15.RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated 简单就是转位int,再使用torch.tensor([x]]) torch.cat() 函数传入的tuple里面的tensor维度不可以为0,而我们直接创建一个非list的tensor的时候,就有了zero-dimensional tensor, 这在pytorch0.3 版本没有什么问题,但pytorch0.4版本...
Any iterable object, such as a dictionary or a tuple, can be concatenated. Two objects of different data types cannot be concatenated. This means you cannot concatenate a list with a dictionary, or an integer with a list. You encounter the “TypeError: can only concatenate list (not “int...
iterable: The iterable object whose elements will be concatenated together. Common iterables include strings, lists, tuples, etc. The fix to this error is simple: only use the valid data types that are iterable. A very simple example of thejoin()function with an iterable is shown below. ...
A tuple containingfeaturesandclass_name. Usage TheCustomDatasetclass is used to encapsulate and preprocess data from a CSV file for machine learning tasks in PyTorch. Suitable for tasks like supervised learning where data needs to be loaded, preprocessed, and transformed into PyTorch tensors. Notes ...
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The codes in each tuple are concatenated ... V Raman,G Swart - International Conference on Very Large Data Bases 被引量: 82发表: 2006年 Energy savings for a wood based modular pre-fabricated facade refurbishment system compared to other measures legal framework is, and tried to sort out how...
任何语言对集合的分类可以分为两种类型,一种是以一个整数为索引的类数组类型 和 一个可以用key来访问值的表类型。在不同的语言中,前者一般叫做数组(array),列表(list)和元组(tuples)。后者一般叫做哈希表(hash table),关联数组 (associative arrays), Map(maps),字典(dictonaries)....
Currently, this method is a stub and needs to be implemented. Args: df_puls (DataFrame): The dataframe containing gene and protein data. Yields --- dict: A dictionary containing gene and protein information. """ for _ in df_puls.itertuples(): yield { # "Gene_Type": row.gene_type,...
result = { k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items() } result["labels"] = result["input_ids"].copy() return result # Note that with `batched=True`, this map processes 1,0...