# Image Classification - TensorFlow Hyperparameters Hyperparameters are parameters that are set before a machine learning model begins learning. The following hyperparameters are supported by the Amazon SageMaker AI built-in Image Classification - TensorFlow algorithm. See [Tune an Image Classification - TensorFlow model](IC-TF-tuning.md) 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\$1random\$1flip | Indicates which flip mode to use for data augmentation when `augmentation` is set to `"True"`. For more information, see [RandomFlip](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomFlip) in the TensorFlow documentation. Valid values: string, any of the following: (`"horizontal_and_vertical"`, `"vertical"`, or `"None"`). Default value: `"horizontal_and_vertical"`. | | augmentation\$1random\$1rotation | 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](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomRotation) in the TensorFlow documentation. Valid values: float, range: [`-1.0`, `1.0`]. Default value: `0.2`. | | augmentation\$1random\$1zoom | 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](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomZoom) in the TensorFlow documentation. Valid values: float, range: [`-1.0`, `1.0`]. Default value: `0.1`. | | batch\$1size | 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\$11 | 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\$12 | 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\$1mode | 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\$1rate | 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\$1stopping | 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\$1stopping\$1min\$1delta | The minimum change needed to qualify as an improvement. An absolute change less than the value of early\$1stopping\$1min\$1delta does not qualify as improvement. Used only when early\$1stopping is set to "True".Valid values: float, range: [`0.0`, `1.0`].Default value: `0.0`. | | early\$1stopping\$1patience | 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\$1metric | 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](https://www.tensorflow.org/api_docs/python/tf/keras/metrics) in the TensorFlow documentation. Valid values: string, any of the following: (`"accuracy"`, `"precision"`, `"recall"`, `"auc"`, or `"prc"`). Default value: `"accuracy"`. | | image\$1resize\$1interpolation | Indicates interpolation method used when resizing images. For more information, see [image.resize](https://www.tensorflow.org/api_docs/python/tf/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\$1accumulator\$1value | 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\$1smoothing | 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\$1rate | 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](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) in the TensorFlow documentation. Valid values: string, any of the following: (`"adam"`, `"sgd"`, `"nesterov"`, `"rmsprop"`,` "adagrad"` , `"adadelta"`). Default value: `"adam"`. | | regularizers\$1l2 | The L2 regularization factor for the dense layer in the classification layer. Valid values: float, range: [`0.0`, `1.0`]. Default value: `.0001`. | | reinitialize\$1top\$1layer | 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\$1only\$1top\$1layer | 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"`. |