a d=ic¡ã@sfdZddlmZddlmZejZejZejZejZejZej Z ej Z ej Z ej Z ej Z ejZ[[dS)aî Public APIs for the HParams plugin. This module supports a spectrum of use cases, depending on how much structure you want. In the simplest case, you can simply collect your hparams into a dict, and use a Keras callback to record them: >>> from tensorboard.plugins.hparams import api as hp >>> hparams = { ... "optimizer": "adam", ... "fc_dropout": 0.2, ... "neurons": 128, ... # ... ... } >>> >>> model = model_fn(hparams) >>> callbacks = [ >>> tf.keras.callbacks.TensorBoard(logdir), >>> hp.KerasCallback(logdir, hparams), >>> ] >>> model.fit(..., callbacks=callbacks) The Keras callback requires that TensorFlow eager execution be enabled. If not using Keras, use the `hparams` function to write the values directly: >>> # In eager mode: >>> with tf.create_file_writer(logdir).as_default(): ... hp.hparams(hparams) >>> >>> # In legacy graph mode: >>> with tf.compat.v2.create_file_writer(logdir).as_default() as w: ... sess.run(w.init()) ... sess.run(hp.hparams(hparams)) ... sess.run(w.flush()) To control how hyperparameters and metrics appear in the TensorBoard UI, you can define `HParam` and `Metric` objects, and write an experiment summary to the top-level log directory: >>> HP_OPTIMIZER = hp.HParam("optimizer") >>> HP_FC_DROPOUT = hp.HParam( ... "fc_dropout", ... display_name="f.c. dropout", ... description="Dropout rate for fully connected subnet.", ... ) >>> HP_NEURONS = hp.HParam("neurons", description="Neurons per dense layer") >>> >>> with tf.summary.create_file_writer(base_logdir).as_default(): ... hp.hparams_config( ... hparams=[ ... HP_OPTIMIZER, ... HP_FC_DROPOUT, ... HP_NEURONS, ... ], ... metrics=[ ... hp.Metric("xent", group="validation", display_name="cross-entropy"), ... hp.Metric("f1", group="validation", display_name="F₁ score"), ... hp.Metric("loss", group="train", display_name="training loss"), ... ], ... ) You can continue to pass a string-keyed dict to the Keras callback or the `hparams` function, or you can use `HParam` objects as the keys. The latter approach enables better static analysis: your favorite Python linter can tell you if you misspell a hyperparameter name, your IDE can help you find all the places where a hyperparameter is used, etc: >>> hparams = { ... HP_OPTIMIZER: "adam", ... HP_FC_DROPOUT: 0.2, ... HP_NEURONS: 128, ... # ... ... } >>> >>> model = model_fn(hparams) >>> callbacks = [ >>> tf.keras.callbacks.TensorBoard(logdir), >>> hp.KerasCallback(logdir, hparams), >>> ] Finally, you can choose to annotate your hparam definitions with domain information: >>> HP_OPTIMIZER = hp.HParam("optimizer", hp.Discrete(["adam", "sgd"])) >>> HP_FC_DROPOUT = hp.HParam("fc_dropout", hp.RealInterval(0.1, 0.4)) >>> HP_NEURONS = hp.HParam("neurons", hp.IntInterval(64, 256)) The TensorBoard HParams plugin does not provide tuners, but you can integrate these domains into your preferred tuning framework if you so desire. The domains will also be reflected in the TensorBoard UI. See the `Experiment`, `HParam`, `Metric`, and `KerasCallback` classes for API specifications. Consult the `hparams_demo.py` script in the TensorBoard repository for an end-to-end MNIST example. é)Ú_keras)Ú summary_v2N)Ú__doc__Ztensorboard.plugins.hparamsrrZDiscreteÚDomainZHParamZ IntIntervalZMetricZ RealIntervalZhparamsZ hparams_pbZhparams_configZhparams_config_pbZCallbackZ KerasCallback©rrúp/home/droni/.local/share/virtualenvs/DPS-5Je3_V2c/lib/python3.9/site-packages/tensorboard/plugins/hparams/api.pyÚsb