CV：基于Keras利用CNN主流架构之mini_XCEPTION训练性别分类模型hdf5并保存到指定文件夹下

CV：基于Keras利用CNN主流架构之mini_XCEPTION训练性别分类模型hdf5并保存到指定文件夹下

 

 

目录

图示过程

核心代码

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图示过程

 

核心代码

from keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping
from keras.callbacks import ReduceLROnPlateau
from models.cnn import mini_XCEPTION

# parameters1、定义参数：每个batch的采样本数、训练轮数、输入shape、部分比例分离用于验证、冗长参数、分类个数、patience、do_random_crop
batch_size = 32
num_epochs = 1000
validation_split = .2
do_random_crop = False  #random crop only works for classification since the current implementation does no transform bounding boxes
patience = 100
num_classes = 2
dataset_name = 'imdb'
input_shape = (64, 64, 1)

#if判断，然后指定图像、log、loghdf5各自保存路径
if input_shape[2] == 1:
    grayscale = True
images_path = '../datasets/imdb_crop/'
log_file_path = '../trained_models/gender_models/gender_training.log'
trained_models_path = '../trained_models/gender_models/gender_mini_XCEPTION'


# data loader
data_loader = DataManager(dataset_name) #自定义DataManager函数实现根据数据集name进行加载
ground_truth_data = data_loader.get_data() #自定义get_data函数根据不同数据集name得到各自的ground truth data，
train_keys, val_keys = split_imdb_data(ground_truth_data, validation_split)
print('Number of training samples:', len(train_keys))
print('Number of validation samples:', len(val_keys))

#调用ImageDataGenerator函数实现实时数据增强生成小批量的图像数据。
image_generator = ImageGenerator(ground_truth_data, batch_size,
                                 input_shape[:2],
                                 train_keys, val_keys, None,
                                 path_prefix=images_path,
                                 vertical_flip_probability=0,
                                 grayscale=grayscale,
                                 do_random_crop=do_random_crop)

# model parameters/compilation2、建立XCEPTION模型并compile编译配置参数，最后输出网络摘要
model = mini_XCEPTION(input_shape, num_classes)
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.summary()

#3、指定要训练的数据集(gender→imdb即男女数据集)

# model callbacks
# callbacks4、回调：通过调用CSVLogger、EarlyStopping、ReduceLROnPlateau、ModelCheckpoint等函数得到训练参数存到一个list内
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1,
                              patience=int(patience/2), verbose=1)
csv_logger = CSVLogger(log_file_path, append=False)
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names,
                                   monitor='val_loss',
                                   verbose=1,
                                   save_best_only=True,
                                   save_weights_only=False)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]

# training model5、调用fit_generator函数训练模型
model.fit_generator(image_generator.flow(mode='train'),
                    steps_per_epoch=int(len(train_keys) / batch_size),
                    epochs=num_epochs, verbose=1,
                    callbacks=callbacks,
                    validation_data=image_generator.flow('val'),
                    validation_steps=int(len(val_keys) / batch_size))