Transfer Learning with MobileNet-v2

introduction to transfer learning

In transfer learning, we use a pre-trained model which is trained on a large and general enough dataset to serve as a generic model for our needs.
We can use these pre-trained models without having to train a model from scratch on a large dataset.

transfer_learning

Find the Jupyter Notebook with Code for Transfer Learning here.

It is a model developed at Google.
It is trained on the ImageNet dataset, a large dataset of 1.4M images and 1000 classes.

Here in the cats_vs_dogs dataset we use the very last layer before the flatten operation for feature extraction. This layer is called a ‘bottleneck layer’.
The bottleneck layer features retain more generality as compared to the final/top layer.
Here we set the include_top=False, so that we load a model that doesn’t include the classification layers at the top.

Examine and understand the data.
Build an input pipeline
Compose a. Loading the pre-trained model and pre-trained weights. b. Stack the classification layers on top.
Train
Evaluate

When working with a small dataset, we can take advantage of features learned by a model trained on a larger dataset in the same domain.
This is done by instantiating the pre-trained model and adding a fully-connected classifier on top.
The pre-trained model is ‘frozen’ and only the weights of the classifier get updated during training.
This in turn helps us achieve better accuracy for our model, even if we have a small dataset and perform only fewer computations for training.

The model has a initial Accuracy of 0.54 and initial loss of 0.63 for the Validation set before training.
After training the model on Train set, it has an accuracy of 0.9501 and loss of 0.1020.

Link to Dockerfile: https://hub.docker.com/repository/docker/afrozchakure/tl_tensorflow