We train the model using the fit_generator() method to leverage the data augmentation prepared in the previous step. Hence, it is sometimes confusing to differentiate between transfer learning, domain adaptation, and multi-task learning. The architecture of the VGG-16 model is depicted in the following figure. Transfer learning Once downloaded, unzip it into a folder. Lets improve upon our regularized CNN model by adding in more data using a proper image augmentation strategy. We will be using the Inception V3 Model from Google as our pre-trained model. We explore a transfer learning setting, In this case, we have a total of 10222 samples and 120 classes. We will now scale each image with pixel values between (0, 255) to values between (0, 1) because deep learning models work really well with small input values. Our worst model is our basic CNN model, with a model accuracy and F1-score of around 78%, and our best model is our fine-tuned model with transfer learning and image augmentation, which gives us a model accuracy and F1-score of 96%, which is really amazing considering we trained our model from our 3,000 image training dataset. We will be leveraging the dataset available through Kaggle available here. However, the training time and the amount of data required for such deep learning systems are much more than that of traditional ML systems. Thus, we are mostly concerned with leveraging the convolution blocks of the VGG-16 model and then flattening the final output (from the feature maps) so that we can feed it into our own dense layers for our classifier. We will now leverage our VGG-16 model object stored in the vgg_model variable and unfreeze convolution blocks 4 and 5 while keeping the first three blocks frozen. Initial learning is necessary for transfer, and a considerable amount is known about the kinds of learning experiences that support transfer. You can clearly see in the previous output that we generate a new version of our training image each time (with translations, rotations, and zoom) and also we assign a label of cat to it so that the model can extract relevant features from these images and also remember that these are cats. The case studies depicted here and their results are purely based on actual experiments which we conducted when we implemented and tested these models while working on our book: Hands on Transfer Learning with Python (details at the end of this article). Also, as we have depicted in the earlier figure, knowledge from an existing task acts as an additional input when learning a new target task. An April 28th Machine Design-hosted live webinar sponsored by Bishop-Wisecarver. We can see that our model has an overall validation accuracy of 90%, which is a slight improvement from our previous model, and also the train and validation accuracy are quite close to each other, indicating that the model is not overfitting. al. The models have to be rebuilt from scratch once the feature-space distribution changes. The important thing to remember here is that the test dataset has to undergo similar pre-processing as the training dataset. The adult learner is motivated to apply his newly-acquired knowledge only if he is confident that it will help him tackle his real-life challenges. We can see that accuracy values are really excellent here, and although the model looks like it might be slightly overfitting on the training data, we still get great validation accuracy. We encourage you to try out similar strategies with your own data! Pre-trained models are used in the following two popular ways when building new models or reusing them: We will cover both of them in detail in this section. This is going to be a challenge! Luckily, the deep learning world believes in sharing. Alternative Perspectives on the Transfer of Learning: History, Issues, and Challenges for Future Research. The VGG-16 model is a 16-layer (convolution and fully connected) network built on the ImageNet database, which is built for the purpose of image recognition and classification. It could be argued that the problem came into existence the day we separated training from the workplace. We had implemented some distance learning in the past, but nothing at this scale. Transfer Learning Contests: Name: Sponsor: Status: Unsupervised and Transfer Learning Challenge (Phase 2) IJCNN'11: Finished: Learning to Rank Challenge (Task 2) Yahoo! These include the following. The objective for inductive-learning algorithms is to infer a mapping from a set of training examples. Motivating learners is one of the common challenges faced by the eLearning developers. There is a stark difference between the traditional approach of building and training machine learning models, and using a methodology following transfer learning principles. Since students will need to apply what they learn new circumstances, it is important to conceive of transfer of knowledge with a recognition of the complex relationship of co mponents involved in transfer. The dataset that we will be using, comes from the very popular Dogs vs. Cats Challenge, where our primary objective is to build a deep learning model that can successfully recognize and categorize images into either a cat or a dog. Challenges of Using EDI. )wvA.'*y^ K1jW>b)nQ.Kul~~bf%pB. In their book on Deep Learning, Goodfellow and their co-authors present zero-shot learning as a scenario where three variables are learned, such as the traditional input variable, x, the traditional output variable, y, and the additional random variable that describes the task, T. The model is thus trained to learn the conditional probability distribution of P(y | x, T). Transfer learning wolves many of the problems of training AI models in an efficient and affordable way. In the case of multitask learning, several tasks are learned simultaneously without distinction between the source and targets. This might sound unbelievable, especially when learning using examples is what most supervised learning algorithms are about. Using this insight, we may freeze (fix weights) certain layers while retraining, or fine-tune the rest of them to suit our needs. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? This can be achieved by applying certain pre-processing steps directly to the representations themselves. The primary challenge is educating students as much as possible from their homes. We reduce the default learning rate by a factor of 10 here for our optimizer to prevent the model from getting stuck in a local minima or overfit, as we will be sending a lot of images with random transformations. This output from the penultimate layer is then fed into an additional set of layers, followed by a classification layer. Check your inboxMedium sent you an email at to complete your subscription. When you deploy the course and leave the learners to their devices, chances are that the learners may not take up the eLearning course effectively as they would do in classroom training. Dont have the time to read through the book or cant spend right now? All of this is part of the model training process, so lets train our model using the following snippet which leverages the fit() function. In order for such a learner to generalize well on unseen data, its algorithm works with a set of assumptions related to the distribution of the training data. Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. While we can use all 25,000 images and build some nice models on them, if you remember, our problem objective includes the added constraint of having a small number of images per category. A pre-trained model like the VGG-16 is an already pre-trained model on a huge dataset (ImageNet) with a lot of diverse image categories. Now that we have our scaled dataset ready, lets evaluate each model by making predictions for all the test images, and then evaluate the model performance by checking how accurate are the predictions. The following code helps us achieve this. 2020-05-13 Update: This blog post is now TensorFlow 2+ compatible! You can clearly see that after 23 epochs the model starts overfitting on the training data. What we acquire as knowledge while learning about one task, we utilize in the same way to solve related tasks. We have also built a nifty utility module called model_evaluation_utils, which we will be using to evaluate the performance of our deep learning models. One study found that learners who used relevant visuals were able to retain more information and scored higher on transfer tests than those who used only text. Learn how transfer learning works using PyTorch and how it ties into using pre-trained models. The following are a few examples: Transfer learning for NLP: Textual data presents all sorts of challenges when it comes to ML and deep learning. Transportation Management Systems (TMS) To attend class online, youll need a certain degree of technological proficiencyincluding the ability to successfully log in, participate in classes, submit work, and communicate with teachers and classmates. A section of this chapter discusses approaches for avoiding negative transfer. For instance, once a child is shown what an apple looks like, they can easily identify a different variety of apple (with one or a few training examples); this is not the case with ML and deep learning algorithms. Deep learning systems and models are layered architectures that learn different features at different layers (hierarchical representations of layered features). Now, if you remember from the previous case study, image augmentation is a great way to deal with having less data per class. In particular, we discussed how feature-representation transfer can be useful. Date: This is one of the limiting aspects of deep neural networks, though such is not the case with human learning. Yet, there are certain pertinent issues related to transfer learning that need more research and exploration. Unsupervised and Transfer Learning Challenge: a Deep Learning Approach 109 G. Mesnil et al; JMLR W&CP 27:97110, 2012. Latest commit fbcce40 Nov 26, 2018 History. In most cases, teams/people share the details of these networks for others to use. Thus, we can utilize this property to use them as feature extractors. Before we jump into modeling, lets load and prepare our datasets. transfer learning is a topic of ongoing interest in the machine-learning community. Thus, the key motivation, especially considering the context of deep learning is the fact that most models which solve complex problems need a whole lot of data, and getting vast amounts of labeled data for supervised models can be really difficult, considering the time and effort it takes to label data points. Considering this fact, the model should have learned a robust hierarchy of features, which are spatial, rotation, and translation invariant with regard to features learned by CNN models. There are different transfer learning strategies and techniques, which can be applied based on the domain, task at hand, and the availability of data. We represent the preceding architecture, along with the two variants (basic feature extractor and fine-tuning) that we will be using, in the following block diagram, so you can get a better visual perspective. Transfer of learning occurs when people apply information, strategies, and skills they have learned to a new situation or context. We do not need the last three layers since we will be using our own fully connected dense layers to predict whether images will be a dog or a cat. These span across different domains, such as computer vision and NLP, the two most popular domains for deep learning applications. From the preceding grid, we can see that there is a lot of variation, in terms of resolution, lighting, zoom levels, and so on, available along with the fact that images do not just contain just a single dog but other dogs and surrounding items as well. The first thing to remember here is that, transfer learning, is not a new concept which is very specific to deep learning. Learning tools and apps are being designed, improved and deployed at unprecedented speeds. A simple example would be the ImageNet dataset, which has millions of images pertaining to different categories, thanks to years of hard work starting at Stanford! In this case-study, we will be concentrating toward the task of fine-grained image classification. This brings us to the question, should we freeze layers in the network to use them as feature extractors or should we also fine-tune layers in the process? We already know how to build a deep convolutional network from scratch. Here's how it's gone so far. Note: Many of the transfer learning concepts Ill be covering in this series tutorials also appear in my book, Deep Learning for Computer Vision with Python. A big shoutout goes to my co-authors Raghav & Tamoghna for working with me on the book which paved the way for this content! This is essentially helpful in real-world scenarios where it is not possible to have labeled data for every possible class (if it is a classification task), and in scenarios where new classes can be added often. Have feedback for me? In the span of little more than a year, transfer learning in the form of pretrained language models has become ubiquitous in NLP and has contributed to the state of the art on a wide range of tasks. For instance, if we utilize AlexNet without its final classification layer, it will help us transform images from a new domain task into a 4096-dimensional vector based on its hidden states, thus enabling us to extract features from a new domain task, utilizing the knowledge from a source-domain task. This is made possible by removing the final layer or using the output from the penultimate layer. This means, an average of only 85 images per class! The dataset for our problem is available on Kaggle and is one of the most popular computer vision based datasets out there. We transfer and leverage our knowledge from what we have learnt in the past! The key idea here is to just leverage the pre-trained models weighted layers to extract features but not to update the weights of the models layers during training with new data for the new task. Transfer is not a discrete activity, but is rather an integral part of the learning process. Now that our datasets are ready, lets get started with the modeling process. We train the model for a total of 30 epochs and validate it consequently on our validation set of 1,000 images. The preceding output shows us our basic CNN model summary. Transfer learning can help us deal with these novel scenarios and is necessary for production-scale use of machine learning that goes beyond tasks and domains were labeled data is plentiful. We learned different transfer learning strategies and even discussed the three questions of what, when, and how to transfer knowledge from the source to the target. In fact, transfer learning is not a concept which just cropped up in the 2010s. The flatten layer is used to flatten out 128 of the 17 x 17 feature maps that we get as output from the third convolution layer. An important dimension of this study was to consider how learning community program strengths and challenges can be used to meet self-efficacy The literature on transfer learning has gone through a lot of iterations, and as mentioned at the start of this chapter, the terms associated with it have been used loosely and often interchangeably. We can also visualize model predictions in a visually appealing way using the following code. Lets train this model now. Lets try using our image augmentation strategy on this model. The task Transfer of learning occurs when people apply information, strategies, and skills they have learned to a new situation or context. Researchers attempt to identify when and how transfer occurs and to offer strategies to improve transfer. stream Lets extract out the bottleneck features from our training and validation sets now. Lets now build our deep learning model and train it. There Will be a Shortage Of Data Science Jobs in the Next 5 Years? We can see that we definitely have some interesting results. The reason for model overfitting is because we have much less training data and the model keeps seeing the same instances over time across each epoch. Apart from inductive transfer, inductive-learning algorithms also utilize Bayesian and Hierarchical transfer techniques to assist with improvements in the learning and performance of the target task. As we can see, in most of the cases, the model is not only predicting the correct dog breed, it also does so with very high confidence. We will use the same model architecture from before. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. In their paper, A Survey on Transfer Learning, Pan and Yang use domain, task, and marginal probabilities to present a framework for understanding transfer learning. Online Learning Challenges (& Ways to Solve Them) The spread of COVID-19 has forced millions of students and teachers to move their communication online. To account for this, we scale the test dataset as well, before feeding it into the function. In this case, the task is to identify each of those dog breeds. So, that's it for transfer learning where you learn from one task and try to transfer to a different task. Transfer learning has immense potential and is a commonly required enhancement for existing learning algorithms. Since our previous model was trained on the same small sample of data points each time, it wasnt able to generalize well and ended up overfitting after a few epochs. The transfer of learning from the gym to other areas of participants lives has always been a core component of the Teaching Personal and Social Responsibility Model. The idea behind image augmentation is that we follow a set process of taking in existing images from our training dataset and applying some image transformation operations to them, such as rotation, shearing, translation, zooming, and so on, to produce new, altered versions of existing images. The inductive bias or assumptions can be characterized by multiple factors, such as the hypothesis space it restricts to and the search process through the hypothesis space. Its time now for the final test, where we literally test the performance of our models by making predictions on our test dataset. Your home for data science. In the case of problems in the computer vision domain, certain low-level features, such as edges, shapes, corners and intensity, can be shared across tasks, and thus enable knowledge transfer among tasks! These algorithms are trained to solve specific tasks. We will also briey address the impact that differ-ences among datasets (source and target problem) may have in the learn-ing rates. Zero-shot learning is another extreme variant of transfer learning, which relies on no labeled examples to learn a task.
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