데이터셋 링크 : https://www.kaggle.com/asdasdasasdas/garbage-classification
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
|
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import load_img, img_to_array, ImageDataGenerator
import matplotlib.pyplot as plt
import os, shutil
# 데이터셋 생성
dataset_dir = './data/Garbage classification/'
train_dir = os.path.join(dataset_dir, 'train')
os.mkdir(train_dir)
validation_dir = os.path.join(dataset_dir, 'val')
os.mkdir(validation_dir)
train_dir_cardboard = os.path.join(train_dir, 'cardboard')
os.mkdir(train_dir_cardboard)
train_dir_glass = os.path.join(train_dir, 'glass')
os.mkdir(train_dir_glass)
train_dir_metal = os.path.join(train_dir, 'metal')
os.mkdir(train_dir_metal)
train_dir_paper = os.path.join(train_dir, 'paper')
os.mkdir(train_dir_paper)
train_dir_plastic = os.path.join(train_dir, 'plastic')
os.mkdir(train_dir_plastic)
train_dir_trash = os.path.join(train_dir, 'trash')
os.mkdir(train_dir_trash)
validation_dir_cardboard = os.path.join(validation_dir, 'cardboard')
os.mkdir(validation_dir_cardboard)
validation_dir_glass = os.path.join(validation_dir, 'glass')
os.mkdir(validation_dir_glass)
validation_dir_metal = os.path.join(validation_dir, 'metal')
os.mkdir(validation_dir_metal)
validation_dir_paper = os.path.join(validation_dir, 'paper')
os.mkdir(validation_dir_paper)
validation_dir_plastic = os.path.join(validation_dir, 'plastic')
os.mkdir(validation_dir_plastic)
validation_dir_trash = os.path.join(validation_dir, 'trash')
os.mkdir(validation_dir_trash)
file_names = ['cardboard{}.jpg'.format(i) for i in range(1, 101)]
for file_name in file_names:
src = os.path.join(dataset_dir + '/cardboard', file_name)
dst = os.path.join(validation_dir_cardboard, file_name)
shutil.copyfile(src, dst)
file_names = ['glass{}.jpg'.format(i) for i in range(1, 101)]
for file_name in file_names:
src = os.path.join(dataset_dir + '/glass', file_name)
dst = os.path.join(validation_dir_glass, file_name)
shutil.copyfile(src, dst)
file_names = ['metal{}.jpg'.format(i) for i in range(1, 101)]
for file_name in file_names:
src = os.path.join(dataset_dir + '/metal', file_name)
dst = os.path.join(validation_dir_metal, file_name)
shutil.copyfile(src, dst)
file_names = ['paper{}.jpg'.format(i) for i in range(1, 101)]
for file_name in file_names:
src = os.path.join(dataset_dir + '/paper', file_name)
dst = os.path.join(validation_dir_paper, file_name)
shutil.copyfile(src, dst)
file_names = ['plastic{}.jpg'.format(i) for i in range(1, 101)]
for file_name in file_names:
src = os.path.join(dataset_dir + '/plastic', file_name)
dst = os.path.join(validation_dir_plastic, file_name)
shutil.copyfile(src, dst)
file_names = ['trash{}.jpg'.format(i) for i in range(1, 31)]
for file_name in file_names:
src = os.path.join(dataset_dir + '/trash', file_name)
dst = os.path.join(validation_dir_trash, file_name)
shutil.copyfile(src, dst)
file_names = ['cardboard{}.jpg'.format(i) for i in range(101, len(os.listdir(dataset_dir + '/cardboard')))]
for file_name in file_names:
src = os.path.join(dataset_dir + '/cardboard', file_name)
dst = os.path.join(train_dir_cardboard, file_name)
shutil.copyfile(src, dst)
file_names = ['glass{}.jpg'.format(i) for i in range(101, len(os.listdir(dataset_dir + '/glass')))]
for file_name in file_names:
src = os.path.join(dataset_dir + '/glass', file_name)
dst = os.path.join(train_dir_glass, file_name)
shutil.copyfile(src, dst)
file_names = ['metal{}.jpg'.format(i) for i in range(101, len(os.listdir(dataset_dir + '/metal')))]
for file_name in file_names:
src = os.path.join(dataset_dir + '/metal', file_name)
dst = os.path.join(train_dir_metal, file_name)
shutil.copyfile(src, dst)
file_names = ['paper{}.jpg'.format(i) for i in range(101, len(os.listdir(dataset_dir + '/paper')))]
for file_name in file_names:
src = os.path.join(dataset_dir + '/paper', file_name)
dst = os.path.join(train_dir_paper, file_name)
shutil.copyfile(src, dst)
file_names = ['plastic{}.jpg'.format(i) for i in range(101, len(os.listdir(dataset_dir + '/plastic')))]
for file_name in file_names:
src = os.path.join(dataset_dir + '/plastic', file_name)
dst = os.path.join(train_dir_plastic, file_name)
shutil.copyfile(src, dst)
file_names = ['trash{}.jpg'.format(i) for i in range(31, len(os.listdir(dataset_dir + '/trash')))]
for file_name in file_names:
src = os.path.join(dataset_dir + '/trash', file_name)
dst = os.path.join(train_dir_trash, file_name)
shutil.copyfile(src, dst)
train_gen = ImageDataGenerator(rotation_range=60, width_shift_range=0.3, shear_range=0.3,
horizontal_flip=True, zoom_range=0.3, rescale=1./255)
val_gen = ImageDataGenerator(rotation_range=60, width_shift_range=0.3, shear_range=0.3,
horizontal_flip=True, zoom_range=0.3, rescale=1./255)
train_generator = train_gen.flow_from_directory(train_dir, target_size=(240, 240), batch_size=32, class_mode='sparse')
validation_generator = train_gen.flow_from_directory(validation_dir, target_size=(240, 240), batch_size=32, class_mode='sparse')
# 모델 생성
def make_model():
model = Sequential()
model.add(Conv2D(input_shape=(240, 240, 3), kernel_size=(3, 3), filters=32, activation='relu'))
model.add(Conv2D(kernel_size=(3, 3), filters=32, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(kernel_size=(3, 3), padding='same', filters=64, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(kernel_size=(3, 3), filters=128, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(kernel_size=(3, 3), filters=128, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(6, activation='softmax'))
return model
model = make_model()
# 모델 컴파일 및 훈련시키기
model.compile(loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
history = model.fit_generator(train_generator, steps_per_epoch=10, epochs=200,
validation_data=validation_generator, validation_steps=10)
# 훈련의 정확도 확인
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.legend(['train', 'validation'], loc='best')
plt.show()
|
cs |
위 소스코드를 실행시키면 아래와 같은 출력값을 얻을 수 있다.
반응형
'머신러닝_딥러닝 > Tensorflow + Keras' 카테고리의 다른 글
| (Tensorflow 2.x) 함수형 API (0) | 2021.10.23 |
|---|---|
| (Tensorflow 2.x) CNN 3탄 (Transfer Learning) (0) | 2021.10.23 |
| (Tensorflow 2.x) CNN 1탄 (With CIFAR-10 ) (0) | 2021.10.23 |
| (Tensorflow 2.x) Fashion MNIST (With CNN) (0) | 2021.10.23 |
| (Tensorflow 2.x) MNIST (With CNN) (0) | 2021.10.23 |