发现升级到1.10版本后,mindspore.dataset ImageFolderDataset生成的数据集,在有extensions=[".jpg", ".jpeg", ".JPEG"] 参数的时候,get_dataset_size()会错误的返回0 ,而没有这个参数,返回正常。 hymenoptera_data 目录下是train目录,train目录下是两个目录,分别放着蜜蜂和蚂蚁的jpg图片。 import mindspore.dataset...
(self.device) train_loader = DataLoader(dataset=train_tensor_data, shuffle=shuffle, batch_size=batch_size) sample_num = len(train_tensor_data) steps_per_epoch = (sample_num - 1) // batch_size + 1 # train print("Train on {0} samples, validate on {1} samples, {2} steps per ...
df_train, df_test = generate_seq_feature_match(data,user_col,item_col,time_col="timestamp",item_attribute_cols=[],sample_method=1, mode=0,neg_ratio=3,min_item=0)# 该函数将在 1.5 中讲解print(df_train.head()) x_train = gen_model_input(df_train, user_profile, user_col, item_pr...
2.2. 清洗图片,按Paddlepaddle2.0 的格式 建立dataset 2.2.1. 过滤图片 2.2.2. 归一化 2.2.3 style图片数量 3. 训练 3.1 载入modelhub的msgnet 预训练模型 或 自己训练到一半的模型 3.2. 训练的参数——backbone部分要stop_gradient(重要): 3.3 训练情况: 3.4 使用visualDL查看训练过程(已熟悉的请忽略): 4....
句子 a句子 b句子 c句子 a0.92480.23420.4242句子 b0.31420.91230.1422句子 c0.29030.18570.9983 矩阵中第(i,j)个元素代表 origin 列表中的第 i 个元素和 repetition 列表中第 j 个元素
I will never tire of highlighting that MS researchers have always had a pioneering attitude towards how to train/create models by reducing their size while simultaneously increasing their capabilities; papers on models like Orca 2, Phi-3, and Florence-2 serve as a sort of manual on data prepar...
trainer = SFTTrainer( model=model, tokenizer=tokenizer, train_dataset=train_data, dataset_text_field="text", max_seq_length=max_seq_length, dataset_num_proc=2, args=TrainingArguments( per_device_train_batch_size=2, num_train_epochs=3, gradient_accumulation_steps=4, # Use num_train_epochs...
azureml-train-automl-client azureml-train-automl-runtime azureml-train-core azureml-training-tabular azureml-widgets azureml-contrib-automl-pipeline-steps azureml-contrib-dataset azureml-contrib-fairness azureml-contrib-functions azureml-contrib-notebook ...
trainer =SFTTrainer(model=model,tokenizer=tokenizer,train_dataset=train_data,dataset_text_field="text",max_seq_length=max_seq_length,dataset_num_proc=2,args=TrainingArguments(per_device_train_batch_size=2,num_train_epochs=6,gradient_accumulation_steps=4,# Use num_train_epochs = 1, warmup_rat...
parser.add_argument("--reason_seg_data", default="ReasonSeg|train", type=str) parser.add_argument("--val_dataset", default="ReasonSeg|val", type=str) parser.add_argument("--dataset_dir", default="./dataset", type=str) parser.add_argument("--log_base_dir", default="./runs",...