Specificities = [] kf = KFold(n_splits=10) for train_i, test_i in kf.split(X): x_train = X[train_i].reshape(X[train_i].shape[0], X[train_i].shape[1] * X[train_i].shape[2]) x_test = X[test_i].reshape(X[test_i].shape[0], X[test_i].shape[1] * X[test_i].shape[2]) y_train, y_test = Y[train_i], Y[test_i] svm_model = SVC(C=100, kernel='linear', gamma='scale') svm_model.fit(x_train, y_train) y_pred = svm_model.predict(x_test) print("-------------------------------") ACC, TPR, TNR, PPV, NPV, FPR = per.GetPerformanceMetrics(y_test, y_pred, weighted=True) Accuracies.append(ACC) Recalls.append(TPR) Specificities.append(TNR) Precisions.append(PPV) NPVs.append(NPV) FPRs.append(FPR) print("Accuracy: ", ACC) print("Recall: ", TPR) print("Specificity: ", TNR) print("Precision: ", PPV) print("Negative Predictive Value: ", NPV) print("FP rate(fall-out): ", FPR) print(confusion_matrix(y_test, y_pred))
]) model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=['accuracy']) model.summary() model.fit(train_x, train_y, verbose=2, batch_size=2, epochs=50) model.evaluate(test_x, test_y, batch_size=2, verbose=2) ypred = np.argmax(model.predict(test_x, batch_size=2), axis=-1) print("-------------------------------") ACC, TPR, TNR, PPV, NPV, FPR = per.GetPerformanceMetrics(test_y, ypred, weighted=True) print("Accuracy: ", ACC) print("Recall: ", TPR) print("Specificity: ", TNR) print("Precision: ", PPV) print("Negative Predictive Value: ", NPV) print("FP rate(fall-out): ", FPR) print(confusion_matrix(test_y, ypred)) print("-------------------------------") print( "0: fighting\n 1: front\n 2: ready\n 3: cat\n 4: horse \n 5: hicho \n 6: seiza" )