The Trainer defines the model and training loop within the TrainingPipeline.
FeedForwardTrainer step is provided in the source code, which defines a simple feed-forward neural
network in Tensorflow.
Create custom trainers¶
In the case of the Trainer, the built-in methods are just convenience to access popular model types. Most of the times, custom model code is required. This is how to create custom trainer steps:
ZenML comes equipped with a
BaseTrainer that all trainers should inherit from. This is how the interface
def run_fn(self): pass def input_fn(self, file_pattern: List[Text], tf_transform_output: tft.TFTransformOutput): pass def model_fn(train_dataset: tf.data.Dataset, eval_dataset: tf.data.Dataset): pass
This doc section is incomplete. Please refer to the docstrings in source code while you wait to complete this section.