Updates

Author: madeline-underwood

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/fine-tune-2.md

@@ -6,15 +6,15 @@ weight: 3
 layout: learningpathall
 ---
 
-## Build the Model
+## Build the model
 
-Navigate to the Arm examples directory in the ExecuTorch repository.
+Navigate to the Arm examples directory in the ExecuTorch repository:
 
 ```bash
 cd $HOME/executorch/examples/arm
 ```
 
-Using a file editor of your choice, create a file named `rps_tiny.py`, copy and paste the code shown below:
+Create a file named `rps_tiny.py` and paste the following code:
 
 ```python
 #!/usr/bin/env python3
@@ -369,24 +369,24 @@ if __name__ == "__main__":
 ```
 
 
-### About the Script
+### About the script
 The script handles the entire workflow: data generation, model training, and a simple command-line game.
 
-- **Synthetic Data Generation:** The script includes a function `render_rps()` that generates 28x28 grayscale images of the letters 'R', 'P', and 'S' with random rotations, blurs, and noise. This creates a diverse dataset that's used to train the model.
-- **Model Architecture:** The model, a TinyRPS class, is a simple Convolutional Neural Network (CNN). It uses a series of 2D convolutional layers, followed by pooling layers to reduce spatial dimensions, and finally, fully connected linear layers to produce a final prediction. This architecture is efficient and well-suited for edge devices.
-- **Training:** The script generates synthetic training and validation datasets. It then trains the CNN model using the **Adam optimizer** and **Cross-Entropy Loss**. It tracks validation accuracy and saves the best-performing model to `rps_best.pt`.
-- **ExecuTorch Export:** A key part of the script is the `export_to_pte()` function. This function uses the `torch.export module` (or a fallback) to trace the trained PyTorch model and convert it into an ExecuTorch program (`.pte`). This compiled program is highly optimized for deployment on any target hardware, for example Cortex-M or Cortex-A CPUs for embedded devices.
-- **CLI Mini-Game**: After training, you can play an interactive game. The script generates an image of your move and a random opponent's move. It then uses the trained model to classify both images and determines the winner based on the model's predictions.
+- Synthetic Data Generation: the script includes a function `render_rps()` that generates 28x28 grayscale images of the letters 'R', 'P', and 'S' with random rotations, blurs, and noise. This creates a diverse dataset that's used to train the model.
+- Model Architecture: the model, a TinyRPS class, is a simple Convolutional Neural Network (CNN). It uses a series of 2D convolutional layers, followed by pooling layers to reduce spatial dimensions, and finally, fully connected linear layers to produce a final prediction. This architecture is efficient and well-suited for edge devices.
+- Training: the script generates synthetic training and validation datasets. It then trains the CNN model using the **Adam optimizer** and **Cross-Entropy Loss**. It tracks validation accuracy and saves the best-performing model to `rps_best.pt`.
+- ExecuTorch Export: a key part of the script is the `export_to_pte()` function. This function uses the `torch.export module` (or a fallback) to trace the trained PyTorch model and convert it into an ExecuTorch program (`.pte`). This compiled program is highly optimized for deployment on any target hardware, for example Cortex-M or Cortex-A CPUs for embedded devices.
+- CLI Mini-Game: after training, you can play an interactive game. The script generates an image of your move and a random opponent's move. It then uses the trained model to classify both images and determines the winner based on the model's predictions.
 
-### Running the Script:
+## Running the Script:
 
-To train the model, export it, and play the game, run the following command:
+Train the model, export it, and play the game:
 
 ```bash
 python rps_tiny.py --epochs 8 --export --play
 ```
 
-You'll see the training progress, where the model's accuracy rapidly improves on the synthetic data.
+You’ll see training progress similar to:
 
 ```output
 == Building synthetic datasets ==
@@ -402,7 +402,8 @@ Training done.
 Loaded weights from rps_best.pt
 [export] wrote rps_tiny.pte
 ```
-After training and export, the game will start. Type rock, paper, or scissors and see the model's predictions and what your opponent played.
+
+After training and export, the game starts. Type rock, paper, or scissors, and review the model’s predictions for you and a random opponent:
 
 ```output
 === Rock–Paper–Scissors: Play vs Tiny CNN ===