預訓練的卷積神經網路特徵提取及應用
阿新 • • 發佈:2020-12-11
使用keras上的VGG16模型對ImageNet的訓練結果進行特徵提取,並在貓狗分類中應用,同時進行了資料增強。程式碼如下:
from keras import models from keras import layers from keras import optimizers from keras.applications import VGG16 from keras.preprocessing.image import ImageDataGenerator import matplotlib.pyplot as plt conv_base = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
#建立模型 model = models.Sequential() model.add(conv_base) model.add(layers.Flatten()) model.add(layers.Dense(256, activation='relu')) model.add(layers.Dense(1, activation='sigmoid')) print(model.summary()) print(len(model.trainable_weights))
#凍結卷積基 conv_base.trainable = False print(len(model.trainable_weights)) #貓狗圖片集,訓練集2000張,驗證和測試集各1000張 train_dir = './datasets/train/' validation_dir = './datasets/validation' test_dir = './datasets/test'
#資料增強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,
fill_mode='nearest'
)
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'
)
model.compile(optimizer=optimizers.RMSprop(lr=2e-5),
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit_generator(
train_generator,steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50
)
model.save('cat_and_dog_pre_train_gpu.h5')
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='Traing 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='Traing loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()