# Import model and loss
from examples.mnist.mnist import create_model
from starttf.examples.mnist.loss import create_loss

# Import utility functions for training and hyper parameter management.
from starttf.estimators.scientific_estimator import easy_train_and_evaluate
from starttf.utils.hyperparams import load_params

if __name__ == "__main__":
    # Load the hyperparameters
    hyper_params = load_params("starttf/examples/mnist/hyper_params.json")

    # Invoke the training
    easy_train_and_evaluate(hyper_params, create_model, create_loss)

f5a35dbbcd4842a2dcf778b34e1e7335ec80147a
<mask token>

<mask token>
if __name__ == '__main__':
    hyper_params = load_params('starttf/examples/mnist/hyper_params.json')
    easy_train_and_evaluate(hyper_params, create_model, create_loss)

from examples.mnist.mnist import create_model
from starttf.examples.mnist.loss import create_loss
from starttf.estimators.scientific_estimator import easy_train_and_evaluate
from starttf.utils.hyperparams import load_params
if __name__ == '__main__':
    hyper_params = load_params('starttf/examples/mnist/hyper_params.json')
    easy_train_and_evaluate(hyper_params, create_model, create_loss)

# Import model and loss
from examples.mnist.mnist import create_model
from starttf.examples.mnist.loss import create_loss

# Import utility functions for training and hyper parameter management.
from starttf.estimators.scientific_estimator import easy_train_and_evaluate
from starttf.utils.hyperparams import load_params

if __name__ == "__main__":
    # Load the hyperparameters
    hyper_params = load_params("starttf/examples/mnist/hyper_params.json")

    # Invoke the training
    easy_train_and_evaluate(hyper_params, create_model, create_loss)
