# 模型训练 model.fit(train_dataset, epochs=5, batch_size=64, verbose=1) # 模型评估 model.evaluate(test_dataset, batch_size=64, verbose=1) The loss value printed in the log is the current step, and the metric is the average value of previous steps. Epoch 1/5 step 938/938 [===]...
format(epoch, batch_id, acc.numpy(),loss.numpy())) loss.backward() opt.minimize(loss) model.clear_gradients() scheduler.step() #训练期间验证 model.eval() meaniou = [] losses = [] for batch_id, data in enumerate(valid_loader()): x_data = paddle.to_tensor(data[0], dtype='float...
首先是将run.sh里的MODEL_PATH修改为你刚保存的模型文件夹: 我这里最后一次训练保存的文件夹是step_1200,因此填入step_1200,要依据自己的情况填入。然后一句命令就够了: 代码语言:javascript 代码运行次数:0 复制 Cloud Studio代码运行 $ sh run.sh eval 可以看到我的模型准确率大概有98%,还是挺不错的。 5.预...
(batch, seq_len, embed_dim)` attention_mask (`paddle.Tensor`, *optional*): attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, query_sequence_length, key_sequence_length)` if default attention is used. output_attentions (`bool`, *...
per_device_eval_batch_size:指定每个GPU核心/CPU在评估时使用的批处理大小。 gradient_accumulation_steps:在执行反向传播和更新参数之前,累积梯度的步数。使用梯度累积时,一次步数对应一次反向传播。 eval_accumulation_steps:在将预测结果移动到CPU之前,累积的预测步数。如果未设置,则整个预测结果将在GPU/TPU上累积后再...
eval_batch_step: [0, 2000] cal_metric_during_train: true pretrained_model: checkpoints: save_inference_dir: use_visualdl: false infer_img: doc/imgs_words/ch/word_1.jpg character_dict_path: ppocr/utils/ppocr_keys_v1.txt max_text_length: &max_text_length 70 ...
eval_batch_step: [0,1200] #模型评估间隔 # if pretrained_model is saved in static mode, load_static_weights must set to True load_static_weights: True #是否将预训练模型保存在静态图形模式 cal_metric_during_train: False #是否设置中值评估 ...
to(self.device) predict_data_loader = [(input_ids, token_type_ids, mask)] batch_probs = [] self.model.eval() with torch.no_grad(): for x in predict_data_loader: batch_prob = self.model(x) batch_probs.append(batch_prob.cpu().numpy()) batch_probs = np.concatenate(batch_pr...
eval() loss_all = 0 eval_steps = 0 formatted_outputs = [] current_idx = 0 for batch in tqdm(data_loader, total=len(data_loader)): eval_steps += 1 input_ids, seq_len, tok_to_orig_start_index, tok_to_orig_end_index, labels = batch logits = model(input_ids=input_ids) mask ...
因此选择一个合适的 batch_size 是很重要的一步;log_interval:每隔 10 step 打印一次训练日志;eval_interval:每隔 50 step 在验证集上进行一次性能评估;checkpoint_dir:将训练的参数和数据保存到 cv_Fine-tune_turtorial_demo 目录中;strategy:使用 DefaultFine-tuneStrategy 策略进行 Fine-tune;更多运行配置,请查看...