如何使用 keras 微调 inception v3 进行多类分类?

Question

我想使用 Keras 使用 Kaggle.com 中的猫与狗数据集进行两类图像分类。 但是我对参数"class_mode"有一些问题，如下面的代码。 如果我使用"二进制"模式，准确率约为95%，但如果使用"分类"，准确率异常低，仅超过50%。

二进制模式意味着最后一层只有一个输出，并使用 S 形激活进行分类。 样本的标签只有一个整数。

分类意味着最后一层有两个输出，并使用 softmax 激活进行分类。 样本的标签是一种热门格式，例如。(1,0(， (0,1(.

我认为这两种方式应该有类似的结果。有人知道差异的原因吗？非常感谢！

import os
import sys
import glob
import argparse
import matplotlib.pyplot as plt
from keras import __version__
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD

在此处设置一些参数

IM_WIDTH, IM_HEIGHT = 299, 299 #fixed size for InceptionV3
NB_EPOCHS = 1
BAT_SIZE = 32
FC_SIZE = 1024
NB_IV3_LAYERS_TO_FREEZE = 172
loss_mode = "binary_crossentropy"
def get_nb_files(directory):
"""Get number of files by searching directory recursively"""
if not os.path.exists(directory):
return 0
cnt = 0
for r, dirs, files in os.walk(directory):
for dr in dirs:
cnt += len(glob.glob(os.path.join(r, dr + "/*")))
return cnt

transfer_learn，保留初始 v3 中的权重

def setup_to_transfer_learn(model, base_model):
"""Freeze all layers and compile the model"""
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='rmsprop', loss=loss_mode, metrics=['accuracy'])

添加最后一个图层以执行两类分类。

def add_new_last_layer(base_model, nb_classes):
"""Add last layer to the convnet
Args:
base_model: keras model excluding top
nb_classes: # of classes
Returns:
new keras model with last layer
"""
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(FC_SIZE, activation='relu')(x) #new FC layer, random init
if args.class_mode == "binary":
predictions = Dense(1, activation='sigmoid')(x) #new softmax layer
else:
predictions = Dense(nb_classes, activation='softmax')(x) #new softmax layer
model = Model(inputs=base_model.input, outputs=predictions)
return model

冻结底层NB_IV3_LAYERS并重新训练剩余的顶层， 并微调重量。

def setup_to_finetune(model):
"""Freeze the bottom NB_IV3_LAYERS and retrain the remaining top layers.
note: NB_IV3_LAYERS corresponds to the top 2 inception blocks in the inceptionv3 arch
Args:
model: keras model
"""
for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:
layer.trainable = False
for layer in model.layers[NB_IV3_LAYERS_TO_FREEZE:]:
layer.trainable = True
model.compile(optimizer="rmsprop", loss=loss_mode, metrics=['accuracy'])
#model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])

def train(args):
"""Use transfer learning and fine-tuning to train a network on a new dataset"""
nb_train_samples = get_nb_files(args.train_dir)
nb_classes = len(glob.glob(args.train_dir + "/*"))
nb_val_samples = get_nb_files(args.val_dir)
nb_epoch = int(args.nb_epoch)
batch_size = int(args.batch_size)
print("nb_classes:{}".format(nb_classes))

数据准备

train_datagen =  ImageDataGenerator(
preprocessing_function=preprocess_input,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
test_datagen = ImageDataGenerator(
preprocessing_function=preprocess_input,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
train_generator = train_datagen.flow_from_directory(
args.train_dir,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,
#class_mode='binary'
class_mode=args.class_mode
)
validation_generator = test_datagen.flow_from_directory(
args.val_dir,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,
#class_mode='binary'
class_mode=args.class_mode
)

设置模型

base_model = InceptionV3(weights='imagenet', include_top=False) #include_top=False excludes final FC layer
model = add_new_last_layer(base_model, nb_classes)

迁移学习

setup_to_transfer_learn(model, base_model)
#model.summary()
history_tl = model.fit_generator(
train_generator,
epochs=nb_epoch,
steps_per_epoch=nb_train_samples//BAT_SIZE,
validation_data=validation_generator,
validation_steps=nb_val_samples//BAT_SIZE)

微调

setup_to_finetune(model)
history_ft = model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples//BAT_SIZE,
epochs=nb_epoch,
validation_data=validation_generator,
validation_steps=nb_val_samples//BAT_SIZE)
model.save(args.output_model_file)
if args.plot:
plot_training(history_ft)

def plot_training(history):
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'r.')
plt.plot(epochs, val_acc, 'r')
plt.title('Training and validation accuracy')
plt.figure()
plt.plot(epochs, loss, 'r.')
plt.plot(epochs, val_loss, 'r-')
plt.title('Training and validation loss')
plt.show()

主要功能

if __name__=="__main__":
a = argparse.ArgumentParser()
a.add_argument("--train_dir", default="train2")
a.add_argument("--val_dir", default="test2")
a.add_argument("--nb_epoch", default=NB_EPOCHS)
a.add_argument("--batch_size", default=BAT_SIZE)
a.add_argument("--output_model_file", default="inceptionv3-ft.model")
a.add_argument("--plot", action="store_true")
a.add_argument("--class_mode", default="binary")

args = a.parse_args()
if args.train_dir is None or args.val_dir is None:
a.print_help()
sys.exit(1)
if args.class_mode != "binary" and args.class_mode != "categorical":
print("set class_mode as 'binary' or 'categorical'")
if args.class_mode == "categorical":
loss_mode = "categorical_crossentropy"
#set class_mode
print("class_mode:{}, loss_mode:{}".format(args.class_mode, loss_mode))
if (not os.path.exists(args.train_dir)) or (not os.path.exists(args.val_dir)):
print("directories do not exist")
sys.exit(1)
train(args)

Answer 1

当学习率太高时，我在几个任务上遇到了这个问题。尝试 0.0001 甚至更小。

根据 Keras 文档，默认速率为 0.001：

keras.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)

见 https://keras.io/optimizers/#rmsprop

Answer 2

我发现如果我使用 SDG 或 Adam 优化器，精度可以正常提高。那么使用默认学习率=0.001 的 RMSprop 优化器有什么问题吗？