def test_deepfm():
data_config = DataConfig()
train_config = TrainConfig()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
device_id=device_id)
rank_size = None
rank_id = None
dataset_path = "/home/workspace/mindspore_dataset/criteo_data/criteo_h5/"
print("dataset_path:", dataset_path)
ds_train = create_dataset(dataset_path,
train_mode=True,
epochs=1,
batch_size=train_config.batch_size,
data_type=DataType(data_config.data_format),
rank_size=rank_size,
rank_id=rank_id)
model_builder = ModelBuilder(ModelConfig, TrainConfig)
train_net, eval_net = model_builder.get_train_eval_net()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
loss_file_name = './loss.log'
time_callback = TimeMonitor(data_size=ds_train.get_dataset_size())
loss_callback = LossCallBack(loss_file_path=loss_file_name)
callback_list = [time_callback, loss_callback]
eval_file_name = './auc.log'
ds_eval = create_dataset(dataset_path,
train_mode=False,
epochs=1,
batch_size=train_config.batch_size,
data_type=DataType(data_config.data_format))
eval_callback = EvalCallBack(model,
ds_eval,
auc_metric,
eval_file_path=eval_file_name)
callback_list.append(eval_callback)
print("train_config.train_epochs:", train_config.train_epochs)
model.train(train_config.train_epochs, ds_train, callbacks=callback_list)
export_loss_value = 0.51
print("loss_callback.loss:", loss_callback.loss)
assert loss_callback.loss < export_loss_value
export_per_step_time = 40.0
print("time_callback:", time_callback.per_step_time)
assert time_callback.per_step_time < export_per_step_time
print("*******test case pass!********")
default="Ascend",
help='Ascend, GPU, or CPU')
args_opt, _ = parser.parse_known_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target=args_opt.device_target,
device_id=device_id)
def add_write(file_path, print_str):
with open(file_path, 'a+', encoding='utf-8') as file_out:
file_out.write(print_str + '\n')
if __name__ == '__main__':
data_config = DataConfig()
model_config = ModelConfig()
train_config = TrainConfig()
ds_eval = create_dataset(args_opt.dataset_path,
train_mode=False,
epochs=1,
batch_size=train_config.batch_size,
data_type=DataType(data_config.data_format))
model_builder = ModelBuilder(ModelConfig, TrainConfig)
train_net, eval_net = model_builder.get_train_eval_net()
train_net.set_train()
eval_net.set_train(False)
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
