Upload folder using huggingface_hub
Browse files- __pycache__/predict.cpython-311.pyc +0 -0
- model_pong_direct.pt +2 -2
- predict.py +2 -2
- train.log +52 -77
__pycache__/predict.cpython-311.pyc
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model_pong_direct.pt
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size
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version https://git-lfs.github.com/spec/v1
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size 2437546
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predict.py
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@@ -167,7 +167,7 @@ def predict_next_frame(ens, context_frames: np.ndarray) -> np.ndarray:
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predicted = torch.zeros_like(direct_pred)
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for step in range(PRED_FRAMES):
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ar_weight = 0.
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direct_weight = 1.0 - ar_weight
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predicted[:, step] = ar_weight * ar_pred[:, step] + direct_weight * direct_pred[:, step]
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@@ -227,7 +227,7 @@ def predict_next_frame(ens, context_frames: np.ndarray) -> np.ndarray:
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predicted = torch.zeros_like(direct_pred)
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for step in range(PRED_FRAMES):
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ar_weight = 0.
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direct_weight = 1.0 - ar_weight
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predicted[:, step] = ar_weight * ar_pred[:, step] + direct_weight * direct_pred[:, step]
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predicted = torch.zeros_like(direct_pred)
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for step in range(PRED_FRAMES):
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ar_weight = 0.85 - (step / (PRED_FRAMES - 1)) * 0.2
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direct_weight = 1.0 - ar_weight
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predicted[:, step] = ar_weight * ar_pred[:, step] + direct_weight * direct_pred[:, step]
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predicted = torch.zeros_like(direct_pred)
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for step in range(PRED_FRAMES):
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ar_weight = 0.7 - (step / (PRED_FRAMES - 1)) * 0.4
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direct_weight = 1.0 - ar_weight
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predicted[:, step] = ar_weight * ar_pred[:, step] + direct_weight * direct_pred[:, step]
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train.log
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[2026-04-12 05:25:10] P1 E90/100 | T:0.007718(S:0.9892) V:0.040199(S:0.9431) LR:8.32e-06
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[2026-04-12 05:25:17] P1 E91/100 | T:0.007692(S:0.9892) V:0.040185(S:0.9431) LR:6.94e-06
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[2026-04-12 05:25:24] P1 E92/100 | T:0.007651(S:0.9893) V:0.040164(S:0.9431) LR:5.70e-06
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[2026-04-12 05:25:32] P1 E93/100 | T:0.007631(S:0.9893) V:0.040117(S:0.9432) LR:4.60e-06
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[2026-04-12 05:26:21] P1 E100/100 | T:0.007589(S:0.9893) V:0.040109(S:0.9432) LR:1.00e-06
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[2026-04-12 05:26:21] Phase 1 done. Best val loss: 0.040109
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[2026-04-12 05:26:21] Phase 2: 2-step AR + hflip aug, 50 epochs, lr=5e-05
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[2026-04-12 05:26:22] pong train: 8398 sequences (seq_len=10)
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[2026-04-12 05:26:22] pong val: 988 sequences (seq_len=10)
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[2026-04-12 05:26:44] P2 E1/50 | T:0.014117(S:0.9801) V:0.056007(S:0.9206) LR:5.00e-05
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[2026-04-12 05:27:05] P2 E2/50 | T:0.013481(S:0.9810) V:0.054888(S:0.9222) LR:4.98e-05
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[2026-04-12 05:27:26] P2 E3/50 | T:0.013067(S:0.9816) V:0.054390(S:0.9229) LR:4.96e-05
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[2026-04-12 05:28:52] P2 E7/50 | T:0.011823(S:0.9834) V:0.053736(S:0.9238) LR:4.77e-05
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[2026-04-12 05:30:01] P2 E10/50 | T:0.011506(S:0.9838) V:0.054305(S:0.9230) LR:4.53e-05
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[2026-04-12 05:33:34] P2 E20/50 | T:0.010052(S:0.9859) V:0.053792(S:0.9237) LR:3.31e-05
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[2026-04-12 05:34:17] P2 E22/50 | T:0.009761(S:0.9863) V:0.053448(S:0.9242) LR:3.01e-05
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[2026-04-12 05:34:39] P2 E23/50 | T:0.009488(S:0.9867) V:0.053285(S:0.9245) LR:2.86e-05
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[2026-04-12 05:35:22] P2 E25/50 | T:0.009336(S:0.9869) V:0.052888(S:0.9250) LR:2.55e-05
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[2026-04-12 05:37:11] P2 E30/50 | T:0.008903(S:0.9875) V:0.053298(S:0.9244) LR:1.79e-05
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[2026-04-12 05:40:56] P2 E40/50 | T:0.008178(S:0.9885) V:0.053040(S:0.9248) LR:5.68e-06
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[2026-04-12 05:42:25] P2 E44/50 | T:0.008011(S:0.9888) V:0.052805(S:0.9251) LR:2.72e-06
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[2026-04-12 05:44:41] P2 E50/50 | T:0.007865(S:0.9890) V:0.052993(S:0.9249) LR:1.00e-06
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[2026-04-12 05:44:41] Phase 2 done. Best val loss: 0.052805
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[2026-04-12 05:44:41] Pong model: 2.3 MB
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[2026-04-12 05:44:41] Training complete!
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[2026-04-12 05:59:24] Starting Pong direct 8-frame training for 2026-04-12-133000-pong-direct-v2
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[2026-04-12 05:59:24] Device: cuda
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[2026-04-12 05:59:25] Pong direct: 1,199,224 params (2.3 MB fp16)
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[2026-04-12 05:59:25] pong train: 8194 seqs (len=16)
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[2026-04-12 05:59:26] pong val: 964 seqs (len=16)
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[2026-04-12 05:59:47] E1/150 | T:0.232601(S:0.6492) V:0.206121(S:0.6850) LR:3.00e-04
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[2026-04-12 06:00:07] E2/150 | T:0.173923(S:0.7357) V:0.175883(S:0.7317) LR:3.00e-04
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[2026-04-12 06:00:28] E3/150 | T:0.138439(S:0.7891) V:0.157651(S:0.7599) LR:3.00e-04
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[2026-04-12 06:00:50] E4/150 | T:0.117363(S:0.8211) V:0.140205(S:0.7864) LR:2.99e-04
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[2026-04-12 06:01:09] E5/150 | T:0.103357(S:0.8425) V:0.136901(S:0.7912) LR:2.99e-04
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[2026-04-12 06:01:30] E6/150 | T:0.093950(S:0.8569) V:0.126921(S:0.8063) LR:2.99e-04
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[2026-04-12 06:01:51] E7/150 | T:0.086157(S:0.8691) V:0.121485(S:0.8144) LR:2.98e-04
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[2026-04-12 06:02:53] E10/150 | T:0.071323(S:0.8921) V:0.119263(S:0.8183) LR:2.97e-04
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[2026-04-12 06:03:13] E11/150 | T:0.067633(S:0.8979) V:0.113872(S:0.8260) LR:2.96e-04
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[2026-04-12 06:03:34] E12/150 | T:0.064540(S:0.9027) V:0.112058(S:0.8288) LR:2.95e-04
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[2026-04-12 06:04:38] E15/150 | T:0.057899(S:0.9133) V:0.109369(S:0.8330) LR:2.93e-04
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[2026-04-12 06:05:00] E16/150 | T:0.054467(S:0.9185) V:0.108353(S:0.8339) LR:2.92e-04
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[2026-04-12 06:05:20] E17/150 | T:0.053424(S:0.9202) V:0.106683(S:0.8371) LR:2.91e-04
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[2026-04-12 06:06:19] E20/150 | T:0.049003(S:0.9271) V:0.106042(S:0.8383) LR:2.87e-04
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[2026-04-12 06:07:00] E22/150 | T:0.046678(S:0.9307) V:0.102919(S:0.8428) LR:2.84e-04
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[2026-04-12 06:08:19] E26/150 | T:0.042844(S:0.9367) V:0.102117(S:0.8444) LR:2.78e-04
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[2026-04-12 06:08:39] E27/150 | T:0.042220(S:0.9377) V:0.102082(S:0.8444) LR:2.77e-04
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[2026-04-12 06:09:38] E30/150 | T:0.039701(S:0.9416) V:0.101520(S:0.8453) LR:2.71e-04
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[2026-04-12 06:09:58] E31/150 | T:0.038995(S:0.9427) V:0.100438(S:0.8467) LR:2.70e-04
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[2026-04-12 06:10:16] E32/150 | T:0.039062(S:0.9426) V:0.100245(S:0.8473) LR:2.68e-04
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[2026-04-12 06:10:36] E33/150 | T:0.038450(S:0.9436) V:0.099826(S:0.8476) LR:2.66e-04
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[2026-04-12 06:11:13] E35/150 | T:0.037610(S:0.9449) V:0.099788(S:0.8479) LR:2.62e-04
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[2026-04-12 06:11:33] E36/150 | T:0.036675(S:0.9463) V:0.098966(S:0.8496) LR:2.59e-04
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[2026-04-12 06:12:52] E40/150 | T:0.034903(S:0.9490) V:0.099786(S:0.8485) LR:2.51e-04
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[2026-04-12 06:13:50] E43/150 | T:0.033734(S:0.9509) V:0.098349(S:0.8506) LR:2.43e-04
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[2026-04-12 06:14:10] E44/150 | T:0.033505(S:0.9512) V:0.098003(S:0.8510) LR:2.41e-04
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[2026-04-12 06:14:50] E46/150 | T:0.033140(S:0.9518) V:0.097767(S:0.8514) LR:2.36e-04
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[2026-04-12 06:15:49] E49/150 | T:0.032213(S:0.9533) V:0.097305(S:0.8522) LR:2.28e-04
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[2026-04-12 06:16:09] E50/150 | T:0.031634(S:0.9541) V:0.097047(S:0.8524) LR:2.25e-04
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[2026-04-12 06:17:28] E54/150 | T:0.030719(S:0.9556) V:0.096493(S:0.8531) LR:2.14e-04
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[2026-04-12 06:18:27] E57/150 | T:0.030218(S:0.9563) V:0.095355(S:0.8549) LR:2.06e-04
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[2026-04-12 06:18:47] E58/150 | T:0.029896(S:0.9568) V:0.094789(S:0.8558) LR:2.03e-04
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[2026-04-12 06:19:27] E60/150 | T:0.029534(S:0.9574) V:0.096519(S:0.8532) LR:1.97e-04
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[2026-04-12 06:22:43] E70/150 | T:0.027838(S:0.9600) V:0.095992(S:0.8544) LR:1.66e-04
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[2026-04-12 06:25:55] E80/150 | T:0.026637(S:0.9619) V:0.095754(S:0.8547) LR:1.35e-04
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[2026-04-12 06:28:12] E87/150 | T:0.025836(S:0.9631) V:0.094571(S:0.8563) LR:1.13e-04
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[2026-04-12 06:28:52] E89/150 | T:0.025732(S:0.9633) V:0.094485(S:0.8564) LR:1.07e-04
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[2026-04-12 06:29:11] E90/150 | T:0.025655(S:0.9634) V:0.094835(S:0.8561) LR:1.04e-04
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[2026-04-12 06:32:25] E100/150 | T:0.024915(S:0.9645) V:0.094847(S:0.8561) LR:7.57e-05
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[2026-04-12 06:35:38] E110/150 | T:0.024432(S:0.9653) V:0.094407(S:0.8567) LR:5.05e-05
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[2026-04-12 06:38:57] E120/150 | T:0.024090(S:0.9658) V:0.094761(S:0.8562) LR:2.96e-05
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[2026-04-12 06:42:12] E130/150 | T:0.023891(S:0.9661) V:0.094652(S:0.8564) LR:1.39e-05
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[2026-04-12 06:45:25] E140/150 | T:0.023785(S:0.9663) V:0.094651(S:0.8564) LR:4.27e-06
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[2026-04-12 06:48:40] E150/150 | T:0.023744(S:0.9663) V:0.094663(S:0.8564) LR:1.00e-06
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[2026-04-12 06:48:40] Done. Best val loss: 0.094407
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[2026-04-12 06:48:40] Model size: 2.3 MB
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[2026-04-12 06:48:40] Training complete!
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