wangyu
6ae24cb14d
- Train 3 MLP networks (acid speed 14→1, tension 4→10, quality 6→2) on 12,000 synthetic samples generated from physics models + noise - Export pre-trained ONNX models to pt_models/ directory - Rewrite prediction.py: ONNX inference first, physics fallback if unavailable - Add onnxruntime + numpy to requirements.txt (Aliyun mirror for Docker) - Use Tsinghua mirror in Dockerfile for pip installs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
118 lines
2.3 KiB
JSON
118 lines
2.3 KiB
JSON
{
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"acid_speed": {
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69.26476287841797
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"X_std": [
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"y_std": [
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]
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},
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"tension": {
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"zone_names": [
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"inlet",
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"s1_roller",
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"acid_entry",
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"acid1",
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"acid2",
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"acid3",
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"rinse",
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"leveler",
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"s2_roller",
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"outlet"
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]
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},
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"quality": {
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70.02484893798828,
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"y_mean": [
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}
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} |