Hyperparameters are parameters that are set before a machine learning model begins learning. The following hyperparameters are supported by the Amazon SageMaker built-in Image Classification - TensorFlow algorithm. See Tune an Image Classification - TensorFlow model for information on hyperparameter tuning.
| Parameter Name | Description |
|---|---|
| augmentation | Set to "True" to apply augmentation_random_flip, augmentation_random_rotation, and augmentation_random_zoom to the training data. Valid values: string, either: ("True" or "False"). Default value: "False". |
| augmentation_random_flip | Indicates which flip mode to use for data augmentation when augmentation is set to "True". For more information, see RandomFlip in the TensorFlow documentation. Valid values: string, any of the following: ("horizontal_and_vertical", "vertical", or "None"). Default value: "horizontal_and_vertical". |
| augmentation_random_rotation | Indicates how much rotation to use for data augmentation when augmentation is set to "True". Values represent a fraction of 2π. Positive values rotate counterclockwise while negative values rotate clockwise. 0 means no rotation. For more information, see RandomRotation in the TensorFlow documentation. Valid values: float, range: [-1.0, 1.0]. Default value: 0.2. |
| augmentation_random_zoom | Indicates how much vertical zoom to use for data augmentation when augmentation is set to "True". Positive values zoom out while negative values zoom in. 0 means no zoom. For more information, see RandomZoom in the TensorFlow documentation. Valid values: float, range: [-1.0, 1.0]. Default value: 0.1. |
| batch_size | The batch size for training. For training on instances with multiple GPUs, this batch size is used across the GPUs. Valid values: positive integer. Default value: 32. |
| beta_1 | The beta1 for the "adam" optimizer. Represents the exponential decay rate for the first moment estimates. Ignored for other optimizers. Valid values: float, range: [0.0, 1.0]. Default value: 0.9. |
| beta_2 | The beta2 for the "adam" optimizer. Represents the exponential decay rate for the second moment estimates. Ignored for other optimizers. Valid values: float, range: [0.0, 1.0]. Default value: 0.999. |
| binary_mode | When binary_mode is set to "True", the model returns a single probability number for the positive class and can use additional eval_metric options. Use only for binary classification problems. Valid values: string, either: ("True" or "False"). Default value: "False". |
| dropout_rate | The dropout rate for the dropout layer in the top classification layer. Valid values: float, range: [0.0, 1.0]. Default value: 0.2 |
| early_stopping | Set to "True" to use early stopping logic during training. If "False", early stopping is not used. Valid values: string, either: ("True" or "False"). Default value: "False". |
| early_stopping_min_delta | The minimum change needed to qualify as an improvement. An absolute change less than the value of early_stopping_min_delta does not qualify as improvement. Used only when early_stopping is set to "True".Valid values: float, range: [0.0, 1.0].Default value: 0.0. |
| early_stopping_patience | The number of epochs to continue training with no improvement. Used only when early_stopping is set to "True". Valid values: positive integer. Default value: 5. |
| epochs | The number of training epochs. Valid values: positive integer. Default value: 3. |
| epsilon | The epsilon for "adam", "rmsprop", "adadelta", and "adagrad" optimizers. Usually set to a small value to avoid division by 0. Ignored for other optimizers. Valid values: float, range: [0.0, 1.0]. Default value: 1e-7. |
| eval_metric | If binary_mode is set to "False", eval_metric can only be "accuracy". If binary_mode is "True", select any of the valid values. For more information, see Metrics in the TensorFlow documentation. Valid values: string, any of the following: ("accuracy", "precision", "recall", "auc", or "prc"). Default value: "accuracy". |
| image_resize_interpolation | Indicates interpolation method used when resizing images. For more information, see image.resize in the TensorFlow documentation. Valid values: string, any of the following: ("bilinear", "nearest", "bicubic", "area", "lanczos3" , "lanczos5", "gaussian", or "mitchellcubic"). Default value: "bilinear". |
| initial_accumulator_value | The starting value for the accumulators, or the per-parameter momentum values, for the "adagrad" optimizer. Ignored for other optimizers. Valid values: float, range: [0.0, 1.0]. Default value: 0.0001. |
| label_smoothing | Indicates how much to relax the confidence on label values. For example, if label_smoothing is 0.1, then non-target labels are 0.1/num_classes and target labels are 0.9+0.1/num_classes. Valid values: float, range: [0.0, 1.0]. Default value: 0.1. |
| learning_rate | The optimizer learning rate. Valid values: float, range: [0.0, 1.0].Default value: 0.001. |
| momentum | The momentum for "sgd", "nesterov", and "rmsprop" optimizers. Ignored for other optimizers. Valid values: float, range: [0.0, 1.0]. Default value: 0.9. |
| optimizer | The optimizer type. For more information, see Optimizers in the TensorFlow documentation. Valid values: string, any of the following: ("adam", "sgd", "nesterov", "rmsprop", "adagrad" , "adadelta"). Default value: "adam". |
| regularizers_l2 | The L2 regularization factor for the dense layer in the classification layer. Valid values: float, range: [0.0, 1.0]. Default value: .0001. |
| reinitialize_top_layer | If set to "Auto", the top classification layer parameters are re-initialized during fine-tuning. For incremental training, top classification layer parameters are not re-initialized unless set to "True". Valid values: string, any of the following: ("Auto", "True" or "False"). Default value: "Auto". |
| rho | The discounting factor for the gradient of the "adadelta" and "rmsprop" optimizers. Ignored for other optimizers. Valid values: float, range: [0.0, 1.0]. Default value: 0.95. |
| train_only_on_top_layer | If "True", only the top classification layer parameters are fine-tuned. If "False", all model parameters are fine-tuned. Valid values: string, either: ("True" or "False"). Default value: "False". |