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: