss: 0.0756 - val_accuracy: 0.9828
In [ ]:
#학습 진행사항을 plt로 출력
# hist3의 accuracy plt의 plot을 이용하여 출력
plt.plot(hist3.history['accuracy'], label='accuracy')
plt.plot(hist3.history['loss'], label='loss')
plt.plot(hist3.history['val_accuracy'], label='val_accuracy')
plt.plot(hist3.history['val_loss'], label='val_loss')
plt.ylim(0.0, 1.0)
plt.legend(loc='upper left')
plt.show()
In [ ]:
hist4 = model4.fit(train_sound2.reshape(-1,40,65,1), train_labels2,
validation_data = (test_sound2.reshape((-1,40,65,1)), test_labels2),
batch_size=128, epochs=3000, verbose=1)
스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.
2/2 [==============================] - 0s 33ms/step - loss: 0.0497 - accuracy: 0.9957 - val_loss: 0.1189 - val_accuracy: 0.9310
Epoch 502/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0970 - accuracy: 0.9696 - val_loss: 0.1169 - val_accuracy: 0.9310
Epoch 503/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0817 - accuracy: 0.9696 - val_loss: 0.1158 - val_accuracy: 0.9310
Epoch 504/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0574 - accuracy: 0.9783 - val_loss: 0.1150 - val_accuracy: 0.9310
Epoch 505/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0391 - accuracy: 0.9826 - val_loss: 0.1141 - val_accuracy: 0.9310
Epoch 506/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0663 - accuracy: 0.9739 - val_loss: 0.1132 - val_accuracy: 0.9310
Epoch 507/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0539 - accuracy: 0.9870 - val_loss: 0.1126 - val_accuracy: 0.9310
Epoch 508/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0699 - accuracy: 0.9609 - val_loss: 0.1118 - val_accuracy: 0.9310
Epoch 509/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0739 - accuracy: 0.9696 - val_loss: 0.1111 - val_accuracy: 0.9483
Epoch 510/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0766 - accuracy: 0.9783 - val_loss: 0.1101 - val_accuracy: 0.9483
Epoch 511/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0448 - accuracy: 0.9957 - val_loss: 0.1094 - val_accuracy: 0.9483
Epoch 512/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0738 - accuracy: 0.9783 - val_loss: 0.1084 - val_accuracy: 0.9483
Epoch 513/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0671 - accuracy: 0.9826 - val_loss: 0.1072 - val_accuracy: 0.9483
Epoch 514/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0448 - accuracy: 0.9913 - val_loss: 0.1078 - val_accuracy: 0.9483
Epoch 515/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0711 - accuracy: 0.9739 - val_loss: 0.1108 - val_accuracy: 0.9483
Epoch 516/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0472 - accuracy: 0.9913 - val_loss: 0.1123 - val_accuracy: 0.9483
Epoch 517/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9483
Epoch 518/3000
2/2 [==============================] - 0s 52ms/step - loss: 0.0474 - accuracy: 0.9826 - val_loss: 0.1116 - val_accuracy: 0.9483
Epoch 519/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0604 - accuracy: 0.9826 - val_loss: 0.1094 - val_accuracy: 0.9483
Epoch 520/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0421 - accuracy: 0.9870 - val_loss: 0.1073 - val_accuracy: 0.9483
Epoch 521/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0509 - accuracy: 0.9826 - val_loss: 0.1059 - val_accuracy: 0.9483
Epoch 522/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0482 - accuracy: 0.9783 - val_loss: 0.1055 - val_accuracy: 0.9483
Epoch 523/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0725 - accuracy: 0.9783 - val_loss: 0.1062 - val_accuracy: 0.9483
Epoch 524/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0382 - accuracy: 0.9913 - val_loss: 0.1055 - val_accuracy: 0.9483
Epoch 525/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0202 - accuracy: 0.9957 - val_loss: 0.1049 - val_accuracy: 0.9483
Epoch 526/3000
2/2 [==============================] - 0s 29ms/step - loss: 0.0654 - accuracy: 0.9739 - val_loss: 0.1045 - val_accuracy: 0.9483
Epoch 527/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0566 - accuracy: 0.9783 - val_loss: 0.1040 - val_accuracy: 0.9483
Epoch 528/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0715 - accuracy: 0.9739 - val_loss: 0.1048 - val_accuracy: 0.9483
Epoch 529/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0620 - accuracy: 0.9783 - val_loss: 0.1045 - val_accuracy: 0.9483
Epoch 530/3000
2/2 [==============================] - 0s 45ms/step - loss: 0.0427 - accuracy: 0.9826 - val_loss: 0.1026 - val_accuracy: 0.9483
Epoch 531/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0656 - accuracy: 0.9783 - val_loss: 0.1020 - val_accuracy: 0.9483
Epoch 532/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0853 - accuracy: 0.9652 - val_loss: 0.1009 - val_accuracy: 0.9483
Epoch 533/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0508 - accuracy: 0.9739 - val_loss: 0.1002 - val_accuracy: 0.9483
Epoch 534/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0479 - accuracy: 0.9913 - val_loss: 0.1016 - val_accuracy: 0.9483
Epoch 535/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0338 - accuracy: 1.0000 - val_loss: 0.1037 - val_accuracy: 0.9483
Epoch 536/3000
2/2 [==============================] - 0s 47ms/step - loss: 0.0588 - accuracy: 0.9826 - val_loss: 0.1057 - val_accuracy: 0.9483
Epoch 537/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0623 - accuracy: 0.9870 - val_loss: 0.1065 - val_accuracy: 0.9483
Epoch 538/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0317 - accuracy: 0.9957 - val_loss: 0.1065 - val_accuracy: 0.9483
Epoch 539/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0417 - accuracy: 0.9913 - val_loss: 0.1072 - val_accuracy: 0.9483
Epoch 540/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0833 - accuracy: 0.9696 - val_loss: 0.1066 - val_accuracy: 0.9483
Epoch 541/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0495 - accuracy: 0.9870 - val_loss: 0.1066 - val_accuracy: 0.9483
Epoch 542/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0731 - accuracy: 0.9739 - val_loss: 0.1067 - val_accuracy: 0.9483
Epoch 543/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0828 - accuracy: 0.9826 - val_loss: 0.1058 - val_accuracy: 0.9483
Epoch 544/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0526 - accuracy: 0.9870 - val_loss: 0.1054 - val_accuracy: 0.9483
Epoch 545/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0523 - accuracy: 0.9783 - val_loss: 0.1054 - val_accuracy: 0.9483
Epoch 546/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0361 - accuracy: 0.9870 - val_loss: 0.1054 - val_accuracy: 0.9483
Epoch 547/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0957 - accuracy: 0.9739 - val_loss: 0.1049 - val_accuracy: 0.9483
Epoch 548/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0710 - accuracy: 0.9870 - val_loss: 0.1046 - val_accuracy: 0.9483
Epoch 549/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0295 - accuracy: 0.9957 - val_loss: 0.1041 - val_accuracy: 0.9483
Epoch 550/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0627 - accuracy: 0.9870 - val_loss: 0.1040 - val_accuracy: 0.9483
Epoch 551/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0414 - accuracy: 0.9870 - val_loss: 0.1046 - val_accuracy: 0.9483
Epoch 552/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0415 - accuracy: 0.9913 - val_loss: 0.1067 - val_accuracy: 0.9483
Epoch 553/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0528 - accuracy: 0.9826 - val_loss: 0.1095 - val_accuracy: 0.9310
Epoch 554/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0548 - accuracy: 0.9826 - val_loss: 0.1106 - val_accuracy: 0.9310
Epoch 555/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0480 - accuracy: 0.9826 - val_loss: 0.1109 - val_accuracy: 0.9310
Epoch 556/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0593 - accuracy: 0.9783 - val_loss: 0.1090 - val_accuracy: 0.9310
Epoch 557/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0825 - accuracy: 0.9652 - val_loss: 0.1082 - val_accuracy: 0.9483
Epoch 558/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0460 - accuracy: 0.9783 - val_loss: 0.1078 - val_accuracy: 0.9483
Epoch 559/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0536 - accuracy: 0.9826 - val_loss: 0.1075 - val_accuracy: 0.9483
Epoch 560/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0427 - accuracy: 0.9870 - val_loss: 0.1069 - val_accuracy: 0.9483
Epoch 561/3000
2/2 [==============================] - 0s 49ms/step - loss: 0.0599 - accuracy: 0.9826 - val_loss: 0.1069 - val_accuracy: 0.9483
Epoch 562/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0319 - accuracy: 0.9870 - val_loss: 0.1072 - val_accuracy: 0.9483
Epoch 563/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0635 - accuracy: 0.9870 - val_loss: 0.1079 - val_accuracy: 0.9483
Epoch 564/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0514 - accuracy: 0.9783 - val_loss: 0.1079 - val_accuracy: 0.9483
Epoch 565/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0525 - accuracy: 0.9783 - val_loss: 0.1073 - val_accuracy: 0.9310
Epoch 566/3000
2/2 [==============================] - 0s 53ms/step - loss: 0.0405 - accuracy: 0.9826 - val_loss: 0.1063 - val_accuracy: 0.9310
Epoch 567/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0440 - accuracy: 0.9783 - val_loss: 0.1070 - val_accuracy: 0.9483
Epoch 568/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0519 - accuracy: 0.9826 - val_loss: 0.1053 - val_accuracy: 0.9483
Epoch 569/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0530 - accuracy: 0.9783 - val_loss: 0.1039 - val_accuracy: 0.9483
Epoch 570/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0300 - accuracy: 0.9957 - val_loss: 0.1048 - val_accuracy: 0.9483
Epoch 571/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0269 - accuracy: 0.9957 - val_loss: 0.1058 - val_accuracy: 0.9483
Epoch 572/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0391 - accuracy: 0.9913 - val_loss: 0.1069 - val_accuracy: 0.9483
Epoch 573/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0574 - accuracy: 0.9783 - val_loss: 0.1089 - val_accuracy: 0.9483
Epoch 574/3000
2/2 [==============================] - 0s 56ms/step - loss: 0.0315 - accuracy: 0.9913 - val_loss: 0.1108 - val_accuracy: 0.9483
Epoch 575/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0401 - accuracy: 0.9826 - val_loss: 0.1119 - val_accuracy: 0.9483
Epoch 576/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0546 - accuracy: 0.9826 - val_loss: 0.1094 - val_accuracy: 0.9483
Epoch 577/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0878 - accuracy: 0.9696 - val_loss: 0.1072 - val_accuracy: 0.9310
Epoch 578/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0566 - accuracy: 0.9739 - val_loss: 0.1090 - val_accuracy: 0.9483
Epoch 579/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0459 - accuracy: 0.9870 - val_loss: 0.1093 - val_accuracy: 0.9483
Epoch 580/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0637 - accuracy: 0.9913 - val_loss: 0.1068 - val_accuracy: 0.9483
Epoch 581/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0886 - accuracy: 0.9696 - val_loss: 0.1012 - val_accuracy: 0.9483
Epoch 582/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0439 - accuracy: 0.9826 - val_loss: 0.0986 - val_accuracy: 0.9483
Epoch 583/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0291 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9483
Epoch 584/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0343 - accuracy: 0.9913 - val_loss: 0.0992 - val_accuracy: 0.9655
Epoch 585/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0346 - accuracy: 0.9913 - val_loss: 0.0977 - val_accuracy: 0.9655
Epoch 586/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0631 - accuracy: 0.9826 - val_loss: 0.0944 - val_accuracy: 0.9483
Epoch 587/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0419 - accuracy: 0.9913 - val_loss: 0.0934 - val_accuracy: 0.9483
Epoch 588/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0404 - accuracy: 0.9870 - val_loss: 0.0944 - val_accuracy: 0.9483
Epoch 589/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0625 - accuracy: 0.9652 - val_loss: 0.0983 - val_accuracy: 0.9483
Epoch 590/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0293 - accuracy: 0.9913 - val_loss: 0.1015 - val_accuracy: 0.9483
Epoch 591/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0659 - accuracy: 0.9696 - val_loss: 0.1042 - val_accuracy: 0.9483
Epoch 592/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0363 - accuracy: 0.9870 - val_loss: 0.1017 - val_accuracy: 0.9483
Epoch 593/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0339 - accuracy: 0.9913 - val_loss: 0.0956 - val_accuracy: 0.9483
Epoch 594/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0526 - accuracy: 0.9826 - val_loss: 0.0937 - val_accuracy: 0.9483
Epoch 595/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0763 - accuracy: 0.9739 - val_loss: 0.0940 - val_accuracy: 0.9483
Epoch 596/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0343 - accuracy: 0.9870 - val_loss: 0.0946 - val_accuracy: 0.9483
Epoch 597/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0230 - accuracy: 0.9957 - val_loss: 0.0953 - val_accuracy: 0.9483
Epoch 598/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0546 - accuracy: 0.9826 - val_loss: 0.0956 - val_accuracy: 0.9483
Epoch 599/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0219 - accuracy: 0.9957 - val_loss: 0.0965 - val_accuracy: 0.9483
Epoch 600/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0531 - accuracy: 0.9826 - val_loss: 0.0968 - val_accuracy: 0.9483
Epoch 601/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0421 - accuracy: 0.9826 - val_loss: 0.0973 - val_accuracy: 0.9483
Epoch 602/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0455 - accuracy: 0.9870 - val_loss: 0.0983 - val_accuracy: 0.9483
Epoch 603/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0257 - accuracy: 0.9957 - val_loss: 0.0997 - val_accuracy: 0.9483
Epoch 604/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0268 - accuracy: 0.9957 - val_loss: 0.1014 - val_accuracy: 0.9483
Epoch 605/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0558 - accuracy: 0.9826 - val_loss: 0.1026 - val_accuracy: 0.9483
Epoch 606/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0571 - accuracy: 0.9826 - val_loss: 0.1022 - val_accuracy: 0.9483
Epoch 607/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0365 - accuracy: 0.9870 - val_loss: 0.1028 - val_accuracy: 0.9483
Epoch 608/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0349 - accuracy: 0.9913 - val_loss: 0.1012 - val_accuracy: 0.9483
Epoch 609/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0300 - accuracy: 0.9957 - val_loss: 0.0990 - val_accuracy: 0.9483
Epoch 610/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0375 - accuracy: 0.9870 - val_loss: 0.0990 - val_accuracy: 0.9483
Epoch 611/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0370 - accuracy: 0.9870 - val_loss: 0.0988 - val_accuracy: 0.9655
Epoch 612/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0285 - accuracy: 0.9913 - val_loss: 0.0971 - val_accuracy: 0.9655
Epoch 613/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0265 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9655
Epoch 614/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0155 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 0.9655
Epoch 615/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0354 - accuracy: 0.9913 - val_loss: 0.0954 - val_accuracy: 0.9655
Epoch 616/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0671 - accuracy: 0.9826 - val_loss: 0.0940 - val_accuracy: 0.9655
Epoch 617/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0323 - accuracy: 0.9913 - val_loss: 0.0930 - val_accuracy: 0.9483
Epoch 618/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0412 - accuracy: 0.9913 - val_loss: 0.0919 - val_accuracy: 0.9483
Epoch 619/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0421 - accuracy: 0.9957 - val_loss: 0.0909 - val_accuracy: 0.9483
Epoch 620/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0351 - accuracy: 0.9913 - val_loss: 0.0901 - val_accuracy: 0.9483
Epoch 621/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0519 - accuracy: 0.9826 - val_loss: 0.0904 - val_accuracy: 0.9483
Epoch 622/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0424 - accuracy: 0.9826 - val_loss: 0.0913 - val_accuracy: 0.9483
Epoch 623/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0284 - accuracy: 0.9957 - val_loss: 0.0931 - val_accuracy: 0.9483
Epoch 624/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0271 - accuracy: 0.9957 - val_loss: 0.0948 - val_accuracy: 0.9483
Epoch 625/3000
2/2 [==============================] - 0s 52ms/step - loss: 0.0392 - accuracy: 0.9870 - val_loss: 0.0938 - val_accuracy: 0.9483
Epoch 626/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0819 - accuracy: 0.9696 - val_loss: 0.0921 - val_accuracy: 0.9483
Epoch 627/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0231 - accuracy: 0.9913 - val_loss: 0.0912 - val_accuracy: 0.9483
Epoch 628/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0613 - accuracy: 0.9826 - val_loss: 0.0912 - val_accuracy: 0.9483
Epoch 629/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0332 - accuracy: 0.9913 - val_loss: 0.0914 - val_accuracy: 0.9483
Epoch 630/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0230 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9483
Epoch 631/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0329 - accuracy: 0.9913 - val_loss: 0.0916 - val_accuracy: 0.9483
Epoch 632/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0278 - accuracy: 0.9957 - val_loss: 0.0917 - val_accuracy: 0.9483
Epoch 633/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0393 - accuracy: 0.9826 - val_loss: 0.0912 - val_accuracy: 0.9483
Epoch 634/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0231 - accuracy: 0.9913 - val_loss: 0.0914 - val_accuracy: 0.9483
Epoch 635/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0218 - accuracy: 0.9957 - val_loss: 0.0925 - val_accuracy: 0.9483
Epoch 636/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0411 - accuracy: 0.9783 - val_loss: 0.0931 - val_accuracy: 0.9483
Epoch 637/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0507 - accuracy: 0.9870 - val_loss: 0.0903 - val_accuracy: 0.9483
Epoch 638/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0367 - accuracy: 0.9957 - val_loss: 0.0881 - val_accuracy: 0.9483
Epoch 639/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0281 - accuracy: 0.9957 - val_loss: 0.0875 - val_accuracy: 0.9483
Epoch 640/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0399 - accuracy: 0.9913 - val_loss: 0.0875 - val_accuracy: 0.9483
Epoch 641/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0761 - accuracy: 0.9783 - val_loss: 0.0866 - val_accuracy: 0.9483
Epoch 642/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0236 - accuracy: 0.9957 - val_loss: 0.0865 - val_accuracy: 0.9483
Epoch 643/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0339 - accuracy: 0.9870 - val_loss: 0.0871 - val_accuracy: 0.9483
Epoch 644/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0263 - accuracy: 0.9957 - val_loss: 0.0868 - val_accuracy: 0.9483
Epoch 645/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.0862 - val_accuracy: 0.9483
Epoch 646/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0491 - accuracy: 0.9826 - val_loss: 0.0855 - val_accuracy: 0.9483
Epoch 647/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0241 - accuracy: 0.9957 - val_loss: 0.0850 - val_accuracy: 0.9483
Epoch 648/3000
2/2 [==============================] - 0s 29ms/step - loss: 0.0456 - accuracy: 0.9826 - val_loss: 0.0851 - val_accuracy: 0.9483
Epoch 649/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0359 - accuracy: 0.9913 - val_loss: 0.0859 - val_accuracy: 0.9483
Epoch 650/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0397 - accuracy: 0.9826 - val_loss: 0.0875 - val_accuracy: 0.9483
Epoch 651/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0165 - accuracy: 0.9957 - val_loss: 0.0897 - val_accuracy: 0.9483
Epoch 652/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0549 - accuracy: 0.9870 - val_loss: 0.0921 - val_accuracy: 0.9483
Epoch 653/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0188 - accuracy: 0.9957 - val_loss: 0.0936 - val_accuracy: 0.9483
Epoch 654/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0464 - accuracy: 0.9870 - val_loss: 0.0934 - val_accuracy: 0.9483
Epoch 655/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0511 - accuracy: 0.9739 - val_loss: 0.0929 - val_accuracy: 0.9483
Epoch 656/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0233 - accuracy: 0.9957 - val_loss: 0.0930 - val_accuracy: 0.9483
Epoch 657/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0212 - accuracy: 0.9957 - val_loss: 0.0922 - val_accuracy: 0.9483
Epoch 658/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0394 - accuracy: 0.9913 - val_loss: 0.0911 - val_accuracy: 0.9483
Epoch 659/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0312 - accuracy: 0.9913 - val_loss: 0.0903 - val_accuracy: 0.9483
Epoch 660/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0259 - accuracy: 0.9957 - val_loss: 0.0887 - val_accuracy: 0.9483
Epoch 661/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0468 - accuracy: 0.9870 - val_loss: 0.0874 - val_accuracy: 0.9483
Epoch 662/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0489 - accuracy: 0.9783 - val_loss: 0.0936 - val_accuracy: 0.9655
Epoch 663/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0362 - accuracy: 0.9783 - val_loss: 0.1033 - val_accuracy: 0.9655
Epoch 664/3000
2/2 [==============================] - 0s 29ms/step - loss: 0.0370 - accuracy: 0.9870 - val_loss: 0.1122 - val_accuracy: 0.9483
Epoch 665/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0572 - accuracy: 0.9783 - val_loss: 0.1152 - val_accuracy: 0.9483
Epoch 666/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0384 - accuracy: 0.9826 - val_loss: 0.1133 - val_accuracy: 0.9483
Epoch 667/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0572 - accuracy: 0.9826 - val_loss: 0.1063 - val_accuracy: 0.9483
Epoch 668/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0761 - accuracy: 0.9739 - val_loss: 0.0965 - val_accuracy: 0.9483
Epoch 669/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0351 - accuracy: 0.9870 - val_loss: 0.0895 - val_accuracy: 0.9655
Epoch 670/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0348 - accuracy: 0.9957 - val_loss: 0.0884 - val_accuracy: 0.9483
Epoch 671/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0362 - accuracy: 0.9826 - val_loss: 0.0925 - val_accuracy: 0.9483
Epoch 672/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0405 - accuracy: 0.9783 - val_loss: 0.0943 - val_accuracy: 0.9483
Epoch 673/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0844 - accuracy: 0.9739 - val_loss: 0.0913 - val_accuracy: 0.9483
Epoch 674/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0289 - accuracy: 0.9870 - val_loss: 0.0960 - val_accuracy: 0.9655
Epoch 675/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0452 - accuracy: 0.9783 - val_loss: 0.1008 - val_accuracy: 0.9483
Epoch 676/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0398 - accuracy: 0.9913 - val_loss: 0.1033 - val_accuracy: 0.9483
Epoch 677/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0384 - accuracy: 0.9913 - val_loss: 0.1003 - val_accuracy: 0.9655
Epoch 678/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0431 - accuracy: 0.9826 - val_loss: 0.0960 - val_accuracy: 0.9483
Epoch 679/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0350 - accuracy: 0.9870 - val_loss: 0.0933 - val_accuracy: 0.9483
Epoch 680/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9483
Epoch 681/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0257 - accuracy: 0.9913 - val_loss: 0.0957 - val_accuracy: 0.9483
Epoch 682/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.0981 - val_accuracy: 0.9483
Epoch 683/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0362 - accuracy: 0.9870 - val_loss: 0.0977 - val_accuracy: 0.9483
Epoch 684/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0674 - accuracy: 0.9826 - val_loss: 0.0935 - val_accuracy: 0.9483
Epoch 685/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0284 - accuracy: 0.9870 - val_loss: 0.0920 - val_accuracy: 0.9483
Epoch 686/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0253 - accuracy: 0.9957 - val_loss: 0.0934 - val_accuracy: 0.9483
Epoch 687/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0470 - accuracy: 0.9783 - val_loss: 0.0942 - val_accuracy: 0.9483
Epoch 688/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0430 - accuracy: 0.9739 - val_loss: 0.0935 - val_accuracy: 0.9483
Epoch 689/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0618 - accuracy: 0.9783 - val_loss: 0.0922 - val_accuracy: 0.9483
Epoch 690/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0278 - accuracy: 0.9913 - val_loss: 0.0902 - val_accuracy: 0.9483
Epoch 691/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0445 - accuracy: 0.9913 - val_loss: 0.0864 - val_accuracy: 0.9483
Epoch 692/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0281 - accuracy: 0.9957 - val_loss: 0.0840 - val_accuracy: 0.9483
Epoch 693/3000
2/2 [==============================] - 0s 55ms/step - loss: 0.0251 - accuracy: 0.9957 - val_loss: 0.0830 - val_accuracy: 0.9483
Epoch 694/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0307 - accuracy: 0.9913 - val_loss: 0.0821 - val_accuracy: 0.9483
Epoch 695/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0227 - accuracy: 0.9957 - val_loss: 0.0822 - val_accuracy: 0.9483
Epoch 696/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0592 - accuracy: 0.9783 - val_loss: 0.0829 - val_accuracy: 0.9655
Epoch 697/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0454 - accuracy: 0.9913 - val_loss: 0.0838 - val_accuracy: 0.9655
Epoch 698/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0201 - accuracy: 1.0000 - val_loss: 0.0833 - val_accuracy: 0.9655
Epoch 699/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0414 - accuracy: 0.9913 - val_loss: 0.0822 - val_accuracy: 0.9655
Epoch 700/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0277 - accuracy: 0.9913 - val_loss: 0.0801 - val_accuracy: 0.9655
Epoch 701/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0284 - accuracy: 0.9957 - val_loss: 0.0789 - val_accuracy: 0.9655
Epoch 702/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0328 - accuracy: 0.9913 - val_loss: 0.0782 - val_accuracy: 0.9655
Epoch 703/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0334 - accuracy: 0.9957 - val_loss: 0.0777 - val_accuracy: 0.9655
Epoch 704/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0611 - accuracy: 0.9870 - val_loss: 0.0753 - val_accuracy: 0.9655
Epoch 705/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0277 - accuracy: 0.9913 - val_loss: 0.0734 - val_accuracy: 0.9655
Epoch 706/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0330 - accuracy: 0.9913 - val_loss: 0.0738 - val_accuracy: 0.9655
Epoch 707/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0246 - accuracy: 0.9957 - val_loss: 0.0738 - val_accuracy: 0.9655
Epoch 708/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0337 - accuracy: 0.9913 - val_loss: 0.0723 - val_accuracy: 0.9655
Epoch 709/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0234 - accuracy: 0.9957 - val_loss: 0.0720 - val_accuracy: 0.9483
Epoch 710/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0264 - accuracy: 0.9957 - val_loss: 0.0725 - val_accuracy: 0.9483
Epoch 711/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0610 - accuracy: 0.9913 - val_loss: 0.0725 - val_accuracy: 0.9483
Epoch 712/3000
2/2 [==============================] - 0s 49ms/step - loss: 0.0594 - accuracy: 0.9826 - val_loss: 0.0715 - val_accuracy: 0.9483
Epoch 713/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9655
Epoch 714/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0230 - accuracy: 0.9957 - val_loss: 0.0779 - val_accuracy: 0.9655
Epoch 715/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0180 - accuracy: 0.9957 - val_loss: 0.0813 - val_accuracy: 0.9655
Epoch 716/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0356 - accuracy: 0.9870 - val_loss: 0.0797 - val_accuracy: 0.9655
Epoch 717/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0402 - accuracy: 0.9913 - val_loss: 0.0778 - val_accuracy: 0.9655
Epoch 718/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0301 - accuracy: 0.9826 - val_loss: 0.0742 - val_accuracy: 0.9655
Epoch 719/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0237 - accuracy: 0.9957 - val_loss: 0.0737 - val_accuracy: 0.9655
Epoch 720/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0223 - accuracy: 0.9957 - val_loss: 0.0732 - val_accuracy: 0.9655
Epoch 721/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0207 - accuracy: 0.9957 - val_loss: 0.0735 - val_accuracy: 0.9655
Epoch 722/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0550 - accuracy: 0.9957 - val_loss: 0.0716 - val_accuracy: 0.9655
Epoch 723/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0198 - accuracy: 0.9957 - val_loss: 0.0722 - val_accuracy: 0.9655
Epoch 724/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0242 - accuracy: 0.9913 - val_loss: 0.0724 - val_accuracy: 0.9655
Epoch 725/3000
2/2 [==============================] - 0s 56ms/step - loss: 0.0306 - accuracy: 0.9913 - val_loss: 0.0733 - val_accuracy: 0.9655
Epoch 726/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0269 - accuracy: 0.9957 - val_loss: 0.0756 - val_accuracy: 0.9655
Epoch 727/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0452 - accuracy: 0.9870 - val_loss: 0.0762 - val_accuracy: 0.9655
Epoch 728/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0135 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9655
Epoch 729/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0168 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9655
Epoch 730/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0199 - accuracy: 0.9957 - val_loss: 0.0759 - val_accuracy: 0.9483
Epoch 731/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0559 - accuracy: 0.9913 - val_loss: 0.0772 - val_accuracy: 0.9483
Epoch 732/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0276 - accuracy: 0.9957 - val_loss: 0.0786 - val_accuracy: 0.9483
Epoch 733/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0405 - accuracy: 0.9826 - val_loss: 0.0798 - val_accuracy: 0.9655
Epoch 734/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0733 - accuracy: 0.9826 - val_loss: 0.0798 - val_accuracy: 0.9655
Epoch 735/3000
2/2 [==============================] - 0s 49ms/step - loss: 0.0142 - accuracy: 0.9957 - val_loss: 0.0795 - val_accuracy: 0.9655
Epoch 736/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0447 - accuracy: 0.9870 - val_loss: 0.0784 - val_accuracy: 0.9483
Epoch 737/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0291 - accuracy: 0.9913 - val_loss: 0.0773 - val_accuracy: 0.9483
Epoch 738/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0287 - accuracy: 0.9913 - val_loss: 0.0768 - val_accuracy: 0.9483
Epoch 739/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0309 - accuracy: 0.9913 - val_loss: 0.0763 - val_accuracy: 0.9655
Epoch 740/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0370 - accuracy: 0.9783 - val_loss: 0.0747 - val_accuracy: 0.9655
Epoch 741/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0239 - accuracy: 0.9957 - val_loss: 0.0733 - val_accuracy: 0.9655
Epoch 742/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0539 - accuracy: 0.9870 - val_loss: 0.0733 - val_accuracy: 0.9655
Epoch 743/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0766 - accuracy: 0.9739 - val_loss: 0.0729 - val_accuracy: 0.9655
Epoch 744/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.0718 - val_accuracy: 0.9655
Epoch 745/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0150 - accuracy: 1.0000 - val_loss: 0.0716 - val_accuracy: 0.9655
Epoch 746/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0344 - accuracy: 0.9957 - val_loss: 0.0708 - val_accuracy: 0.9655
Epoch 747/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0418 - accuracy: 0.9957 - val_loss: 0.0695 - val_accuracy: 0.9655
Epoch 748/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0282 - accuracy: 0.9957 - val_loss: 0.0684 - val_accuracy: 0.9655
Epoch 749/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0507 - accuracy: 0.9913 - val_loss: 0.0679 - val_accuracy: 0.9655
Epoch 750/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0259 - accuracy: 0.9913 - val_loss: 0.0672 - val_accuracy: 0.9655
Epoch 751/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0219 - accuracy: 0.9957 - val_loss: 0.0670 - val_accuracy: 0.9655
Epoch 752/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0350 - accuracy: 0.9870 - val_loss: 0.0672 - val_accuracy: 0.9655
Epoch 753/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0355 - accuracy: 0.9826 - val_loss: 0.0676 - val_accuracy: 0.9655
Epoch 754/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.0691 - val_accuracy: 0.9655
Epoch 755/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0367 - accuracy: 0.9870 - val_loss: 0.0693 - val_accuracy: 0.9655
Epoch 756/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.0696 - val_accuracy: 0.9655
Epoch 757/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0170 - accuracy: 0.9957 - val_loss: 0.0703 - val_accuracy: 0.9655
Epoch 758/3000
2/2 [==============================] - 0s 49ms/step - loss: 0.0282 - accuracy: 0.9913 - val_loss: 0.0712 - val_accuracy: 0.9655
Epoch 759/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0309 - accuracy: 0.9913 - val_loss: 0.0715 - val_accuracy: 0.9655
Epoch 760/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0314 - accuracy: 0.9913 - val_loss: 0.0715 - val_accuracy: 0.9655
Epoch 761/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0339 - accuracy: 0.9913 - val_loss: 0.0728 - val_accuracy: 0.9655
Epoch 762/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0238 - accuracy: 0.9913 - val_loss: 0.0754 - val_accuracy: 0.9655
Epoch 763/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0304 - accuracy: 0.9913 - val_loss: 0.0796 - val_accuracy: 0.9655
Epoch 764/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0328 - accuracy: 0.9957 - val_loss: 0.0864 - val_accuracy: 0.9655
Epoch 765/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0283 - accuracy: 0.9957 - val_loss: 0.0915 - val_accuracy: 0.9655
Epoch 766/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0490 - accuracy: 0.9826 - val_loss: 0.0945 - val_accuracy: 0.9655
Epoch 767/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0483 - accuracy: 0.9783 - val_loss: 0.0853 - val_accuracy: 0.9655
Epoch 768/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0187 - accuracy: 0.9913 - val_loss: 0.0761 - val_accuracy: 0.9655
Epoch 769/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0190 - accuracy: 0.9913 - val_loss: 0.0698 - val_accuracy: 0.9655
Epoch 770/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0227 - accuracy: 0.9957 - val_loss: 0.0657 - val_accuracy: 0.9655
Epoch 771/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0257 - accuracy: 0.9913 - val_loss: 0.0617 - val_accuracy: 0.9655
Epoch 772/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0349 - accuracy: 0.9957 - val_loss: 0.0602 - val_accuracy: 0.9655
Epoch 773/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0458 - accuracy: 0.9913 - val_loss: 0.0602 - val_accuracy: 0.9655
Epoch 774/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0148 - accuracy: 0.9957 - val_loss: 0.0619 - val_accuracy: 0.9655
Epoch 775/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0506 - accuracy: 0.9913 - val_loss: 0.0647 - val_accuracy: 0.9655
Epoch 776/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0242 - accuracy: 0.9957 - val_loss: 0.0704 - val_accuracy: 0.9655
Epoch 777/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0610 - accuracy: 0.9870 - val_loss: 0.0788 - val_accuracy: 0.9655
Epoch 778/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0286 - accuracy: 0.9913 - val_loss: 0.0835 - val_accuracy: 0.9655
Epoch 779/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0156 - accuracy: 0.9913 - val_loss: 0.0873 - val_accuracy: 0.9655
Epoch 780/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0482 - accuracy: 0.9826 - val_loss: 0.0865 - val_accuracy: 0.9655
Epoch 781/3000
2/2 [==============================] - 0s 52ms/step - loss: 0.0335 - accuracy: 0.9870 - val_loss: 0.0828 - val_accuracy: 0.9655
Epoch 782/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0355 - accuracy: 0.9913 - val_loss: 0.0808 - val_accuracy: 0.9655
Epoch 783/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0287 - accuracy: 0.9913 - val_loss: 0.0769 - val_accuracy: 0.9655
Epoch 784/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0522 - accuracy: 0.9826 - val_loss: 0.0712 - val_accuracy: 0.9655
Epoch 785/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0359 - accuracy: 0.9870 - val_loss: 0.0628 - val_accuracy: 0.9655
Epoch 786/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0216 - accuracy: 0.9957 - val_loss: 0.0594 - val_accuracy: 0.9655
Epoch 787/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0326 - accuracy: 0.9870 - val_loss: 0.0597 - val_accuracy: 0.9655
Epoch 788/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0208 - accuracy: 1.0000 - val_loss: 0.0611 - val_accuracy: 0.9655
Epoch 789/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0323 - accuracy: 0.9957 - val_loss: 0.0636 - val_accuracy: 0.9655
Epoch 790/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0478 - accuracy: 0.9913 - val_loss: 0.0690 - val_accuracy: 0.9655
Epoch 791/3000
2/2 [==============================] - 0s 51ms/step - loss: 0.0138 - accuracy: 0.9957 - val_loss: 0.0761 - val_accuracy: 0.9655
Epoch 792/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0217 - accuracy: 0.9913 - val_loss: 0.0824 - val_accuracy: 0.9655
Epoch 793/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.1213 - accuracy: 0.9739 - val_loss: 0.0738 - val_accuracy: 0.9655
Epoch 794/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.0634 - val_accuracy: 0.9655
Epoch 795/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0157 - accuracy: 1.0000 - val_loss: 0.0580 - val_accuracy: 0.9655
Epoch 796/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0133 - accuracy: 1.0000 - val_loss: 0.0569 - val_accuracy: 0.9655
Epoch 797/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0235 - accuracy: 0.9913 - val_loss: 0.0575 - val_accuracy: 0.9655
Epoch 798/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0364 - accuracy: 0.9870 - val_loss: 0.0614 - val_accuracy: 0.9655
Epoch 799/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0187 - accuracy: 0.9957 - val_loss: 0.0635 - val_accuracy: 0.9655
Epoch 800/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0157 - accuracy: 1.0000 - val_loss: 0.0643 - val_accuracy: 0.9655
Epoch 801/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.0651 - val_accuracy: 0.9655
Epoch 802/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0322 - accuracy: 0.9870 - val_loss: 0.0654 - val_accuracy: 0.9655
Epoch 803/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0247 - accuracy: 0.9913 - val_loss: 0.0639 - val_accuracy: 0.9655
Epoch 804/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0272 - accuracy: 0.9913 - val_loss: 0.0633 - val_accuracy: 0.9655
Epoch 805/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0214 - accuracy: 0.9913 - val_loss: 0.0649 - val_accuracy: 0.9655
Epoch 806/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0547 - accuracy: 0.9783 - val_loss: 0.0683 - val_accuracy: 0.9655
Epoch 807/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.0727 - val_accuracy: 0.9655
Epoch 808/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0151 - accuracy: 0.9957 - val_loss: 0.0756 - val_accuracy: 0.9655
Epoch 809/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0281 - accuracy: 0.9957 - val_loss: 0.0759 - val_accuracy: 0.9655
Epoch 810/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0179 - accuracy: 0.9957 - val_loss: 0.0734 - val_accuracy: 0.9655
Epoch 811/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0175 - accuracy: 0.9957 - val_loss: 0.0714 - val_accuracy: 0.9655
Epoch 812/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0143 - accuracy: 0.9957 - val_loss: 0.0719 - val_accuracy: 0.9655
Epoch 813/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0237 - accuracy: 0.9957 - val_loss: 0.0716 - val_accuracy: 0.9655
Epoch 814/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0199 - accuracy: 0.9957 - val_loss: 0.0721 - val_accuracy: 0.9655
Epoch 815/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0257 - accuracy: 0.9913 - val_loss: 0.0713 - val_accuracy: 0.9655
Epoch 816/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0341 - accuracy: 0.9913 - val_loss: 0.0704 - val_accuracy: 0.9655
Epoch 817/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0120 - accuracy: 0.9957 - val_loss: 0.0723 - val_accuracy: 0.9655
Epoch 818/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0333 - accuracy: 0.9826 - val_loss: 0.0747 - val_accuracy: 0.9655
Epoch 819/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0190 - accuracy: 0.9913 - val_loss: 0.0745 - val_accuracy: 0.9655
Epoch 820/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0227 - accuracy: 0.9913 - val_loss: 0.0692 - val_accuracy: 0.9655
Epoch 821/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0373 - accuracy: 0.9870 - val_loss: 0.0622 - val_accuracy: 0.9655
Epoch 822/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0299 - accuracy: 0.9913 - val_loss: 0.0568 - val_accuracy: 0.9655
Epoch 823/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0192 - accuracy: 1.0000 - val_loss: 0.0533 - val_accuracy: 0.9655
Epoch 824/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.0520 - val_accuracy: 0.9655
Epoch 825/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0199 - accuracy: 0.9913 - val_loss: 0.0519 - val_accuracy: 0.9655
Epoch 826/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0262 - accuracy: 0.9957 - val_loss: 0.0538 - val_accuracy: 0.9655
Epoch 827/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0095 - accuracy: 0.9957 - val_loss: 0.0569 - val_accuracy: 0.9655
Epoch 828/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0159 - accuracy: 0.9957 - val_loss: 0.0618 - val_accuracy: 0.9655
Epoch 829/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0570 - accuracy: 0.9783 - val_loss: 0.0713 - val_accuracy: 0.9655
Epoch 830/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0270 - accuracy: 0.9870 - val_loss: 0.0788 - val_accuracy: 0.9655
Epoch 831/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0367 - accuracy: 0.9957 - val_loss: 0.0845 - val_accuracy: 0.9655
Epoch 832/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0330 - accuracy: 0.9957 - val_loss: 0.0804 - val_accuracy: 0.9655
Epoch 833/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0221 - accuracy: 0.9913 - val_loss: 0.0750 - val_accuracy: 0.9655
Epoch 834/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0152 - accuracy: 1.0000 - val_loss: 0.0695 - val_accuracy: 0.9655
Epoch 835/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0529 - accuracy: 0.9913 - val_loss: 0.0631 - val_accuracy: 0.9655
Epoch 836/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0330 - accuracy: 0.9913 - val_loss: 0.0672 - val_accuracy: 0.9655
Epoch 837/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.0713 - val_accuracy: 0.9655
Epoch 838/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0207 - accuracy: 0.9913 - val_loss: 0.0753 - val_accuracy: 0.9655
Epoch 839/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0440 - accuracy: 0.9826 - val_loss: 0.0748 - val_accuracy: 0.9655
Epoch 840/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0179 - accuracy: 0.9957 - val_loss: 0.0713 - val_accuracy: 0.9655
Epoch 841/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0167 - accuracy: 0.9957 - val_loss: 0.0674 - val_accuracy: 0.9655
Epoch 842/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0280 - accuracy: 0.9913 - val_loss: 0.0619 - val_accuracy: 0.9655
Epoch 843/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0264 - accuracy: 0.9913 - val_loss: 0.0597 - val_accuracy: 0.9655
Epoch 844/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0164 - accuracy: 1.0000 - val_loss: 0.0611 - val_accuracy: 0.9655
Epoch 845/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0465 - accuracy: 0.9826 - val_loss: 0.0697 - val_accuracy: 0.9655
Epoch 846/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0184 - accuracy: 0.9957 - val_loss: 0.0815 - val_accuracy: 0.9483
Epoch 847/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0345 - accuracy: 0.9957 - val_loss: 0.0930 - val_accuracy: 0.9483
Epoch 848/3000
2/2 [==============================] - 0s 30ms/step - loss: 0.0260 - accuracy: 0.9957 - val_loss: 0.0998 - val_accuracy: 0.9483
Epoch 849/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0280 - accuracy: 0.9870 - val_loss: 0.1009 - val_accuracy: 0.9483
Epoch 850/3000
2/2 [==============================] - 0s 43ms/step - loss: 0.0396 - accuracy: 0.9739 - val_loss: 0.0976 - val_accuracy: 0.9483
Epoch 851/3000
2/2 [==============================] - 0s 54ms/step - loss: 0.0151 - accuracy: 0.9957 - val_loss: 0.0899 - val_accuracy: 0.9483
Epoch 852/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0414 - accuracy: 0.9870 - val_loss: 0.0816 - val_accuracy: 0.9483
Epoch 853/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0418 - accuracy: 0.9913 - val_loss: 0.0781 - val_accuracy: 0.9483
Epoch 854/3000
2/2 [==============================] - 0s 43ms/step - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.0783 - val_accuracy: 0.9483
Epoch 855/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0530 - accuracy: 0.9870 - val_loss: 0.0820 - val_accuracy: 0.9483
Epoch 856/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0216 - accuracy: 0.9957 - val_loss: 0.0925 - val_accuracy: 0.9483
Epoch 857/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0288 - accuracy: 0.9870 - val_loss: 0.1026 - val_accuracy: 0.9483
Epoch 858/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0190 - accuracy: 0.9957 - val_loss: 0.1086 - val_accuracy: 0.9483
Epoch 859/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0237 - accuracy: 0.9913 - val_loss: 0.1098 - val_accuracy: 0.9483
Epoch 860/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0316 - accuracy: 0.9913 - val_loss: 0.1005 - val_accuracy: 0.9483
Epoch 861/3000
2/2 [==============================] - 0s 31ms/step - loss: 0.0302 - accuracy: 0.9913 - val_loss: 0.0888 - val_accuracy: 0.9483
Epoch 862/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0146 - accuracy: 0.9913 - val_loss: 0.0802 - val_accuracy: 0.9483
Epoch 863/3000
2/2 [==============================] - 0s 55ms/step - loss: 0.0163 - accuracy: 0.9957 - val_loss: 0.0765 - val_accuracy: 0.9655
Epoch 864/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0222 - accuracy: 0.9957 - val_loss: 0.0752 - val_accuracy: 0.9655
Epoch 865/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0357 - accuracy: 0.9870 - val_loss: 0.0774 - val_accuracy: 0.9483
Epoch 866/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0157 - accuracy: 0.9957 - val_loss: 0.0806 - val_accuracy: 0.9483
Epoch 867/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0182 - accuracy: 0.9957 - val_loss: 0.0824 - val_accuracy: 0.9483
Epoch 868/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9483
Epoch 869/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.0840 - val_accuracy: 0.9483
Epoch 870/3000
2/2 [==============================] - 0s 53ms/step - loss: 0.0158 - accuracy: 0.9913 - val_loss: 0.0824 - val_accuracy: 0.9483
Epoch 871/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0236 - accuracy: 0.9957 - val_loss: 0.0813 - val_accuracy: 0.9483
Epoch 872/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.0809 - val_accuracy: 0.9483
Epoch 873/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.0812 - val_accuracy: 0.9483
Epoch 874/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0205 - accuracy: 0.9957 - val_loss: 0.0813 - val_accuracy: 0.9483
Epoch 875/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.0833 - val_accuracy: 0.9483
Epoch 876/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0289 - accuracy: 0.9913 - val_loss: 0.0888 - val_accuracy: 0.9483
Epoch 877/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9483
Epoch 878/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0126 - accuracy: 0.9957 - val_loss: 0.0992 - val_accuracy: 0.9483
Epoch 879/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0272 - accuracy: 0.9826 - val_loss: 0.0999 - val_accuracy: 0.9483
Epoch 880/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0226 - accuracy: 0.9957 - val_loss: 0.0954 - val_accuracy: 0.9483
Epoch 881/3000
2/2 [==============================] - 0s 59ms/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9483
Epoch 882/3000
2/2 [==============================] - 0s 57ms/step - loss: 0.0189 - accuracy: 0.9957 - val_loss: 0.0808 - val_accuracy: 0.9655
Epoch 883/3000
2/2 [==============================] - 0s 53ms/step - loss: 0.0208 - accuracy: 0.9913 - val_loss: 0.0778 - val_accuracy: 0.9655
Epoch 884/3000
2/2 [==============================] - 0s 46ms/step - loss: 0.0313 - accuracy: 0.9957 - val_loss: 0.0769 - val_accuracy: 0.9655
Epoch 885/3000
2/2 [==============================] - 0s 67ms/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.0760 - val_accuracy: 0.9655
Epoch 886/3000
2/2 [==============================] - 0s 51ms/step - loss: 0.0373 - accuracy: 0.9957 - val_loss: 0.0817 - val_accuracy: 0.9655
Epoch 887/3000
2/2 [==============================] - 0s 52ms/step - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9483
Epoch 888/3000
2/2 [==============================] - 0s 55ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9483
Epoch 889/3000
2/2 [==============================] - 0s 47ms/step - loss: 0.0176 - accuracy: 0.9870 - val_loss: 0.0976 - val_accuracy: 0.9483
Epoch 890/3000
2/2 [==============================] - 0s 51ms/step - loss: 0.0124 - accuracy: 0.9957 - val_loss: 0.0976 - val_accuracy: 0.9483
Epoch 891/3000
2/2 [==============================] - 0s 59ms/step - loss: 0.0165 - accuracy: 0.9957 - val_loss: 0.0985 - val_accuracy: 0.9655
Epoch 892/3000
2/2 [==============================] - 0s 57ms/step - loss: 0.0171 - accuracy: 0.9913 - val_loss: 0.0992 - val_accuracy: 0.9655
Epoch 893/3000
2/2 [==============================] - 0s 71ms/step - loss: 0.0234 - accuracy: 0.9913 - val_loss: 0.0998 - val_accuracy: 0.9655
Epoch 894/3000
2/2 [==============================] - 0s 65ms/step - loss: 0.0205 - accuracy: 0.9957 - val_loss: 0.0929 - val_accuracy: 0.9655
Epoch 895/3000
2/2 [==============================] - 0s 51ms/step - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9655
Epoch 896/3000
2/2 [==============================] - 0s 66ms/step - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9655
Epoch 897/3000
2/2 [==============================] - 0s 51ms/step - loss: 0.0180 - accuracy: 0.9913 - val_loss: 0.0732 - val_accuracy: 0.9655
Epoch 898/3000
2/2 [==============================] - 0s 47ms/step - loss: 0.0199 - accuracy: 0.9870 - val_loss: 0.0696 - val_accuracy: 0.9655
Epoch 899/3000
2/2 [==============================] - 0s 58ms/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.0681 - val_accuracy: 0.9655
Epoch 900/3000
2/2 [==============================] - 0s 70ms/step - loss: 0.0325 - accuracy: 0.9826 - val_loss: 0.0662 - val_accuracy: 0.9655
Epoch 901/3000
2/2 [==============================] - 0s 64ms/step - loss: 0.0219 - accuracy: 0.9913 - val_loss: 0.0696 - val_accuracy: 0.9655
Epoch 902/3000
2/2 [==============================] - 0s 61ms/step - loss: 0.0105 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9655
Epoch 903/3000
2/2 [==============================] - 0s 78ms/step - loss: 0.0207 - accuracy: 0.9870 - val_loss: 0.0868 - val_accuracy: 0.9655
Epoch 904/3000
2/2 [==============================] - 0s 49ms/step - loss: 0.0150 - accuracy: 0.9957 - val_loss: 0.0932 - val_accuracy: 0.9483
Epoch 905/3000
2/2 [==============================] - 0s 62ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9655
Epoch 906/3000
2/2 [==============================] - 0s 54ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.0867 - val_accuracy: 0.9655
Epoch 907/3000
2/2 [==============================] - 0s 73ms/step - loss: 0.0111 - accuracy: 1.0000 - val_loss: 0.0786 - val_accuracy: 0.9655
Epoch 908/3000
2/2 [==============================] - 0s 52ms/step - loss: 0.0194 - accuracy: 0.9957 - val_loss: 0.0687 - val_accuracy: 0.9655
Epoch 909/3000
2/2 [==============================] - 0s 59ms/step - loss: 0.0191 - accuracy: 0.9957 - val_loss: 0.0598 - val_accuracy: 0.9655
Epoch 910/3000
2/2 [==============================] - 0s 60ms/step - loss: 0.0814 - accuracy: 0.9870 - val_loss: 0.0524 - val_accuracy: 0.9655
Epoch 911/3000
2/2 [==============================] - 0s 59ms/step - loss: 0.0119 - accuracy: 0.9913 - val_loss: 0.0486 - val_accuracy: 0.9655
Epoch 912/3000
2/2 [==============================] - 0s 60ms/step - loss: 0.0157 - accuracy: 0.9913 - val_loss: 0.0480 - val_accuracy: 0.9655
Epoch 913/3000
2/2 [==============================] - 0s 60ms/step - loss: 0.0328 - accuracy: 0.9870 - val_loss: 0.0476 - val_accuracy: 0.9828
Epoch 914/3000
2/2 [==============================] - 0s 61ms/step - loss: 0.0265 - accuracy: 0.9870 - val_loss: 0.0490 - val_accuracy: 0.9828
Epoch 915/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0272 - accuracy: 0.9870 - val_loss: 0.0539 - val_accuracy: 0.9828
Epoch 916/3000
2/2 [==============================] - 0s 66ms/step - loss: 0.0134 - accuracy: 0.9957 - val_loss: 0.0590 - val_accuracy: 0.9655
Epoch 917/3000
2/2 [==============================] - 0s 70ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.0625 - val_accuracy: 0.9655
Epoch 918/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0221 - accuracy: 0.9826 - val_loss: 0.0638 - val_accuracy: 0.9655
Epoch 919/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.0641 - val_accuracy: 0.9655
Epoch 920/3000
2/2 [==============================] - 0s 77ms/step - loss: 0.0210 - accuracy: 0.9913 - val_loss: 0.0653 - val_accuracy: 0.9655
Epoch 921/3000
2/2 [==============================] - 0s 55ms/step - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.0680 - val_accuracy: 0.9655
Epoch 922/3000
2/2 [==============================] - 0s 58ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.0693 - val_accuracy: 0.9655
Epoch 923/3000
2/2 [==============================] - 0s 68ms/step - loss: 0.0346 - accuracy: 0.9913 - val_loss: 0.0670 - val_accuracy: 0.9655
Epoch 924/3000
2/2 [==============================] - 0s 54ms/step - loss: 0.0131 - accuracy: 1.0000 - val_loss: 0.0648 - val_accuracy: 0.9655
Epoch 925/3000
2/2 [==============================] - 0s 49ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0617 - val_accuracy: 0.9655
Epoch 926/3000
2/2 [==============================] - 0s 80ms/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.0591 - val_accuracy: 0.9655
Epoch 927/3000
2/2 [==============================] - 0s 60ms/step - loss: 0.0157 - accuracy: 1.0000 - val_loss: 0.0586 - val_accuracy: 0.9655
Epoch 928/3000
2/2 [==============================] - 0s 63ms/step - loss: 0.0186 - accuracy: 0.9957 - val_loss: 0.0593 - val_accuracy: 0.9655
Epoch 929/3000
2/2 [==============================] - 0s 57ms/step - loss: 0.0307 - accuracy: 0.9870 - val_loss: 0.0581 - val_accuracy: 0.9655
Epoch 930/3000
2/2 [==============================] - 0s 60ms/step - loss: 0.0214 - accuracy: 0.9870 - val_loss: 0.0617 - val_accuracy: 0.9655
Epoch 931/3000
2/2 [==============================] - 0s 74ms/step - loss: 0.0223 - accuracy: 0.9870 - val_loss: 0.0699 - val_accuracy: 0.9655
Epoch 932/3000
2/2 [==============================] - 0s 62ms/step - loss: 0.0296 - accuracy: 0.9870 - val_loss: 0.0730 - val_accuracy: 0.9655
Epoch 933/3000
2/2 [==============================] - 0s 51ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.0715 - val_accuracy: 0.9655
Epoch 934/3000
2/2 [==============================] - 0s 65ms/step - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.0693 - val_accuracy: 0.9655
Epoch 935/3000
2/2 [==============================] - 0s 54ms/step - loss: 0.0101 - accuracy: 0.9957 - val_loss: 0.0668 - val_accuracy: 0.9655
Epoch 936/3000
2/2 [==============================] - 0s 79ms/step - loss: 0.0235 - accuracy: 0.9957 - val_loss: 0.0636 - val_accuracy: 0.9655
Epoch 937/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0408 - accuracy: 0.9870 - val_loss: 0.0649 - val_accuracy: 0.9655
Epoch 938/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.0709 - val_accuracy: 0.9655
Epoch 939/3000
2/2 [==============================] - 0s 55ms/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9655
Epoch 940/3000
2/2 [==============================] - 0s 67ms/step - loss: 0.0159 - accuracy: 0.9913 - val_loss: 0.0771 - val_accuracy: 0.9655
Epoch 941/3000
2/2 [==============================] - 0s 72ms/step - loss: 0.0235 - accuracy: 0.9913 - val_loss: 0.0802 - val_accuracy: 0.9655
Epoch 942/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0326 - accuracy: 0.9913 - val_loss: 0.0804 - val_accuracy: 0.9655
Epoch 943/3000
2/2 [==============================] - 0s 52ms/step - loss: 0.0315 - accuracy: 0.9957 - val_loss: 0.0773 - val_accuracy: 0.9655
Epoch 944/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0181 - accuracy: 0.9913 - val_loss: 0.0719 - val_accuracy: 0.9655
Epoch 945/3000
2/2 [==============================] - 0s 47ms/step - loss: 0.0114 - accuracy: 1.0000 - val_loss: 0.0651 - val_accuracy: 0.9655
Epoch 946/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0221 - accuracy: 0.9913 - val_loss: 0.0615 - val_accuracy: 0.9655
Epoch 947/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0185 - accuracy: 0.9957 - val_loss: 0.0622 - val_accuracy: 0.9655
Epoch 948/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.0626 - val_accuracy: 0.9655
Epoch 949/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0132 - accuracy: 0.9913 - val_loss: 0.0661 - val_accuracy: 0.9655
Epoch 950/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0101 - accuracy: 1.0000 - val_loss: 0.0699 - val_accuracy: 0.9655
Epoch 951/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9655
Epoch 952/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0150 - accuracy: 0.9913 - val_loss: 0.0732 - val_accuracy: 0.9655
Epoch 953/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0215 - accuracy: 0.9870 - val_loss: 0.0721 - val_accuracy: 0.9655
Epoch 954/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0156 - accuracy: 0.9913 - val_loss: 0.0650 - val_accuracy: 0.9655
Epoch 955/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0129 - accuracy: 0.9957 - val_loss: 0.0578 - val_accuracy: 0.9655
Epoch 956/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0237 - accuracy: 0.9913 - val_loss: 0.0558 - val_accuracy: 0.9655
Epoch 957/3000
2/2 [==============================] - 0s 53ms/step - loss: 0.0248 - accuracy: 0.9957 - val_loss: 0.0577 - val_accuracy: 0.9655
Epoch 958/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.0624 - val_accuracy: 0.9655
Epoch 959/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0095 - accuracy: 0.9957 - val_loss: 0.0691 - val_accuracy: 0.9655
Epoch 960/3000
2/2 [==============================] - 0s 47ms/step - loss: 0.0289 - accuracy: 0.9913 - val_loss: 0.0759 - val_accuracy: 0.9655
Epoch 961/3000
2/2 [==============================] - 0s 49ms/step - loss: 0.0150 - accuracy: 0.9957 - val_loss: 0.0753 - val_accuracy: 0.9655
Epoch 962/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0078 - accuracy: 0.9957 - val_loss: 0.0727 - val_accuracy: 0.9655
Epoch 963/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0163 - accuracy: 0.9957 - val_loss: 0.0731 - val_accuracy: 0.9655
Epoch 964/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9655
Epoch 965/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9655
Epoch 966/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0155 - accuracy: 0.9957 - val_loss: 0.0805 - val_accuracy: 0.9655
Epoch 967/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.0817 - val_accuracy: 0.9655
Epoch 968/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0124 - accuracy: 0.9957 - val_loss: 0.0841 - val_accuracy: 0.9655
Epoch 969/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0105 - accuracy: 0.9957 - val_loss: 0.0858 - val_accuracy: 0.9655
Epoch 970/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0186 - accuracy: 0.9913 - val_loss: 0.0848 - val_accuracy: 0.9655
Epoch 971/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0362 - accuracy: 0.9913 - val_loss: 0.0843 - val_accuracy: 0.9655
Epoch 972/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0232 - accuracy: 0.9957 - val_loss: 0.0778 - val_accuracy: 0.9655
Epoch 973/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0106 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9655
Epoch 974/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0159 - accuracy: 0.9957 - val_loss: 0.0704 - val_accuracy: 0.9655
Epoch 975/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.0677 - val_accuracy: 0.9655
Epoch 976/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0182 - accuracy: 0.9957 - val_loss: 0.0694 - val_accuracy: 0.9655
Epoch 977/3000
2/2 [==============================] - 0s 52ms/step - loss: 0.0181 - accuracy: 0.9957 - val_loss: 0.0619 - val_accuracy: 0.9655
Epoch 978/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.0574 - val_accuracy: 0.9655
Epoch 979/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0220 - accuracy: 0.9913 - val_loss: 0.0569 - val_accuracy: 0.9655
Epoch 980/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0145 - accuracy: 0.9957 - val_loss: 0.0551 - val_accuracy: 0.9655
Epoch 981/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.0537 - val_accuracy: 0.9655
Epoch 982/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0300 - accuracy: 0.9870 - val_loss: 0.0544 - val_accuracy: 0.9655
Epoch 983/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0264 - accuracy: 0.9957 - val_loss: 0.0600 - val_accuracy: 0.9655
Epoch 984/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.0693 - val_accuracy: 0.9655
Epoch 985/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.0779 - val_accuracy: 0.9655
Epoch 986/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0170 - accuracy: 0.9913 - val_loss: 0.0838 - val_accuracy: 0.9655
Epoch 987/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0136 - accuracy: 0.9957 - val_loss: 0.0888 - val_accuracy: 0.9655
Epoch 988/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9655
Epoch 989/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0271 - accuracy: 0.9957 - val_loss: 0.0883 - val_accuracy: 0.9655
Epoch 990/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0165 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9655
Epoch 991/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0712 - val_accuracy: 0.9655
Epoch 992/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0298 - accuracy: 0.9957 - val_loss: 0.0655 - val_accuracy: 0.9655
Epoch 993/3000
2/2 [==============================] - 0s 46ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.0634 - val_accuracy: 0.9655
Epoch 994/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.0625 - val_accuracy: 0.9655
Epoch 995/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0140 - accuracy: 0.9957 - val_loss: 0.0643 - val_accuracy: 0.9655
Epoch 996/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0103 - accuracy: 0.9957 - val_loss: 0.0662 - val_accuracy: 0.9655
Epoch 997/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0647 - accuracy: 0.9739 - val_loss: 0.0726 - val_accuracy: 0.9655
Epoch 998/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0169 - accuracy: 1.0000 - val_loss: 0.0792 - val_accuracy: 0.9655
Epoch 999/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0175 - accuracy: 0.9957 - val_loss: 0.0875 - val_accuracy: 0.9655
Epoch 1000/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.0968 - val_accuracy: 0.9655
Epoch 1001/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0246 - accuracy: 0.9957 - val_loss: 0.1098 - val_accuracy: 0.9655
Epoch 1002/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0221 - accuracy: 0.9957 - val_loss: 0.1182 - val_accuracy: 0.9655
Epoch 1003/3000
2/2 [==============================] - 0s 51ms/step - loss: 0.0157 - accuracy: 0.9957 - val_loss: 0.1267 - val_accuracy: 0.9483
Epoch 1004/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0520 - accuracy: 0.9739 - val_loss: 0.1218 - val_accuracy: 0.9483
Epoch 1005/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0419 - accuracy: 0.9826 - val_loss: 0.0991 - val_accuracy: 0.9655
Epoch 1006/3000
2/2 [==============================] - 0s 46ms/step - loss: 0.0180 - accuracy: 0.9957 - val_loss: 0.0748 - val_accuracy: 0.9655
Epoch 1007/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0281 - accuracy: 0.9913 - val_loss: 0.0554 - val_accuracy: 0.9655
Epoch 1008/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0062 - accuracy: 1.0000 - val_loss: 0.0450 - val_accuracy: 0.9655
Epoch 1009/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0288 - accuracy: 0.9913 - val_loss: 0.0418 - val_accuracy: 0.9655
Epoch 1010/3000
2/2 [==============================] - 0s 43ms/step - loss: 0.0409 - accuracy: 0.9913 - val_loss: 0.0408 - val_accuracy: 0.9655
Epoch 1011/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0149 - accuracy: 0.9957 - val_loss: 0.0427 - val_accuracy: 0.9655
Epoch 1012/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0291 - accuracy: 0.9826 - val_loss: 0.0500 - val_accuracy: 0.9655
Epoch 1013/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0204 - accuracy: 0.9870 - val_loss: 0.0614 - val_accuracy: 0.9655
Epoch 1014/3000
2/2 [==============================] - 0s 45ms/step - loss: 0.0154 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9655
Epoch 1015/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0122 - accuracy: 0.9957 - val_loss: 0.0845 - val_accuracy: 0.9655
Epoch 1016/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0108 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9655
Epoch 1017/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0361 - accuracy: 0.9826 - val_loss: 0.0858 - val_accuracy: 0.9655
Epoch 1018/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0313 - accuracy: 0.9957 - val_loss: 0.0731 - val_accuracy: 0.9655
Epoch 1019/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0535 - accuracy: 0.9870 - val_loss: 0.0555 - val_accuracy: 0.9655
Epoch 1020/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0142 - accuracy: 0.9913 - val_loss: 0.0465 - val_accuracy: 0.9655
Epoch 1021/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0394 - accuracy: 0.9913 - val_loss: 0.0446 - val_accuracy: 0.9655
Epoch 1022/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0211 - accuracy: 0.9957 - val_loss: 0.0483 - val_accuracy: 0.9655
Epoch 1023/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0276 - accuracy: 0.9870 - val_loss: 0.0555 - val_accuracy: 0.9655
Epoch 1024/3000
2/2 [==============================] - 0s 46ms/step - loss: 0.0052 - accuracy: 1.0000 - val_loss: 0.0631 - val_accuracy: 0.9655
Epoch 1025/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.0702 - val_accuracy: 0.9655
Epoch 1026/3000
2/2 [==============================] - 0s 45ms/step - loss: 0.0285 - accuracy: 0.9870 - val_loss: 0.0782 - val_accuracy: 0.9655
Epoch 1027/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0161 - accuracy: 0.9957 - val_loss: 0.0881 - val_accuracy: 0.9655
Epoch 1028/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0077 - accuracy: 0.9957 - val_loss: 0.0935 - val_accuracy: 0.9655
Epoch 1029/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0193 - accuracy: 0.9957 - val_loss: 0.0899 - val_accuracy: 0.9655
Epoch 1030/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.0862 - val_accuracy: 0.9655
Epoch 1031/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0112 - accuracy: 0.9957 - val_loss: 0.0819 - val_accuracy: 0.9655
Epoch 1032/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0110 - accuracy: 0.9957 - val_loss: 0.0778 - val_accuracy: 0.9655
Epoch 1033/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0153 - accuracy: 0.9913 - val_loss: 0.0753 - val_accuracy: 0.9655
Epoch 1034/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.0728 - val_accuracy: 0.9655
Epoch 1035/3000
2/2 [==============================] - 0s 53ms/step - loss: 0.0239 - accuracy: 0.9913 - val_loss: 0.0777 - val_accuracy: 0.9655
Epoch 1036/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0302 - accuracy: 0.9870 - val_loss: 0.0868 - val_accuracy: 0.9655
Epoch 1037/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.0986 - val_accuracy: 0.9655
Epoch 1038/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0142 - accuracy: 0.9957 - val_loss: 0.1104 - val_accuracy: 0.9655
Epoch 1039/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0184 - accuracy: 0.9957 - val_loss: 0.1220 - val_accuracy: 0.9655
Epoch 1040/3000
2/2 [==============================] - 0s 46ms/step - loss: 0.0101 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9655
Epoch 1041/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.1304 - val_accuracy: 0.9655
Epoch 1042/3000
2/2 [==============================] - 0s 52ms/step - loss: 0.0288 - accuracy: 0.9870 - val_loss: 0.1225 - val_accuracy: 0.9655
Epoch 1043/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0290 - accuracy: 0.9913 - val_loss: 0.1051 - val_accuracy: 0.9655
Epoch 1044/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0125 - accuracy: 0.9957 - val_loss: 0.0864 - val_accuracy: 0.9655
Epoch 1045/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0322 - accuracy: 0.9913 - val_loss: 0.0693 - val_accuracy: 0.9655
Epoch 1046/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0262 - accuracy: 0.9913 - val_loss: 0.0630 - val_accuracy: 0.9655
Epoch 1047/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0315 - accuracy: 0.9826 - val_loss: 0.0632 - val_accuracy: 0.9655
Epoch 1048/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0228 - accuracy: 0.9870 - val_loss: 0.0661 - val_accuracy: 0.9655
Epoch 1049/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.0704 - val_accuracy: 0.9655
Epoch 1050/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0164 - accuracy: 0.9957 - val_loss: 0.0737 - val_accuracy: 0.9655
Epoch 1051/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0134 - accuracy: 0.9957 - val_loss: 0.0733 - val_accuracy: 0.9655
Epoch 1052/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.0721 - val_accuracy: 0.9655
Epoch 1053/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.0720 - val_accuracy: 0.9655
Epoch 1054/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0202 - accuracy: 0.9913 - val_loss: 0.0752 - val_accuracy: 0.9655
Epoch 1055/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0113 - accuracy: 0.9957 - val_loss: 0.0762 - val_accuracy: 0.9655
Epoch 1056/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.0769 - val_accuracy: 0.9655
Epoch 1057/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0347 - accuracy: 0.9957 - val_loss: 0.0754 - val_accuracy: 0.9655
Epoch 1058/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0108 - accuracy: 1.0000 - val_loss: 0.0687 - val_accuracy: 0.9655
Epoch 1059/3000
2/2 [==============================] - 0s 45ms/step - loss: 0.0154 - accuracy: 0.9913 - val_loss: 0.0714 - val_accuracy: 0.9655
Epoch 1060/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0173 - accuracy: 0.9957 - val_loss: 0.0748 - val_accuracy: 0.9655
Epoch 1061/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0139 - accuracy: 0.9957 - val_loss: 0.0812 - val_accuracy: 0.9655
Epoch 1062/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0148 - accuracy: 0.9957 - val_loss: 0.0819 - val_accuracy: 0.9655
Epoch 1063/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.0824 - val_accuracy: 0.9655
Epoch 1064/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0187 - accuracy: 0.9913 - val_loss: 0.0887 - val_accuracy: 0.9655
Epoch 1065/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0135 - accuracy: 0.9957 - val_loss: 0.0950 - val_accuracy: 0.9655
Epoch 1066/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0076 - accuracy: 0.9957 - val_loss: 0.0992 - val_accuracy: 0.9655
Epoch 1067/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0190 - accuracy: 0.9913 - val_loss: 0.0977 - val_accuracy: 0.9655
Epoch 1068/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9655
Epoch 1069/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0185 - accuracy: 0.9913 - val_loss: 0.0973 - val_accuracy: 0.9655
Epoch 1070/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0095 - accuracy: 0.9957 - val_loss: 0.0933 - val_accuracy: 0.9655
Epoch 1071/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0107 - accuracy: 0.9957 - val_loss: 0.0858 - val_accuracy: 0.9655
Epoch 1072/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0147 - accuracy: 0.9957 - val_loss: 0.0748 - val_accuracy: 0.9655
Epoch 1073/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0162 - accuracy: 0.9957 - val_loss: 0.0677 - val_accuracy: 0.9655
Epoch 1074/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0116 - accuracy: 0.9957 - val_loss: 0.0597 - val_accuracy: 0.9655
Epoch 1075/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.0550 - val_accuracy: 0.9655
Epoch 1076/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0149 - accuracy: 0.9957 - val_loss: 0.0532 - val_accuracy: 0.9655
Epoch 1077/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0333 - accuracy: 0.9913 - val_loss: 0.0516 - val_accuracy: 0.9655
Epoch 1078/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.0514 - val_accuracy: 0.9655
Epoch 1079/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0094 - accuracy: 0.9957 - val_loss: 0.0529 - val_accuracy: 0.9655
Epoch 1080/3000
2/2 [==============================] - 0s 45ms/step - loss: 0.0118 - accuracy: 0.9957 - val_loss: 0.0570 - val_accuracy: 0.9655
Epoch 1081/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0234 - accuracy: 0.9913 - val_loss: 0.0603 - val_accuracy: 0.9655
Epoch 1082/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.0657 - val_accuracy: 0.9655
Epoch 1083/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9655
Epoch 1084/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0195 - accuracy: 0.9913 - val_loss: 0.0852 - val_accuracy: 0.9655
Epoch 1085/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9655
Epoch 1086/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0506 - accuracy: 0.9870 - val_loss: 0.0847 - val_accuracy: 0.9655
Epoch 1087/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0121 - accuracy: 1.0000 - val_loss: 0.0700 - val_accuracy: 0.9655
Epoch 1088/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0130 - accuracy: 0.9957 - val_loss: 0.0584 - val_accuracy: 0.9655
Epoch 1089/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.0510 - val_accuracy: 0.9655
Epoch 1090/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.0467 - val_accuracy: 0.9655
Epoch 1091/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0091 - accuracy: 1.0000 - val_loss: 0.0452 - val_accuracy: 0.9828
Epoch 1092/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0424 - accuracy: 0.9870 - val_loss: 0.0453 - val_accuracy: 0.9828
Epoch 1093/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.0464 - val_accuracy: 0.9655
Epoch 1094/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0170 - accuracy: 0.9870 - val_loss: 0.0475 - val_accuracy: 0.9655
Epoch 1095/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0158 - accuracy: 0.9957 - val_loss: 0.0510 - val_accuracy: 0.9655
Epoch 1096/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0116 - accuracy: 0.9957 - val_loss: 0.0546 - val_accuracy: 0.9655
Epoch 1097/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.0583 - val_accuracy: 0.9655
Epoch 1098/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0168 - accuracy: 0.9913 - val_loss: 0.0629 - val_accuracy: 0.9655
Epoch 1099/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0116 - accuracy: 0.9957 - val_loss: 0.0645 - val_accuracy: 0.9655
Epoch 1100/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0101 - accuracy: 0.9957 - val_loss: 0.0637 - val_accuracy: 0.9655
Epoch 1101/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0625 - val_accuracy: 0.9655
Epoch 1102/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0056 - accuracy: 0.9957 - val_loss: 0.0605 - val_accuracy: 0.9655
Epoch 1103/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.0594 - val_accuracy: 0.9655
Epoch 1104/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0245 - accuracy: 0.9913 - val_loss: 0.0568 - val_accuracy: 0.9655
Epoch 1105/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0304 - accuracy: 0.9913 - val_loss: 0.0528 - val_accuracy: 0.9655
Epoch 1106/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0215 - accuracy: 0.9957 - val_loss: 0.0489 - val_accuracy: 0.9655
Epoch 1107/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0247 - accuracy: 0.9913 - val_loss: 0.0460 - val_accuracy: 0.9828
Epoch 1108/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0146 - accuracy: 0.9957 - val_loss: 0.0459 - val_accuracy: 0.9828
Epoch 1109/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0122 - accuracy: 0.9957 - val_loss: 0.0473 - val_accuracy: 0.9828
Epoch 1110/3000
2/2 [==============================] - 0s 60ms/step - loss: 0.0167 - accuracy: 0.9957 - val_loss: 0.0489 - val_accuracy: 0.9828
Epoch 1111/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.0503 - val_accuracy: 0.9828
Epoch 1112/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0090 - accuracy: 0.9957 - val_loss: 0.0522 - val_accuracy: 0.9828
Epoch 1113/3000
2/2 [==============================] - 0s 58ms/step - loss: 0.0128 - accuracy: 0.9957 - val_loss: 0.0549 - val_accuracy: 0.9828
Epoch 1114/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0176 - accuracy: 0.9913 - val_loss: 0.0582 - val_accuracy: 0.9828
Epoch 1115/3000
2/2 [==============================] - 0s 55ms/step - loss: 0.0213 - accuracy: 0.9913 - val_loss: 0.0593 - val_accuracy: 0.9828
Epoch 1116/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0099 - accuracy: 0.9957 - val_loss: 0.0605 - val_accuracy: 0.9828
Epoch 1117/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0356 - accuracy: 0.9957 - val_loss: 0.0638 - val_accuracy: 0.9655
Epoch 1118/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0163 - accuracy: 0.9957 - val_loss: 0.0659 - val_accuracy: 0.9655
Epoch 1119/3000
2/2 [==============================] - 0s 50ms/step - loss: 0.0123 - accuracy: 0.9957 - val_loss: 0.0659 - val_accuracy: 0.9655
Epoch 1120/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0230 - accuracy: 0.9957 - val_loss: 0.0636 - val_accuracy: 0.9655
Epoch 1121/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0301 - accuracy: 0.9913 - val_loss: 0.0597 - val_accuracy: 0.9655
Epoch 1122/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0274 - accuracy: 0.9913 - val_loss: 0.0559 - val_accuracy: 0.9655
Epoch 1123/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0165 - accuracy: 0.9957 - val_loss: 0.0554 - val_accuracy: 0.9655
Epoch 1124/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0232 - accuracy: 0.9913 - val_loss: 0.0583 - val_accuracy: 0.9655
Epoch 1125/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0122 - accuracy: 0.9957 - val_loss: 0.0587 - val_accuracy: 0.9655
Epoch 1126/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0256 - accuracy: 0.9957 - val_loss: 0.0613 - val_accuracy: 0.9655
Epoch 1127/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0243 - accuracy: 0.9957 - val_loss: 0.0613 - val_accuracy: 0.9655
Epoch 1128/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.0603 - val_accuracy: 0.9655
Epoch 1129/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.0617 - val_accuracy: 0.9655
Epoch 1130/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0103 - accuracy: 0.9957 - val_loss: 0.0634 - val_accuracy: 0.9655
Epoch 1131/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.0659 - val_accuracy: 0.9655
Epoch 1132/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0183 - accuracy: 0.9913 - val_loss: 0.0650 - val_accuracy: 0.9655
Epoch 1133/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0635 - val_accuracy: 0.9655
Epoch 1134/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0157 - accuracy: 0.9957 - val_loss: 0.0631 - val_accuracy: 0.9655
Epoch 1135/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.0641 - val_accuracy: 0.9655
Epoch 1136/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.0649 - val_accuracy: 0.9655
Epoch 1137/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.0653 - val_accuracy: 0.9655
Epoch 1138/3000
2/2 [==============================] - 0s 33ms/step - loss: 0.0081 - accuracy: 0.9957 - val_loss: 0.0665 - val_accuracy: 0.9655
Epoch 1139/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.0678 - val_accuracy: 0.9655
Epoch 1140/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0271 - accuracy: 0.9870 - val_loss: 0.0756 - val_accuracy: 0.9655
Epoch 1141/3000
2/2 [==============================] - 0s 56ms/step - loss: 0.0208 - accuracy: 0.9957 - val_loss: 0.0853 - val_accuracy: 0.9655
Epoch 1142/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0121 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9655
Epoch 1143/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1013 - val_accuracy: 0.9655
Epoch 1144/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0227 - accuracy: 0.9913 - val_loss: 0.1029 - val_accuracy: 0.9655
Epoch 1145/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.0994 - val_accuracy: 0.9655
Epoch 1146/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0238 - accuracy: 0.9913 - val_loss: 0.0864 - val_accuracy: 0.9655
Epoch 1147/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0068 - accuracy: 0.9957 - val_loss: 0.0746 - val_accuracy: 0.9655
Epoch 1148/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0148 - accuracy: 0.9913 - val_loss: 0.0621 - val_accuracy: 0.9655
Epoch 1149/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0419 - accuracy: 0.9957 - val_loss: 0.0551 - val_accuracy: 0.9655
Epoch 1150/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0154 - accuracy: 0.9913 - val_loss: 0.0517 - val_accuracy: 0.9655
Epoch 1151/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0270 - accuracy: 0.9913 - val_loss: 0.0518 - val_accuracy: 0.9655
Epoch 1152/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0158 - accuracy: 0.9957 - val_loss: 0.0555 - val_accuracy: 0.9655
Epoch 1153/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.0610 - val_accuracy: 0.9655
Epoch 1154/3000
2/2 [==============================] - 0s 44ms/step - loss: 0.0189 - accuracy: 0.9957 - val_loss: 0.0719 - val_accuracy: 0.9655
Epoch 1155/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0149 - accuracy: 0.9957 - val_loss: 0.0785 - val_accuracy: 0.9655
Epoch 1156/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0092 - accuracy: 0.9957 - val_loss: 0.0814 - val_accuracy: 0.9655
Epoch 1157/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0176 - accuracy: 0.9957 - val_loss: 0.0787 - val_accuracy: 0.9655
Epoch 1158/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9655
Epoch 1159/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0296 - accuracy: 0.9913 - val_loss: 0.0646 - val_accuracy: 0.9655
Epoch 1160/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.0552 - val_accuracy: 0.9655
Epoch 1161/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0123 - accuracy: 0.9957 - val_loss: 0.0522 - val_accuracy: 0.9655
Epoch 1162/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0108 - accuracy: 0.9957 - val_loss: 0.0519 - val_accuracy: 0.9655
Epoch 1163/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0133 - accuracy: 0.9957 - val_loss: 0.0549 - val_accuracy: 0.9655
Epoch 1164/3000
2/2 [==============================] - 0s 41ms/step - loss: 0.0176 - accuracy: 0.9913 - val_loss: 0.0591 - val_accuracy: 0.9655
Epoch 1165/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0482 - accuracy: 0.9957 - val_loss: 0.0678 - val_accuracy: 0.9655
Epoch 1166/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9655
Epoch 1167/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0108 - accuracy: 0.9957 - val_loss: 0.0801 - val_accuracy: 0.9655
Epoch 1168/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0108 - accuracy: 1.0000 - val_loss: 0.0844 - val_accuracy: 0.9655
Epoch 1169/3000
2/2 [==============================] - 0s 46ms/step - loss: 0.0099 - accuracy: 0.9957 - val_loss: 0.0885 - val_accuracy: 0.9655
Epoch 1170/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0106 - accuracy: 0.9957 - val_loss: 0.0872 - val_accuracy: 0.9655
Epoch 1171/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9655
Epoch 1172/3000
2/2 [==============================] - 0s 43ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9655
Epoch 1173/3000
2/2 [==============================] - 0s 48ms/step - loss: 0.0240 - accuracy: 0.9913 - val_loss: 0.0684 - val_accuracy: 0.9655
Epoch 1174/3000
2/2 [==============================] - 0s 32ms/step - loss: 0.0122 - accuracy: 0.9957 - val_loss: 0.0593 - val_accuracy: 0.9655
Epoch 1175/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0524 - val_accuracy: 0.9655
Epoch 1176/3000
2/2 [==============================] - 0s 39ms/step - loss: 0.0131 - accuracy: 0.9913 - val_loss: 0.0482 - val_accuracy: 0.9828
Epoch 1177/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0459 - val_accuracy: 0.9828
Epoch 1178/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0336 - accuracy: 0.9870 - val_loss: 0.0484 - val_accuracy: 0.9828
Epoch 1179/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0242 - accuracy: 0.9913 - val_loss: 0.0558 - val_accuracy: 0.9655
Epoch 1180/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.0635 - val_accuracy: 0.9655
Epoch 1181/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.0704 - val_accuracy: 0.9655
Epoch 1182/3000
2/2 [==============================] - 0s 36ms/step - loss: 0.0149 - accuracy: 0.9957 - val_loss: 0.0760 - val_accuracy: 0.9655
Epoch 1183/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0094 - accuracy: 0.9957 - val_loss: 0.0834 - val_accuracy: 0.9655
Epoch 1184/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0087 - accuracy: 0.9957 - val_loss: 0.0851 - val_accuracy: 0.9655
Epoch 1185/3000
2/2 [==============================] - 0s 42ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0767 - val_accuracy: 0.9655
Epoch 1186/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.0692 - val_accuracy: 0.9655
Epoch 1187/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0201 - accuracy: 0.9957 - val_loss: 0.0585 - val_accuracy: 0.9655
Epoch 1188/3000
2/2 [==============================] - 0s 47ms/step - loss: 0.0166 - accuracy: 0.9870 - val_loss: 0.0492 - val_accuracy: 0.9655
Epoch 1189/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0163 - accuracy: 0.9913 - val_loss: 0.0444 - val_accuracy: 0.9828
Epoch 1190/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0341 - accuracy: 0.9913 - val_loss: 0.0441 - val_accuracy: 0.9828
Epoch 1191/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0127 - accuracy: 0.9957 - val_loss: 0.0500 - val_accuracy: 0.9655
Epoch 1192/3000
2/2 [==============================] - 0s 34ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0562 - val_accuracy: 0.9655
Epoch 1193/3000
2/2 [==============================] - 0s 35ms/step - loss: 0.0101 - accuracy: 0.9957 - val_loss: 0.0605 - val_accuracy: 0.9655
Epoch 1194/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0257 - accuracy: 0.9913 - val_loss: 0.0549 - val_accuracy: 0.9655
Epoch 1195/3000
2/2 [==============================] - 0s 40ms/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.0480 - val_accuracy: 0.9828
Epoch 1196/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.0440 - val_accuracy: 0.9828
Epoch 1197/3000
2/2 [==============================] - 0s 37ms/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.0424 - val_accuracy: 0.9828
Epoch 1198/3000
2/2 [==============================] - 0s 38ms/step - loss: 0.0503 - accuracy: 0.9870 - val_lo
In [ ]:
#학습 진행사항을 plt로 출력
# hist4의 accuracy plt의 plot을 이용하여 출력
plt.plot(hist4.history['accuracy'], label='accuracy')
plt.plot(hist4.history['loss'], label='loss')
plt.plot(hist4.history['val_accuracy'], label='val_accuracy')
plt.plot(hist4.history['val_loss'], label='val_loss')
plt.ylim(0.0, 1.0)
plt.legend(loc='upper left')
plt.show()
7.4.모델 평가하기
In [ ]:
# 3번 모델 평가하기
model3.evaluate(test_sound2.reshape(-1,40,65,1), test_labels2, batch_size=32)
2/2 [==============================] - 0s 96ms/step - loss: 0.0756 - accuracy: 0.9828
Out[ ]:
[0.07556033879518509, 0.982758641242981]
In [ ]:
# 4번 모델 평가하기
model4.evaluate(test_sound2.reshape(-1,40,65,1), test_labels2, batch_size=32)
2/2 [==============================] - 0s 62ms/step - loss: 0.1305 - accuracy: 0.9655
Out[ ]:
[0.130483940243721, 0.9655172228813171]
In [ ]:
# 학습된 모델에 넣고 예측하기
yun_result5 = model3.predict(test_yun)
yun_result6 = model4.predict(test_yun)
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 17ms/step
In [ ]:
print("1번 모델의 결과:", yun_result5)
print("2번 모델의 결과:", yun_result6)
1번 모델의 결과: [[1.000000e+00 7.845508e-21]]
2번 모델의 결과: [[0.00762401 0.992376 ]]
In [ ]:
# 시각화
plt.subplot(2,1,1)
plt.bar(range(2), yun_result5[0])
plt.subplot(2,1,2)
plt.bar(range(2), yun_result6[0])
plt.show()
print("날리면이면 0, 바이든이면1") #여기서는 클래스가 2개
# labels_dic = { 0:'nali', 1:'biden' }
날리면이면 0, 바이든이면1
