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@@ -413,15 +413,8 @@ class VAE:
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dllogger.log(data=metrics_scores, step=(epoch,))
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def log_final_stats(self):
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- data = {"total_train_time": np.sum(self.time_elapsed_training_history),
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- "total_valid_time": np.sum(self.time_elapsed_validation_history),
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- "average_train_epoch time": np.mean(self.time_elapsed_training_history),
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- "average_validation_time": np.mean(self.time_elapsed_validation_history),
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- "total_elapsed_time" : self.total_time,
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- "mean_training_throughput": np.mean(self.training_throughputs[10:]),
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- "mean_inference_throughput": np.mean(self.inference_throughputs),
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- "max_training_throughput": np.max(self.training_throughputs[10:]),
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- "max_inference_throughput": np.max(self.inference_throughputs)}
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+ data = {"mean_training_throughput": np.mean(self.training_throughputs[10:]),
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+ "mean_inference_throughput": np.mean(self.inference_throughputs[2:])}
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for metric_name, metric_values in self.metrics_history.items():
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data["final_" + metric_name] = metric_values[-1]
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