mlp_classifier_medsiglip.metadata.json

{
  "timestamp_utc": "2026-02-22T11:21:06.858917+00:00",
  "platform": "Windows-10-10.0.26200-SP0",
  "python_version": "3.11.9",
  "torch_version": "2.8.0+cpu",
  "numpy_version": "1.26.3",
  "device": "cpu",
  "seed": 42,
  "deterministic": true,
  "paths": {
    "base": ".\\embed_data",
    "x_train": ".\\embed_data\\X_train_medsiglip.npy",
    "y_train": ".\\embed_data\\Y_train.npy",
    "x_val": ".\\embed_data\\X_val_medsiglip.npy",
    "y_val": ".\\embed_data\\Y_val.npy",
    "x_test": ".\\embed_data\\X_test_medsiglip.npy",
    "y_test": ".\\embed_data\\Y_test.npy"
  },
  "dataset_shapes": {
    "x_train": [
      13028,
      1152
    ],
    "y_train": [
      13028,
      5
    ],
    "x_val": [
      4342,
      1152
    ],
    "y_val": [
      4342,
      5
    ],
    "x_test": [
      4343,
      1152
    ],
    "y_test": [
      4343,
      5
    ]
  },
  "hyperparameters": {
    "model_type": "mlp",
    "batch_size": 256,
    "num_workers": 0,
    "epochs": 100,
    "patience": 10,
    "learning_rate": 0.0001,
    "weight_decay": 1e-05,
    "threshold": 0.3,
    "num_experts": 5,
    "gate_hidden": 512,
    "temperature": 1.0,
    "lambda_balance": 0.1,
    "mlp_hidden_1": 1024,
    "mlp_hidden_2": 512,
    "mlp_dropout": 0.15
  }
}