[DL] ImageDataGenerator의 사용 - 3

ImageDataGenerator3의 사용(개와 고양이)

TF2.X 에서 이미지 증식을 사용해 개와 고양이 문제를 CNN 을 통해 확인해 본다.

사용 Library

사용하는 library들이다.

import numpy as np
import tensorflow as tf

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

데이터셋

train data와 validation date를 정의한다.

train_dir      = './data/cat_dog_full/train'
validation_dir = './data/cat_dog_full/validation'

# 전체 데이터가 25,000장 (고양이 : 12,500, 개 : 12,500)
# 댕댕이 이미지 train      : 7,000장
# 댕댕이 이미지 validation : 3,000장
# 댕댕이 이미지 test       : 2,500장

ImageDataGenerator

train 및 validation directory를 가져와 ImageDataGenerator를 생성한다.

train_datagen = ImageDataGenerator(rescale=1/255,
                                   horizontal_flip=True,
                                   ratation_range = 30,
                                   width_shift_range=0.1,
                                   height_shift_range=0.1,
                                   shear_range = 0.2,  # 층밀림의 강도
                                   zoom_range = 0.2)

validation_datagen = ImageDataGenerator(rescale=1/255)


train_generator = train_datagen.flow_from_directory(train_dir,
                                                    classes=['cats', 'dogs'],
                                                    batch_size=20,
                                                    target_size=(150, 150),
                                                    class_mode = 'binary')


validation_generator = train_datagen.flow_from_directory(validation_dir,
                                                        classes= ['cats', 'dogs'],
                                                        batch_size =20,
                                                        target_size=(150,150),
                                                        class_mode = 'binary')
                                          class_mode = 'binary')

모델 생성 및 학습

CNN 구조를 만들고 학습시킨다.

model = Sequential()

model.add(Conv2D(filters = 32, kernel_size=(3, 3) ,activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(filters = 64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(filters = 128, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(filters = 256, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(filters = 256, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Flatten())

model.add(Dropout(0.5))

model.add(Dense(512, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(optimizer=Adam(learning_rate=1e-4),
              loss='binary_crossentropy',
              metrics=['accuracy'])

history = model.fit(train_generator, steps_per_epoch=700, epochs=100, validation_data=validation_generator, validation_steps=300)

결과

validation accuracy를 알아 본다.

Once deleted, variables cannot be recovered. Proceed (y/[n])? y
Found 14000 images belonging to 2 classes.
Found 6000 images belonging to 2 classes.
Epoch 1/100
700/700 [==============================] - 137s 196ms/step - loss: 0.6516 - accuracy: 0.6048 - val_loss: 0.5806 - val_accuracy: 0.6882
Epoch 2/100
700/700 [==============================] - 120s 171ms/step - loss: 0.5747 - accuracy: 0.6971 - val_loss: 0.5463 - val_accuracy: 0.7183
Epoch 3/100
700/700 [==============================] - 120s 171ms/step - loss: 0.5386 - accuracy: 0.7296 - val_loss: 0.5202 - val_accuracy: 0.7395
Epoch 4/100
700/700 [==============================] - 121s 172ms/step - loss: 0.5162 - accuracy: 0.7448 - val_loss: 0.4975 - val_accuracy: 0.7607
Epoch 5/100
700/700 [==============================] - 121s 173ms/step - loss: 0.4936 - accuracy: 0.7621 - val_loss: 0.4422 - val_accuracy: 0.7962
Epoch 6/100
700/700 [==============================] - 120s 171ms/step - loss: 0.4648 - accuracy: 0.7762 - val_loss: 0.4266 - val_accuracy: 0.8055
Epoch 7/100
700/700 [==============================] - 120s 171ms/step - loss: 0.4510 - accuracy: 0.7854 - val_loss: 0.4085 - val_accuracy: 0.8128
Epoch 8/100
700/700 [==============================] - 120s 172ms/step - loss: 0.4380 - accuracy: 0.7902 - val_loss: 0.3970 - val_accuracy: 0.8257
Epoch 9/100
700/700 [==============================] - 120s 172ms/step - loss: 0.4264 - accuracy: 0.8062 - val_loss: 0.3815 - val_accuracy: 0.8268
Epoch 10/100
700/700 [==============================] - 120s 172ms/step - loss: 0.4150 - accuracy: 0.8079 - val_loss: 0.3803 - val_accuracy: 0.8283
Epoch 11/100
700/700 [==============================] - 120s 171ms/step - loss: 0.4009 - accuracy: 0.8209 - val_loss: 0.3668 - val_accuracy: 0.8388
Epoch 12/100
700/700 [==============================] - 121s 173ms/step - loss: 0.3835 - accuracy: 0.8261 - val_loss: 0.4248 - val_accuracy: 0.8065
Epoch 13/100
700/700 [==============================] - 121s 173ms/step - loss: 0.3731 - accuracy: 0.8339 - val_loss: 0.3682 - val_accuracy: 0.8432
Epoch 14/100
700/700 [==============================] - 120s 171ms/step - loss: 0.3661 - accuracy: 0.8357 - val_loss: 0.3269 - val_accuracy: 0.8562
Epoch 15/100
700/700 [==============================] - 117s 167ms/step - loss: 0.3562 - accuracy: 0.8405 - val_loss: 0.3903 - val_accuracy: 0.8275
Epoch 16/100
700/700 [==============================] - 117s 167ms/step - loss: 0.3487 - accuracy: 0.8481 - val_loss: 0.3428 - val_accuracy: 0.8523
Epoch 17/100
700/700 [==============================] - 118s 168ms/step - loss: 0.3360 - accuracy: 0.8536 - val_loss: 0.3131 - val_accuracy: 0.8632
Epoch 18/100
700/700 [==============================] - 117s 167ms/step - loss: 0.3297 - accuracy: 0.8553 - val_loss: 0.3217 - val_accuracy: 0.8580
Epoch 19/100
700/700 [==============================] - 116s 166ms/step - loss: 0.3226 - accuracy: 0.8596 - val_loss: 0.3052 - val_accuracy: 0.8652
Epoch 20/100
700/700 [==============================] - 115s 164ms/step - loss: 0.3073 - accuracy: 0.8621 - val_loss: 0.2836 - val_accuracy: 0.8773
Epoch 21/100
700/700 [==============================] - 115s 164ms/step - loss: 0.2965 - accuracy: 0.8728 - val_loss: 0.2838 - val_accuracy: 0.8778
Epoch 22/100
700/700 [==============================] - 115s 165ms/step - loss: 0.2996 - accuracy: 0.8688 - val_loss: 0.2891 - val_accuracy: 0.8718
Epoch 23/100
700/700 [==============================] - 115s 164ms/step - loss: 0.2895 - accuracy: 0.8753 - val_loss: 0.2621 - val_accuracy: 0.8853
Epoch 24/100
700/700 [==============================] - 115s 164ms/step - loss: 0.2801 - accuracy: 0.8789 - val_loss: 0.3216 - val_accuracy: 0.8607
Epoch 25/100
700/700 [==============================] - 115s 165ms/step - loss: 0.2740 - accuracy: 0.8841 - val_loss: 0.2734 - val_accuracy: 0.8802
Epoch 26/100
700/700 [==============================] - 115s 165ms/step - loss: 0.2726 - accuracy: 0.8846 - val_loss: 0.2455 - val_accuracy: 0.8938
Epoch 27/100
700/700 [==============================] - 114s 163ms/step - loss: 0.2652 - accuracy: 0.8869 - val_loss: 0.2573 - val_accuracy: 0.8890
Epoch 28/100
700/700 [==============================] - 115s 164ms/step - loss: 0.2594 - accuracy: 0.8913 - val_loss: 0.2812 - val_accuracy: 0.8857
Epoch 29/100
700/700 [==============================] - 115s 164ms/step - loss: 0.2526 - accuracy: 0.8916 - val_loss: 0.2414 - val_accuracy: 0.8982
Epoch 30/100
700/700 [==============================] - 117s 168ms/step - loss: 0.2456 - accuracy: 0.8946 - val_loss: 0.2344 - val_accuracy: 0.9010
Epoch 31/100
700/700 [==============================] - 119s 169ms/step - loss: 0.2385 - accuracy: 0.9014 - val_loss: 0.2583 - val_accuracy: 0.8867
Epoch 32/100
700/700 [==============================] - 115s 165ms/step - loss: 0.2366 - accuracy: 0.8976 - val_loss: 0.2349 - val_accuracy: 0.9013
Epoch 33/100
700/700 [==============================] - 115s 164ms/step - loss: 0.2401 - accuracy: 0.8990 - val_loss: 0.2274 - val_accuracy: 0.9037
Epoch 34/100
700/700 [==============================] - 115s 165ms/step - loss: 0.2265 - accuracy: 0.9036 - val_loss: 0.2223 - val_accuracy: 0.9053
Epoch 35/100
700/700 [==============================] - 116s 165ms/step - loss: 0.2234 - accuracy: 0.9067 - val_loss: 0.2163 - val_accuracy: 0.9030
Epoch 36/100
700/700 [==============================] - 115s 165ms/step - loss: 0.2159 - accuracy: 0.9109 - val_loss: 0.2375 - val_accuracy: 0.8968
Epoch 37/100
700/700 [==============================] - 115s 164ms/step - loss: 0.2084 - accuracy: 0.9117 - val_loss: 0.2109 - val_accuracy: 0.9095
Epoch 38/100
700/700 [==============================] - 115s 165ms/step - loss: 0.2108 - accuracy: 0.9111 - val_loss: 0.2371 - val_accuracy: 0.8962
Epoch 39/100
700/700 [==============================] - 115s 165ms/step - loss: 0.2115 - accuracy: 0.9121 - val_loss: 0.2025 - val_accuracy: 0.9130
Epoch 40/100
700/700 [==============================] - 116s 165ms/step - loss: 0.2009 - accuracy: 0.9144 - val_loss: 0.1986 - val_accuracy: 0.9145
Epoch 41/100
700/700 [==============================] - 115s 165ms/step - loss: 0.2025 - accuracy: 0.9136 - val_loss: 0.1966 - val_accuracy: 0.9162
Epoch 42/100
700/700 [==============================] - 115s 165ms/step - loss: 0.1986 - accuracy: 0.9170 - val_loss: 0.2189 - val_accuracy: 0.9042
Epoch 43/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1924 - accuracy: 0.9208 - val_loss: 0.2323 - val_accuracy: 0.9080
Epoch 44/100
700/700 [==============================] - 114s 163ms/step - loss: 0.1931 - accuracy: 0.9199 - val_loss: 0.1886 - val_accuracy: 0.9202
Epoch 45/100
700/700 [==============================] - 115s 164ms/step - loss: 0.1891 - accuracy: 0.9212 - val_loss: 0.1992 - val_accuracy: 0.9137
Epoch 46/100
700/700 [==============================] - 115s 165ms/step - loss: 0.1836 - accuracy: 0.9239 - val_loss: 0.1839 - val_accuracy: 0.9222
Epoch 47/100
700/700 [==============================] - 115s 165ms/step - loss: 0.1857 - accuracy: 0.9244 - val_loss: 0.1902 - val_accuracy: 0.9217
Epoch 48/100
700/700 [==============================] - 115s 165ms/step - loss: 0.1804 - accuracy: 0.9269 - val_loss: 0.1993 - val_accuracy: 0.9203
Epoch 49/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1729 - accuracy: 0.9299 - val_loss: 0.2044 - val_accuracy: 0.9157
Epoch 50/100
700/700 [==============================] - 114s 163ms/step - loss: 0.1781 - accuracy: 0.9274 - val_loss: 0.1898 - val_accuracy: 0.9200
Epoch 51/100
700/700 [==============================] - 115s 164ms/step - loss: 0.1711 - accuracy: 0.9288 - val_loss: 0.1872 - val_accuracy: 0.9178
Epoch 52/100
700/700 [==============================] - 115s 164ms/step - loss: 0.1755 - accuracy: 0.9281 - val_loss: 0.1845 - val_accuracy: 0.9227
Epoch 53/100
700/700 [==============================] - 115s 164ms/step - loss: 0.1648 - accuracy: 0.9334 - val_loss: 0.1694 - val_accuracy: 0.9290
Epoch 54/100
700/700 [==============================] - 115s 165ms/step - loss: 0.1636 - accuracy: 0.9328 - val_loss: 0.1878 - val_accuracy: 0.9180
Epoch 55/100
700/700 [==============================] - 115s 164ms/step - loss: 0.1645 - accuracy: 0.9302 - val_loss: 0.1703 - val_accuracy: 0.9273
Epoch 56/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1636 - accuracy: 0.9322 - val_loss: 0.1693 - val_accuracy: 0.9297
Epoch 57/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1597 - accuracy: 0.9334 - val_loss: 0.2046 - val_accuracy: 0.9108
Epoch 58/100
700/700 [==============================] - 115s 164ms/step - loss: 0.1603 - accuracy: 0.9356 - val_loss: 0.2020 - val_accuracy: 0.9153
Epoch 59/100
700/700 [==============================] - 115s 164ms/step - loss: 0.1614 - accuracy: 0.9351 - val_loss: 0.2159 - val_accuracy: 0.9048
Epoch 60/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1540 - accuracy: 0.9359 - val_loss: 0.1779 - val_accuracy: 0.9307
Epoch 61/100
700/700 [==============================] - 115s 165ms/step - loss: 0.1522 - accuracy: 0.9386 - val_loss: 0.2063 - val_accuracy: 0.9153
Epoch 62/100
700/700 [==============================] - 115s 164ms/step - loss: 0.1536 - accuracy: 0.9370 - val_loss: 0.1816 - val_accuracy: 0.9233
Epoch 63/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1530 - accuracy: 0.9374 - val_loss: 0.1705 - val_accuracy: 0.9293
Epoch 64/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1472 - accuracy: 0.9401 - val_loss: 0.2004 - val_accuracy: 0.9170
Epoch 65/100
700/700 [==============================] - 118s 169ms/step - loss: 0.1475 - accuracy: 0.9391 - val_loss: 0.1641 - val_accuracy: 0.9313
Epoch 66/100
700/700 [==============================] - 119s 170ms/step - loss: 0.1425 - accuracy: 0.9424 - val_loss: 0.1568 - val_accuracy: 0.9337
Epoch 67/100
700/700 [==============================] - 115s 165ms/step - loss: 0.1443 - accuracy: 0.9419 - val_loss: 0.1604 - val_accuracy: 0.9372
Epoch 68/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1412 - accuracy: 0.9438 - val_loss: 0.2134 - val_accuracy: 0.9197
Epoch 69/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1402 - accuracy: 0.9429 - val_loss: 0.1629 - val_accuracy: 0.9332
Epoch 70/100
700/700 [==============================] - 115s 164ms/step - loss: 0.1332 - accuracy: 0.9449 - val_loss: 0.1569 - val_accuracy: 0.9363
Epoch 71/100
700/700 [==============================] - 116s 166ms/step - loss: 0.1386 - accuracy: 0.9456 - val_loss: 0.1553 - val_accuracy: 0.9333
Epoch 72/100
700/700 [==============================] - 116s 166ms/step - loss: 0.1369 - accuracy: 0.9431 - val_loss: 0.1762 - val_accuracy: 0.9277
Epoch 73/100
700/700 [==============================] - 117s 167ms/step - loss: 0.1360 - accuracy: 0.9434 - val_loss: 0.1760 - val_accuracy: 0.9278
Epoch 74/100
700/700 [==============================] - 117s 167ms/step - loss: 0.1315 - accuracy: 0.9477 - val_loss: 0.1652 - val_accuracy: 0.9315
Epoch 75/100
700/700 [==============================] - 116s 166ms/step - loss: 0.1351 - accuracy: 0.9441 - val_loss: 0.1909 - val_accuracy: 0.9268
Epoch 76/100
700/700 [==============================] - 116s 166ms/step - loss: 0.1346 - accuracy: 0.9467 - val_loss: 0.1539 - val_accuracy: 0.9347
Epoch 77/100
700/700 [==============================] - 116s 166ms/step - loss: 0.1313 - accuracy: 0.9476 - val_loss: 0.1718 - val_accuracy: 0.9307
Epoch 78/100
700/700 [==============================] - 117s 167ms/step - loss: 0.1278 - accuracy: 0.9491 - val_loss: 0.1727 - val_accuracy: 0.9278
Epoch 79/100
700/700 [==============================] - 117s 167ms/step - loss: 0.1228 - accuracy: 0.9502 - val_loss: 0.1553 - val_accuracy: 0.9382
Epoch 80/100
700/700 [==============================] - 117s 167ms/step - loss: 0.1268 - accuracy: 0.9480 - val_loss: 0.1572 - val_accuracy: 0.9395
Epoch 81/100
700/700 [==============================] - 116s 166ms/step - loss: 0.1314 - accuracy: 0.9468 - val_loss: 0.1560 - val_accuracy: 0.9368
Epoch 82/100
700/700 [==============================] - 118s 168ms/step - loss: 0.1228 - accuracy: 0.9510 - val_loss: 0.1612 - val_accuracy: 0.9367
Epoch 83/100
700/700 [==============================] - 116s 165ms/step - loss: 0.1226 - accuracy: 0.9496 - val_loss: 0.1939 - val_accuracy: 0.9242
Epoch 84/100
700/700 [==============================] - 117s 167ms/step - loss: 0.1225 - accuracy: 0.9494 - val_loss: 0.1494 - val_accuracy: 0.9438
Epoch 85/100
700/700 [==============================] - 112s 160ms/step - loss: 0.1258 - accuracy: 0.9504 - val_loss: 0.1532 - val_accuracy: 0.9393
Epoch 86/100
700/700 [==============================] - 112s 161ms/step - loss: 0.1215 - accuracy: 0.9521 - val_loss: 0.1536 - val_accuracy: 0.9405
Epoch 87/100
700/700 [==============================] - 113s 161ms/step - loss: 0.1179 - accuracy: 0.9525 - val_loss: 0.1642 - val_accuracy: 0.9335
Epoch 88/100
700/700 [==============================] - 112s 160ms/step - loss: 0.1185 - accuracy: 0.9524 - val_loss: 0.1491 - val_accuracy: 0.9400
Epoch 89/100
700/700 [==============================] - 113s 161ms/step - loss: 0.1125 - accuracy: 0.9567 - val_loss: 0.1619 - val_accuracy: 0.9400
Epoch 90/100
700/700 [==============================] - 113s 161ms/step - loss: 0.1197 - accuracy: 0.9521 - val_loss: 0.1614 - val_accuracy: 0.9382
Epoch 91/100
700/700 [==============================] - 112s 161ms/step - loss: 0.1197 - accuracy: 0.9530 - val_loss: 0.1662 - val_accuracy: 0.9367
Epoch 92/100
700/700 [==============================] - 112s 160ms/step - loss: 0.1106 - accuracy: 0.9550 - val_loss: 0.1533 - val_accuracy: 0.9403
Epoch 93/100
700/700 [==============================] - 113s 161ms/step - loss: 0.1082 - accuracy: 0.9571 - val_loss: 0.1608 - val_accuracy: 0.9380
Epoch 94/100
  1/700 [..............................] - ETA: 0s - loss: 0.0896 - accuracy: 0.9500