fastAI第一章学习笔记

这是第二篇学习笔记博客，但记录的是fastAI课程第一章的学习笔记. 这里记录一些概念/术语的理解。

术语

Term	Meaning
Label	The data that we’re trying to predict, such as “dog” or “cat”
Architecture	The template of the model that we’re trying to fit; the actual mathematical function that we’re passing the input data and parameters to
Model	The combination of the architecture with a particular set of parameters
Parameters	The values in the model that change what task it can do, and are updated through model training
Fit	Update the parameters of the model such that the predictions of the model using the input data match the target labels
Train	A synonym for fit
Pretrained model	A model that has already been trained, generally using a large dataset, and will be fine-tuned
Fine-tune	Update a pretrained model for a different task
Epoch	One complete pass through the input data
Loss	A measure of how good the model is, chosen to drive training via SGD
Metric	A measurement of how good the model is, using the validation set, chosen for human consumption
Validation set	A set of data held out from training, used only for measuring how good the model is
Training set	The data used for fitting the model; does not include any data from the validation set
Overfitting	Training a model in such a way that it remembers specific features of the input data, rather than generalizing well to data not seen during training
CNN	Convolutional neural network; a type of neural network that works particularly well for computer vision tasks

疑问

关于fine tune这里有个问题，根据定义，fine tune是对一个已经训练好的模型进行“微调”，用于一个 不同 的任务, 但是很多地方在训练之后，使用模型之前也会进行fine tune 如第二章中(当然第一章也有很多这样的例子)

bears = DataBlock(
    blocks=(ImageBlock, CategoryBlock), # what kind of date we want to working with - image
    get_items=get_image_files, #how to get the items - by files
    splitter=RandomSplitter(valid_pct=0.2, seed=42), #how to create validation set
    get_y=parent_label, # how to label these items
    item_tfms=Resize(128))

bears = bears.new(
    item_tfms=RandomResizedCrop(224, min_scale=0.5),
    batch_tfms=aug_transforms())
dls = bears.dataloaders(path)

learn = vision_learner(dls, resnet18, metrics=error_rate)

以上vision_learner就是对模型进行针对这个任务(识别熊的种类)进行训练，后面也是用到该任务，下面的fine tune 是否有必要？

learn.fine_tune(4)

2023年注:

cnn_learner 已经更名为vision_Leanrner，旧API名称以不推荐使用。 对于上面的疑问，fune tune确实是对已训练好的模型进行微调。只不过这里“已经训练好的模型”是 dls 套用 resnet18 它本身就可以看作一个预先训练好的模型。

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