optimizer=opt,
metrics=['accuracy'])
# 否则从磁盘中加载checkpoints模型
else:
print("[INFO] loading {}...".format(args['model']))
model = load_model(args['model'])
# 更新学习率
print("[INFO] old learning rate: {}".format(K.get_value(
model.optimizer.lr)))
K.set_value(model.optimizer.lr, 1e-5)
print("[INFO] new learning rate: {}".format(K.get_value(
model.optimizer.lr)))
# 回调函数列表
callbacks = [
# checkpoint
EPO.EpochCheckpoint(args['checkpoints'],
every=5,
startAt=args['start_epoch']),
# 监控训练过程
TM.TrainingMonitor("output/resnet56_cifar10.png",
jsonPath="output/resnet56_cifar10.json",
startAt=args['start_epoch'])
]
# 训练网络
print("[INFO] training network.....")
model.fit_generator(aug.flow(trainX, trainY, batch_size=128),
validation_data=(testX, testY),
steps_per_epoch=len(trainX) // 128,
epochs=10,
callbacks=callbacks,
verbose=1)
opt = SGD(lr=experiment_learning_rate,momentum=0.9)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
else:
print("[INFO] loading {}...".format(args["model"]))
model = load_model(args["model"])
# update the learning rate
print("[INFO] old learning rate: {}".format(K.get_value(model.optimizer.lr)))
K.set_value(model.optimizer.lr, experiment_learning_rate)
print("[INFO] new learning rate: {}".format(K.get_value(model.optimizer.lr)))
# construct the set of callbacks
callbacks = [
ECP.EpochCheckpoint(args["checkpoints"], every=5, startAt=args["start_epoch"]),
TM.TrainingMonitor(config.FIG_PATH, jsonPath=config.JSON_PATH,startAt=args["start_epoch"])]
# train the network
model.fit_generator(
trainGen.generator(),
steps_per_epoch=trainGen.numImages // 64,
validation_data=valGen.generator(),
validation_steps=valGen.numImages // 64,
epochs=experiment_epoch,
max_queue_size=64 * 2,
callbacks=callbacks,
verbose=1)
# close the databases
trainGen.close()