TensorFlow Lite Converter Deployment Guide

This guide provides an overview of how to deploy TensorFlow Lite models for various platforms and devices.

Supported Platforms

• Android
• iOS
• WebAssembly
• Raspberry Pi

Steps for Deployment

1. Convert the TensorFlow model to TensorFlow Lite format.

   • Use the TensorFlow Lite Converter to convert your TensorFlow model to TensorFlow Lite format.

2. Optimize the TensorFlow Lite model for performance.

   • Use the TensorFlow Lite Optimization Toolkit to optimize your model for performance.

3. Deploy the TensorFlow Lite model to your target platform.

   • Follow the platform-specific deployment guides to deploy your model to your target platform.

Example

Here's an example of how to convert a TensorFlow model to TensorFlow Lite format:

python tensorflow/lite/tensorflow/lite/tools/convert.py \
  --input_graph=/path/to/input_graph.pb \
  --input_tensor=import/layer_name:0 \
  --output_file=/path/to/output.tflite \
  --input_shape=1,224,224,3

Resources

• TensorFlow Lite Documentation
• TensorFlow Lite GitHub Repository

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