Coronavirus disease 2019 (COVID-19) is defined as illness caused by a novel coronavirus now called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; formerly called 2019-nCoV), which was first identified amid an outbreak of respiratory illness cases in Wuhan City, Hubei Province, China. It was initially reported to the WHO on December 31, 2019. On January 30, 2020, the WHO declared the COVID-19 outbreak a global health emergency. On March 11, 2020, the WHO declared COVID-19 a global pandemic, its first such designation since declaring H1N1 influenza a pandemic in 2009.
ResNet-50 is a convolutional neural network that is 50 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals, ResNet is the short name for residual Network.
👪 팀명: Are you still there?
- 김대영
- 지우근
(1) 데이터 로드
(2) 데이터 정보 시각화
(3) 데이터 전처리 (정규화)
(4) train, validation set 구분
(1) Resnet50 Transfer Learning
(2) 손실함수, 옵티마이저, 학습률, 학습 스케쥴러 설정
(1) 경진대회 아닌 경우 : 평가 (정답이 있음)
(2) 경진대회인 경우 : 예측 및 제출(캐글에서 평가받을 수 있음)
(1) 예측 결과 확인 (Confusion Matrix, Plot)
(2) Test Image 시험
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import cv2
import os
from tqdm import tqdm
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from keras.utils.np_utils import to_categorical
from keras.models import Model,Sequential, Input, load_model
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, AveragePooling2D, GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
disease_types=['COVID', 'non-COVID']
data_dir = '../input/sarscov2-ctscan-dataset'
train_dir = os.path.join(data_dir)
train_data = []
for defects_id, sp in enumerate(disease_types):
for file in os.listdir(os.path.join(train_dir, sp)):
train_data.append(['{}/{}'.format(sp, file), defects_id, sp])
train = pd.DataFrame(train_data, columns=['File', 'DiseaseID','Disease Type'])
train.head()
| File | DiseaseID | Disease Type | |
|---|---|---|---|
| 0 | COVID/Covid (230).png | 0 | COVID |
| 1 | COVID/Covid (1195).png | 0 | COVID |
| 2 | COVID/Covid (182).png | 0 | COVID |
| 3 | COVID/Covid (817).png | 0 | COVID |
| 4 | COVID/Covid (631).png | 0 | COVID |
SEED = 42
train = train.sample(frac=1, random_state=SEED)
train.index = np.arange(len(train)) # Reset indices
train.head()
| File | DiseaseID | Disease Type | |
|---|---|---|---|
| 0 | COVID/Covid (54).png | 0 | COVID |
| 1 | COVID/Covid (1035).png | 0 | COVID |
| 2 | non-COVID/Non-Covid (21).png 1 | non-COVID | |
| 3 | non-COVID/Non-Covid (248).png | 1 | non-COVID |
| 4 | COVID/Covid (409).png | 0 | COVID |
import pandas_profiling as pp
pp.ProfileReport(train)
Details about Dataset
plt.hist(train['DiseaseID'])
plt.title('Frequency Histogram of Species')
plt.figure(figsize=(12, 12))
plt.show()
def plot_defects(defect_types, rows, cols):
fig, ax = plt.subplots(rows, cols, figsize=(12, 12))
defect_files = train['File'][train['Disease Type'] == defect_types].values
n = 0
for i in range(rows):
for j in range(cols):
image_path = os.path.join(data_dir, defect_files[n])
ax[i, j].set_xticks([])
ax[i, j].set_yticks([])
ax[i, j].imshow(cv2.imread(image_path))
n += 1
# Displays first n images of class from training set
plot_defects('COVID', 3, 3)
def plot_defects(defect_types, rows, cols):
fig, ax = plt.subplots(rows, cols, figsize=(12, 12))
defect_files = train['File'][train['Disease Type'] == defect_types].values
n = 0
for i in range(rows):
for j in range(cols):
image_path = os.path.join(data_dir, defect_files[n])
ax[i, j].set_xticks([])
ax[i, j].set_yticks([])
ax[i, j].imshow(cv2.imread(image_path))
n += 1
# Displays first n images of class from training set
plot_defects('non-COVID', 3, 3)
IMAGE_SIZE = 64
def read_image(filepath):
return cv2.imread(os.path.join(data_dir, filepath)) # Loading a color image is the default flag
# Resize image to target size
def resize_image(image, image_size):
return cv2.resize(image.copy(), image_size, interpolation=cv2.INTER_AREA)
X_train = np.zeros((train.shape[0], IMAGE_SIZE, IMAGE_SIZE, 3))
for i, file in tqdm(enumerate(train['File'].values)):
image = read_image(file)
if image is not None:
X_train[i] = resize_image(image, (IMAGE_SIZE, IMAGE_SIZE))
# Normalize the data
X_Train = X_train / 255.
print('Train Shape: {}'.format(X_Train.shape))
2481it [00:30, 82.16it/s]
Train Shape: (2481, 64, 64, 3)
Y_train = train['DiseaseID'].values
Y_train = to_categorical(Y_train, num_classes=2)
print(Y_train.shape)
(2481, 2)
BATCH_SIZE = 64
# Split the train and validation sets
X_train, X_val, Y_train, Y_val = train_test_split(X_Train, Y_train, test_size=0.2, random_state=SEED)
print(f'X_train:',X_train.shape)
print(f'X_val:',X_val.shape)
print(f'Y_train:',Y_train.shape)
print(f'Y_val:',Y_val.shape)
X_train: (1984, 64, 64, 3)
X_val: (497, 64, 64, 3)
Y_train: (1984, 2)
Y_val: (497, 2)
fig, ax = plt.subplots(1, 3, figsize=(15, 15))
for i in range(3):
ax[i].set_axis_off()
ax[i].imshow(X_train[i])
ax[i].set_title(disease_types[np.argmax(Y_train[i])])
EPOCHS = 100
SIZE=64
N_ch=3
def build_resnet50():
resnet50 = ResNet50(weights='imagenet', include_top=False)
input = Input(shape=(SIZE, SIZE, N_ch))
x = Conv2D(3, (3, 3), padding='same')(input)
x = resnet50(x)
x = GlobalAveragePooling2D()(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Dense(256, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
# multi output
output = Dense(2,activation = 'softmax', name='root')(x)
# model
model = Model(input,output)
optimizer = Adam(lr=0.003, beta_1=0.9, beta_2=0.999, epsilon=0.1, decay=0.0)
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
model.summary()
return model
model = build_resnet50()
annealer = ReduceLROnPlateau(monitor='val_accuracy', factor=0.70, patience=5, verbose=1, min_lr=1e-4)
checkpoint = ModelCheckpoint('model.h5', verbose=1, save_best_only=True)
# Generates batches of image data with data augmentation
datagen = ImageDataGenerator(rotation_range=360, # Degree range for random rotations
width_shift_range=0.2, # Range for random horizontal shifts
height_shift_range=0.2, # Range for random vertical shifts
zoom_range=0.2, # Range for random zoom
horizontal_flip=True, # Randomly flip inputs horizontally
vertical_flip=True) # Randomly flip inputs vertically
datagen.fit(X_train)
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94773248/94765736 [==============================] - 0s 0us/step
Model: "functional_1"
from tensorflow.keras.utils import plot_model
from IPython.display import Image
plot_model(model, to_file='convnet.png', show_shapes=True,show_layer_names=True)
Image(filename='convnet.png')
# Fits the model on batches with real-time data augmentation
hist = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=BATCH_SIZE),
steps_per_epoch=X_train.shape[0] // BATCH_SIZE,
epochs=EPOCHS,
verbose=1,
callbacks=[annealer, checkpoint],
validation_data=(X_val, Y_val))
Epoch 1/100 31/31 [==============================] - ETA: 0s - loss: 1.1960 - accuracy: 0.5383 Epoch 00001: val_loss improved from inf to 4.50768, saving model to model.h5 31/31 [==============================] - 5s 176ms/step - loss: 1.1960 - accuracy: 0.5383 - val_loss: 4.5077 - val_accuracy: 0.4487
Epoch 2/100 31/31 [==============================] - ETA: 0s - loss: 1.0704 - accuracy: 0.5827 Epoch 00002: val_loss did not improve from 4.50768 31/31 [==============================] - 3s 96ms/step - loss: 1.0704 - accuracy: 0.5827 - val_loss: 9.4418 - val_accuracy: 0.4487
Epoch 3/100 31/31 [==============================] - ETA: 0s - loss: 0.9108 - accuracy: 0.6195 Epoch 00003: val_loss did not improve from 4.50768 31/31 [==============================] - 3s 98ms/step - loss: 0.9108 - accuracy: 0.6195 - val_loss: 5.3648 - val_accuracy: 0.4487
Epoch 4/100 31/31 [==============================] - ETA: 0s - loss: 0.8254 - accuracy: 0.6517 Epoch 00004: val_loss did not improve from 4.50768 31/31 [==============================] - 4s 129ms/step - loss: 0.8254 - accuracy: 0.6517 - val_loss: 18.8762 - val_accuracy: 0.4487
Epoch 5/100 31/31 [==============================] - ETA: 0s - loss: 0.7385 - accuracy: 0.6825 Epoch 00005: val_loss did not improve from 4.50768 31/31 [==============================] - 3s 95ms/step - loss: 0.7385 - accuracy: 0.6825 - val_loss: 16.6468 - val_accuracy: 0.4487
Epoch 6/100 31/31 [==============================] - ETA: 0s - loss: 0.6954 - accuracy: 0.6749 Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.002100000018253922.
Epoch 00006: val_loss did not improve from 4.50768 31/31 [==============================] - 3s 97ms/step - loss: 0.6954 - accuracy: 0.6749 - val_loss: 14.8384 - val_accuracy: 0.4487
Epoch 7/100 31/31 [==============================] - ETA: 0s - loss: 0.5987 - accuracy: 0.7374 Epoch 00007: val_loss did not improve from 4.50768 31/31 [==============================] - 3s 106ms/step - loss: 0.5987 - accuracy: 0.7374 - val_loss: 6.9381 - val_accuracy: 0.4487
Epoch 8/100 31/31 [==============================] - ETA: 0s - loss: 0.5406 - accuracy: 0.7510 Epoch 00008: val_loss did not improve from 4.50768 31/31 [==============================] - 3s 97ms/step - loss: 0.5406 - accuracy: 0.7510 - val_loss: 20.0613 - val_accuracy: 0.4487
Epoch 9/100 31/31 [==============================] - ETA: 0s - loss: 0.5255 - accuracy: 0.7641 Epoch 00009: val_loss improved from 4.50768 to 1.45244, saving model to model.h5 31/31 [==============================] - 4s 135ms/step - loss: 0.5255 - accuracy: 0.7641 - val_loss: 1.4524 - val_accuracy: 0.5513
Epoch 10/100 31/31 [==============================] - ETA: 0s - loss: 0.5246 - accuracy: 0.7671 Epoch 00010: val_loss did not improve from 1.45244 31/31 [==============================] - 3s 104ms/step - loss: 0.5246 - accuracy: 0.7671 - val_loss: 9.2468 - val_accuracy: 0.4487
Epoch 11/100 31/31 [==============================] - ETA: 0s - loss: 0.4565 - accuracy: 0.7994 Epoch 00011: val_loss did not improve from 1.45244 31/31 [==============================] - 3s 98ms/step - loss: 0.4565 - accuracy: 0.7994 - val_loss: 18.6173 - val_accuracy: 0.4487
Epoch 12/100 31/31 [==============================] - ETA: 0s - loss: 0.4693 - accuracy: 0.8014 Epoch 00012: val_loss did not improve from 1.45244 31/31 [==============================] - 3s 95ms/step - loss: 0.4693 - accuracy: 0.8014 - val_loss: 23.2059 - val_accuracy: 0.4487
Epoch 13/100 31/31 [==============================] - ETA: 0s - loss: 0.4297 - accuracy: 0.8165 Epoch 00013: val_loss did not improve from 1.45244 31/31 [==============================] - 3s 97ms/step - loss: 0.4297 - accuracy: 0.8165 - val_loss: 2.5663 - val_accuracy: 0.4507
Epoch 14/100 31/31 [==============================] - ETA: 0s - loss: 0.4311 - accuracy: 0.8065 Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.0014699999475851653.
Epoch 00014: val_loss did not improve from 1.45244 31/31 [==============================] - 4s 119ms/step - loss: 0.4311 - accuracy: 0.8065 - val_loss: 6.5573 - val_accuracy: 0.4487
Epoch 15/100 31/31 [==============================] - ETA: 0s - loss: 0.4150 - accuracy: 0.8201 Epoch 00015: val_loss did not improve from 1.45244 31/31 [==============================] - 3s 96ms/step - loss: 0.4150 - accuracy: 0.8201 - val_loss: 3.2344 - val_accuracy: 0.5513
Epoch 16/100 31/31 [==============================] - ETA: 0s - loss: 0.3838 - accuracy: 0.8216 Epoch 00016: val_loss improved from 1.45244 to 0.86896, saving model to model.h5 31/31 [==============================] - 4s 132ms/step - loss: 0.3838 - accuracy: 0.8216 - val_loss: 0.8690 - val_accuracy: 0.5030
Epoch 17/100 31/31 [==============================] - ETA: 0s - loss: 0.4117 - accuracy: 0.8327 Epoch 00017: val_loss did not improve from 0.86896 31/31 [==============================] - 3s 101ms/step - loss: 0.4117 - accuracy: 0.8327 - val_loss: 1.0979 - val_accuracy: 0.5513
Epoch 18/100 31/31 [==============================] - ETA: 0s - loss: 0.4132 - accuracy: 0.8276 Epoch 00018: val_loss improved from 0.86896 to 0.69180, saving model to model.h5 31/31 [==============================] - 4s 134ms/step - loss: 0.4132 - accuracy: 0.8276 - val_loss: 0.6918 - val_accuracy: 0.5533
Epoch 19/100 31/31 [==============================] - ETA: 0s - loss: 0.3775 - accuracy: 0.8311 Epoch 00019: val_loss improved from 0.69180 to 0.66311, saving model to model.h5 31/31 [==============================] - 4s 140ms/step - loss: 0.3775 - accuracy: 0.8311 - val_loss: 0.6631 - val_accuracy: 0.5775
Epoch 20/100 31/31 [==============================] - ETA: 0s - loss: 0.3969 - accuracy: 0.8327 Epoch 00020: val_loss improved from 0.66311 to 0.64736, saving model to model.h5 31/31 [==============================] - 4s 134ms/step - loss: 0.3969 - accuracy: 0.8327 - val_loss: 0.6474 - val_accuracy: 0.6016
Epoch 21/100 31/31 [==============================] - ETA: 0s - loss: 0.3692 - accuracy: 0.8493 Epoch 00021: val_loss improved from 0.64736 to 0.61495, saving model to model.h5 31/31 [==============================] - 4s 138ms/step - loss: 0.3692 - accuracy: 0.8493 - val_loss: 0.6150 - val_accuracy: 0.6761
Epoch 22/100 31/31 [==============================] - ETA: 0s - loss: 0.3691 - accuracy: 0.8453 Epoch 00022: val_loss did not improve from 0.61495 31/31 [==============================] - 4s 121ms/step - loss: 0.3691 - accuracy: 0.8453 - val_loss: 0.6224 - val_accuracy: 0.6338
Epoch 23/100 31/31 [==============================] - ETA: 0s - loss: 0.3648 - accuracy: 0.8448 Epoch 00023: val_loss improved from 0.61495 to 0.61105, saving model to model.h5 31/31 [==============================] - 4s 134ms/step - loss: 0.3648 - accuracy: 0.8448 - val_loss: 0.6110 - val_accuracy: 0.6680
Epoch 24/100 31/31 [==============================] - ETA: 0s - loss: 0.3633 - accuracy: 0.8463 Epoch 00024: val_loss did not improve from 0.61105 31/31 [==============================] - 3s 96ms/step - loss: 0.3633 - accuracy: 0.8463 - val_loss: 0.6246 - val_accuracy: 0.6338
Epoch 25/100 31/31 [==============================] - ETA: 0s - loss: 0.3526 - accuracy: 0.8438 Epoch 00025: val_loss did not improve from 0.61105 31/31 [==============================] - 3s 107ms/step - loss: 0.3526 - accuracy: 0.8438 - val_loss: 0.6163 - val_accuracy: 0.6519
Epoch 26/100 31/31 [==============================] - ETA: 0s - loss: 0.3359 - accuracy: 0.8553 Epoch 00026: val_loss improved from 0.61105 to 0.55062, saving model to model.h5 31/31 [==============================] - 4s 134ms/step - loss: 0.3359 - accuracy: 0.8553 - val_loss: 0.5506 - val_accuracy: 0.7264
Epoch 27/100 31/31 [==============================] - ETA: 0s - loss: 0.3360 - accuracy: 0.8604 Epoch 00027: val_loss improved from 0.55062 to 0.53647, saving model to model.h5 31/31 [==============================] - 5s 171ms/step - loss: 0.3360 - accuracy: 0.8604 - val_loss: 0.5365 - val_accuracy: 0.7123
Epoch 28/100 31/31 [==============================] - ETA: 0s - loss: 0.3257 - accuracy: 0.8664 Epoch 00028: val_loss improved from 0.53647 to 0.50145, saving model to model.h5 31/31 [==============================] - 4s 134ms/step - loss: 0.3257 - accuracy: 0.8664 - val_loss: 0.5015 - val_accuracy: 0.7626
Epoch 29/100 31/31 [==============================] - ETA: 0s - loss: 0.3153 - accuracy: 0.8654 Epoch 00029: val_loss improved from 0.50145 to 0.48864, saving model to model.h5 31/31 [==============================] - 4s 133ms/step - loss: 0.3153 - accuracy: 0.8654 - val_loss: 0.4886 - val_accuracy: 0.7626
Epoch 30/100 31/31 [==============================] - ETA: 0s - loss: 0.3167 - accuracy: 0.8679 Epoch 00030: val_loss improved from 0.48864 to 0.47768, saving model to model.h5 31/31 [==============================] - 4s 145ms/step - loss: 0.3167 - accuracy: 0.8679 - val_loss: 0.4777 - val_accuracy: 0.7565
Epoch 31/100 31/31 [==============================] - ETA: 0s - loss: 0.3113 - accuracy: 0.8700 Epoch 00031: val_loss improved from 0.47768 to 0.43511, saving model to model.h5 31/31 [==============================] - 4s 133ms/step - loss: 0.3113 - accuracy: 0.8700 - val_loss: 0.4351 - val_accuracy: 0.7827
Epoch 32/100 31/31 [==============================] - ETA: 0s - loss: 0.3219 - accuracy: 0.8634 Epoch 00032: val_loss improved from 0.43511 to 0.41982, saving model to model.h5 31/31 [==============================] - 5s 176ms/step - loss: 0.3219 - accuracy: 0.8634 - val_loss: 0.4198 - val_accuracy: 0.7847
Epoch 33/100 31/31 [==============================] - ETA: 0s - loss: 0.3171 - accuracy: 0.8674 Epoch 00033: val_loss did not improve from 0.41982 31/31 [==============================] - 3s 96ms/step - loss: 0.3171 - accuracy: 0.8674 - val_loss: 0.5310 - val_accuracy: 0.7304
Epoch 34/100 31/31 [==============================] - ETA: 0s - loss: 0.3015 - accuracy: 0.8705 Epoch 00034: val_loss improved from 0.41982 to 0.41760, saving model to model.h5 31/31 [==============================] - 4s 132ms/step - loss: 0.3015 - accuracy: 0.8705 - val_loss: 0.4176 - val_accuracy: 0.7787
Epoch 35/100 31/31 [==============================] - ETA: 0s - loss: 0.3247 - accuracy: 0.8659 Epoch 00035: val_loss improved from 0.41760 to 0.41057, saving model to model.h5 31/31 [==============================] - 4s 142ms/step - loss: 0.3247 - accuracy: 0.8659 - val_loss: 0.4106 - val_accuracy: 0.8008
Epoch 36/100 31/31 [==============================] - ETA: 0s - loss: 0.3005 - accuracy: 0.8730 Epoch 00036: val_loss did not improve from 0.41057 31/31 [==============================] - 3s 96ms/step - loss: 0.3005 - accuracy: 0.8730 - val_loss: 0.4865 - val_accuracy: 0.7746
Epoch 37/100 31/31 [==============================] - ETA: 0s - loss: 0.3253 - accuracy: 0.8564 Epoch 00037: val_loss improved from 0.41057 to 0.31435, saving model to model.h5 31/31 [==============================] - 4s 134ms/step - loss: 0.3253 - accuracy: 0.8564 - val_loss: 0.3143 - val_accuracy: 0.8491
Epoch 38/100 31/31 [==============================] - ETA: 0s - loss: 0.3135 - accuracy: 0.8684 Epoch 00038: val_loss improved from 0.31435 to 0.29319, saving model to model.h5 31/31 [==============================] - 5s 171ms/step - loss: 0.3135 - accuracy: 0.8684 - val_loss: 0.2932 - val_accuracy: 0.8692
Epoch 39/100 31/31 [==============================] - ETA: 0s - loss: 0.3023 - accuracy: 0.8800 Epoch 00039: val_loss did not improve from 0.29319 31/31 [==============================] - 3s 95ms/step - loss: 0.3023 - accuracy: 0.8800 - val_loss: 0.3662 - val_accuracy: 0.8169
Epoch 40/100 31/31 [==============================] - ETA: 0s - loss: 0.3011 - accuracy: 0.8770 Epoch 00040: val_loss improved from 0.29319 to 0.28929, saving model to model.h5 31/31 [==============================] - 4s 141ms/step - loss: 0.3011 - accuracy: 0.8770 - val_loss: 0.2893 - val_accuracy: 0.8712
Epoch 41/100 31/31 [==============================] - ETA: 0s - loss: 0.2955 - accuracy: 0.8821 Epoch 00041: val_loss improved from 0.28929 to 0.20949, saving model to model.h5 31/31 [==============================] - 5s 172ms/step - loss: 0.2955 - accuracy: 0.8821 - val_loss: 0.2095 - val_accuracy: 0.9195
Epoch 42/100 31/31 [==============================] - ETA: 0s - loss: 0.2872 - accuracy: 0.8821 Epoch 00042: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 95ms/step - loss: 0.2872 - accuracy: 0.8821 - val_loss: 0.3225 - val_accuracy: 0.8410
Epoch 43/100 31/31 [==============================] - ETA: 0s - loss: 0.2841 - accuracy: 0.8816 Epoch 00043: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 102ms/step - loss: 0.2841 - accuracy: 0.8816 - val_loss: 0.2451 - val_accuracy: 0.8893
Epoch 44/100 31/31 [==============================] - ETA: 0s - loss: 0.2768 - accuracy: 0.8831 Epoch 00044: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 98ms/step - loss: 0.2768 - accuracy: 0.8831 - val_loss: 0.2400 - val_accuracy: 0.8893
Epoch 45/100 31/31 [==============================] - ETA: 0s - loss: 0.2872 - accuracy: 0.8730 Epoch 00045: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 95ms/step - loss: 0.2872 - accuracy: 0.8730 - val_loss: 0.2129 - val_accuracy: 0.9074
Epoch 46/100 31/31 [==============================] - ETA: 0s - loss: 0.2698 - accuracy: 0.8967 Epoch 00046: ReduceLROnPlateau reducing learning rate to 0.0010289999307133257.
Epoch 00046: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 104ms/step - loss: 0.2698 - accuracy: 0.8967 - val_loss: 0.2646 - val_accuracy: 0.8773
Epoch 47/100 31/31 [==============================] - ETA: 0s - loss: 0.2682 - accuracy: 0.8775 Epoch 00047: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 95ms/step - loss: 0.2682 - accuracy: 0.8775 - val_loss: 0.4491 - val_accuracy: 0.8129
Epoch 48/100 31/31 [==============================] - ETA: 0s - loss: 0.2574 - accuracy: 0.8851 Epoch 00048: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 96ms/step - loss: 0.2574 - accuracy: 0.8851 - val_loss: 0.2930 - val_accuracy: 0.8732
Epoch 49/100 31/31 [==============================] - ETA: 0s - loss: 0.2746 - accuracy: 0.8876 Epoch 00049: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 101ms/step - loss: 0.2746 - accuracy: 0.8876 - val_loss: 0.2216 - val_accuracy: 0.9095
Epoch 50/100 31/31 [==============================] - ETA: 0s - loss: 0.2846 - accuracy: 0.8836 Epoch 00050: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 96ms/step - loss: 0.2846 - accuracy: 0.8836 - val_loss: 0.2179 - val_accuracy: 0.9074
Epoch 51/100 31/31 [==============================] - ETA: 0s - loss: 0.2552 - accuracy: 0.8921 Epoch 00051: ReduceLROnPlateau reducing learning rate to 0.0007202999433502554.
Epoch 00051: val_loss did not improve from 0.20949 31/31 [==============================] - 4s 114ms/step - loss: 0.2552 - accuracy: 0.8921 - val_loss: 0.5933 - val_accuracy: 0.7626
Epoch 52/100 31/31 [==============================] - ETA: 0s - loss: 0.2626 - accuracy: 0.8816 Epoch 00052: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 96ms/step - loss: 0.2626 - accuracy: 0.8816 - val_loss: 0.2280 - val_accuracy: 0.8934
Epoch 53/100 31/31 [==============================] - ETA: 0s - loss: 0.2684 - accuracy: 0.8906 Epoch 00053: val_loss did not improve from 0.20949 31/31 [==============================] - 3s 105ms/step - loss: 0.2684 - accuracy: 0.8906 - val_loss: 0.2276 - val_accuracy: 0.9074
Epoch 54/100 31/31 [==============================] - ETA: 0s - loss: 0.2703 - accuracy: 0.8805 Epoch 00054: val_loss improved from 0.20949 to 0.18132, saving model to model.h5 31/31 [==============================] - 4s 131ms/step - loss: 0.2703 - accuracy: 0.8805 - val_loss: 0.1813 - val_accuracy: 0.9235
Epoch 55/100 31/31 [==============================] - ETA: 0s - loss: 0.2603 - accuracy: 0.8926 Epoch 00055: val_loss did not improve from 0.18132 31/31 [==============================] - 3s 95ms/step - loss: 0.2603 - accuracy: 0.8926 - val_loss: 0.1863 - val_accuracy: 0.9256
Epoch 56/100 31/31 [==============================] - ETA: 0s - loss: 0.2702 - accuracy: 0.8891 Epoch 00056: val_loss did not improve from 0.18132 31/31 [==============================] - 3s 106ms/step - loss: 0.2702 - accuracy: 0.8891 - val_loss: 0.4952 - val_accuracy: 0.7928
Epoch 57/100 31/31 [==============================] - ETA: 0s - loss: 0.2564 - accuracy: 0.8926 Epoch 00057: val_loss improved from 0.18132 to 0.17801, saving model to model.h5 31/31 [==============================] - 4s 135ms/step - loss: 0.2564 - accuracy: 0.8926 - val_loss: 0.1780 - val_accuracy: 0.9276
Epoch 58/100 31/31 [==============================] - ETA: 0s - loss: 0.2778 - accuracy: 0.8856 Epoch 00058: val_loss did not improve from 0.17801 31/31 [==============================] - 3s 95ms/step - loss: 0.2778 - accuracy: 0.8856 - val_loss: 0.2364 - val_accuracy: 0.8833
Epoch 59/100 31/31 [==============================] - ETA: 0s - loss: 0.2559 - accuracy: 0.8891 Epoch 00059: val_loss did not improve from 0.17801 31/31 [==============================] - 3s 107ms/step - loss: 0.2559 - accuracy: 0.8891 - val_loss: 0.2141 - val_accuracy: 0.8974
Epoch 60/100 31/31 [==============================] - ETA: 0s - loss: 0.2584 - accuracy: 0.8891 Epoch 00060: val_loss did not improve from 0.17801 31/31 [==============================] - 4s 116ms/step - loss: 0.2584 - accuracy: 0.8891 - val_loss: 0.3432 - val_accuracy: 0.8290
Epoch 61/100 31/31 [==============================] - ETA: 0s - loss: 0.2622 - accuracy: 0.8916 Epoch 00061: val_loss did not improve from 0.17801 31/31 [==============================] - 3s 95ms/step - loss: 0.2622 - accuracy: 0.8916 - val_loss: 0.2799 - val_accuracy: 0.8692
Epoch 62/100 31/31 [==============================] - ETA: 0s - loss: 0.2483 - accuracy: 0.8947 Epoch 00062: ReduceLROnPlateau reducing learning rate to 0.0005042099684942513.
Epoch 00062: val_loss did not improve from 0.17801 31/31 [==============================] - 3s 105ms/step - loss: 0.2483 - accuracy: 0.8947 - val_loss: 0.1943 - val_accuracy: 0.9095
Epoch 63/100 31/31 [==============================] - ETA: 0s - loss: 0.2592 - accuracy: 0.8891 Epoch 00063: val_loss did not improve from 0.17801 31/31 [==============================] - 3s 96ms/step - loss: 0.2592 - accuracy: 0.8891 - val_loss: 0.1807 - val_accuracy: 0.9215
Epoch 64/100 31/31 [==============================] - ETA: 0s - loss: 0.2521 - accuracy: 0.8906 Epoch 00064: val_loss did not improve from 0.17801 31/31 [==============================] - 3s 95ms/step - loss: 0.2521 - accuracy: 0.8906 - val_loss: 0.1829 - val_accuracy: 0.9235
Epoch 65/100 31/31 [==============================] - ETA: 0s - loss: 0.2507 - accuracy: 0.8911 Epoch 00065: val_loss improved from 0.17801 to 0.17014, saving model to model.h5 31/31 [==============================] - 4s 137ms/step - loss: 0.2507 - accuracy: 0.8911 - val_loss: 0.1701 - val_accuracy: 0.9276
Epoch 66/100 31/31 [==============================] - ETA: 0s - loss: 0.2351 - accuracy: 0.9022 Epoch 00066: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 96ms/step - loss: 0.2351 - accuracy: 0.9022 - val_loss: 0.1901 - val_accuracy: 0.9155
Epoch 67/100 31/31 [==============================] - ETA: 0s - loss: 0.2603 - accuracy: 0.8896 Epoch 00067: ReduceLROnPlateau reducing learning rate to 0.0003529469657223671.
Epoch 00067: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 102ms/step - loss: 0.2603 - accuracy: 0.8896 - val_loss: 0.2167 - val_accuracy: 0.8853
Epoch 68/100 31/31 [==============================] - ETA: 0s - loss: 0.2464 - accuracy: 0.8936 Epoch 00068: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 98ms/step - loss: 0.2464 - accuracy: 0.8936 - val_loss: 0.1829 - val_accuracy: 0.9175
Epoch 69/100 31/31 [==============================] - ETA: 0s - loss: 0.2380 - accuracy: 0.8942 Epoch 00069: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 112ms/step - loss: 0.2380 - accuracy: 0.8942 - val_loss: 0.1748 - val_accuracy: 0.9256
Epoch 70/100 31/31 [==============================] - ETA: 0s - loss: 0.2429 - accuracy: 0.8997 Epoch 00070: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 99ms/step - loss: 0.2429 - accuracy: 0.8997 - val_loss: 0.1778 - val_accuracy: 0.9215
Epoch 71/100 31/31 [==============================] - ETA: 0s - loss: 0.2566 - accuracy: 0.8901 Epoch 00071: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 96ms/step - loss: 0.2566 - accuracy: 0.8901 - val_loss: 0.1713 - val_accuracy: 0.9256
Epoch 72/100 31/31 [==============================] - ETA: 0s - loss: 0.2492 - accuracy: 0.8967 Epoch 00072: ReduceLROnPlateau reducing learning rate to 0.0002470628678565845.
Epoch 00072: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 109ms/step - loss: 0.2492 - accuracy: 0.8967 - val_loss: 0.2305 - val_accuracy: 0.8793
Epoch 73/100 31/31 [==============================] - ETA: 0s - loss: 0.2436 - accuracy: 0.9007 Epoch 00073: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 96ms/step - loss: 0.2436 - accuracy: 0.9007 - val_loss: 0.1905 - val_accuracy: 0.9155
Epoch 74/100 31/31 [==============================] - ETA: 0s - loss: 0.2570 - accuracy: 0.8942 Epoch 00074: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 96ms/step - loss: 0.2570 - accuracy: 0.8942 - val_loss: 0.1711 - val_accuracy: 0.9256
Epoch 75/100 31/31 [==============================] - ETA: 0s - loss: 0.2452 - accuracy: 0.8931 Epoch 00075: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 106ms/step - loss: 0.2452 - accuracy: 0.8931 - val_loss: 0.1974 - val_accuracy: 0.9054
Epoch 76/100 31/31 [==============================] - ETA: 0s - loss: 0.2451 - accuracy: 0.8896 Epoch 00076: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 95ms/step - loss: 0.2451 - accuracy: 0.8896 - val_loss: 0.2238 - val_accuracy: 0.8853
Epoch 77/100 31/31 [==============================] - ETA: 0s - loss: 0.2416 - accuracy: 0.8952 Epoch 00077: ReduceLROnPlateau reducing learning rate to 0.00017294401768594978.
Epoch 00077: val_loss did not improve from 0.17014 31/31 [==============================] - 3s 96ms/step - loss: 0.2416 - accuracy: 0.8952 - val_loss: 0.1707 - val_accuracy: 0.9276
Epoch 78/100 31/31 [==============================] - ETA: 0s - loss: 0.2478 - accuracy: 0.8936 Epoch 00078: val_loss improved from 0.17014 to 0.16120, saving model to model.h5 31/31 [==============================] - 4s 139ms/step - loss: 0.2478 - accuracy: 0.8936 - val_loss: 0.1612 - val_accuracy: 0.9296
Epoch 79/100 31/31 [==============================] - ETA: 0s - loss: 0.2451 - accuracy: 0.8962 Epoch 00079: val_loss did not improve from 0.16120 31/31 [==============================] - 3s 96ms/step - loss: 0.2451 - accuracy: 0.8962 - val_loss: 0.1667 - val_accuracy: 0.9316
Epoch 80/100 31/31 [==============================] - ETA: 0s - loss: 0.2266 - accuracy: 0.8982 Epoch 00080: val_loss did not improve from 0.16120 31/31 [==============================] - 3s 96ms/step - loss: 0.2266 - accuracy: 0.8982 - val_loss: 0.1659 - val_accuracy: 0.9276
Epoch 81/100 31/31 [==============================] - ETA: 0s - loss: 0.2445 - accuracy: 0.9012 Epoch 00081: val_loss did not improve from 0.16120 31/31 [==============================] - 3s 96ms/step - loss: 0.2445 - accuracy: 0.9012 - val_loss: 0.1697 - val_accuracy: 0.9235
Epoch 82/100 31/31 [==============================] - ETA: 0s - loss: 0.2272 - accuracy: 0.9017 Epoch 00082: val_loss did not improve from 0.16120 31/31 [==============================] - 3s 103ms/step - loss: 0.2272 - accuracy: 0.9017 - val_loss: 0.1703 - val_accuracy: 0.9235
Epoch 83/100 31/31 [==============================] - ETA: 0s - loss: 0.2419 - accuracy: 0.8992 Epoch 00083: val_loss did not improve from 0.16120 31/31 [==============================] - 3s 113ms/step - loss: 0.2419 - accuracy: 0.8992 - val_loss: 0.1717 - val_accuracy: 0.9296
Epoch 84/100 31/31 [==============================] - ETA: 0s - loss: 0.2437 - accuracy: 0.8936 Epoch 00084: ReduceLROnPlateau reducing learning rate to 0.00012106080830562859.
Epoch 00084: val_loss did not improve from 0.16120 31/31 [==============================] - 3s 96ms/step - loss: 0.2437 - accuracy: 0.8936 - val_loss: 0.1936 - val_accuracy: 0.9054
Epoch 85/100 31/31 [==============================] - ETA: 0s - loss: 0.2483 - accuracy: 0.8936 Epoch 00085: val_loss did not improve from 0.16120 31/31 [==============================] - 3s 104ms/step - loss: 0.2483 - accuracy: 0.8936 - val_loss: 0.1758 - val_accuracy: 0.9155
Epoch 86/100 31/31 [==============================] - ETA: 0s - loss: 0.2271 - accuracy: 0.9012 Epoch 00086: val_loss improved from 0.16120 to 0.16092, saving model to model.h5 31/31 [==============================] - 4s 133ms/step - loss: 0.2271 - accuracy: 0.9012 - val_loss: 0.1609 - val_accuracy: 0.9256
Epoch 87/100 31/31 [==============================] - ETA: 0s - loss: 0.2330 - accuracy: 0.9052 Epoch 00087: val_loss did not improve from 0.16092 31/31 [==============================] - 3s 95ms/step - loss: 0.2330 - accuracy: 0.9052 - val_loss: 0.1614 - val_accuracy: 0.9276
Epoch 88/100 31/31 [==============================] - ETA: 0s - loss: 0.2531 - accuracy: 0.8891 Epoch 00088: val_loss improved from 0.16092 to 0.16062, saving model to model.h5 31/31 [==============================] - 4s 142ms/step - loss: 0.2531 - accuracy: 0.8891 - val_loss: 0.1606 - val_accuracy: 0.9296
Epoch 89/100 31/31 [==============================] - ETA: 0s - loss: 0.2369 - accuracy: 0.8982 Epoch 00089: ReduceLROnPlateau reducing learning rate to 0.0001.
Epoch 00089: val_loss improved from 0.16062 to 0.15927, saving model to model.h5 31/31 [==============================] - 5s 168ms/step - loss: 0.2369 - accuracy: 0.8982 - val_loss: 0.1593 - val_accuracy: 0.9296
Epoch 90/100 31/31 [==============================] - ETA: 0s - loss: 0.2335 - accuracy: 0.9042 Epoch 00090: val_loss improved from 0.15927 to 0.15853, saving model to model.h5 31/31 [==============================] - 5s 145ms/step - loss: 0.2335 - accuracy: 0.9042 - val_loss: 0.1585 - val_accuracy: 0.9276
Epoch 91/100 31/31 [==============================] - ETA: 0s - loss: 0.2274 - accuracy: 0.9083 Epoch 00091: val_loss did not improve from 0.15853 31/31 [==============================] - 4s 117ms/step - loss: 0.2274 - accuracy: 0.9083 - val_loss: 0.1655 - val_accuracy: 0.9235
Epoch 92/100 31/31 [==============================] - ETA: 0s - loss: 0.2346 - accuracy: 0.9052 Epoch 00092: val_loss improved from 0.15853 to 0.15741, saving model to model.h5 31/31 [==============================] - 4s 135ms/step - loss: 0.2346 - accuracy: 0.9052 - val_loss: 0.1574 - val_accuracy: 0.9256
Epoch 93/100 31/31 [==============================] - ETA: 0s - loss: 0.2390 - accuracy: 0.9027 Epoch 00093: val_loss improved from 0.15741 to 0.15642, saving model to model.h5 31/31 [==============================] - 5s 167ms/step - loss: 0.2390 - accuracy: 0.9027 - val_loss: 0.1564 - val_accuracy: 0.9276
Epoch 94/100 31/31 [==============================] - ETA: 0s - loss: 0.2491 - accuracy: 0.8962 Epoch 00094: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 96ms/step - loss: 0.2491 - accuracy: 0.8962 - val_loss: 0.1660 - val_accuracy: 0.9256
Epoch 95/100 31/31 [==============================] - ETA: 0s - loss: 0.2367 - accuracy: 0.9002 Epoch 00095: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 98ms/step - loss: 0.2367 - accuracy: 0.9002 - val_loss: 0.1700 - val_accuracy: 0.9256
Epoch 96/100 31/31 [==============================] - ETA: 0s - loss: 0.2502 - accuracy: 0.8926 Epoch 00096: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 105ms/step - loss: 0.2502 - accuracy: 0.8926 - val_loss: 0.1703 - val_accuracy: 0.9195
Epoch 97/100 31/31 [==============================] - ETA: 0s - loss: 0.2262 - accuracy: 0.9042 Epoch 00097: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 95ms/step - loss: 0.2262 - accuracy: 0.9042 - val_loss: 0.1842 - val_accuracy: 0.9074
Epoch 98/100 31/31 [==============================] - ETA: 0s - loss: 0.2206 - accuracy: 0.9057 Epoch 00098: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 95ms/step - loss: 0.2206 - accuracy: 0.9057 - val_loss: 0.1932 - val_accuracy: 0.9054
Epoch 99/100 31/31 [==============================] - ETA: 0s - loss: 0.2227 - accuracy: 0.9042 Epoch 00099: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 105ms/step - loss: 0.2227 - accuracy: 0.9042 - val_loss: 0.1845 - val_accuracy: 0.9155
Epoch 100/100 31/31 [==============================] - ETA: 0s - loss: 0.2426 - accuracy: 0.8977 Epoch 00100: val_loss did not improve from 0.15642 31/31 [==============================] - 4s 115ms/step - loss: 0.2426 - accuracy: 0.8977 - val_loss: 0.1877 - val_accuracy: 0.9115
# 불필요한 반복 (코딩 수정 중 오류)
# 한번 더 훈련시키므로 예측값이 처음부터 좋을 수 밖에 없다.
# Fits the model on batches with real-time data augmentation
hist = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=BATCH_SIZE),
steps_per_epoch=X_train.shape[0] // BATCH_SIZE,
epochs=EPOCHS,
verbose=1,
callbacks=[annealer, checkpoint],
validation_data=(X_val, Y_val))
Epoch 1/100 31/31 [==============================] - ETA: 0s - loss: 0.2345 - accuracy: 0.9042 Epoch 00001: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 97ms/step - loss: 0.2345 - accuracy: 0.9042 - val_loss: 0.1807 - val_accuracy: 0.9195
Epoch 2/100 31/31 [==============================] - ETA: 0s - loss: 0.2437 - accuracy: 0.8906 Epoch 00002: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 106ms/step - loss: 0.2437 - accuracy: 0.8906 - val_loss: 0.1679 - val_accuracy: 0.9195
Epoch 3/100 31/31 [==============================] - ETA: 0s - loss: 0.2300 - accuracy: 0.9037 Epoch 00003: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 96ms/step - loss: 0.2300 - accuracy: 0.9037 - val_loss: 0.1589 - val_accuracy: 0.9316
Epoch 4/100 31/31 [==============================] - ETA: 0s - loss: 0.2393 - accuracy: 0.8967 Epoch 00004: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 96ms/step - loss: 0.2393 - accuracy: 0.8967 - val_loss: 0.1585 - val_accuracy: 0.9276
Epoch 5/100 31/31 [==============================] - ETA: 0s - loss: 0.2144 - accuracy: 0.9113 Epoch 00005: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 96ms/step - loss: 0.2144 - accuracy: 0.9113 - val_loss: 0.1685 - val_accuracy: 0.9235
Epoch 6/100 31/31 [==============================] - ETA: 0s - loss: 0.2415 - accuracy: 0.8947 Epoch 00006: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 105ms/step - loss: 0.2415 - accuracy: 0.8947 - val_loss: 0.1683 - val_accuracy: 0.9235
Epoch 7/100 31/31 [==============================] - ETA: 0s - loss: 0.2365 - accuracy: 0.9012 Epoch 00007: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 95ms/step - loss: 0.2365 - accuracy: 0.9012 - val_loss: 0.1572 - val_accuracy: 0.9276
Epoch 8/100 31/31 [==============================] - ETA: 0s - loss: 0.2295 - accuracy: 0.9022 Epoch 00008: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 95ms/step - loss: 0.2295 - accuracy: 0.9022 - val_loss: 0.1577 - val_accuracy: 0.9215
Epoch 9/100 31/31 [==============================] - ETA: 0s - loss: 0.2356 - accuracy: 0.9108 Epoch 00009: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 106ms/step - loss: 0.2356 - accuracy: 0.9108 - val_loss: 0.1571 - val_accuracy: 0.9235
Epoch 10/100 31/31 [==============================] - ETA: 0s - loss: 0.2145 - accuracy: 0.9047 Epoch 00010: val_loss did not improve from 0.15642 31/31 [==============================] - 4s 115ms/step - loss: 0.2145 - accuracy: 0.9047 - val_loss: 0.1572 - val_accuracy: 0.9215
Epoch 11/100 31/31 [==============================] - ETA: 0s - loss: 0.2393 - accuracy: 0.8972 Epoch 00011: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 95ms/step - loss: 0.2393 - accuracy: 0.8972 - val_loss: 0.1639 - val_accuracy: 0.9256
Epoch 12/100 31/31 [==============================] - ETA: 0s - loss: 0.2309 - accuracy: 0.9022 Epoch 00012: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 100ms/step - loss: 0.2309 - accuracy: 0.9022 - val_loss: 0.1671 - val_accuracy: 0.9155
Epoch 13/100 31/31 [==============================] - ETA: 0s - loss: 0.2392 - accuracy: 0.8977 Epoch 00013: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 96ms/step - loss: 0.2392 - accuracy: 0.8977 - val_loss: 0.1669 - val_accuracy: 0.9215
Epoch 14/100 31/31 [==============================] - ETA: 0s - loss: 0.2257 - accuracy: 0.9098 Epoch 00014: val_loss did not improve from 0.15642 31/31 [==============================] - 3s 97ms/step - loss: 0.2257 - accuracy: 0.9098 - val_loss: 0.1605 - val_accuracy: 0.9235
Epoch 15/100 31/31 [==============================] - ETA: 0s - loss: 0.2271 - accuracy: 0.9073 Epoch 00015: val_loss improved from 0.15642 to 0.15639, saving model to model.h5 31/31 [==============================] - 4s 134ms/step - loss: 0.2271 - accuracy: 0.9073 - val_loss: 0.1564 - val_accuracy: 0.9235
Epoch 16/100 31/31 [==============================] - ETA: 0s - loss: 0.2255 - accuracy: 0.9007 Epoch 00016: val_loss did not improve from 0.15639 31/31 [==============================] - 3s 100ms/step - loss: 0.2255 - accuracy: 0.9007 - val_loss: 0.1581 - val_accuracy: 0.9235
Epoch 17/100 31/31 [==============================] - ETA: 0s - loss: 0.2311 - accuracy: 0.9052 Epoch 00017: val_loss did not improve from 0.15639 31/31 [==============================] - 3s 98ms/step - loss: 0.2311 - accuracy: 0.9052 - val_loss: 0.1574 - val_accuracy: 0.9195
Epoch 18/100 31/31 [==============================] - ETA: 0s - loss: 0.2310 - accuracy: 0.9022 Epoch 00018: val_loss did not improve from 0.15639 31/31 [==============================] - 3s 97ms/step - loss: 0.2310 - accuracy: 0.9022 - val_loss: 0.1571 - val_accuracy: 0.9195
Epoch 19/100 31/31 [==============================] - ETA: 0s - loss: 0.2249 - accuracy: 0.9148 Epoch 00019: val_loss did not improve from 0.15639 31/31 [==============================] - 3s 108ms/step - loss: 0.2249 - accuracy: 0.9148 - val_loss: 0.1589 - val_accuracy: 0.9235
Epoch 20/100 31/31 [==============================] - ETA: 0s - loss: 0.2282 - accuracy: 0.9037 Epoch 00020: val_loss did not improve from 0.15639 31/31 [==============================] - 3s 112ms/step - loss: 0.2282 - accuracy: 0.9037 - val_loss: 0.1572 - val_accuracy: 0.9256
Epoch 21/100 31/31 [==============================] - ETA: 0s - loss: 0.2405 - accuracy: 0.9088 Epoch 00021: val_loss improved from 0.15639 to 0.15581, saving model to model.h5 31/31 [==============================] - 4s 132ms/step - loss: 0.2405 - accuracy: 0.9088 - val_loss: 0.1558 - val_accuracy: 0.9235
Epoch 22/100 31/31 [==============================] - ETA: 0s - loss: 0.2318 - accuracy: 0.9037 Epoch 00022: val_loss did not improve from 0.15581 31/31 [==============================] - 3s 104ms/step - loss: 0.2318 - accuracy: 0.9037 - val_loss: 0.1561 - val_accuracy: 0.9215
Epoch 23/100 31/31 [==============================] - ETA: 0s - loss: 0.2319 - accuracy: 0.8972 Epoch 00023: val_loss did not improve from 0.15581 31/31 [==============================] - 3s 97ms/step - loss: 0.2319 - accuracy: 0.8972 - val_loss: 0.1560 - val_accuracy: 0.9235
Epoch 24/100 31/31 [==============================] - ETA: 0s - loss: 0.2311 - accuracy: 0.9017 Epoch 00024: val_loss did not improve from 0.15581 31/31 [==============================] - 3s 95ms/step - loss: 0.2311 - accuracy: 0.9017 - val_loss: 0.1589 - val_accuracy: 0.9316 Epoch 25/100 31/31 [==============================] - ETA: 0s - loss: 0.2401 - accuracy: 0.9022 Epoch 00025: val_loss improved from 0.15581 to 0.15480, saving model to model.h5 31/31 [==============================] - 4s 141ms/step - loss: 0.2401 - accuracy: 0.9022 - val_loss: 0.1548 - val_accuracy: 0.9296
Epoch 26/100 31/31 [==============================] - ETA: 0s - loss: 0.2405 - accuracy: 0.8957 Epoch 00026: val_loss improved from 0.15480 to 0.15372, saving model to model.h5 31/31 [==============================] - 5s 166ms/step - loss: 0.2405 - accuracy: 0.8957 - val_loss: 0.1537 - val_accuracy: 0.9296
Epoch 27/100 31/31 [==============================] - ETA: 0s - loss: 0.2229 - accuracy: 0.9052 Epoch 00027: val_loss improved from 0.15372 to 0.15358, saving model to model.h5 31/31 [==============================] - 4s 139ms/step - loss: 0.2229 - accuracy: 0.9052 - val_loss: 0.1536 - val_accuracy: 0.9336
Epoch 28/100 31/31 [==============================] - ETA: 0s - loss: 0.2307 - accuracy: 0.9057 Epoch 00028: val_loss did not improve from 0.15358 31/31 [==============================] - 3s 96ms/step - loss: 0.2307 - accuracy: 0.9057 - val_loss: 0.1554 - val_accuracy: 0.9296
Epoch 29/100 31/31 [==============================] - ETA: 0s - loss: 0.2256 - accuracy: 0.9088 Epoch 00029: val_loss improved from 0.15358 to 0.15259, saving model to model.h5 31/31 [==============================] - 4s 133ms/step - loss: 0.2256 - accuracy: 0.9088 - val_loss: 0.1526 - val_accuracy: 0.9336
Epoch 30/100 31/31 [==============================] - ETA: 0s - loss: 0.2215 - accuracy: 0.9057 Epoch 00030: val_loss improved from 0.15259 to 0.15197, saving model to model.h5 31/31 [==============================] - 5s 171ms/step - loss: 0.2215 - accuracy: 0.9057 - val_loss: 0.1520 - val_accuracy: 0.9296
Epoch 31/100 31/31 [==============================] - ETA: 0s - loss: 0.2102 - accuracy: 0.9103 Epoch 00031: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2102 - accuracy: 0.9103 - val_loss: 0.1616 - val_accuracy: 0.9276
Epoch 32/100 31/31 [==============================] - ETA: 0s - loss: 0.2129 - accuracy: 0.9073 Epoch 00032: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 96ms/step - loss: 0.2129 - accuracy: 0.9073 - val_loss: 0.1531 - val_accuracy: 0.9316
Epoch 33/100 31/31 [==============================] - ETA: 0s - loss: 0.2329 - accuracy: 0.8997 Epoch 00033: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 104ms/step - loss: 0.2329 - accuracy: 0.8997 - val_loss: 0.1530 - val_accuracy: 0.9336
Epoch 34/100 31/31 [==============================] - ETA: 0s - loss: 0.2320 - accuracy: 0.9032 Epoch 00034: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 96ms/step - loss: 0.2320 - accuracy: 0.9032 - val_loss: 0.1563 - val_accuracy: 0.9316
Epoch 35/100 31/31 [==============================] - ETA: 0s - loss: 0.2294 - accuracy: 0.8987 Epoch 00035: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 96ms/step - loss: 0.2294 - accuracy: 0.8987 - val_loss: 0.1544 - val_accuracy: 0.9276
Epoch 36/100 31/31 [==============================] - ETA: 0s - loss: 0.2336 - accuracy: 0.9032 Epoch 00036: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 98ms/step - loss: 0.2336 - accuracy: 0.9032 - val_loss: 0.1546 - val_accuracy: 0.9296
Epoch 37/100 31/31 [==============================] - ETA: 0s - loss: 0.2271 - accuracy: 0.9062 Epoch 00037: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 99ms/step - loss: 0.2271 - accuracy: 0.9062 - val_loss: 0.1563 - val_accuracy: 0.9316
Epoch 38/100 31/31 [==============================] - ETA: 0s - loss: 0.2136 - accuracy: 0.9093 Epoch 00038: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2136 - accuracy: 0.9093 - val_loss: 0.1586 - val_accuracy: 0.9316
Epoch 39/100 31/31 [==============================] - ETA: 0s - loss: 0.2094 - accuracy: 0.9128 Epoch 00039: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2094 - accuracy: 0.9128 - val_loss: 0.1550 - val_accuracy: 0.9276
Epoch 40/100 31/31 [==============================] - ETA: 0s - loss: 0.2242 - accuracy: 0.9007 Epoch 00040: val_loss did not improve from 0.15197 31/31 [==============================] - 4s 126ms/step - loss: 0.2242 - accuracy: 0.9007 - val_loss: 0.1621 - val_accuracy: 0.9276
Epoch 41/100 31/31 [==============================] - ETA: 0s - loss: 0.2394 - accuracy: 0.8962 Epoch 00041: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 96ms/step - loss: 0.2394 - accuracy: 0.8962 - val_loss: 0.1639 - val_accuracy: 0.9276
Epoch 42/100 31/31 [==============================] - ETA: 0s - loss: 0.2133 - accuracy: 0.9118 Epoch 00042: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 96ms/step - loss: 0.2133 - accuracy: 0.9118 - val_loss: 0.1573 - val_accuracy: 0.9316
Epoch 43/100 31/31 [==============================] - ETA: 0s - loss: 0.2223 - accuracy: 0.9078 Epoch 00043: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 105ms/step - loss: 0.2223 - accuracy: 0.9078 - val_loss: 0.1543 - val_accuracy: 0.9316
Epoch 44/100 31/31 [==============================] - ETA: 0s - loss: 0.2113 - accuracy: 0.9088 Epoch 00044: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2113 - accuracy: 0.9088 - val_loss: 0.1575 - val_accuracy: 0.9256
Epoch 45/100 31/31 [==============================] - ETA: 0s - loss: 0.2317 - accuracy: 0.9022 Epoch 00045: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2317 - accuracy: 0.9022 - val_loss: 0.1606 - val_accuracy: 0.9296
Epoch 46/100 31/31 [==============================] - ETA: 0s - loss: 0.2205 - accuracy: 0.9052 Epoch 00046: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 96ms/step - loss: 0.2205 - accuracy: 0.9052 - val_loss: 0.1671 - val_accuracy: 0.9215
Epoch 47/100 31/31 [==============================] - ETA: 0s - loss: 0.2233 - accuracy: 0.9098 Epoch 00047: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 105ms/step - loss: 0.2233 - accuracy: 0.9098 - val_loss: 0.1536 - val_accuracy: 0.9296
Epoch 48/100 31/31 [==============================] - ETA: 0s - loss: 0.2276 - accuracy: 0.9052 Epoch 00048: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2276 - accuracy: 0.9052 - val_loss: 0.1719 - val_accuracy: 0.9235
Epoch 49/100 31/31 [==============================] - ETA: 0s - loss: 0.2267 - accuracy: 0.9078 Epoch 00049: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2267 - accuracy: 0.9078 - val_loss: 0.1716 - val_accuracy: 0.9215
Epoch 50/100 31/31 [==============================] - ETA: 0s - loss: 0.2285 - accuracy: 0.8977 Epoch 00050: val_loss did not improve from 0.15197 31/31 [==============================] - 4s 124ms/step - loss: 0.2285 - accuracy: 0.8977 - val_loss: 0.1591 - val_accuracy: 0.9276
Epoch 51/100 31/31 [==============================] - ETA: 0s - loss: 0.2410 - accuracy: 0.9022 Epoch 00051: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2410 - accuracy: 0.9022 - val_loss: 0.1552 - val_accuracy: 0.9336
Epoch 52/100 31/31 [==============================] - ETA: 0s - loss: 0.2462 - accuracy: 0.8926 Epoch 00052: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2462 - accuracy: 0.8926 - val_loss: 0.1642 - val_accuracy: 0.9276
Epoch 53/100 31/31 [==============================] - ETA: 0s - loss: 0.2353 - accuracy: 0.8982 Epoch 00053: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 99ms/step - loss: 0.2353 - accuracy: 0.8982 - val_loss: 0.1723 - val_accuracy: 0.9195
Epoch 54/100 31/31 [==============================] - ETA: 0s - loss: 0.2230 - accuracy: 0.9047 Epoch 00054: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 96ms/step - loss: 0.2230 - accuracy: 0.9047 - val_loss: 0.1609 - val_accuracy: 0.9235 Epoch 55/100 31/31 [==============================] - ETA: 0s - loss: 0.2259 - accuracy: 0.9068 Epoch 00055: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2259 - accuracy: 0.9068 - val_loss: 0.1563 - val_accuracy: 0.9276
Epoch 56/100 31/31 [==============================] - ETA: 0s - loss: 0.2301 - accuracy: 0.9032 Epoch 00056: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2301 - accuracy: 0.9032 - val_loss: 0.1565 - val_accuracy: 0.9276
Epoch 57/100 31/31 [==============================] - ETA: 0s - loss: 0.2092 - accuracy: 0.9123 Epoch 00057: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 105ms/step - loss: 0.2092 - accuracy: 0.9123 - val_loss: 0.1602 - val_accuracy: 0.9276
Epoch 58/100 31/31 [==============================] - ETA: 0s - loss: 0.2263 - accuracy: 0.9012 Epoch 00058: val_loss did not improve from 0.15197 31/31 [==============================] - 3s 95ms/step - loss: 0.2263 - accuracy: 0.9012 - val_loss: 0.1545 - val_accuracy: 0.9296
Epoch 59/100 31/31 [==============================] - ETA: 0s - loss: 0.2254 - accuracy: 0.9052 Epoch 00059: val_loss improved from 0.15197 to 0.15149, saving model to model.h5 31/31 [==============================] - 4s 132ms/step - loss: 0.2254 - accuracy: 0.9052 - val_loss: 0.1515 - val_accuracy: 0.9336
Epoch 60/100 31/31 [==============================] - ETA: 0s - loss: 0.2214 - accuracy: 0.9078 Epoch 00060: val_loss did not improve from 0.15149 31/31 [==============================] - 4s 124ms/step - loss: 0.2214 - accuracy: 0.9078 - val_loss: 0.1576 - val_accuracy: 0.9296
Epoch 61/100 31/31 [==============================] - ETA: 0s - loss: 0.2101 - accuracy: 0.9083 Epoch 00061: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 98ms/step - loss: 0.2101 - accuracy: 0.9083 - val_loss: 0.1650 - val_accuracy: 0.9235
Epoch 62/100 31/31 [==============================] - ETA: 0s - loss: 0.2205 - accuracy: 0.9062 Epoch 00062: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 96ms/step - loss: 0.2205 - accuracy: 0.9062 - val_loss: 0.1576 - val_accuracy: 0.9276
Epoch 63/100 31/31 [==============================] - ETA: 0s - loss: 0.2093 - accuracy: 0.9098 Epoch 00063: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 105ms/step - loss: 0.2093 - accuracy: 0.9098 - val_loss: 0.1551 - val_accuracy: 0.9276
Epoch 64/100 31/31 [==============================] - ETA: 0s - loss: 0.2189 - accuracy: 0.9027 Epoch 00064: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 97ms/step - loss: 0.2189 - accuracy: 0.9027 - val_loss: 0.1562 - val_accuracy: 0.9256
Epoch 65/100 31/31 [==============================] - ETA: 0s - loss: 0.2238 - accuracy: 0.9007 Epoch 00065: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 96ms/step - loss: 0.2238 - accuracy: 0.9007 - val_loss: 0.1759 - val_accuracy: 0.9195
Epoch 66/100 31/31 [==============================] - ETA: 0s - loss: 0.2208 - accuracy: 0.9052 Epoch 00066: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 96ms/step - loss: 0.2208 - accuracy: 0.9052 - val_loss: 0.1698 - val_accuracy: 0.9235
Epoch 67/100 31/31 [==============================] - ETA: 0s - loss: 0.2249 - accuracy: 0.9032 Epoch 00067: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 105ms/step - loss: 0.2249 - accuracy: 0.9032 - val_loss: 0.1603 - val_accuracy: 0.9195
Epoch 68/100 31/31 [==============================] - ETA: 0s - loss: 0.2235 - accuracy: 0.9088 Epoch 00068: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 95ms/step - loss: 0.2235 - accuracy: 0.9088 - val_loss: 0.1577 - val_accuracy: 0.9195
Epoch 69/100 31/31 [==============================] - ETA: 0s - loss: 0.2408 - accuracy: 0.8936 Epoch 00069: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 95ms/step - loss: 0.2408 - accuracy: 0.8936 - val_loss: 0.1762 - val_accuracy: 0.9235
Epoch 70/100 31/31 [==============================] - ETA: 0s - loss: 0.2178 - accuracy: 0.9047 Epoch 00070: val_loss did not improve from 0.15149 31/31 [==============================] - 4s 124ms/step - loss: 0.2178 - accuracy: 0.9047 - val_loss: 0.1613 - val_accuracy: 0.9235
Epoch 71/100 31/31 [==============================] - ETA: 0s - loss: 0.2223 - accuracy: 0.8987 Epoch 00071: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 96ms/step - loss: 0.2223 - accuracy: 0.8987 - val_loss: 0.1549 - val_accuracy: 0.9235
Epoch 72/100 31/31 [==============================] - ETA: 0s - loss: 0.2172 - accuracy: 0.9093 Epoch 00072: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 96ms/step - loss: 0.2172 - accuracy: 0.9093 - val_loss: 0.1606 - val_accuracy: 0.9235
Epoch 73/100 31/31 [==============================] - ETA: 0s - loss: 0.2119 - accuracy: 0.9103 Epoch 00073: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 102ms/step - loss: 0.2119 - accuracy: 0.9103 - val_loss: 0.1853 - val_accuracy: 0.9095
Epoch 74/100 31/31 [==============================] - ETA: 0s - loss: 0.2138 - accuracy: 0.9214 Epoch 00074: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 95ms/step - loss: 0.2138 - accuracy: 0.9214 - val_loss: 0.2074 - val_accuracy: 0.8974
Epoch 75/100 31/31 [==============================] - ETA: 0s - loss: 0.2214 - accuracy: 0.9017 Epoch 00075: val_loss did not improve from 0.15149 31/31 [==============================] - 3s 95ms/step - loss: 0.2214 - accuracy: 0.9017 - val_loss: 0.1758 - val_accuracy: 0.9215
Epoch 76/100 31/31 [==============================] - ETA: 0s - loss: 0.2047 - accuracy: 0.9123 Epoch 00076: val_loss improved from 0.15149 to 0.15097, saving model to model.h5 31/31 [==============================] - 4s 133ms/step - loss: 0.2047 - accuracy: 0.9123 - val_loss: 0.1510 - val_accuracy: 0.9316
Epoch 77/100 31/31 [==============================] - ETA: 0s - loss: 0.2146 - accuracy: 0.9108 Epoch 00077: val_loss improved from 0.15097 to 0.14935, saving model to model.h5 31/31 [==============================] - 5s 161ms/step - loss: 0.2146 - accuracy: 0.9108 - val_loss: 0.1494 - val_accuracy: 0.9356
Epoch 78/100 31/31 [==============================] - ETA: 0s - loss: 0.2225 - accuracy: 0.9093 Epoch 00078: val_loss improved from 0.14935 to 0.14915, saving model to model.h5 31/31 [==============================] - 5s 155ms/step - loss: 0.2225 - accuracy: 0.9093 - val_loss: 0.1491 - val_accuracy: 0.9316
Epoch 79/100 31/31 [==============================] - ETA: 0s - loss: 0.2233 - accuracy: 0.9098 Epoch 00079: val_loss improved from 0.14915 to 0.14742, saving model to model.h5 31/31 [==============================] - 5s 146ms/step - loss: 0.2233 - accuracy: 0.9098 - val_loss: 0.1474 - val_accuracy: 0.9316
Epoch 80/100 31/31 [==============================] - ETA: 0s - loss: 0.2184 - accuracy: 0.9148 Epoch 00080: val_loss did not improve from 0.14742 31/31 [==============================] - 3s 95ms/step - loss: 0.2184 - accuracy: 0.9148 - val_loss: 0.1491 - val_accuracy: 0.9336
Epoch 81/100 31/31 [==============================] - ETA: 0s - loss: 0.2088 - accuracy: 0.9098 Epoch 00081: val_loss did not improve from 0.14742 31/31 [==============================] - 3s 97ms/step - loss: 0.2088 - accuracy: 0.9098 - val_loss: 0.1577 - val_accuracy: 0.9296
Epoch 82/100 31/31 [==============================] - ETA: 0s - loss: 0.2094 - accuracy: 0.9108 Epoch 00082: val_loss did not improve from 0.14742 31/31 [==============================] - 3s 104ms/step - loss: 0.2094 - accuracy: 0.9108 - val_loss: 0.1701 - val_accuracy: 0.9256
Epoch 83/100 31/31 [==============================] - ETA: 0s - loss: 0.2125 - accuracy: 0.9088 Epoch 00083: val_loss did not improve from 0.14742 31/31 [==============================] - 3s 96ms/step - loss: 0.2125 - accuracy: 0.9088 - val_loss: 0.1661 - val_accuracy: 0.9256
Epoch 84/100 31/31 [==============================] - ETA: 0s - loss: 0.2337 - accuracy: 0.9022 Epoch 00084: val_loss did not improve from 0.14742 31/31 [==============================] - 3s 95ms/step - loss: 0.2337 - accuracy: 0.9022 - val_loss: 0.1490 - val_accuracy: 0.9316
Epoch 85/100 31/31 [==============================] - ETA: 0s - loss: 0.2046 - accuracy: 0.9093 Epoch 00085: val_loss did not improve from 0.14742 31/31 [==============================] - 3s 104ms/step - loss: 0.2046 - accuracy: 0.9093 - val_loss: 0.1485 - val_accuracy: 0.9356
Epoch 86/100 31/31 [==============================] - ETA: 0s - loss: 0.2055 - accuracy: 0.9098 Epoch 00086: val_loss did not improve from 0.14742 31/31 [==============================] - 3s 95ms/step - loss: 0.2055 - accuracy: 0.9098 - val_loss: 0.1571 - val_accuracy: 0.9296
Epoch 87/100 31/31 [==============================] - ETA: 0s - loss: 0.2291 - accuracy: 0.9002 Epoch 00087: val_loss did not improve from 0.14742 31/31 [==============================] - 3s 96ms/step - loss: 0.2291 - accuracy: 0.9002 - val_loss: 0.1493 - val_accuracy: 0.9296
Epoch 88/100 31/31 [==============================] - ETA: 0s - loss: 0.2271 - accuracy: 0.9057 Epoch 00088: val_loss improved from 0.14742 to 0.14703, saving model to model.h5 31/31 [==============================] - 5s 159ms/step - loss: 0.2271 - accuracy: 0.9057 - val_loss: 0.1470 - val_accuracy: 0.9296
Epoch 89/100 31/31 [==============================] - ETA: 0s - loss: 0.2251 - accuracy: 0.9057 Epoch 00089: val_loss improved from 0.14703 to 0.14538, saving model to model.h5 31/31 [==============================] - 5s 165ms/step - loss: 0.2251 - accuracy: 0.9057 - val_loss: 0.1454 - val_accuracy: 0.9356
Epoch 90/100 31/31 [==============================] - ETA: 0s - loss: 0.2303 - accuracy: 0.9047 Epoch 00090: val_loss improved from 0.14538 to 0.14396, saving model to model.h5 31/31 [==============================] - 5s 146ms/step - loss: 0.2303 - accuracy: 0.9047 - val_loss: 0.1440 - val_accuracy: 0.9316
Epoch 91/100 31/31 [==============================] - ETA: 0s - loss: 0.2213 - accuracy: 0.9078 Epoch 00091: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 96ms/step - loss: 0.2213 - accuracy: 0.9078 - val_loss: 0.1444 - val_accuracy: 0.9316
Epoch 92/100 31/31 [==============================] - ETA: 0s - loss: 0.2071 - accuracy: 0.9118 Epoch 00092: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 97ms/step - loss: 0.2071 - accuracy: 0.9118 - val_loss: 0.1480 - val_accuracy: 0.9336
Epoch 93/100 31/31 [==============================] - ETA: 0s - loss: 0.2279 - accuracy: 0.9123 Epoch 00093: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 95ms/step - loss: 0.2279 - accuracy: 0.9123 - val_loss: 0.1462 - val_accuracy: 0.9336
Epoch 94/100 31/31 [==============================] - ETA: 0s - loss: 0.1996 - accuracy: 0.9183 Epoch 00094: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 106ms/step - loss: 0.1996 - accuracy: 0.9183 - val_loss: 0.1707 - val_accuracy: 0.9235
Epoch 95/100 31/31 [==============================] - ETA: 0s - loss: 0.2098 - accuracy: 0.9143 Epoch 00095: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 95ms/step - loss: 0.2098 - accuracy: 0.9143 - val_loss: 0.1634 - val_accuracy: 0.9276
Epoch 96/100 31/31 [==============================] - ETA: 0s - loss: 0.2165 - accuracy: 0.9073 Epoch 00096: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 106ms/step - loss: 0.2165 - accuracy: 0.9073 - val_loss: 0.1547 - val_accuracy: 0.9235
Epoch 97/100 31/31 [==============================] - ETA: 0s - loss: 0.2225 - accuracy: 0.9098 Epoch 00097: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 111ms/step - loss: 0.2225 - accuracy: 0.9098 - val_loss: 0.1605 - val_accuracy: 0.9276
Epoch 98/100 31/31 [==============================] - ETA: 0s - loss: 0.2195 - accuracy: 0.9098 Epoch 00098: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 105ms/step - loss: 0.2195 - accuracy: 0.9098 - val_loss: 0.1502 - val_accuracy: 0.9316
Epoch 99/100 31/31 [==============================] - ETA: 0s - loss: 0.2082 - accuracy: 0.9073 Epoch 00099: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 95ms/step - loss: 0.2082 - accuracy: 0.9073 - val_loss: 0.1447 - val_accuracy: 0.9336
Epoch 100/100 31/31 [==============================] - ETA: 0s - loss: 0.2142 - accuracy: 0.9108 Epoch 00100: val_loss did not improve from 0.14396 31/31 [==============================] - 3s 98ms/step - loss: 0.2142 - accuracy: 0.9108 - val_loss: 0.1445 - val_accuracy: 0.9376
Y_pred = model.predict(X_val)
Y_pred = np.argmax(Y_pred, axis=1)
Y_true = np.argmax(Y_val, axis=1)
cm = confusion_matrix(Y_true, Y_pred)
plt.figure(figsize=(12, 12))
ax = sns.heatmap(cm, cmap=plt.cm.Purples, annot=True, square=True, xticklabels=disease_types, yticklabels=disease_types)
ax.set_ylabel('Actual', fontsize=40)
ax.set_xlabel('Predicted', fontsize=40)
# accuracy plot
plt.plot(hist.history['accuracy'])
plt.plot(hist.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='best')
plt.show()
# loss plot
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='best')
plt.show()
처음부터 좋은 결과인 이유는 모델을 실수로 2번 돌린 코드이기 때문
from skimage import io
from keras.preprocessing import image
img = image.load_img('../input/tifpng-dataset/ID_0204_Z_0066.png', grayscale=False, target_size=(64, 64))
show_img=image.load_img('../input/tifpng-dataset/ID_0204_Z_0066.png', grayscale=False, target_size=(200, 200))
disease_class=['Covid-19','Non Covid-19']
x = image.img_to_array(img)
print(img)
x = np.expand_dims(x, axis = 0)
x /= 255
custom = model.predict(x)
print(custom)
print(custom[0])
plt.imshow(show_img)
plt.show()
a=custom[0]
ind=np.argmax(a)
print(ind)
print('Prediction:',disease_class[ind])
pix = np.array(img)
print(img.size)
print(pix)
(64, 64) [[[245 245 245] [245 245 245] [245 245 245] ... [246 246 246] [246 246 246] [246 246 246]]
[[245 245 245] [246 246 246] [246 246 246] ... [245 245 245] [245 245 245] [245 245 245]]
[[245 245 245] [245 245 245] [246 246 246] ... [245 245 245] [245 245 245] [245 245 245]]
...
[[246 246 246] [245 245 245] [246 246 246] ... [245 245 245] [245 245 245] [245 245 245]]
[[245 245 245] [245 245 245] [246 246 246] ... [245 245 245] [245 245 245] [245 245 245]]
[[245 245 245] [245 245 245] [246 246 246] ... [245 245 245] [245 245 245] [245 245 245]]]