Keras vs Tensorflow Lite: What are the differences?
Introduction
Keras and TensorFlow Lite are both popular frameworks used in machine learning and deep learning projects. While both of them have their own unique features and functionalities, there are several key differences between the two.
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Model Complexity and Flexibility: Keras is known for its high-level abstraction, providing a simple and user-friendly interface for building and training neural networks. It allows faster prototyping and easier model building, making it suitable for beginners and quick experimentation. On the other hand, TensorFlow Lite offers a lower-level approach, providing more control and flexibility for advanced users. It allows fine-grained optimization and customization for resource-constrained devices.
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Supported Platforms: Keras is a framework built on top of TensorFlow, which means it can run on top of TensorFlow and other deep learning platforms. It supports a wide variety of platforms, including CPU, GPU, and even distributed systems. TensorFlow Lite, on the other hand, is specifically designed for mobile and embedded devices. It is optimized for low-latency inference and efficient usage of computational resources on devices like smartphones, IoT devices, and microcontrollers.
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Model Deployment: Keras primarily focuses on model development and training, providing a high-level API for building models. It simplifies the process of model deployment by automatically converting and saving models in the HDF5 format. TensorFlow Lite, on the other hand, focuses on model deployment and inference on resource-constrained devices. It provides tools and techniques for optimizing and converting models to a format suitable for deployment on mobile and embedded devices.
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Model Size and Performance: Keras models tend to have a larger size compared to TensorFlow Lite models due to the additional high-level abstraction layer. This can be a concern when deploying models on devices with limited storage capacity. TensorFlow Lite, on the other hand, focuses on model compression and optimization techniques to reduce the model size while maintaining reasonable accuracy. It also provides hardware acceleration options, like the Neural Processing Unit (NPU), to improve the performance and efficiency of model inference on devices.
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Quantization and Pruning: TensorFlow Lite offers built-in support for model quantization and pruning techniques. Model quantization reduces the precision of numbers in the model, leading to smaller model size and accelerated inference. Model pruning removes unnecessary weights or connections in the network, further reducing the model size and improving inference speed. Keras, on the other hand, does not have built-in support for these techniques, although they can be implemented using TensorFlow APIs.
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Community and Ecosystem: TensorFlow Lite benefits from the extensive TensorFlow community and ecosystem, providing access to a wide range of pre-trained models, tools, and resources. It has gained popularity in the mobile and embedded device community due to its optimization techniques and deployment support. Keras, although part of the TensorFlow ecosystem, has its own community and ecosystem with a focus on high-level model building and ease of use.
In Summary, Keras and TensorFlow Lite have key differences in terms of model complexity and flexibility, supported platforms, model deployment, model size and performance, quantization and pruning support, and community and ecosystem. Keras is designed for high-level model building and faster prototyping, while TensorFlow Lite is focused on model deployment and optimization for resource-constrained devices.
