# Create output network output_network = OutputNetwork(n_input_features=args.n_kernels, n_output_features=task_definition.get_n_output_features(), n_layers=1, n_units=32) # Combine networks to DeepRC network model = DeepRC(max_seq_len=30, sequence_embedding_network=sequence_embedding_network, attention_network=attention_network, output_network=output_network, consider_seq_counts=False, n_input_features=20, add_positional_information=True, sequence_reduction_fraction=0.1, reduction_mb_size=int(5e4), device=device).to(device=device) # # Train DeepRC model # train(model, task_definition=task_definition, trainingset_dataloader=trainingset, trainingset_eval_dataloader=trainingset_eval, learning_rate=args.learning_rate, early_stopping_target_id='binary_target_1', # Get model that performs best for this task validationset_eval_dataloader=validationset_eval, n_updates=args.n_updates, evaluate_at=args.evaluate_at, device=device, results_directory="results/multitask_cnn" # Here our results and trained models will be stored ) # You can use "tensorboard --logdir [results_directory] --port=6060" and open "http://localhost:6060/" in your # web-browser to view the progress # # Evaluate trained model on testset # scores = evaluate(model=model, dataloader=testset_eval, task_definition=task_definition, device=device) print(f"Test scores:\n{scores}")
n_input_features=20, add_positional_information=True, sequence_reduction_fraction=0.1, reduction_mb_size=int(5e4), device=device).to(device=device) # # Train DeepRC model # train( model, task_definition=task_definition, trainingset_dataloader=trainingset, trainingset_eval_dataloader=trainingset_eval, early_stopping_target_id= 'status', # Get model that performs best for this task validationset_eval_dataloader=validationset_eval, n_updates=args.n_updates, evaluate_at=args.evaluate_at, device=device, results_directory= f"results/cmv_with_implanted_signals_{args.id}" # Here our results and trained models will be stored ) # You can use "tensorboard --logdir [results_directory] --port=6060" and open "http://localhost:6060/" in your # web-browser to view the progress # # Evaluate trained model on testset # scores = evaluate(model=model, dataloader=testset_eval, task_definition=task_definition,