embedding_table = np.loadtxt(
os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32)
network = SentimentNet(vocab_size=embedding_table.shape[0],
embed_size=cfg.embed_size,
num_hiddens=cfg.num_hiddens,
num_layers=cfg.num_layers,
bidirectional=cfg.bidirectional,
num_classes=cfg.num_classes,
weight=Tensor(embedding_table),
batch_size=cfg.batch_size)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
opt = nn.Momentum(network.trainable_params(), cfg.learning_rate,
cfg.momentum)
loss_cb = LossMonitor()
model = Model(network, loss, opt, {'acc': Accuracy()})
print("============== Starting Testing ==============")
ds_eval = lstm_create_dataset(args.preprocess_path,
cfg.batch_size,
training=False)
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
if args.device_target == "CPU":
acc = model.eval(ds_eval, dataset_sink_mode=False)
else:
acc = model.eval(ds_eval)
print("============== {} ==============".format(acc))
embedding_table = np.pad(embedding_table, [(0, 0), (0, pad_num)],
'constant')
cfg.embed_size = int(np.ceil(cfg.embed_size / 16) * 16)
network = SentimentNet(vocab_size=embedding_table.shape[0],
embed_size=cfg.embed_size,
num_hiddens=cfg.num_hiddens,
num_layers=cfg.num_layers,
bidirectional=cfg.bidirectional,
num_classes=cfg.num_classes,
weight=Tensor(embedding_table),
batch_size=cfg.batch_size)
# pre_trained
if args.pre_trained:
load_param_into_net(network, load_checkpoint(args.pre_trained))
ds_train = lstm_create_dataset(args.preprocess_path, cfg.batch_size, 1)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
if cfg.dynamic_lr:
lr = Tensor(
get_lr(global_step=cfg.global_step,
lr_init=cfg.lr_init,
lr_end=cfg.lr_end,
lr_max=cfg.lr_max,
warmup_epochs=cfg.warmup_epochs,
total_epochs=cfg.num_epochs,
steps_per_epoch=ds_train.get_dataset_size(),
lr_adjust_epoch=cfg.lr_adjust_epoch))
else:
lr = cfg.learning_rate
cfg.embed_size = int(np.ceil(cfg.embed_size / 16) * 16)
network = SentimentNet(vocab_size=embedding_table.shape[0],
embed_size=cfg.embed_size,
num_hiddens=cfg.num_hiddens,
num_layers=cfg.num_layers,
bidirectional=cfg.bidirectional,
num_classes=cfg.num_classes,
weight=Tensor(embedding_table),
batch_size=cfg.batch_size)
# pre_trained
if args.pre_trained:
load_param_into_net(network, load_checkpoint(args.pre_trained))
ds_train = lstm_create_dataset(args.preprocess_path,
cfg.batch_size,
1,
device_num=device_num,
rank=rank)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
if cfg.dynamic_lr:
lr = Tensor(
get_lr(global_step=cfg.global_step,
lr_init=cfg.lr_init,
lr_end=cfg.lr_end,
lr_max=cfg.lr_max,
warmup_epochs=cfg.warmup_epochs,
total_epochs=cfg.num_epochs,
steps_per_epoch=ds_train.get_dataset_size(),
lr_adjust_epoch=cfg.lr_adjust_epoch))
else:
