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The network was optimized for 100 epochs working with the Adam optimization algorithm [37] although the finding out rate was modified making use of the A single Cycle Finding out Rate Scheduler [38]. We extensively experimented with the hyperparameters (e.g., the number of layers in GRUs, hidden state size, and number of neurons in the linear layers) and settled with the ones pointed out earlier. We utilised mini-batch gradient descent for parameter updates with a batch size of 64. To make the findings statistically steady, we applied nested cross-validation [39] with 5 folds in each the inner and outer configuration. On a machine with an AMD Ryzen X, 64 GB of DDR4 dual-channel memory, and an NVIDIA RTX 3090 GPU, for the final model, each and every experiment takes 4 hours to comprehensive. The total number of parameters in our proposed model is 103,27,242. Parameter counts for every single layer of the network are shown in Table four. In certain, we propose 4 tasks primarily based on new soccer-related annotations and information.

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