Transfer Learning Made Easy: Coding a Powerful Technique - KDnuggets

Fig: The model summary of the second network showing the fixed and trainable weights. The fixed weights are transferred directly from the first network. Now we train the second model and observe how it takes less overall time and still gets equal or higher performance. The accuracy of the second model is even higher than the first model, although this may not be the case all the time, and depends on the model architecture and dataset. Fig: Validation set accuracy over epochs while training the second network.