第7课：迁移学习—

欢迎来到第7课，我们将介绍迁移学习。该技术通过复用已在大规模通用数据集（如ImageNet）上训练过的深度学习模型，并将其适配到新的具体任务（如我们的FoodVision挑战）中来解决问题。在标注数据集有限的情况下，它对于高效实现顶尖性能至关重要。

1. 预训练权重的力量

深度神经网络以层次化的方式学习特征。底层学习基础概念（边缘、角点、纹理），而深层则将这些组合成复杂概念（眼睛、车轮、特定物体）。关键洞察在于，早期学到的基础特征在大多数视觉领域都具有普遍适用性跨大多数视觉领域均适用。

迁移学习的组成部分

源任务： 在1400万张图像和1000个类别上进行训练（例如，ImageNet）。
目标任务： 将权重调整以分类一个更小的数据集（例如，我们特定的FoodVision类别）。
被利用的部分： 网络参数中的绝大多数——即特征提取层——被直接复用。

效率提升

迁移学习显著降低了两大资源障碍： 计算成本 （你无需花费数天时间训练整个模型）以及 数据需求 （仅需数百个而非数千个训练样本即可达到高精度）。

TERMINALbash — pytorch-env

> Ready. Click "Run" to execute.

TENSOR INSPECTOR Live

Run code to inspect active tensors

Question 1

What is the primary advantage of using a model pre-trained on ImageNet for a new vision task?

It requires less labeled data than training from scratch.

It completely eliminates the need for any training data.

It guarantees 100% accuracy immediately.

Question 2

In a Transfer Learning workflow, which part of the neural network is typically frozen?

The final Output Layer (Classifier Head).

The Convolutional Base (Feature Extractor layers).

The entire network is usually unfrozen.

Question 3

When replacing the classifier head in PyTorch, what parameter must you first determine from the frozen base?

The batch size of the target data.

The input feature size (the output dimensions of the last convolutional layer).

The total number of model parameters.

Challenge: Adapting the Classifier Head

Designing a new classifier for FoodVision.

You load a ResNet model pre-trained on ImageNet. Its last feature layer outputs a vector of size 512. Your 'FoodVision' project has 7 distinct food classes.

Step 1

What is the required Input Feature size for the new, trainable Linear Layer?

Solution:
The Input Feature size must match the output of the frozen base layer.
Size: 512.

Step 2

What is the PyTorch code snippet to create this new classification layer (assuming the output is named `new_layer`)?

Solution:
The output size of 512 is the input, and the class count 7 is the output.
Code: new_layer = torch.nn.Linear(512, 7)

Step 3

What is the required Output Feature size for the new Linear Layer?

Solution:
The Output Feature size must match the number of target classes.
Size: 7.