optimizer=optimizer, iterator_seed=1, cropped=True) print('compiled') num_epochs = 30 model.fit(train_set.X, train_set.y, epochs=num_epochs, batch_size=64, scheduler='cosine', input_time_length=input_time_length) print(model.epochs_df) model.network.eval() print(model.predict(test_set.X)) scores = model.evaluate(test_set.X, test_set.y) Accuracy = 1 - scores['misclass'] print('Accuracy (%) :', Accuracy) # save key values Accuracies[count, CV] = Accuracy Losses[count, :] = model.epochs_df['train_loss'] #th.save({'Acc':Accuracies,'Losses': Losses}, var_save_path) print('Overall Acc Subject {}: {}'.format(i, np.mean(Accuracies[count]))) count += 1 print('last_step')
test_set = SignalAndTarget(X[(trainingSampleSize + valudationSampleSize):], y=y[(trainingSampleSize + valudationSampleSize):]) eval = model.evaluate(test_set.X, test_set.y) print(eval) print(eval['misclass']) np.save( "finetuneCrossSubjects\{}-{}-singleSubjectNum{}-2.5sec-{}epoches-testSetMisclass" .format(model_type, train_type, single_subject_num, epoches), eval['misclass']) from sklearn.metrics import confusion_matrix try: print("prediction") y_pred = model.predict(test_set.X) print(y_pred) print("real labels") print(test_set.y) confusion_matrix = confusion_matrix(test_set.y, y_pred) print(confusion_matrix) except: try: y_pred = model.predict_classes(test_set.X) print(y_pred) print("real labels") print(test_set.y) confusion_matrix = confusion_matrix(test_set.y, y_pred) print(confusion_matrix) except: print("predict_classes method failed")