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():
parser = argparse.ArgumentParser(description='Run MNIST experiment')
parser.add_argument('--config', type=str, default='experiments/config.yaml',
help='Path to config file')
parser.add_argument('--device', type=str, default=None,
help='Device to use (cuda/cpu)')
args = parser.parse_args()
print("Starting training...")
model, history, test_loader = train_model(args.config)
print("\nEvaluating model...")
device = model.device if hasattr(model, 'device') else next(model.parameters()).device
accuracy, cm = evaluate_model(model, test_loader, device)
print(f"\nFinal test accuracy: {accuracy:.4f}")
print("Results saved to ./results/")
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Enterprise Order Management System
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PyTorch MNIST Experiments
Set up Cursor Rules for Dashboard
Bioinformatics Tools
PyTorch MNIST Experiments
Add mixed precision training, learning rate scheduling, and proper validation. Also create an experiment config system so I can easily run different hyperparameter settings.
Thought
10sSearched
PyTorch mixed precision training best practicesI'll enhance your MNIST trainer with a complete experiment framework including mixed precision, validation splitting, and proper configuration management. Let me start by rewriting the training module:
train_model.py
Now let me update the evaluation module to save results and generate a detailed report:
evaluation.py
Let me create a command-line runner script to make experiments easy to execute:
run_experiment.py
Done, configurable MNIST experiment framework with AMP and reports.
- Training : AMP, train/val split, cosine schedule, gradient clipping, checkpoints
- Experimentation : YAML config, saved history, confusion matrix + classification report, CLI runner
#!/usr/bin/env python3
import argparse
from train_model import train_model
from evaluation import evaluate_model
def main():
parser = argparse.ArgumentParser(description='Run MNIST experiment')
parser.add_argument('--config', type=str, default='experiments/config.yaml',
help='Path to config file')
parser.add_argument('--device', type=str, default=None,
help='Device to use (cuda/cpu)')
args = parser.parse_args()
print("Starting training...")
model, history, test_loader = train_model(args.config)
print("\nEvaluating model...")
device = model.device if hasattr(model, 'device') else next(model.parameters()).device
accuracy, cm = evaluate_model(model, test_loader, device)
print(f"\nFinal test accuracy: {accuracy:.4f}")
print("Results saved to ./results/")
if __name__ == "__main__":
main()
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Subagents, Skills, and Image Generation
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