This repository provides a practical guide on using transfer learning for binary classification tasks using TensorFlow. Transfer learning is a technique that leverages pre-trained models on large-scale datasets and fine-tunes them for specific tasks, allowing us to achieve high accuracy even with limited training data.
Transfer learning is a popular approach in deep learning, where the knowledge gained from training a model on one task is transferred and applied to a different, but related, task. This technique is especially useful when the target task has limited labeled data available.
In this repository, we demonstrate how to perform transfer learning for binary classification using TensorFlow, a popular deep learning framework. We will utilize a pre-trained model as a feature extractor and then fine-tune it on our specific binary classification task.
To get started, follow the steps below:
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Click this link to open the notebook in Colab: https://colab.research.google.com/github/barzansaeedpour/transfer-learning-for-binary-classification-tensorflow/blob/main/01_transfer_learning_cats_dogs.ipynb
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The instruction and explaination of the code is mentioned in the notebook
In the transfer learning phase, we utilize a pre-trained model as a feature extractor. By freezing the weights of the pre-trained layers, we can extract meaningful features from our dataset. This step helps to leverage the knowledge learned by the pre-trained model on a large-scale dataset.
The steps for transfer learning are as follows:
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Load the pre-trained model weights. TensorFlow provides various pre-trained models like VGG16, ResNet, or Inception, which can be easily loaded using the
tf.keras.applicationsmodule. -
Create a new model by removing the original classification layer from the pre-trained model. We retain the convolutional layers to extract features.
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Freeze the weights of the pre-trained layers to prevent them from being updated during training.
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Add new trainable layers on top of the pre-trained model. These layers will learn to classify the extracted features for our specific task.
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Compile the model with an appropriate loss function, optimizer, and evaluation metrics.
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Train the model on your labeled training data, using the features extracted from the pre-trained layers as input.
After the transfer learning phase, we can further improve the model's performance through fine-tuning. Fine-tuning involves unfreezing some of the pre-trained layers and training them along with the newly added layers.
Here are the steps for fine-tuning:
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Unfreeze some of the top layers of the pre-trained model while keeping the lower layers frozen. This allows the network to adapt to more specific features related to our task.
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Lower the learning rate to avoid catastrophic forgetting. Smaller learning rates help the model to fine-tune while preserving the learned knowledge from the pre-trained layers.
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Continue training the model using the labeled training data, allowing the unfrozen layers to update their weights based on the specific task.
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Monitor the validation accuracy during training and stop when the performance no longer improves or starts to overfit.
This repository provides a practical guide on using transfer learning for binary classification tasks with TensorFlow. By leveraging pre-trained models and fine-tuning them on specific datasets, we can achieve excellent performance even with limited labeled data.
Remember to customize the model architecture, hyperparameters, and training pipeline based on your specific task and dataset. Experiment with different pre-trained models and fine-tuning strategies to find the best approach for your binary classification problem.
Contributions to this repository are welcome. If you have any suggestions, improvements, or bug fixes, please submit a pull request.
This repository is licensed under the MIT License. Feel free to use and modify the code for your own purposes.