ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size() * config.epochs,
keep_checkpoint_max=10)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path + '/ckpt_' +
str(get_rank()) + '/',
config=ckptconfig)
callback_list = [
TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback
]
if int(get_rank()) == 0:
callback_list.append(ckpoint_cb)
model.train(epochs,
ds_train,
callbacks=callback_list,
sink_size=ds_train.get_dataset_size())
if __name__ == "__main__":
wide_and_deep_config = WideDeepConfig()
wide_and_deep_config.argparse_init()
compute_emb_dim(wide_and_deep_config)
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
save_graphs=True)
init()
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True,
device_num=get_group_size())
train_and_eval(wide_and_deep_config)
def test_eval(config):
"""
test evaluate
"""
data_path = config.data_path
batch_size = config.batch_size
ds_eval = create_dataset(data_path, train_mode=False, epochs=2,
batch_size=batch_size)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
param_dict = load_checkpoint(config.ckpt_path)
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
if __name__ == "__main__":
widedeep_config = WideDeepConfig()
widedeep_config.argparse_init()
test_eval(widedeep_config)
batch_size=batch_size)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
net_builder = ModelBuilder()
train_net, _ = net_builder.get_net(configure)
train_net.set_train()
model = Model(train_net)
callback = LossCallBack(config=configure)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=configure.ckpt_path,
config=ckptconfig)
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), callback,
ckpoint_cb
])
if __name__ == "__main__":
config = WideDeepConfig()
config.argparse_init()
context.set_context(mode=context.GRAPH_MODE,
device_target=config.device_target)
test_train(config)
