best val_rmse 0.0000

config.json

@@ -12,7 +12,7 @@
     "split_past_future": false,
     "type": "pk_peak_half_life"
   },
-  "debug_test": true,
+  "debug_test": false,
   "dosing": {
     "logdose_mean_range": [
       -2.0,
@@ -34,7 +34,7 @@
     "same_route": true,
     "time": 0.0
   },
-  "experiment_dir": "/home/cesarali/Pharma/sim_priors_pk/results/comet/aistats/09523eeea77d480c8fc176c41a45d133",
+  "experiment_dir": "/work/ojedamarin/Projects/Pharma/Results/comet/aistats/f8f0addc790f44b7aa272cc128352c14",
   "experiment_indentifier": null,
   "experiment_name": "aistats",
   "hf_model_card_path": [
@@ -171,15 +171,15 @@
       "cesarali/Theophylline"
     ],
     "test_protocol": "simulated",
-    "test_size": 64,
+    "test_size": 256,
     "tqdm_progress": false,
-    "train_size": 320,
+    "train_size": 1280,
     "val_protocol": "simulated",
-    "val_size": 64,
+    "val_size": 256,
     "z_score_normalization": false
   },
   "model_type": "node_pk",
-  "my_results_path": null,
+  "my_results_path": "/work/ojedamarin/Projects/Pharma/Results/",
   "name_str": "AICMEPK",
   "network": {
     "activation": "ReLU",
@@ -243,17 +243,17 @@
   },
   "train": {
     "amsgrad": false,
-    "batch_size": 32,
+    "batch_size": 128,
     "betas": [
       0.9,
       0.999
     ],
-    "epochs": 10,
+    "epochs": 1000,
     "eps": 1e-08,
     "gradient_clip_val": 1.0,
     "learning_rate": 0.0001,
-    "log_empirical_evaluation_pct": 2.0,
-    "log_image_every_epoch_pct": 2.0,
+    "log_empirical_evaluation_pct": 0.2,
+    "log_image_every_epoch_pct": 0.1,
     "log_interval": 1,
     "log_prediction_in_val": true,
     "log_reconstruction_in_val": true,

pytorch_model.bin

@@ -1,3 +1,3 @@
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+oid sha256:d478b08a8388662be6ea3687e914b18c32bbe35229c28dfe31edc7521e920902
 size 30610195