fine-tuning的二三事

本文探讨了在数据集大小不一及与ImageNet相似度不同的情况下如何进行模型微调。介绍了三种微调方法:固定特征提取器、部分微调卷积网络及整个预训练模型的微调,并给出了具体的实施步骤。

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日常的应用中,我们会很经常遇到一个问题:

如何应用强大的model(比如ResNet)去训练我们自己的数据?

考虑到这样的几个事实:

  1. 通常我们自己的数据集都不会大(<1w)
  2. 从头开始训练耗时

解决方法就是fine-tuning.


方式

参考CS231的资料,有三种方式

  • ConvNet as fixed feature extractor.
    其实这里有两种做法:
    (1) 使用最后一个fc layer之前的fc layer获得的特征,学习个线性分类器(比如SVM)
    (2) 重新训练最后一个fc layer
  • Fine-tuning the ConvNet.
    固定前几层的参数,只对最后几层进行fine-tuning
  • Pretrained models.
    这个其实和第二种是一个意思,不过比较极端,使用整个pre-trained的model作为初始化,然后fine-tuning整个网络而不是某些层

选择

考虑两个问题:

  • 你的数据集大小
  • 你的数据集和ImageNet(假设在ImageNet上训练的)的相似性

分为四种情况,解决方法基于的原则就是:

NN中的低层特征是比较generic的,比如说线、边缘的信息,高层特征是Dataset Specific的,基于此,如果你的数据集和ImageNet差异比较大,这个时候你应该尽可能的少用pre-trained model的高层特征.

  1. 数据集小(比如<5000),相似度高

    这是最常见的情况,可以仅重新训练最后一层(fc layer)

  2. 数据集大(比如>10000),相似度高

    fine-tuning后几层,保持前面几层不变或者干脆直接使用pre-trained model作为初始化,fine-tuning整个网络

  3. 数据集小,相似度低

    小数据集没有办法进行多层或者整个网络的fine-tuning,建议保持前几层不动,fine-tuning后几层(效果可能也不会很好)

  4. 数据集大,相似度低

    虽然相似度低,但是数据集大,可以和2一样处理

从上面我们可以看出,数据集大有优势,否则最好是数据集和原始的相似度比较高;如果出现数据集小同时相似度低的情况,这个时候去fine-tuning后几层未必会有比较好的效果.

choise

Caffe中如何进行fine-tuning

Caffe做fine-tuning相对tensorflow很简单,只需要简单修改下配置文件就行了.

此处假设你的数据集比较小,同时相似度比较高,仅需重新训练最后一层(fc)的情况.

(1) 降低solver中lr和stepsize

这个很明显,因为相似度比较高我们可以期望原始获得的feature和需要的是很接近的,此时需要降低学习率(lr)和迭代次数(stepsize).

solver.prototxt

(2) 修改最后一层fc的名字,设置好lr_mult

应为需要训练最后一层,我们把之前的层的学习率设置的很低(比如0.001),或者你干脆设置为0,最后一层设置一定的学习率(比如0.01),所以需要乘以10.

deploy.prototxt

(3) 训练

其实就已经改好了,是不是很简单,按照之前标准化的训练测试就好了

知乎上fine-tuning的介绍上有更加详细的介绍,可以移步去看.


参考

(1) NodYoung的博客

(2) CS231的transfer-learning

(3) 知乎上关于caffe下做fine-tuning的介绍

### Ollama Fine-Tuning Guide and Documentation #### Overview of Ollama Fine-Tuning Fine-tuning is a process where pre-trained models are adapted to specific tasks or domains, improving performance on specialized datasets. This approach leverages the knowledge captured by large-scale language models during their initial training phase while allowing customization for particular applications. For Ollama-specific fine-tuning, several key aspects must be considered: - **Data Preparation**: Ensuring that data used for fine-tuning aligns closely with target application scenarios. - **Model Selection**: Choosing an appropriate base model based on task requirements and resource constraints. - **Hyperparameter Tuning**: Adjusting parameters such as learning rate, batch size, etc., to optimize results. - **Evaluation Metrics**: Defining clear criteria to assess improvements post-fine-tuning. #### Practical Steps for Implementing Ollama Fine-Tuning To implement effective fine-tuning strategies using Ollama frameworks, consider following these guidelines: ```python from ollama import load_model, prepare_data, train_model # Load pretrained model model = load_model('pretrained_ollama') # Prepare dataset tailored towards desired use case data = prepare_data(path_to_custom_dataset) # Define hyperparameters suitable for your scenario hyperparams = { 'learning_rate': 0.001, 'batch_size': 32, 'epochs': 5 } # Train the model with custom settings train_model(model=model, data=data, params=hyperparams) ``` This code snippet demonstrates how one might set up a basic pipeline for adapting a generic Ollama-based system into something more domain-specific through targeted adjustments at both input preparation stages alongside parameter selection processes[^1]. #### Best Practices When Performing Ollama Fine-Tuning When engaging in this type of work, adhering to best practices can significantly enhance outcomes: - Utilize high-quality labeled examples relevant to intended deployment contexts. - Experiment systematically across multiple configurations before settling on final choices. - Monitor progress carefully throughout experimentation phases via logging mechanisms built within development environments. - Validate findings rigorously against benchmarks established prior to initiating any modifications. By incorporating these recommendations when working with Ollama's capabilities, developers stand better positioned not only achieve superior technical achievements but also ensure ethical considerations remain paramount during all facets involved from conception through execution cycles[^2]. --related questions-- 1. What preprocessing steps should be taken before feeding text inputs into Ollama models? 2. How does transfer learning differ between general-purpose versus industry-focused NLP solutions like those offered under Ollama umbrella? 3. Can you provide real-world success stories showcasing benefits derived after applying advanced tuning techniques discussed here?
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