Tensorflow_Transfer_learning_modified

Tensorflow/Transfer_learning.md

@@ -1,5 +1,8 @@
 # Dogs vs. Cats
 
+- Google Colab 사용
+  - Runtime에서 TPU 사용 추천
+
 - Dogs vs Cats dataset 
   - https://www.kaggle.com/c/dogs-vs-cats/data 
   - 25000 images (12500 cats and 12500 dogs) 
@@ -109,3 +112,206 @@ model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4),
 ```
 
 ![image-20220307174011390](../image.assets/image-20220307174011390.png)
+
+- 문제 유형마다 함수가 어느정도 정해져있음
+
+![image-20220308133622867](../image.assets/image-20220308133622867.png)
+
+---
+
+
+
+#### Data Preprocessing
+
+```python
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+
+train_datagen = ImageDataGenerator(rescale=1./255)
+test_datagen = ImageDataGenerator(rescale=1./255)
+train_generator = train_datagen.flow_from_directory(
+ train_dir,
+ target_size=(150,150),
+ batch_size=20,
+ class_mode='binary'
+)
+validation_generator = test_datagen.flow_from_directory(
+ validation_dir,
+ target_size=(150,150),
+ batch_size=20,
+ class_mode='binary'
+)
+```
+
+- ImageDataGenerator 클래스는 디스크에 있는 이미지 파일을 읽어 전처리 된 배치 텐서로 자동으로 바꾸어 주는 파이썬 제너레이터를 만들어 줌
+
+```python
+for data_batch, labels_batch in train_generator:
+  print('배치 데이터 크기:', data_batch.shape)
+  print('배치 레이블 크기:',  labels_batch.shape)
+  break
+
+'''
+배치 데이터 크기: (20, 150, 150, 3)
+배치 레이블 크기: (20,)
+'''
+```
+
+```python
+history = model.fit_generator( # 배치 제너레이터를 사용하여 모델 훈련
+ train_generator,
+ steps_per_epoch=100,
+ epochs=30,
+ validation_data=validation_generator,
+ validation_steps=50
+)
+model.save('/content/drive/MyDrive/tensflow/cats_and_dogs_small_1.h5') # 모델 저장
+```
+
+```python
+import matplotlib.pyplot as plt
+
+acc = history.history['acc']
+val_acc = history.history['val_acc']
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+
+epochs = range(1, len(acc)+1)
+
+plt.plot(epochs, acc, 'bo', label='Training acc')
+plt.plot(epochs, val_acc, 'b', label='Validation acc')
+plt.title('Training and validation accuracy')
+plt.legend()
+plt.figure()
+
+plt.plot(epochs, loss, 'bo', label='Training loss')
+plt.plot(epochs, val_loss, 'b', label='Validation loss')
+plt.title('Training and validation loss')
+plt.legend()
+
+plt.show()
+```
+
+![image-20220308185343204](../image.assets/image-20220308185343204.png)
+
+---
+
+
+
+#### Data Augmentation
+
+- 성능 향상을 위해 원래 데이터에 랜덤변환을 적용하여 부풀림
+
+```python
+datagen = ImageDataGenerator(
+ rotation_range=20,
+ width_shift_range=0.1,
+ height_shift_range=0.1,
+ shear_range=0.1,
+ zoom_range=0.1,
+ horizontal_flip=True,
+ fill_mode='nearest'
+)
+```
+
+```python
+from keras.preprocessing import image
+fnames = sorted([os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)])
+img_path = fnames[3] # 증식할 이미지 선택
+img = image.load_img(img_path, target_size=(150,150)) # 이미지를 읽고 크기 변경
+x = image.img_to_array(img) # (150,150,3) 크기의 numpy 배열로 변환
+x = x.reshape((1,)+x.shape) # (1,150,150,3) 크기로 변환
+i=0
+for batch in datagen.flow(x, batch_size=1):
+ plt.figure(i)
+ imgplot = plt.imshow(image.array_to_img(batch[0]))
+ i += 1
+ if i%4 == 0:
+ break
+plt.show()
+```
+
+![image-20220308185403163](../image.assets/image-20220308185403163.png)
+
+```python
+model = models.Sequential()
+model.add(Conv2D(32, (3,3), activation='relu', input_shape=(150,150,3)))
+model.add(MaxPooling2D((2,2)))
+model.add(Conv2D(64, (3,3), activation='relu'))
+model.add(MaxPooling2D((2,2)))
+model.add(Conv2D(128, (3,3), activation='relu'))
+model.add(MaxPooling2D((2,2)))
+model.add(Conv2D(128, (3,3), activation='relu'))
+model.add(MaxPooling2D((2,2)))
+model.add(Flatten())
+model.add(Dropout(0.5))
+model.add(Dense(512, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+model.summary()
+from tensorflow.keras import optimizers
+model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4), metrics=['acc'])
+```
+
+![image-20220308185444644](../image.assets/image-20220308185444644.png)
+
+```python
+train_datagen = ImageDataGenerator(
+ rescale=1./255,
+ rotation_range=40,
+ width_shift_range=0.2,
+ height_shift_range=0.2,
+ shear_range=0.2,
+ zoom_range=0.2,
+ horizontal_flip=True,
+ )
+test_datagen = ImageDataGenerator(rescale=1./255) # 검증 데이터는 증식되민 안됨
+train_generator = train_datagen.flow_from_directory(
+ train_dir,
+ target_size=(150,150),
+ batch_size=32,
+ class_mode='binary'
+)
+validation_generator = test_datagen.flow_from_directory(
+ validation_dir,
+ target_size=(150,150),
+ batch_size=32,
+ class_mode='binary'
+)
+'''
+Found 2000 images belonging to 2 classes.
+Found 2000 images belonging to 2 classes.
+'''
+```
+
+```python
+history = model.fit_generator( # 배치 제너레이터를 사용하여 모델 훈련
+ train_generator,
+ steps_per_epoch=50,
+ epochs=300,
+ validation_data=validation_generator,
+ validation_steps=50
+)
+# 마운드한 구글 드라이브에 저장
+model.save('/content/drive/MyDrive/tensflow/cats_and_dogs_small_2.h5') 
+```
+
+```python
+import matplotlib.pyplot as plt
+acc = history.history['acc']
+val_acc = history.history['val_acc']
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+epochs = range(1, len(acc)+1)
+plt.plot(epochs, acc, 'bo', label='Training acc')
+plt.plot(epochs, val_acc, 'b', label='Validation acc')
+plt.title('Training and validation accuracy')
+plt.legend()
+plt.figure()
+plt.plot(epochs, loss, 'bo', label='Training loss')
+plt.plot(epochs, val_loss, 'b', label='Validation loss')
+plt.title('Training and validation loss')
+plt.legend()
+plt.show()
+```
+
+![image-20220308192540554](../image.assets/image-20220308192540554.png)