config.json

{
    "model_type": "cnn",
    "input_shape": [152, 152, 3],
    "conv_layers": [
      {
        "filters": 32,
        "kernel_size": [3, 3],
        "activation": "relu",
        "pool_size": [2, 2]
      },
      {
        "filters": 64,
        "kernel_size": [3, 3],
        "activation": "relu",
        "pool_size": [2, 2]
      },
      {
        "filters": 128,
        "kernel_size": [3, 3],
        "activation": "relu",
        "pool_size": [2, 2]
      }
    ],
    "dense_layers": [
      {
        "units": 64,
        "activation": "relu"
      }
    ],
    "output_layer": {
      "units": 69216,
      "activation": "sigmoid"
    },
    "reshape_layer": {
      "target_shape": [152, 152, 3]
    },
    "compile": {
      "optimizer": "adam",
      "loss": "mean_squared_error"
    },
    "data_augmentation": {
      "rescale": 1.0,
      "horizontal_flip": true,
      "rotation_range": 20,
      "zoom_range": 0.2,
      "shear_range": 0.2,
      "brightness_range": [0.8, 1.2]
    },
    "training": {
      "batch_size": 10,
      "target_size": [152, 152],
      "epochs": 50,
      "steps_per_epoch": "auto",
      "validation_steps": "auto"
    },
    "save_path": "cnn_similarity_model.keras",
    "similarity_metric": "euclidean"
  }