fix bug

train.ipynb

     "import ray\n",
     "from ray import tune\n",
     "from ray.tune.schedulers import ASHAScheduler\n",
+    "from ray.tune import CLIReporter\n",
     "\n",
     "import torch\n",
     "from torch import nn\n",
     "    train_loader = DataLoader(train_set, batch_size=1, shuffle=False, num_workers=1)\n",
     "    val_loader = DataLoader(val_set, batch_size=1, shuffle=False, num_workers=1)\n",
     "    \n",
-    "    \n",
     "    conf = {\n",
     "        'capture_input_dim' : 112,\n",
     "        'score_input_dim' : 50,\n",
     "    logger = TensorBoardLogger(save_dir='logs/', name=name)\n",
     "    checkpoint = ModelCheckpoint(\n",
     "        dirpath=save_dir, \n",
-    "        filename='{epoch}-{val_loss:.2f}', \n",
-    "        monitor='val_loss',\n",
-    "        mode='min',\n",
-    "        save_top_k=10, \n",
+    "        filename='{epoch}-{val_loss:.2f}-{val_acc:.2f}', \n",
+    "        monitor='val_acc',\n",
+    "        mode='max',\n",
+    "        save_top_k=5, \n",
     "        every_n_epochs = 5\n",
     "    )\n",
-    "#     reporter = TuneReportCallback(\n",
-    "#         {\n",
-    "#             \"loss\": \"ptl/val_loss\",\n",
-    "#             \"mean_accuracy\": \"ptl/val_acc\"\n",
-    "#         },\n",
-    "#         on=\"validation_end\"\n",
-    "#     )\n",
     "    os.makedirs(save_dir, exist_ok=True)\n",
     "    json.dump(conf, open(save_dir + 'config.json', 'w'))\n",
     "\n",
     "    trainer = Trainer(\n",
     "        benchmark=True, \n",
-    "        gpus=[1], \n",
+    "        gpus=[0], \n",
     "        accumulate_grad_batches=64,\n",
     "        logger=logger, \n",
     "        enable_progress_bar=False,\n",
-    "        max_epochs=10,\n",
-    "        callbacks=[checkpoint]\n",
+    "        max_epochs=20,\n",
+    "        callbacks=[\n",
+    "            checkpoint,\n",
+    "        ]\n",
     "    )\n",
     "    trainer.fit(model, train_loader, val_loader)\n",
-    "    res = trainer.validate(val_loader)[0]\n",
-    "    tune.report(**res)"
+    "    res = trainer.validate(model, val_loader)[0]\n",
+    "    tune.report(**res)\n",
+    "    return"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "== Status ==<br>Current time: 2022-03-09 01:53:36 (running for 00:01:52.55)<br>Memory usage on this node: 14.1/125.6 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 1.0/18 CPUs, 2.0/2 GPUs, 0.0/71.06 GiB heap, 0.0/34.44 GiB objects (0.0/1.0 accelerator_type:GTX)<br>Result logdir: /home/ramezani/ray_results/experiment_2022-03-09_01-51-43<br>Number of trials: 16/16 (15 PENDING, 1 RUNNING)<br><br>"
+       "== Status ==<br>Current time: 2022-03-11 21:39:55 (running for 00:09:12.46)<br>Memory usage on this node: 15.5/125.6 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 2.0/18 CPUs, 2.0/2 GPUs, 0.0/70.74 GiB heap, 0.0/34.31 GiB objects (0.0/1.0 accelerator_type:GTX)<br>Result logdir: /home/ramezani/ray_results/CSI-weibo<br>Number of trials: 20/20 (19 PENDING, 1 RUNNING)<br><br>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
    "source": [
     "analysis = tune.run(\n",
     "    experiment,\n",
-    "    num_samples=4,\n",
-    "    resources_per_trial={\"cpu\": 1, \"gpu\": 2},\n",
+    "    num_samples=5,\n",
+    "    resources_per_trial={\"cpu\": 2, \"gpu\": 2},\n",
     "    verbose=1,\n",
     "    config={\n",
     "        \"weight_decay\": tune.grid_search([0., 0.1, 0.01, 0.001]),\n",
-    "        \"lr\": tune.loguniform(1e-5, 1e-1),\n",
+    "        \"lr\": tune.loguniform(1e-4, 1e-1),\n",
     "        \"dropout\": tune.uniform(0., 0.3)\n",
-    "    }\n",
+    "    },\n",
+    "    name='CSI-weibo'\n",
     ")\n"
    ]
   },
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = analysis.results_df"
+    "analysis.results_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis.results_df.to_csv('results.csv', index=False)"
    ]
   }
  ],