TensorFlow to PyTorch Conversion Guide 🧠🔄

Overview

Converting models from TensorFlow to PyTorch is a common task for developers transitioning between frameworks. This guide provides a step-by-step approach to help you migrate your TensorFlow models to PyTorch seamlessly.

Key Steps:

1. Export TensorFlow Model
   Use tf.saved_model.save() to save your model in .pb format or tf.keras to export in .h5 format.
   📌 Example:

   tf.saved_model.save(model, 'tf_model')  
   

2. Convert to ONNX Format
   Convert the TensorFlow model to ONNX using tf2onnx tool.
   🛠️ Command:

   python -m tf2onnx.convert --saved-model tf_model --output model.onnx  
   

3. Load ONNX in PyTorch
   Use torch.onnx module to load the model and restructure it in PyTorch.
   🧩 Code snippet:

   import torch  
   model = torch.jit.load("model.onnx")  
   

4. Fine-tune in PyTorch
   Adjust hyperparameters and train the model using PyTorch’s training loop.

Tools & Resources:

• tf2onnx GitHub: Official tool for TensorFlow to ONNX conversion.
• PyTorch Tutorials: Learn advanced PyTorch techniques for model optimization.

Tips for Smooth Migration

• 🚀 Use torchscript for exporting PyTorch models if further conversion is needed.
• ⚠️ Ensure input/output shapes match between frameworks to avoid errors.
• 📚 Check Model Compatibility Guide for detailed framework-specific notes.

Visual Aids

For deeper insights, explore our PyTorch Ecosystem documentation! 📘