增加图像分类器中 类 的数量
Increase the number of classes in image classifier
我在 keras 中使用 CNN 完成了一个关于两个对象即狗和猫的图像分类的程序。现在如何增加 类，即狗、猫和青蛙的数量？
代码如下：
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
filepath="weights-improvment-{epoch:02d}-{val_acc:.2f}.hdf5"
checpoint=ModelCheckpoint(filepath,monitor='val_acc',verbose=1,save_best_only=True,mode='max')
callback_list=[checpoint]
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 10,
validation_data = test_set,
validation_steps = 2000)
classifier.save('model_after_trained.h5')
为了class化两个以上的classes，最后一层的神经元（单元）数必须改为classes的数量预测。
假设你要预测3个物体，最后一层必须改成：
classifier.add(Dense(units = 3, activation = 'sigmoid'))
请找到下面的link，这将帮助您使用 CNN 进行多class class化：https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html
希望对您有所帮助！！！
我在 keras 中使用 CNN 完成了一个关于两个对象即狗和猫的图像分类的程序。现在如何增加 类，即狗、猫和青蛙的数量？
代码如下：
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
filepath="weights-improvment-{epoch:02d}-{val_acc:.2f}.hdf5"
checpoint=ModelCheckpoint(filepath,monitor='val_acc',verbose=1,save_best_only=True,mode='max')
callback_list=[checpoint]
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 10,
validation_data = test_set,
validation_steps = 2000)
classifier.save('model_after_trained.h5')
为了class化两个以上的classes，最后一层的神经元（单元）数必须改为classes的数量预测。
假设你要预测3个物体，最后一层必须改成：
classifier.add(Dense(units = 3, activation = 'sigmoid'))
请找到下面的link，这将帮助您使用 CNN 进行多class class化：https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html
希望对您有所帮助！！！