{"id":8997,"date":"2021-03-17T19:06:45","date_gmt":"2021-03-17T13:36:45","guid":{"rendered":"https:\/\/www.h2kinfosys.com\/blog\/?p=8997"},"modified":"2025-10-27T02:37:06","modified_gmt":"2025-10-27T06:37:06","slug":"transfer-learning-in-keras-with-examples","status":"publish","type":"post","link":"https:\/\/www.h2kinfosys.com\/blog\/transfer-learning-in-keras-with-examples\/","title":{"rendered":"Transfer learning in Keras with Examples"},"content":{"rendered":"\n

Introduction: The Power of Transfer Learning in AI<\/strong><\/h3>\n\n\n\n

In today\u2019s era of Artificial Intelligence, achieving high model accuracy often requires enormous datasets and significant computational power. However, not every organization or learner has access to millions of labeled images or high-end GPUs. This is where Transfer Learning<\/strong> comes to the rescue.<\/p>\n\n\n\n

Transfer Learning enables developers to leverage pre-trained models such as VGG16, ResNet, or Inceptionthat have already been trained on massive datasets like ImageNet. By reusing these models, you can adapt them for your own tasks, saving time, effort, and computational resources.<\/p>\n\n\n\n

If you\u2019ve ever enrolled in an Artificial intelligence certification course<\/a><\/strong> or explored online courses for artificial intelligence<\/strong>, you\u2019ve likely encountered Transfer Learning in deep learning modules. It\u2019s one of the most practical and efficient techniques for model optimization and fast experimentation.<\/p>\n\n\n\n

Training a deep neural network on a large dataset takes a lot of time. For instance, the popular AlexNet took 5 to 6 days for training and did that with two GPUs. Not everyone has access to these computational requirements to train such a dataset and even at that, the time it takes. Transfer learning provides a way of avoiding these long training times when building a neural network. This popular technique involves re-using the weights of an already trained model that was built by a robust dataset. An example of such a dataset would be the training data in the ImageNet competition that contained more than 1 million dataset. The models that performed well on this kind of data can then be downloaded and reused in a new computer vision problem. That is transfer learning. In this tutorial, we will take a look at what transfer learning is about and how to build a CNN using transfer learning.<\/p>\n\n\n\n

By the end of this tutorial, you will discover the following:<\/p>\n\n\n\n

Let\u2019s get started. <\/p>\n\n\n\n

What is Transfer Learning<\/h2>\n\n\n\n

Transfer learning is the act of reusing an already built model that was trained on a dataset to solve a particular problem in another closely related problem. Tranfer learning is an important concept for data scientists to understand because not only does it save the time required for training the data. But it also allows you to make use of powerful models without needing so much computing power. It is basically a weight initialization scheme where the weights of a model that has done well on a data is used in another data. <\/p>\n\n\n\n

Using Transfer Learning for Image Recognition<\/h2>\n\n\n\n

During the ImageNet challenge that lasted for 9 years, a lot of high performing image classification models were built. As of the last year of the competition, the winning model had an error rate of less than 5%, way better than human capabilities. These models have paved the way for the massive advancement that deep learning has brought especially through image recognition. What\u2019s more interesting is that the models have been released to the public for personal consumption under a permissive license. Examples of such models include VGGNet, ResNet, Xception, DenseNet etc. <\/p>\n\n\n\n

The availability of these models have formed the bedrock for transfer learning. Researchers do not have to reinvent the wheel by training models over and over again. Since these models were trained on over 1 million dataset and with over 1000 classes, they have proved to generalise well on new dataset that seeks to perform a similar task. <\/p>\n\n\n\n

The model can be accessed through APIs and are downloadable for free. Going forward in this tutorial, we will see how to use one of these models in Keras<\/a>. <\/p>\n\n\n\n

How to use Pretrained Models <\/h2>\n\n\n\n

The possibilities with transfer learning are limitless. We can also apply them to as far as we know. Generally speaking, using pretrained models follows 3 patterns:<\/p>\n\n\n\n