Question

在开始使用Keras的官方二进制分类示例（参见here）之后，我正在实现一个Tensorflow作为后端的多类分类器。在这个例子中，有两个类（狗/猫），我现在有50个类，数据以相同的方式存储在文件夹中。

培训时，损失不会下降，准确性也不会提高。我更改了使用sigmoid函数的最后一层使用softmax，将binary_crossentropy更改为categorical_crossentropy，并将class_mode更改为{{1 }}

这是我的代码：

categorical

有关我可能出错的地方的任何想法？任何输入将非常感谢！

编辑：正如@RobertValencia所问，这是最新培训日志的开始：

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import keras.optimizers



optimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

# dimensions of our images.
img_width, img_height = 224, 224

train_data_dir = 'images/train'
validation_data_dir = 'images/val'
nb_train_samples = 209222
nb_validation_samples = 40000
epochs = 50
batch_size = 16

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(50))
model.add(Activation('softmax'))



model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])


train_datagen = ImageDataGenerator()

train_generator = train_datagen.flow_from_directory(
    directory=train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = train_datagen.flow_from_directory(
    directory=validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

model.save_weights('weights.h5')

Answer 1

考虑到需要区分的类的数量，可能会增加模型的复杂性，以及使用不同的优化器，可能会产生更好的结果。尝试使用这个部分基于VGG-16 CNN架构的模型，但不是那么复杂：

model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(1024, activation='relu'))
model.add(Dense(50, activation='softmax'))

optimizer = Nadam(lr=0.002,
                  beta_1=0.9,
                  beta_2=0.999,
                  epsilon=1e-08,
                  schedule_decay=0.004)

model.compile(loss='categorical_crossentropy',
              optimizer=optimizer,
              metrics=['categorical_accuracy'])

如果您的效果更好，我建议您查看VGG-16型号：

https://github.com/fchollet/keras/blob/master/keras/applications/vgg16.py或
https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3（包括零填充和退出图层）

Keras：CNN多类分类器

1 个答案: