model.compile(loss_fn=net_loss,
optimizer=net_opt,
metrics={"Accuracy": Accuracy()})
epoch_size = args_opt.epoch_size
batch_size = args_opt.batch_size
cifar10_path = args_opt.dataset_path
save_checkpoint_epochs = args_opt.save_checkpoint_epochs
dataset_sink_mode = not args_opt.device_target == "CPU"
if args_opt.do_eval: # as for evaluation, users could use model.eval
ds_eval = create_dataset(cifar10_path,
batch_size=batch_size,
is_training=False)
if args_opt.load_pretrained == 'local':
if args_opt.checkpoint_path:
model.load_checkpoint(args_opt.checkpoint_path)
acc = model.eval(ds_eval, dataset_sink_mode=dataset_sink_mode)
print("============== Accuracy:{} ==============".format(acc))
else: # as for train, users could use model.train
ds_train = create_dataset(cifar10_path, batch_size=batch_size)
ckpoint_cb = ModelCheckpoint(
prefix="vgg16_cifar10",
config=CheckpointConfig(
save_checkpoint_steps=save_checkpoint_epochs *
ds_train.get_dataset_size(),
keep_checkpoint_max=10))
model.train(epoch_size,
ds_train,
callbacks=[ckpoint_cb, LossMonitor()],
dataset_sink_mode=dataset_sink_mode)
net = DeepFM(field_size=39,
vocab_size=184965,
embed_size=80,
convert_dtype=True)
# build train network
train_net = DeepFMTrainModel(DeepFMWithLoss(net))
# build eval network
eval_net = DeepFMEvalModel(net)
# build model
model = Model(train_net)
# loss/ckpt/metric callbacks
loss_tm = LossTimeMonitorV2()
config_ckpt = CheckpointConfig(save_checkpoint_steps=data_size // 100,
keep_checkpoint_max=10)
model_ckpt = ModelCheckpoint(prefix='deepfm',
directory=checkpoint_dir,
config=config_ckpt)
auc_metric = AUCMetric()
model.compile(eval_network=eval_net,
metrics={"auc_metric": auc_metric},
amp_level='O0')
print("====== start train model ======", flush=True)
model.train(epoch=epoch_size,
train_dataset=train_ds,
callbacks=[loss_tm, model_ckpt],
dataset_sink_mode=dataset_sink_mode)
print("====== start eval model ======", flush=True)
acc = model.eval(eval_ds)
print("====== eval acc: {} ======".format(acc), flush=True)