Hyperparameters

Training Parameters

All parameters are set through the training_plan after linking your model with the dataset.

training_plan = user.link_model_dataset(dataset_id='Dataset ID')

To see all current parameter settings, run training_plan.get_training_plan(). To run consecutive experiments, overwrite parameters and re-start training with training_plan.start().

You can refer to the TensorFlow Documentation for more information on TensorFlow augmentation parameters and the PyTorch Documentation for more information on PyTorch augmentation parameters.

Basic training configuration parameters that control the fundamental aspects of your training process.

Parameter	Description	Default	Example
Epochs	Number of complete passes through the entire dataset	10	`training_plan.epochs(100)`
Cycles	Number of complete passes through training and validation datasets	1	`training_plan.cycles(10)`
Batch Size	Number of samples processed at one time. Set automatically from the `batch_size` variable in your model file	Datatype dependent 16 in most cases	Set via `batch_size = 16` in your model `.py` file
Validation Split	Percentage of dataset used for validation (0-1)	Dataset dependent 20% in most cases	`training_plan.validation_split(0.2)`

Core Hyperparameters

1. Optimizer

Controls how the model’s parameters are updated during training. Supports different optimizers for TensorFlow and PyTorch. The default optimizer is SGD for both PyTorch and TensorFlow. Supported Optimizers:

TensorFlow: adam, rmsprop, sgd, adadelta, adagrad, adamax, nadam, ftrl
PyTorch: adam, rmsprop, sgd, adadelta, adagrad, adamax

training_plan.optimizer('rmsprop')

2. Learning Rate

Controls the rate at which the model learns. Supports three different types:

Type	Description	Framework Support	Example
Constant	Fixed learning rate throughout training	TensorFlow, PyTorch	`training_plan.learning_rate({'type': 'constant', 'value': 0.002})`
Adaptive	Learning rate that changes based on schedule	TensorFlow	`training_plan.learning_rate({'type': 'adaptive', 'value': {'decay_rate': 0.9, 'decay_steps': 100, 'initial_learning_rate': 0.1, 'scheduler': 'ExponentialDecay'}})`
Custom	User-defined learning rate function	TensorFlow	`training_plan.learning_rate({'type': 'custom', 'value': {'name': custom_function, 'epoch': 5}})`

Default: {'type': 'constant', 'value': 0.001}

Custom for TensorFlow: Define a custom learning rate function, then pass it via learning_rate() with type: 'custom':

def custom_LearningRate_scheduler(epoch):
        if epoch < 5:
            return 0.01
        else:
            return 0.01 * tf.math.exp(0.1 * (10 - epoch))
training_plan.learning_rate({'type': 'custom', 'value': {'name': custom_LearningRate_scheduler, 'epoch': 5}})

3. Loss Function

Defines how the model measures prediction errors. Supports standard and custom loss functions. It is implemented similarly to the built-in loss function in TensorFlow. Supported Loss Functions:

TensorFlow: binary_crossentropy, categorical_crossentropy, mse, custom loss functions
PyTorch: crossentropy, mse, l1

# Standard loss function
training_plan.loss_function({'type': 'standard', 'value': 'categorical_crossentropy'})

# Custom loss function (TensorFlow only)
def custom_mse(y_true, y_pred):
	# calculating squared difference between target and predicted values
	loss = K.square(y_pred - y_true)  # (batch_size, 2)

	# multiplying the values with weights along batch dimension
	loss = loss * [0.3, 0.7]          # (batch_size, 2)

	# summing both loss values along batch dimension
	loss = K.sum(loss, axis=1)        # (batch_size,)

	return loss
training_plan.loss_function({'type': 'custom', 'value': custom_mse})

Default: {'type': 'standard', 'value': 'mse'}

Training Control

Layer Freezing

Specify which layers should remain unchanged during training (TensorFlow only):

training_plan.layers_freeze(['conv1','fc1'])

Layer freezing is currently supported for TensorFlow models only. PyTorch models will receive a “not supported” message.

Callbacks

Control training behavior with various callbacks:

Callback	Purpose	Parameters	Example
Early Stopping	Stop training when metric stops improving	metric, patience	`training_plan.early_stop_callback('loss', 10)`
Reduce LR	Reduce learning rate when metric plateaus	metric, factor, patience, threshold	`training_plan.reduce_lr_callback('loss', 0.1, 10, 0.0001)`
Model Checkpoint	Save model weights at specific intervals	metric, save_best_only	`training_plan.model_checkpoint_callback('val_loss', True)`
Terminate on NaN	Stop training if validation loss becomes NaN	None	`training_plan.terminate_on_nan_callback()`

Preprocessing (Tabular & Time Series)

Configure the platform’s built-in preprocessing per experiment. Available for tabular classification, tabular regression, time-to-event, and time-series classification; time-series forecasting supports the imputation pair (handle_missing_values / imputation_strategy). The calls below are illustrative alternatives, not a recommended combination — for example, handle_missing_values(False) and imputation_strategy('iterative') contradict each other (the first disables the step the second configures).

# Turn built-in imputation off so your model owns missing-data handling
training_plan.handle_missing_values(False)

# Or pick the imputation strategy: median (default), mean, most_frequent,
# constant, iterative (MICE), or 'none'
training_plan.imputation_strategy('iterative')

# Categorical encoding: label (default) or onehot
training_plan.encoding_strategy('onehot')

# Numeric feature scaling on/off + scaler choice (tabular & time-to-event)
training_plan.normalize_features(True)
training_plan.scaler('RobustScaler')

# Time-series classification only: opt-in missingness-indicator channels —
# a 0/1 <column>__missing feature per numeric column carrying the raw NaN
# mask. The model's input width grows, identically at every edge.
training_plan.missingness_indicators(True)

When to disable imputation: if your model embeds its own imputer (an sklearn Pipeline) or handles NaNs natively (XGBoost, LightGBM, HistGradientBoosting, CatBoost, EBM), set handle_missing_values(False) — otherwise the built-in step fills every gap first and your model never sees a raw NaN.

knn imputation and the QuantileTransformer scaler are deliberately unavailable: their fitted state memorizes raw training data, which would leave the secure environment inside the preprocessing artifact. iterative (MICE) persists only learned coefficients — the same privacy class as model weights.

Defaults reproduce the historical platform behaviour (median imputation on, label encoding, z-score feature scaling on), so an experiment that never touches these knobs is unchanged.

Data Augmentation for Image Data

Enhance your dataset with real-time image transformations. All parameters support both TensorFlow and PyTorch unless noted otherwise.

Geometric Transformations

Parameter	Description	Default	Framework Support	Example
rotation_range	Degree range for random rotations. For example, if the rotation_range is set to 2, images will be rotated by a random degree between -2 and 2	0	TensorFlow, PyTorch	`training_plan.rotation_range(2)`
width_shift_range	Range for horizontal shifts (float: fraction, int: pixels). If the value is a float less than 1, it represents a fraction of total width; otherwise it represents pixels. Integers represent pixel values from the interval (-width_shift_range, +width_shift_range)	0.0	TensorFlow, PyTorch	`training_plan.width_shift_range(0.1)`
height_shift_range	Range for vertical shifts (float: fraction, int: pixels). Works like width_shift_range	0.0	TensorFlow, PyTorch	`training_plan.height_shift_range(0.1)`
shear_range	Shear intensity in degrees in counter-clockwise direction	0.0	TensorFlow	`training_plan.shear_range(0.2)`
zoom_range	Range for zooming (float or list). If the value is a float, then zoom range is defined as [1-zoom_range, 1+zoom_range]. If the value is a list, it represents the range of zoom	0.0	TensorFlow, PyTorch	`training_plan.zoom_range(0.1)`, `training_plan.zoom_range([0.2, 0.8])`
horizontal_flip	Randomly flip images horizontally	False	TensorFlow	`training_plan.horizontal_flip(True)`
vertical_flip	Randomly flip images vertically	False	TensorFlow	`training_plan.vertical_flip(True)`

Color and Intensity Transformations

Parameter	Description	Default	Framework Support	Example
brightness_range	Range for brightness shifts (tuple of floats)	None	TensorFlow, PyTorch	`training_plan.brightness_range((0.1,0.4))`
channel_shift_range	Range for random channel shifts	0	TensorFlow, PyTorch*	`training_plan.channel_shift_range(0.4)`
rescale	Rescaling factor for pixel values (float)	None	TensorFlow, PyTorch	`training_plan.rescale(1.0/255.0)`

*PyTorch: Only supported for RGB images

Normalization

Parameter	Description	Default	Framework Support	Example
samplewise_center	Center each image by subtracting mean	False	TensorFlow	`training_plan.samplewise_center(True)`
samplewise_std_normalization	Standardize each image by subtracting the mean and dividing by the standard deviation of pixel values. Calculated individually	False	TensorFlow	`training_plan.samplewise_std_normalization(True)`

Other Parameters

Parameter	Description	Default	Framework Support	Example
fill_mode	Method for filling points outside boundaries. Supported for TensorFlow: “constant”, “nearest”, “reflect”, “wrap”. For PyTorch: “constant”, “edge”, “symmetric”, “reflect”, “wrap”.	‘constant’	TensorFlow, PyTorch	`training_plan.fill_mode("nearest")`
cval	Fill value for points outside the image boundaries when fill_mode=“constant”	0.0	TensorFlow, PyTorch	`training_plan.cval(0.3)`
shuffle	Whether to shuffle the data	True	TensorFlow, PyTorch	`training_plan.shuffle(True)`

LLM Parameters (Text Classification)

For text classification tasks in PyTorch, you can enable and configure LoRA (Low-Rank Adaptation) parameters:

# Enable LoRA first
training_plan.enable_lora(True)

# Configure LoRA parameters (positional arguments: lora_r, lora_alpha, lora_dropout, q_lora)
training_plan.set_lora_parameters(256, 512, 0.05, False)

Parameter	Description	Type	Default	Example
lora_r	Rank for the LoRA layer	Positive integer	256	`lora_r=256`
lora_alpha	Scaling factor alpha for LoRA	Positive integer	512	`lora_alpha=512`
lora_dropout	Dropout rate for LoRA layers	Float (0-1)	0.05	`lora_dropout=0.05`
q_lora	Enable or disable Q LoRA	Boolean	False	`q_lora=False`

Note: LLM parameters are supported only for PyTorch.

Dataset Parameters (Optional)

Customize your dataset configuration and preprocessing options:

Dataset Customization

Parameter	Description	Example
training_classes	Customize dataset by specifying samples per class	`training_plan.training_classes({'car': 30, 'person': 30})`
data_type	Image format: ‘rgb’ or ‘grayscale’	`training_plan.data_type('rgb')`
seed	Set global random seed	`training_plan.seed(True)`

Next Steps

Submit your best model: Evaluate Model

Need Help?

For more info about available functions and methods, call the help function in your notebook:

user.help()

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Overview

Create a Use Case

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Environment Setup

Tools & Help

Training Parameters

Core Hyperparameters

1. Optimizer

2. Learning Rate

3. Loss Function

Training Control

Layer Freezing

Callbacks

Preprocessing (Tabular & Time Series)

Data Augmentation for Image Data

Geometric Transformations

Color and Intensity Transformations

Normalization

Other Parameters

LLM Parameters (Text Classification)

Dataset Parameters (Optional)

Dataset Customization

Next Steps

Need Help?

​Training Parameters

​Core Hyperparameters

​1. Optimizer

​2. Learning Rate

​3. Loss Function

​Training Control

​Layer Freezing

​Callbacks

​Preprocessing (Tabular & Time Series)

​Data Augmentation for Image Data

​Geometric Transformations

​Color and Intensity Transformations

​Normalization

​Other Parameters

​LLM Parameters (Text Classification)

​Dataset Parameters (Optional)

​Dataset Customization

​Next Steps

​Need Help?

Training Parameters

Core Hyperparameters

1. Optimizer

2. Learning Rate

3. Loss Function

Training Control

Layer Freezing

Callbacks

Preprocessing (Tabular & Time Series)

Data Augmentation for Image Data

Geometric Transformations

Color and Intensity Transformations

Normalization

Other Parameters

LLM Parameters (Text Classification)

Dataset Parameters (Optional)

Dataset Customization

Next Steps

Need Help?