Fine-tuning deep learning algorithms will help to improve the accuracy of a new neural network model by integrating data from an existing neural network and using it as an initialization point to make the training process time and resource-efficient.
Deep learning is a subset of machine learning that has networks capable of learning autonomously without human intervention from data that is unstructured or unlabeled. The deep learning algorithms require a vast amount of data to analyze and learn from. Hence, deep learning can prove to be a resource-intensive task. But, what if the process of deep learning can be simplified to make it time-efficient? Luckily, it is possible now with the help of fine-tuning. Fine-tuning, in general, means making small adjustments to a process to achieve the desired output or performance.
Fine-tuning deep learning involves using weights of a previous deep learning algorithm for programming another similar deep learning process. Weights are used to connect each neuron in one layer to every neuron in the next layer in the neural network. The fine-tuning process significantly decreases the time required for programming and processing a new deep learning algorithm as it already contains vital information from a pre-existing deep learning algorithm. Some of the prominent fine-tuning frameworks include Keras, ModelZoo, TensorFlow, Torch, and MxNet. Some of the common questions one may encounter with respect to fine-tuning deep learning algorithms include:
When to fine-tune deep learning models
Although fine-tuning proves beneficial in training new deep learning algorithms, it can be used only when the dataset of an existing model and the new deep learning model are similar to each other. Fine-tuning takes a model that has already been trained for a particular task and then fine-tuning or tweaking it to make it perform a second similar task. For example, a deep learning network that has been used to recognize cars can be fine-tuned to recognize trucks. As the deep learning neural network can identify features of a car such as edges, windshields, doors, lights, etc., it can use this information to recognize trucks. Since trucks and cars have these features in common, the deep learning model doesn’t need to be trained again to identify these features. The same logic can be applied to build deep learning neural networks for identifying vehicles in general. Thus, a lot of time and resources can be saved by using the data from a similar previously programmed deep learning network.
How to fine-tune deep learning
Since the input information for the new neural network is similar to a pre-existing deep learning model, it becomes a relatively easy task to program the new model. The first step includes importing the data of the existing similar deep learning network. The second step involves removing the output layer of the network as it was programmed for tasks specific to the previous model. If we continue with the previous example, the output layer was programmed to recognize whether a given image was a car or not. However, since our new model requires the deep learning neural network to determine whether the given image is a truck or not; the older output layer becomes unusable. Hence, we need to remove the output layer. The third step is optional and depends upon the similarities of both the learning models. You may require to add or remove certain layers depending upon the similarities of the two models. Once you’ve added or removed layers depending upon the data required, you must then freeze the layers in the new model. Freezing a layer means the layer doesn’t need any modification to the data contained in them, henceforth. The weights for these layers don’t update when we train the new model on the new data for the new task.
The final step involves training the model on the new data. The input layer needs to be modified to train the deep learning network to identify trucks. The weights of all the other layers stay the same, and only the input layer is trained on the new model. The output layer then is trained to display the result intended for the new deep learning neural network. The result of this new model will be to display whether the given image is a truck or not. Thus, using data from a deep learning neural network for identifying cars, we can easily train a new network to identify trucks. These two neural networks carry out different tasks, yet are programmed on similar data.
Why fine-tuning deep learning model is necessary
Whenever we are given the task of training a deep learning neural network, we usually think of training it from scratch. Training a neural network is a time and resource-intensive process. The neural network needs to be fed tons of data for it to actually work as intended. Gathering the data for the neural network can take long periods of time. With fine-tuning, the deep learning neural networks already have most of the data available for the new model from previous ones. Thus, a lot of time and resources are saved when fine-tuning deep learning models is carried out.
Fine-tuning deep learning models can also help when the data available for a new deep learning model is limited. For example, the new deep learning model might not have new data to begin with, and thus training such a model can prove to be a problem. With fine-tuning, most of the missing data can be incorporated from previous models, making the training process much easier. For example, if you want to program a deep learning model to identify trucks, there might not be enough data available for it. But, one can utilize images of vehicles or cars specifically so that the deep learning model can recognize the basic features of a vehicle. The truck-specific features can then be recognized with the other data.
Fine-tuning deep learning models also provide ease of transferring knowledge. The available data from a previous deep learning neural network can easily be imported for the new model. It can include the input layer or a combination of the input layer and the hidden layers. The data can be imported into the new deep learning model fairly easily. Slight modifications might be required to the imported layers to work according to the new deep learning model.
Fine-tuning deep learning models prove useful in training new models. It eases the process by being time-efficient. A huge amount of data is imported from previous models. Hence it can help save a lot of time. It also provides a ton of data for the new model, therefore making the new model much more reliable. Fine-tuning can help push the envelope of deep-learning as developing new algorithms will become much more simplified and time-efficient.
