diff options
Diffstat (limited to 'train_colab.ipynb')
| -rw-r--r-- | train_colab.ipynb | 12 |
1 files changed, 7 insertions, 5 deletions
diff --git a/train_colab.ipynb b/train_colab.ipynb index ac64e50..a591beb 100644 --- a/train_colab.ipynb +++ b/train_colab.ipynb @@ -67,14 +67,14 @@ { "cell_type": "markdown", "source": "## 3. Download model & logs", - "metadata": {}, - "execution_count": null, - "outputs": [] + "metadata": {} }, { "cell_type": "code", "metadata": {}, - "source": "from google.colab import files\nimport glob\n\n# Download final model(s)\nfor f in glob.glob(\"*_final.pt\"):\n print(f\"Downloading {f}...\")\n files.download(f)\n\n# Download training log(s)\nfor f in glob.glob(\"*_log.csv\"):\n print(f\"Downloading {f}...\")\n files.download(f)" + "source": "from google.colab import files\nimport glob\n\n# Download final model(s)\nfor f in glob.glob(\"*_final.pt\"):\n print(f\"Downloading {f}...\")\n files.download(f)\n\n# Download training log(s)\nfor f in glob.glob(\"*_log.csv\"):\n print(f\"Downloading {f}...\")\n files.download(f)", + "execution_count": null, + "outputs": [] }, { "cell_type": "markdown", @@ -129,7 +129,9 @@ { "cell_type": "code", "metadata": {}, - "source": "import sys\nsys.path.insert(0, \".\")\nfrom train import PolicyValueNet, evaluate_vs_greedy_batch\n\ndevice = \"cpu\"\nmodel = PolicyValueNet().to(device)\n\nimport glob\nfinal_models = glob.glob(\"*_final.pt\") + glob.glob(\"models/*_final.pt\")\nif final_models:\n ckpt = torch.load(final_models[0], map_location=device, weights_only=True)\n model.load_state_dict(ckpt[\"model\"])\n model.eval()\n print(f\"Loaded: {final_models[0]}\")\n print(f\"Trained for {ckpt.get('episode', '?')} episodes\")\n print()\n\n for n in [2, 3, 4]:\n wr = evaluate_vs_greedy_batch(model, num_players=n, num_games=2000, device=device)\n print(f\" {n} players: win rate = {wr:.1%} (random baseline: {1/n:.1%})\")\nelse:\n print(\"No model found. Train first!\")" + "source": "import sys\nsys.path.insert(0, \".\")\nfrom train import PolicyValueNet, evaluate_vs_greedy_batch\n\ndevice = \"cpu\"\nmodel = PolicyValueNet().to(device)\n\nimport glob\nfinal_models = glob.glob(\"*_final.pt\") + glob.glob(\"models/*_final.pt\")\nif final_models:\n ckpt = torch.load(final_models[0], map_location=device, weights_only=True)\n model.load_state_dict(ckpt[\"model\"])\n model.eval()\n print(f\"Loaded: {final_models[0]}\")\n print(f\"Trained for {ckpt.get('episode', '?')} episodes\")\n print()\n\n for n in [2, 3, 4]:\n wr = evaluate_vs_greedy_batch(model, num_players=n, num_games=2000, device=device)\n print(f\" {n} players: win rate = {wr:.1%} (random baseline: {1/n:.1%})\")\nelse:\n print(\"No model found. Train first!\")", + "execution_count": null, + "outputs": [] } ] }
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