CNN-Based Transfer Learning (VGG16)

Transfer learning is a powerful technique for addressing problems involving the categorization of images. Essentially, it is a form of machine learning which utilises pre-trained models for a specific task, allowing the model to be applied to new data with minimal additional training. Transfer learning can be used when there is a need to quickly apply a powerful model to a dataset which is not large enough to require the model to be re-trained from scratch. Through the use of the Keras library, transfer learning can be used to quickly and accurately categorise images, saving time and resources. This article provides an in-depth look into transfer learning and its practical use in image categorization with the Keras library.

In order to gain an understanding of transfer learning, it is important to have a basic familiarity with convolutional neural networks (CNN). CNN is a popular technique used for the purpose of picture categorization.

Convolutional Neural Networks (CNNs) employ convolution layers that use philtres to identify pertinent features in images. With the help of large datasets, these networks are trained to recognise the different characteristics of images. By using a deep CNN, the number of parameters (i.e. weights) that need to be learned can be very high, resulting in longer training times. Transfer learning has provided a great way to reduce this training time in scenarios where multiple factors are present.

One of the most popular image classification tasks is ImageNet, which requires millions of photographs to be sorted into predetermined categories. Accurately predicting and classifying these images into hundreds of categories is an essential task. Every year, one version of the model consistently outperforms the other, and when a winning model is determined, all of its learning parameters or weights are made available to the public. The Keras application streamlines this process by allowing users to immediately utilise the best model and all its pre-trained weights, thus saving a considerable amount of time.

On the Keras documentation website, you can find a comprehensive list of all the available models that have been pre-trained using the ImageNet classification problem. These models can be easily implemented without any additional effort.

Both the parameters utilised in these models and their correctness are clearly shown. The levels of detail in each model are also shown.

Have a peek at the source below.

Dataset loading.

Bring in the required library files first.

It is important to note the dimensions of the image that is being processed. It should be kept in mind that the input size of the model may be different due to it being trained on the ImageNet classification task. Consequently, the input layer must be altered if the size of the task and the image being processed differ.

The ImageNet classification challenge yields an output of 1000 distinct classes; however, it is possible to reduce the number of classes. It is important to note that while the hidden layers, convolution layers, and weights of these layers remain the same, there must be an adjustment to the output layer.

As an example, we can examine the dataset of skin cancer, which contains images of both benign and malignant lesions of the skin. This dataset is available for download to users.

The next move is to define both the train and test routes.

Utilising Transferred Knowledge

Transfer learning can be implemented now that the dataset has been imported.

To begin, you must enter the dimensions of the input image and import VGG16 from the Keras Applications library. The ImageNet classification problem is used to obtain the weights, with the Top=False parameter indicating that the weights of the input and output layers should be ignored in favour of the weights generated by the network itself.

If the “trainable” parameter is set to “False” for a layer in the VGG16 model, it indicates that the layer should not be retrained during the model’s training process. It is important to be aware of this parameter and use it explicitly when necessary.

Next, create a flattened layer in order to reduce the output of the final layer to a single dimension. For this purpose, a two-neuron output layer is necessary. The recommended activation function for this layer is Softmax, as this is the more generic approach; however, Sigmoid may also be used, as the output only contains two categories.

Learners are now able to observe the advantages of utilising transfer learning. With only 15,000 of the total 14 million parameters being trainable, the time and effort required to complete the task is substantially lower and the process is much less complex. Consequently, transfer learning is greatly beneficial.

This is followed by a series of trials where the Adam optimizer, the binary crossentropy loss function, and accuracy are all variables.

The testing set is subjected to the same procedure.

Input parameters such as output batch size, class, and output size goal are required.

Teaching the model

Once data augmentation has been completed, the training of the model can commence. In order to preserve the current best model, model checkpoints are employed. This process will involve ten epochs, each with five stages, and 32 validation phases.

Once the training process is complete, the accuracy of the results will become abundantly clear. The most advantageous feature of this approach is that you can obtain the highest performing model for the desired dataset just by taking the weights and implementing them.

Motives why machine learning programs should use transfer learning

The top advantages of transfer learning are:

  • The utilisation of transfer learning can be an advantageous approach when it comes to saving both money and time. This is due to the fact that the majority of machine learning issues require the use of a large amount of labelled data for training purposes. However, with transfer learning, most models come pre-trained, thus reducing the amount of data needed for training and resulting in a more efficient process.
  • The implementation of transfer learning has been shown to effectively enhance the effectiveness of training models. This is due to the fact that a considerable amount of time and effort is required to create machine learning models that are capable of tackling difficult problems. Furthermore, with transfer learning, the need for constructing new models is eliminated as existing models can be reused once their data has been transferred.
  • Transfer learning offers a more generic approach to tackling the issue than would be possible by applying a distinct algorithm for each new case.

The Uses of Transferred Knowledge

Some practical examples of transfer learning are shown below.

  • It is possible to apply transfer learning to anticipate the next word in a sequence in NLP, for example.
  • Transfer learning has proven to be highly effective in visual object recognition. For instance, a pre-trained model designed to identify cats can be used to identify dogs as well. This demonstrates the versatility of transfer learning, as it allows models to be used in applications that extend beyond their initial purpose.
  • When it comes to voice recognition, a language-specific model may be utilised to decode a language spoken by someone else.
  • It is possible to identify CT scans using the same model used for MRI scan recognition.
  • Scanning email for spam may be done using a machine learning model that was originally designed to categorise emails.

Transfer learning is an invaluable technique for image classification tasks; it has been demonstrated to yield positive results. Utilising Convolutional Neural Networks (CNNs) as the learning mechanism enables one to experiment with multiple models and fine-tune their hyperparameters using the Keras tuner.

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