Here's what the first workflow looks like in Keras:įirst, instantiate a base model with pre-trained weights. Such scenarios data augmentation is very important. Your new dataset has too little data to train a full-scale model from scratch, and in Transfer learning is typically used for tasks when Modify the input data of your new model during training, which is required when doingĭata augmentation, for instance. So it's a lot faster & cheaper.Īn issue with that second workflow, though, is that it doesn't allow you to dynamically Your data, rather than once per epoch of training. Use that output as input data for a new, smaller model.Ī key advantage of that second workflow is that you only run the base model once on.Run your new dataset through it and record the output of one (or several) layersįrom the base model.Note that an alternative, more lightweight workflow could also be: Train your new model on your new dataset.Create a new model on top of the output of one (or several) layers from the base.Freeze all layers in the base model by setting trainable = False.Instantiate a base model and load pre-trained weights into it.This leads us to how a typical transfer learning workflow can be implemented in Keras: trainable = False # `trainable` is propagated recursively trainable = False # All layers in `model` are now frozen assert inner_model. trainable = False # Freeze the outer model assert inner_model. Typically they are updated by the model during the forward pass.Įxample: the Dense layer has 2 trainable weights (kernel & bias) non_trainable_weights is the list of those that aren't meant to be trained.trainable_weights is the list of those that are meant to be updated (via gradientĭescent) to minimize the loss during training.weights is the list of all weights variables of the layer.Layers & models have three weight attributes: "building powerful image classification models using very littleįreezing layers: understanding the trainable attribute ImageNet dataset, and retraining it on the Kaggle "cats vs dogs" classification Then, we'll demonstrate the typical workflow by taking a model pretrained on the Transfer learning & fine-tuning workflows. Incrementally adapting the pretrained features to the new data.įirst, we will go over the Keras trainable API in detail, which underlies most This can potentially achieve meaningful improvements, by Model you obtained above (or part of it), and re-training it on the new data with a The old features into predictions on a new dataset.Ī last, optional step, is fine-tuning, which consists of unfreezing the entire Add some new, trainable layers on top of the frozen layers.Freeze them, so as to avoid destroying any of the information they contain during.Take layers from a previously trained model.The most common incarnation of transfer learning in the context of deep learning is the Transfer learning is usually done for tasks where your dataset has too little data to Learned to identify racoons may be useful to kick-start a model meant to identify For instance, features from a model that has Leveraging them on a new, similar problem. Transfer learning consists of taking features learned on one problem, and Import numpy as np import tensorflow as tf from tensorflow import keras
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