mirror of
https://github.com/MLSysBook/TinyTorch.git
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26b4d95bfb
Results: - Perceptron: ✅ (1.92s) 100.0% - XOR: ✅ (1.87s) 54.5% - MNIST: ✅ (1.96s) 11.5% - CIFAR: ❌ (60.00s) - TinyGPT: ✅ (1.92s)
34 lines
1.5 KiB
JSON
34 lines
1.5 KiB
JSON
{
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"Perceptron": {
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"success": true,
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"time": 1.924880027770996,
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"output_preview": "ion\n\n\ud83d\ude80 Next Steps:\n \u2022 Continue to XOR 1969 milestone after Module 06 (Autograd)\n \u2022 YOUR foundation enables solving non-linear problems!\n \u2022 With 100.0% accuracy, YOUR perceptron works perfectly!\n",
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"loss": 0.2038,
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"accuracy": 100.0
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},
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"XOR": {
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"success": true,
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"time": 1.8728010654449463,
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"output_preview": "ayer networks\n\n\ud83d\ude80 Next Steps:\n \u2022 Continue to MNIST MLP after Module 08 (Training)\n \u2022 YOUR XOR solution scales to real vision problems!\n \u2022 Hidden layers principle powers all modern deep learning!\n",
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"loss": 0.2497,
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"accuracy": 54.5
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},
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"MNIST": {
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"success": true,
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"time": 1.9613378047943115,
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"output_preview": " a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n one_hot[i, int(labels_np[i])] = 1.0\n",
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"loss": 0.0,
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"accuracy": 11.5
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},
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"CIFAR": {
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"success": false,
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"time": 60,
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"timeout": true
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},
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"TinyGPT": {
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"success": true,
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"time": 1.9189341068267822,
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"output_preview": "ining\n \u2022 Complete transformer architecture from first principles\n\n\ud83c\udfed Production Note:\n Real PyTorch uses optimized CUDA kernels for attention,\n but you built and understand the core mathematics!\n",
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"loss": 0.3419
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}
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} |