20장 전이 학습을 통해 딥러닝의 성능 극대화하기¶

1. 소규모 데이터셋으로 만드는 강력한 학습 모델¶

실습: 치매 환자의 뇌인지 일반인의 뇌인지 예측하기¶

In [1]:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras import optimizers

import numpy as np
import matplotlib.pyplot as plt

#학습셋의 변형을 설정하는 부분입니다. 
train_datagen = ImageDataGenerator(rescale=1./255,          # 주어진 이미지의 크기를 설정합니다.
                                  horizontal_flip=True,     # 수평 대칭 이미지를 50% 확률로 만들어 추가합니다.
                                  width_shift_range=0.1,    # 전체 크기의 15% 범위에서 좌우로 이동합니다.
                                  height_shift_range=0.1,   # 마찬가지로 위, 아래로 이동합니다.
                                  #rotation_range=5,        # 정해진 각도만큼 회전시킵니다.
                                  #shear_range=0.7,         # 좌표 하나를 고정시키고 나머지를 이동시킵니다.
                                  #zoom_range=1.2,          # 확대 또는 축소시킵니다.
                                  #vertical_flip=True,      # 수직 대칭 이미지를 만듭니다.
                                  #fill_mode='nearest'      # 빈 공간을 채우는 방법입니다. nearest 옵션은 가장 비슷한 색으로 채우게 됩니다.
                                  )      

train_generator = train_datagen.flow_from_directory(
       './data/train',   # 학습셋이 있는 폴더의 위치입니다.
       target_size=(150, 150),
       batch_size=5,
       class_mode='binary')

# 테스트셋은 이미지 부풀리기 과정을 진행하지 않습니다.
test_datagen = ImageDataGenerator(rescale=1./255)  

test_generator = test_datagen.flow_from_directory(
       './data/test',   # 테스트셋이 있는 폴더의 위치입니다.
       target_size=(150, 150),
       batch_size=5,
       class_mode='binary')


# 앞서 배운 CNN 모델을 만들어 적용해 보겠습니다.
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150,150,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.summary()

from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras import optimizers import numpy as np import matplotlib.pyplot as plt #학습셋의 변형을 설정하는 부분입니다. train_datagen = ImageDataGenerator(rescale=1./255, # 주어진 이미지의 크기를 설정합니다. horizontal_flip=True, # 수평 대칭 이미지를 50% 확률로 만들어 추가합니다. width_shift_range=0.1, # 전체 크기의 15% 범위에서 좌우로 이동합니다. height_shift_range=0.1, # 마찬가지로 위, 아래로 이동합니다. #rotation_range=5, # 정해진 각도만큼 회전시킵니다. #shear_range=0.7, # 좌표 하나를 고정시키고 나머지를 이동시킵니다. #zoom_range=1.2, # 확대 또는 축소시킵니다. #vertical_flip=True, # 수직 대칭 이미지를 만듭니다. #fill_mode='nearest' # 빈 공간을 채우는 방법입니다. nearest 옵션은 가장 비슷한 색으로 채우게 됩니다. ) train_generator = train_datagen.flow_from_directory( './data/train', # 학습셋이 있는 폴더의 위치입니다. target_size=(150, 150), batch_size=5, class_mode='binary') # 테스트셋은 이미지 부풀리기 과정을 진행하지 않습니다. test_datagen = ImageDataGenerator(rescale=1./255) test_generator = test_datagen.flow_from_directory( './data/test', # 테스트셋이 있는 폴더의 위치입니다. target_size=(150, 150), batch_size=5, class_mode='binary') # 앞서 배운 CNN 모델을 만들어 적용해 보겠습니다. model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=(150,150,3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1)) model.add(Activation('sigmoid')) model.summary()

Found 160 images belonging to 2 classes.
Found 120 images belonging to 2 classes.
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 148, 148, 32)      896       
_________________________________________________________________
activation (Activation)      (None, 148, 148, 32)      0         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 74, 74, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 72, 72, 32)        9248      
_________________________________________________________________
activation_1 (Activation)    (None, 72, 72, 32)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 36, 36, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 34, 34, 64)        18496     
_________________________________________________________________
activation_2 (Activation)    (None, 34, 34, 64)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 17, 17, 64)        0         
_________________________________________________________________
flatten (Flatten)            (None, 18496)             0         
_________________________________________________________________
dense (Dense)                (None, 64)                1183808   
_________________________________________________________________
activation_3 (Activation)    (None, 64)                0         
_________________________________________________________________
dropout (Dropout)            (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 65        
_________________________________________________________________
activation_4 (Activation)    (None, 1)                 0         
=================================================================
Total params: 1,212,513
Trainable params: 1,212,513
Non-trainable params: 0
_________________________________________________________________

In [2]:

!pip install SciPy

!pip install SciPy

Requirement already satisfied: SciPy in c:\users\gilbut\anaconda3\envs\deep_learning\lib\site-packages (1.7.3)
Requirement already satisfied: numpy<1.23.0,>=1.16.5 in c:\users\gilbut\anaconda3\envs\deep_learning\lib\site-packages (from SciPy) (1.19.5)

In [3]:

#모델 실행의 옵션을 설정합니다. 
model.compile(loss='binary_crossentropy', optimizer=optimizers.Adam(learning_rate=0.0002), metrics=['accuracy'])

# 학습의 조기 중단을 설정합니다.
early_stopping_callback = EarlyStopping(monitor='val_loss', patience=5)

#모델을 실행합니다
history = model.fit(
       train_generator,
       epochs=100,
       validation_data=test_generator,
       validation_steps=10, 
       callbacks=[early_stopping_callback])

#모델 실행의 옵션을 설정합니다. model.compile(loss='binary_crossentropy', optimizer=optimizers.Adam(learning_rate=0.0002), metrics=['accuracy']) # 학습의 조기 중단을 설정합니다. early_stopping_callback = EarlyStopping(monitor='val_loss', patience=5) #모델을 실행합니다 history = model.fit( train_generator, epochs=100, validation_data=test_generator, validation_steps=10, callbacks=[early_stopping_callback])

Epoch 1/100
32/32 [==============================] - 3s 68ms/step - loss: 0.7053 - accuracy: 0.5063 - val_loss: 0.6896 - val_accuracy: 0.5000
Epoch 2/100
32/32 [==============================] - 2s 66ms/step - loss: 0.7028 - accuracy: 0.5125 - val_loss: 0.6904 - val_accuracy: 0.4800
Epoch 3/100
32/32 [==============================] - 2s 74ms/step - loss: 0.6913 - accuracy: 0.5188 - val_loss: 0.6883 - val_accuracy: 0.4800
Epoch 4/100
32/32 [==============================] - 2s 71ms/step - loss: 0.6854 - accuracy: 0.5375 - val_loss: 0.6786 - val_accuracy: 0.8200
Epoch 5/100
32/32 [==============================] - 2s 75ms/step - loss: 0.6756 - accuracy: 0.5750 - val_loss: 0.6573 - val_accuracy: 0.6400
Epoch 6/100
32/32 [==============================] - 2s 76ms/step - loss: 0.6825 - accuracy: 0.5813 - val_loss: 0.6378 - val_accuracy: 0.8400
Epoch 7/100
32/32 [==============================] - 2s 64ms/step - loss: 0.6558 - accuracy: 0.6313 - val_loss: 0.6667 - val_accuracy: 0.5000
Epoch 8/100
32/32 [==============================] - 2s 63ms/step - loss: 0.6345 - accuracy: 0.6500 - val_loss: 0.5991 - val_accuracy: 0.6400
Epoch 9/100
32/32 [==============================] - 2s 67ms/step - loss: 0.6429 - accuracy: 0.6313 - val_loss: 0.6015 - val_accuracy: 0.8200
Epoch 10/100
32/32 [==============================] - 2s 73ms/step - loss: 0.5748 - accuracy: 0.6938 - val_loss: 0.4762 - val_accuracy: 0.9200
Epoch 11/100
32/32 [==============================] - 3s 77ms/step - loss: 0.5332 - accuracy: 0.7437 - val_loss: 0.4443 - val_accuracy: 0.8400
Epoch 12/100
32/32 [==============================] - 2s 75ms/step - loss: 0.4228 - accuracy: 0.8625 - val_loss: 0.4070 - val_accuracy: 0.8400
Epoch 13/100
32/32 [==============================] - 2s 65ms/step - loss: 0.3653 - accuracy: 0.8500 - val_loss: 0.2481 - val_accuracy: 0.9600
Epoch 14/100
32/32 [==============================] - 2s 64ms/step - loss: 0.3076 - accuracy: 0.8813 - val_loss: 0.3884 - val_accuracy: 0.8400
Epoch 15/100
32/32 [==============================] - 2s 66ms/step - loss: 0.2986 - accuracy: 0.9062 - val_loss: 0.2137 - val_accuracy: 0.9200
Epoch 16/100
32/32 [==============================] - 2s 64ms/step - loss: 0.2292 - accuracy: 0.9438 - val_loss: 0.1383 - val_accuracy: 0.9600
Epoch 17/100
32/32 [==============================] - 2s 66ms/step - loss: 0.2399 - accuracy: 0.9062 - val_loss: 0.1318 - val_accuracy: 0.9800
Epoch 18/100
32/32 [==============================] - 2s 65ms/step - loss: 0.2025 - accuracy: 0.9000 - val_loss: 0.1201 - val_accuracy: 0.9800
Epoch 19/100
32/32 [==============================] - 2s 64ms/step - loss: 0.1659 - accuracy: 0.9375 - val_loss: 0.1257 - val_accuracy: 0.9800
Epoch 20/100
32/32 [==============================] - 2s 64ms/step - loss: 0.1465 - accuracy: 0.9438 - val_loss: 0.1550 - val_accuracy: 0.9000
Epoch 21/100
32/32 [==============================] - 2s 64ms/step - loss: 0.2073 - accuracy: 0.9312 - val_loss: 0.1501 - val_accuracy: 0.9400
Epoch 22/100
32/32 [==============================] - 2s 63ms/step - loss: 0.1289 - accuracy: 0.9688 - val_loss: 0.1103 - val_accuracy: 0.9200
Epoch 23/100
32/32 [==============================] - 2s 64ms/step - loss: 0.1045 - accuracy: 0.9688 - val_loss: 0.0534 - val_accuracy: 0.9800
Epoch 24/100
32/32 [==============================] - 2s 63ms/step - loss: 0.0545 - accuracy: 0.9875 - val_loss: 0.1120 - val_accuracy: 0.9600
Epoch 25/100
32/32 [==============================] - 2s 67ms/step - loss: 0.0969 - accuracy: 0.9563 - val_loss: 0.0927 - val_accuracy: 0.9600
Epoch 26/100
32/32 [==============================] - 2s 65ms/step - loss: 0.1004 - accuracy: 0.9625 - val_loss: 0.1100 - val_accuracy: 0.9600
Epoch 27/100
32/32 [==============================] - 2s 64ms/step - loss: 0.0948 - accuracy: 0.9563 - val_loss: 0.0291 - val_accuracy: 1.0000
Epoch 28/100
32/32 [==============================] - 2s 63ms/step - loss: 0.1080 - accuracy: 0.9625 - val_loss: 0.0329 - val_accuracy: 1.0000
Epoch 29/100
32/32 [==============================] - 2s 64ms/step - loss: 0.1362 - accuracy: 0.9563 - val_loss: 0.0802 - val_accuracy: 0.9800
Epoch 30/100
32/32 [==============================] - 2s 73ms/step - loss: 0.0909 - accuracy: 0.9625 - val_loss: 0.0423 - val_accuracy: 0.9800
Epoch 31/100
32/32 [==============================] - 2s 65ms/step - loss: 0.0631 - accuracy: 0.9875 - val_loss: 0.1227 - val_accuracy: 0.9600
Epoch 32/100
32/32 [==============================] - 2s 65ms/step - loss: 0.1099 - accuracy: 0.9563 - val_loss: 0.0388 - val_accuracy: 1.0000

In [4]:

# 검증셋과 학습셋의 오차를 저장합니다.
y_vloss = history.history['val_loss']
y_loss = history.history['loss']

# 그래프로 표현해 봅니다.
x_len = np.arange(len(y_loss))
plt.plot(x_len, y_vloss, marker='.', c="red", label='Testset_loss')
plt.plot(x_len, y_loss, marker='.', c="blue", label='Trainset_loss')

# 그래프에 그리드를 주고 레이블을 표시하겠습니다.
plt.legend(loc='upper right')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()

# 검증셋과 학습셋의 오차를 저장합니다. y_vloss = history.history['val_loss'] y_loss = history.history['loss'] # 그래프로 표현해 봅니다. x_len = np.arange(len(y_loss)) plt.plot(x_len, y_vloss, marker='.', c="red", label='Testset_loss') plt.plot(x_len, y_loss, marker='.', c="blue", label='Trainset_loss') # 그래프에 그리드를 주고 레이블을 표시하겠습니다. plt.legend(loc='upper right') plt.grid() plt.xlabel('epoch') plt.ylabel('loss') plt.show()

2. 전이 학습으로 모델 성능 극대화하기¶

실습: 전이 학습 실습하기¶

In [5]:

from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import Input, models, layers, optimizers, metrics
from tensorflow.keras.layers import Dense, Flatten, Activation, Dropout
from tensorflow.keras.applications import VGG16
from tensorflow.keras.callbacks import EarlyStopping
import numpy as np
import matplotlib.pyplot as plt

# 학습셋의 변형을 설정하는 부분입니다. 
train_datagen = ImageDataGenerator(rescale=1./255,          # 주어진 이미지의 크기를 설정합니다.
                                  horizontal_flip=True,     # 수평 대칭 이미지를 50% 확률로 만들어 추가합니다.
                                  width_shift_range=0.1,    # 전체 크기의 15% 범위에서 좌우로 이동합니다.
                                  height_shift_range=0.1,   # 마찬가지로 위, 아래로 이동합니다.
                                  #rotation_range=5,        # 정해진 각도만큼 회전시킵니다.
                                  #shear_range=0.7,         # 좌표 하나를 고정시키고 나머지를 이동시킵니다.
                                  #zoom_range=1.2,          # 확대 또는 축소시킵니다.
                                  #vertical_flip=True,      # 수직 대칭 이미지를 만듭니다.
                                  #fill_mode='nearest'      # 빈 공간을 채우는 방법입니다. nearest 옵션은 가장 비슷한 색으로 채우게 됩니다.
                                  )      


train_generator = train_datagen.flow_from_directory(
       './data/train',
       target_size=(150, 150),
       batch_size=5,
       class_mode='binary')

# 테스트셋의 정규화를 설정합니다.
test_datagen = ImageDataGenerator(rescale=1./255)

test_generator = test_datagen.flow_from_directory(
       './data/test',
       target_size=(150, 150),
       batch_size=5,
       class_mode='binary')

# VGG16 모델을 불러옵니다.
transfer_model = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
transfer_model.trainable = False
transfer_model.summary()

# 우리의 모델을 설정합니다.
finetune_model = models.Sequential()
finetune_model.add(transfer_model)
finetune_model.add(Flatten())
finetune_model.add(Dense(64))
finetune_model.add(Activation('relu'))
finetune_model.add(Dropout(0.5))
finetune_model.add(Dense(1))
finetune_model.add(Activation('sigmoid'))
finetune_model.summary()

from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras import Input, models, layers, optimizers, metrics from tensorflow.keras.layers import Dense, Flatten, Activation, Dropout from tensorflow.keras.applications import VGG16 from tensorflow.keras.callbacks import EarlyStopping import numpy as np import matplotlib.pyplot as plt # 학습셋의 변형을 설정하는 부분입니다. train_datagen = ImageDataGenerator(rescale=1./255, # 주어진 이미지의 크기를 설정합니다. horizontal_flip=True, # 수평 대칭 이미지를 50% 확률로 만들어 추가합니다. width_shift_range=0.1, # 전체 크기의 15% 범위에서 좌우로 이동합니다. height_shift_range=0.1, # 마찬가지로 위, 아래로 이동합니다. #rotation_range=5, # 정해진 각도만큼 회전시킵니다. #shear_range=0.7, # 좌표 하나를 고정시키고 나머지를 이동시킵니다. #zoom_range=1.2, # 확대 또는 축소시킵니다. #vertical_flip=True, # 수직 대칭 이미지를 만듭니다. #fill_mode='nearest' # 빈 공간을 채우는 방법입니다. nearest 옵션은 가장 비슷한 색으로 채우게 됩니다. ) train_generator = train_datagen.flow_from_directory( './data/train', target_size=(150, 150), batch_size=5, class_mode='binary') # 테스트셋의 정규화를 설정합니다. test_datagen = ImageDataGenerator(rescale=1./255) test_generator = test_datagen.flow_from_directory( './data/test', target_size=(150, 150), batch_size=5, class_mode='binary') # VGG16 모델을 불러옵니다. transfer_model = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3)) transfer_model.trainable = False transfer_model.summary() # 우리의 모델을 설정합니다. finetune_model = models.Sequential() finetune_model.add(transfer_model) finetune_model.add(Flatten()) finetune_model.add(Dense(64)) finetune_model.add(Activation('relu')) finetune_model.add(Dropout(0.5)) finetune_model.add(Dense(1)) finetune_model.add(Activation('sigmoid')) finetune_model.summary()

Found 160 images belonging to 2 classes.
Found 120 images belonging to 2 classes.
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
58892288/58889256 [==============================] - 5s 0us/step
58900480/58889256 [==============================] - 5s 0us/step
Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 150, 150, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         
=================================================================
Total params: 14,714,688
Trainable params: 0
Non-trainable params: 14,714,688
_________________________________________________________________
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
vgg16 (Functional)           (None, 4, 4, 512)         14714688  
_________________________________________________________________
flatten_1 (Flatten)          (None, 8192)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 64)                524352    
_________________________________________________________________
activation_5 (Activation)    (None, 64)                0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 65        
_________________________________________________________________
activation_6 (Activation)    (None, 1)                 0         
=================================================================
Total params: 15,239,105
Trainable params: 524,417
Non-trainable params: 14,714,688
_________________________________________________________________

In [6]:

# 모델의 실행 옵션을 설정합니다. 
finetune_model.compile(loss='binary_crossentropy', optimizer=optimizers.Adam(learning_rate=0.0002), metrics=['accuracy'])

# 학습의 조기 중단을 설정합니다.
early_stopping_callback = EarlyStopping(monitor='val_loss', patience=5)

# 모델을 실행합니다.
history = finetune_model.fit(
       train_generator,
       epochs=20,
       validation_data=test_generator,
       validation_steps=10, 
       callbacks=[early_stopping_callback])

# 모델의 실행 옵션을 설정합니다. finetune_model.compile(loss='binary_crossentropy', optimizer=optimizers.Adam(learning_rate=0.0002), metrics=['accuracy']) # 학습의 조기 중단을 설정합니다. early_stopping_callback = EarlyStopping(monitor='val_loss', patience=5) # 모델을 실행합니다. history = finetune_model.fit( train_generator, epochs=20, validation_data=test_generator, validation_steps=10, callbacks=[early_stopping_callback])

Epoch 1/20
32/32 [==============================] - 7s 217ms/step - loss: 0.7590 - accuracy: 0.5688 - val_loss: 0.5409 - val_accuracy: 0.7800
Epoch 2/20
32/32 [==============================] - 7s 224ms/step - loss: 0.5509 - accuracy: 0.7125 - val_loss: 0.5420 - val_accuracy: 0.7600
Epoch 3/20
32/32 [==============================] - 7s 208ms/step - loss: 0.4324 - accuracy: 0.8188 - val_loss: 0.4058 - val_accuracy: 0.8200
Epoch 4/20
32/32 [==============================] - 7s 217ms/step - loss: 0.4192 - accuracy: 0.8313 - val_loss: 0.3407 - val_accuracy: 0.9200
Epoch 5/20
32/32 [==============================] - 7s 208ms/step - loss: 0.3139 - accuracy: 0.8813 - val_loss: 0.2570 - val_accuracy: 0.9600
Epoch 6/20
32/32 [==============================] - 7s 220ms/step - loss: 0.2795 - accuracy: 0.9125 - val_loss: 0.2508 - val_accuracy: 0.9200
Epoch 7/20
32/32 [==============================] - 7s 228ms/step - loss: 0.2519 - accuracy: 0.9125 - val_loss: 0.3089 - val_accuracy: 0.8600
Epoch 8/20
32/32 [==============================] - 7s 231ms/step - loss: 0.2198 - accuracy: 0.9375 - val_loss: 0.1758 - val_accuracy: 0.9800
Epoch 9/20
32/32 [==============================] - 7s 230ms/step - loss: 0.2399 - accuracy: 0.9312 - val_loss: 0.1661 - val_accuracy: 0.9400
Epoch 10/20
32/32 [==============================] - 7s 228ms/step - loss: 0.2002 - accuracy: 0.9375 - val_loss: 0.1326 - val_accuracy: 0.9800
Epoch 11/20
32/32 [==============================] - 8s 240ms/step - loss: 0.2483 - accuracy: 0.9125 - val_loss: 0.1493 - val_accuracy: 0.9400
Epoch 12/20
32/32 [==============================] - 8s 248ms/step - loss: 0.1724 - accuracy: 0.9438 - val_loss: 0.1812 - val_accuracy: 0.9400
Epoch 13/20
32/32 [==============================] - 8s 265ms/step - loss: 0.1952 - accuracy: 0.9500 - val_loss: 0.1734 - val_accuracy: 0.9200
Epoch 14/20
32/32 [==============================] - 8s 247ms/step - loss: 0.1618 - accuracy: 0.9625 - val_loss: 0.1516 - val_accuracy: 0.9400
Epoch 15/20
32/32 [==============================] - 10s 300ms/step - loss: 0.1693 - accuracy: 0.9563 - val_loss: 0.2223 - val_accuracy: 0.9200

In [7]:

# 검증셋과 학습셋의 오차를 저장합니다.
y_vloss = history.history['val_loss']
y_loss = history.history['loss']

# 그래프로 표현해 봅니다.
x_len = np.arange(len(y_loss))
plt.plot(x_len, y_vloss, marker='.', c="red", label='Testset_loss')
plt.plot(x_len, y_loss, marker='.', c="blue", label='Trainset_loss')

# 그래프에 그리드를 주고 레이블을 표시하겠습니다.
plt.legend(loc='upper right')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()

# 검증셋과 학습셋의 오차를 저장합니다. y_vloss = history.history['val_loss'] y_loss = history.history['loss'] # 그래프로 표현해 봅니다. x_len = np.arange(len(y_loss)) plt.plot(x_len, y_vloss, marker='.', c="red", label='Testset_loss') plt.plot(x_len, y_loss, marker='.', c="blue", label='Trainset_loss') # 그래프에 그리드를 주고 레이블을 표시하겠습니다. plt.legend(loc='upper right') plt.grid() plt.xlabel('epoch') plt.ylabel('loss') plt.show()