Europe Cup Grp B stats & predictions Tomorrow

Basketball Strategies: A Deep Dive

Basketball strategies are crucial for team success. Here's a closer look at some common tactics used by professional teams:

• The Full-Court Press:** An aggressive defensive strategy aimed at disrupting the opponent's offensive flow right from the start of their possession.
• The Isolation Play:** Focuses on creating one-on-one matchups by isolating a strong offensive player against a weaker defender. 0.0: [28]: loss = (1 - epsilon) * nll_loss + epsilon * smooth_loss / lprobs.size(-1) [29]: else: [30]: loss = nll_loss [31]: return loss [32]: class GECCriterion(FairseqCriterion): [33]: def __init__(self, args): [34]: super().__init__(args) [35]: self.eps = args.label_smoothing [36]: @staticmethod [37]: def add_args(parser): [38]: """Add criterion-specific arguments to the parser.""" [39]: # fmt: off [40]: parser.add_argument('--label-smoothing', default=0., type=float, [41]: help='epsilon for label smoothing, 0 means no label smoothing') [42]: # fmt: on [43]: def forward(self, model, sample=None): [44]: """Compute loss. [45]: Args: [46]: model (TransformerModel): model [47]: sample (dict): batch [48]: Returns: 49]: tuple: [50]: - Torch.Tensor: loss value [51]: - dict: dictionary containing logging information """ net_output = model(**sample['net_input']) loss = self.compute_loss(model, net_output, sample, reduce=not self.args.sentence_avg) sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens'] logging_output = { 'loss': utils.item(loss.data) if reduce else loss.data, 'ntokens': sample['ntokens'], 'nsentences': sample['target'].size(0), 'sample_size': sample_size, } return loss, logging_output @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" loss_sum = sum(log.get('loss', 0) for log in logging_outputs) ntokens = sum(log.get('ntokens', 0) for log in logging_outputs) nsentences = sum(log.get('nsentences', 0) for log in logging_outputs) sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3) metrics.log_scalar('nll_loss', loss_sum / ntokens / math.log(2), ntokens, round=3) metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['nll_loss'].avg)) @staticmethod def logging_outputs_can_be_summed() -> bool: """ Return whether the logging outputs returned by `forward` can be summed across workers prior to calling `reduce_metrics`. Setting this to True will improves distributed training speed. """ return True ***** Tag Data ***** ID: 2 description: The GECCriterion class which inherits from FairseqCriterion includes methods that define custom behavior including adding arguments via static methods and implementing forward pass computation. start line: 32 end line: 42 dependencies: [] context description: This class encapsulates custom criterion logic specific for GEC, making it relevant when dealing with specialized tasks beyond generic neural network training. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: 4 self contained: N ************* ## Suggestions for complexity 1. **Dynamic Label Smoothing**: Modify `GECCriterion` such that `label_smoothing` value changes dynamically based on some metric or epoch count during training. 2. **Custom Logging**: Integrate custom logging functionality within `add_args` method that logs each argument addition step-by-step. 3. **Conditional Initialization**: Add conditional logic within `__init__` method based on additional parameters passed through `args`, altering internal state or adding new attributes dynamically. 4. **Multi-Criterion Support**: Expand `GECCriterion` class so it supports multiple types of criteria based on input arguments provided at initialization. 5. **Custom Argument Parsing**: Implement advanced argument parsing within `add_args` method that validates complex interdependencies between arguments. ## Conversation <|user|>`Hi AI I need help with my code [SNIPPET]