To categorize images effectively, transfer learning is a powerful technique. This form of machine learning employs pre-trained models for a particular task, allowing for the model to be used on fresh data with only minimal additional training. If the data set doesn’t necessitate a new model to be developed from the beginning, transfer learning can be used to quickly apply a powerful model. By utilising the Keras library, transfer learning can be used to rapidly and accurately sort images, saving valuable time and resources. In this article, we delve deeper into transfer learning and its practical application in image categorisation with the Keras library.
To comprehend transfer learning, having a basic understanding of convolutional neural networks (CNN) is crucial. CNN is a prevalent method used for image classification.
In Convolutional Neural Networks (CNNs), convolution layers use filters to identify important features in images. These networks are trained to recognise the unique characteristics of images by employing large datasets. The use of deep CNNs can result in an extensive number of parameters (i.e. weights) that require learning, resulting in longer training times. Transfer learning is an excellent approach to reducing training time in scenarios where multiple factors exist.
ImageNet is amongst the most popular image classification tasks that involves sorting millions of photographs into pre-defined categories accurately. The significant task of properly predicting and classifying hundreds of images necessitates proficiency. Every year a version of the model outperforms the others, and upon identifying the winning model, all its learning parameters or weights are publicly accessible. The Keras application simplifies this process by enabling users to instantly use the top model and its pre-trained weights, saving a substantial amount of time.
The Keras documentation website offers a comprehensive list of all available models pre-trained on the ImageNet classification problem. With no added effort, these models can be implemented effortlessly.
The models used in these models and their accuracy levels are evidently displayed. The source code below provides a glimpse into the level of detail for each model.
Loading the Dataset.
Begin by importing the necessary library files.
It is crucial to be aware of the dimensions of the processed image. As models are trained on the ImageNet classification task, their input size may vary. Therefore, if there is a discrepancy between the size of the task and the image being processed, it is necessary to adjust the input layer.
The ImageNet classification challenge results in 1000 different classes; however, it is possible to decrease this number. It is important to remember that while the hidden layers, convolution layers, and their weights remain the same, the output layer must be adjusted.
For instance, let’s take a look at the skin cancer dataset, which includes pictures of both benign and malignant skin lesions. Users may download this dataset.
The next step is to specify both the train and test paths.
Using Transferred Knowledge
With the dataset now imported, transfer learning can be implemented.
To start, input the dimensions of the image and import VGG16 from the Keras Applications library. The weights are acquired through the ImageNet classification problem, with the Top=False parameter indicating that the weights produced by the network should be used instead of the input and output layers’ weights.
If the “trainable” parameter for a layer in the VGG16 model is set to “False”, that specific layer will not be retrained during the model’s training process. It is important to explicitly use this parameter when needed.
Following that, create a flattened layer to reduce the final layer’s output to one dimension. A two-neuron output layer is required for this purpose. Softmax is the recommended activation function for this layer, as it is the more universal approach; however, Sigmoid can also be used since the output only includes two categories.
Learners can now see the benefits of using transfer learning. With only 15,000 out of a total of 14 million parameters being trainable, the time and effort required to complete the task are significantly reduced, and the process is much less complicated. As a result, transfer learning is highly advantageous.
Following this, a series of trials are performed, where the variables include the Adam optimizer, binary crossentropy loss function, and accuracy.
The same procedure is applied to the testing set.
To get started, input parameters like output batch size, class, and output size goal are needed.
Training the Model
After completing data augmentation, the model training can begin. Model checkpoints are used to save the current best model. The process consists of ten epochs, each with five stages, and 32 validation phases.
Upon completion of the training process, the accuracy of the results will become apparent. The most significant advantage of this approach is that you can obtain the best performing model for the desired dataset simply by taking the weights and implementing them.
Reasons Why Machine Learning Programs Should Utilize Transfer Learning
The primary benefits of transfer learning include:
- Transfer learning can be a beneficial approach for saving both time and money. This is because most machine learning problems require large amounts of labelled data for training. However, with transfer learning, many models come pre-trained, reducing the amount of data required for training and resulting in a more efficient process.
- The use of transfer learning has been demonstrated to significantly improve the effectiveness of model training. This is because creating machine learning models capable of addressing complex problems requires a significant amount of time and effort. In addition, transfer learning eliminates the need to build new models, as existing models can be reused once their data has been transferred.
- Transfer learning provides a more general approach to addressing problems than would be feasible by using a separate algorithm for each new case.
Applications of Transferred Knowledge
The following are some practical examples of transfer learning.
- Transfer learning can be utilized to predict the next word in a sequence in NLP, for instance.
- Transfer learning has demonstrated high efficiency in visual object recognition. For example, a pre-trained model created to recognize cats can also recognize dogs. This highlights the flexibility of transfer learning, as it enables models to be used in applications that go beyond their original intent.
- In voice recognition, a language-specific model can be used to interpret a language spoken by another individual.
- CT scans can be identified using the same model employed for MRI scan recognition.
- To scan emails for spam, a machine learning model developed to classify emails can be utilized.
For image classification tasks, transfer learning is an invaluable method that has proved to be productive. By using Convolutional Neural Networks (CNNs) as the learning mechanism, one can test different models and tune their hyperparameters with the Keras tuner.