The Role of Dataset Classes in Transfer Learning

Author(s): Akula Hemanth Kumar

Originally published on Towards AI.

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What do you do with a deep learning model in transfer learning?

These are the steps already done by contributors in pytorch, keras and mxnet

• You take a deep learning architecture, such as resnet, densenet, or even custom network.
• Train the architecture on large datasets such as Imagenet, coco, etc.
• The trained weights become your starting point for transfer learning.

The final layer of this pre-trained model has a number of neurons = number of classes in the large dataset

In transfer learning

• You take the network and load the pre-trained weights on the network.
• Then remove the final layer that has the extra(or less) number of neurons.
• You add a new layer with a number of neurons = number of classes in your custom dataset.
• Optionally you can add more layers in between this newly added final layer and the old network.

Now you have two parts in your network

• One that already existed( the pre-trained one, the base network).
• The new sub-network or a single layer you added.

The hyper-parameter we can see here: Freeze base network

• Freezing base network makes the base network untrainable.
• The base network now acts as a feature extractor and only the next half is trained.
• If you do not freeze the base network the entire network is trained.

Here we have 2 datasets

• Cats-Dogs dataset having 2 classes.
• Logo classification having 16 classes.

Creating and managing experiments

• Provide project name
• Provide experiment name

This creates files and directories as per the following structure

workspace 
 U+007C--Project 
 U+007C--study-num-classes U+007C U+007C--experiment-state.json U+007C U+007C--output U+007C U+007C--logs (All training logs and graphs saved here) U+007C U+007C--models (all trained models saved here)

Setup Default Params with Cats-Dogs dataset

gtf.Default(dataset_path="study_classes/dogs_vs_cats", 
 model_name="resnet18", 
 num_epochs=5)

Visualize network

gtf.Visualize_With_Netron(data_shape=(3, 224, 224), port=8081)

The final layer

Reset Default Params with a new dataset — Logo classification

gtf.Default(dataset_path="study_classes/logos", 
 model_name="resnet18", 
 num_epochs=5)

Visualize network

gtf.Visualize_With_Netron(data_shape=(3, 224, 224), port=8082)

The final layer

You can find the complete jupyter notebook on Github.

If you have any questions, you can reach Abhishek and Akash. Feel free to reach out to them.

I am extremely passionate about computer vision and deep learning in general. I am an open-source contributor to Monk Libraries.

You can also see my other writings at:

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Published via Towards AI