mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-08 20:48:09 -05:00
0ac486f7bb
Results: - Perceptron: ✅ (1.86s) 100.0% - XOR: ✅ (1.90s) 54.5% - MNIST: ✅ (2.05s) 10.0% - CIFAR: ❌ (60.00s) - TinyGPT: ✅ (1.84s)
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": 1.8575267791748047,
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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": 1.8962900638580322,
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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": 2.04866886138916,
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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": 10.0
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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": 1.8439507484436035,
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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": 0.3174
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}
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} |