To learn about this API, you can start by reading the trial definitions from the following examples:
Before loading data, read Prepare Data to understand how to work with different sources of data.
(x, y)of Numpy arrays. x must be a NumPy array (or array-like), a list of arrays (in case the model has multiple inputs), or a dict mapping input names to the corresponding array, if the model has named inputs. y should be a numpy array.
(x, y, sample_weights)of Numpy arrays.
tf.data.datasetreturning a tuple of either (inputs, targets) or (inputs, targets, sample_weights).
keras.utils.Sequencereturning a tuple of either (inputs, targets) or (inputs, targets, sample weights).
tf.data.Dataset, users are required to wrap both their training and validation dataset
wrapper is used to shard the dataset for Introduction to Distributed Training. For optimal performance, users
should wrap a dataset immediately after creating it.
Define the Model¶
Customize Calling Model Fitting Function¶
A checkpoint includes the model definition (Python source code), experiment configuration file, network architecture, and the values of the model’s parameters (i.e., weights) and hyperparameters. When using a stateful optimizer during training, checkpoints will also include the state of the optimizer (i.e., learning rate). Users can also embed arbitrary metadata in checkpoints via a Python API.
TensorFlow Keras trials are checkpointed to a file named
tf.keras.models.save_model. You can learn more from the TF Keras docs.