He won the world heavyweight championship, defeating Sonny Liston in a major upset on February 25, 1964, at age 22. At 18, he won a gold medal in the light heavyweight division at the 1960 Summer Olympics and turned professional later that year. In 1999, he was named Sportsman of the Century by Sports Illustrated and the Sports Personality of the Century by the BBC.īorn and raised in Louisville, Kentucky, he began training as an amateur boxer at age 12. He was the undisputed champion from 1974 to 1978 and the WBA and Ring heavyweight champion from 1978 to 1979. He held the Ring magazine heavyweight title from 1964 to 1970. Nicknamed " the Greatest", he is regarded as one of the most significant sports figures of the 20th century and is often regarded as the greatest heavyweight boxer of all time. Janu– June 3, 2016) was an American professional boxer and activist. Logger ¶ ( Optional) – if True logs to the logger.Muhammad Ali ( / ɑː ˈ l iː/ born Cassius Marcellus Clay Jr. Prog_bar ¶ ( bool) – if True logs to the progress bar. The default behavior per hook is documented here: Automatic Logging. Your model’s output freeze ¶ LightningModule. **kwargs ¶ ( Any) – Keyword arguments are also possible. *args ¶ ( Any) – Whatever you decide to pass into the forward method. If you need to control how often the optimizer steps, override the optimizer_step() hook.įorward ¶ LightningModule. If you use multiple optimizers, you will have to switch to ‘manual optimization’ mode and step them If you use, Lightning handles the closure function automatically for you. If you use 16-bit precision ( precision=16), Lightning will automatically handle the optimizer. step() method automatically in case of automatic optimization. "interval" (default “epoch”) in the scheduler configuration, Lightning will call If a learning rate scheduler is specified in configure_optimizers() with key step() automatically in case of automatic optimization. Self.log('metric_to_track', metric_val) in your LightningModule.
Metrics can be made available to monitor by simply logging it using _shared_eval_step ( batch, batch_idx ) metrics =, ) cross_entropy ( y_hat, y ) return loss def validation_step ( self, batch, batch_idx ): loss, acc = self. model = model def training_step ( self, batch, batch_idx ): x, y = batch y_hat = self. LightningModule ): def _init_ ( self, model ): super (). From torchmetrics.functional import accuracy class ClassificationTask ( pl.
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