tlt.models.tf_model.TFModel

class tlt.models.tf_model.TFModel(model_name: str, framework: FrameworkType, use_case: UseCaseType)

Base class to represent a TF pretrained model

__init__(model_name: str, framework: FrameworkType, use_case: UseCaseType)

Class constructor

Methods

__init__(model_name, framework, use_case)	Class constructor
benchmark(dataset[, saved_model_dir, ...])	Use Intel Neural Compressor to benchmark the model with the dataset argument.
cleanup_saved_objects_for_distributed()
evaluate(dataset)	Evaluate the model using the specified dataset.
export(output_dir)	Exports a trained model as a saved_model.pb file.
export_for_distributed([export_dir, ...])	Exports the model, optimizer, loss, train data and validation data to the export_dir for distributed script to access.
load_from_directory(model_dir)	Loads a saved model from the specified directory
optimize_graph(output_dir[, overwrite_model])	Performs FP32 graph optimization using the Intel Neural Compressor on the model and writes the inference-optimized model to the output_dir.
quantize(output_dir, dataset[, config, ...])	Performs post training quantization using the Intel Neural Compressor on the model using the dataset.
set_auto_mixed_precision(...)	Enable auto mixed precision for training.
train(dataset, output_dir[, epochs, ...])	Train the model using the specified dataset

Attributes

framework	Framework with which the model is compatible
learning_rate	Learning rate for the model
model_name	Name of the model
preprocessor	Preprocessor for the model
use_case	Use case (or category) to which the model belongs