print('3D ResNeXt Test time:', toc7 - tic7)
print('3D ResNeXt Test score:', loss_and_metrics[0])
print('3D ResNeXt Test accuracy:', loss_and_metrics[1])
pred_test = model_ResNeXt.predict(
x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2],
x_test.shape[3], 1)).argmax(axis=1)
collections.Counter(pred_test)
gt_test = gt[test_indices] - 1
overall_acc = metrics.accuracy_score(pred_test, gt_test[:-VAL_SIZE])
confusion_matrix = metrics.confusion_matrix(pred_test, gt_test[:-VAL_SIZE])
each_acc, average_acc = averageAccuracy.AA_andEachClassAccuracy(
confusion_matrix)
kappa = metrics.cohen_kappa_score(pred_test, gt_test[:-VAL_SIZE])
KAPPA_3D_ResNeXt.append(kappa)
OA_3D_ResNeXt.append(overall_acc)
AA_3D_ResNeXt.append(average_acc)
TRAINING_TIME_3D_ResNeXt.append(toc6 - tic6)
TESTING_TIME_3D_ResNeXt.append(toc7 - tic7)
ELEMENT_ACC_3D_ResNeXt[index_iter, :] = each_acc
print("3D ResNeXt finished.")
print("# %d Iteration" % (index_iter + 1))
# save records
modelStatsRecord.outputStats(
KAPPA_3D_ResNeXt, OA_3D_ResNeXt, AA_3D_ResNeXt, ELEMENT_ACC_3D_ResNeXt,
TRAINING_TIME_3D_ResNeXt, TESTING_TIME_3D_ResNeXt, history_3d_ResNeXt,
# tic7 = time.clock()
# model.save("models/houston.h5")
model.load_weights('ckpt/houston.h5')
pred_test = model.predict([x_test, x_test1]).argmax(axis=1)
toc7 = time.clock()
collections.Counter(pred_test)
# gt_test = gt[test_indices] - 1
gt_test = gt1[test_indices] - 1
overall_acc_mss = metrics.accuracy_score(pred_test,
gt_test[:-VAL_SIZE])
oa_all[0][num] = overall_acc_mss
confusion_matrix_mss = metrics.confusion_matrix(
pred_test, gt_test[:-VAL_SIZE])
each_acc_mss, average_acc_mss = averageAccuracy.AA_andEachClassAccuracy(
confusion_matrix_mss)
aa_all[0][num] = average_acc_mss
kappa = metrics.cohen_kappa_score(pred_test, gt_test[:-VAL_SIZE])
kappa_all[0][num] = kappa
KAPPA_RES_SS4.append(kappa)
OA_RES_SS4.append(overall_acc_mss)
AA_RES_SS4.append(average_acc_mss)
print("training finished.")
# print('Training Time: ', toc6 - tic6)
# print('Test time:', toc7 - tic7)
print("# %d Iteration" % (index_iter + 1))
print('each_acc', each_acc_mss)
print("oa", overall_acc_mss)
print("aa", average_acc_mss)
model.save_weights('model_weigh/test3_best.h5')
print("^-^-------------testing-------------------^-^")
model.load_weights('ckpt/best.h5')
tic7 = time.clock()
pred_test = model.predict([x_test, x_test1]).argmax(axis=1)
toc7 = time.clock()
collections.Counter(pred_test)
gt_test = gt[test_indices] - 1
gt_train = gt[train_indices] - 1
overall_acc = metrics.accuracy_score(pred_test, gt_test[:-VAL_SIZE])
confusion_matrix_res4 = metrics.confusion_matrix(pred_test,
gt_test[:-VAL_SIZE])
each_acc_res4, average_acc_res4 = averageAccuracy.AA_andEachClassAccuracy(
confusion_matrix_res4)
kappa = metrics.cohen_kappa_score(pred_test, gt_test[:-VAL_SIZE])
collections.Counter(pred_test)
gt_test = gt[test_indices] - 1
gt_train = gt[train_indices] - 1
overaccy = metrics.accuracy_score(pred_test, gt_test[:-VAL_SIZE])
confusion_matrix_mss = metrics.confusion_matrix(gt_test[:-VAL_SIZE], pred_test)
print(confusion_matrix_mss)
average_acc = averageAccuracy.AA_andEachClassAccuracy(confusion_matrix_mss)
kappa_value = Kappa.kappa(confusion_matrix_mss)
print("training finished.")
print('3D CONV Training Time: ', toc2 - tic2)
print('3D CONV Test time:', toc3 - tic3)
print('3D CONV Test score:', loss_and_metrics[0])
print('3D CONV Test accuracy:', loss_and_metrics[1])
print(history_conv.history.keys())
# 预测
pred_test_conv = model.predict(x_test).argmax(axis=1)
collections.Counter(pred_test_conv)
gt_test = gt[test_indices] - 1
overall_acc_conv = metrics.accuracy_score(pred_test_conv, gt_test)
confusion_matrix_conv = metrics.confusion_matrix(pred_test_conv, gt_test)
each_acc_conv, average_acc_conv = averageAccuracy.AA_andEachClassAccuracy(
confusion_matrix_conv)
kappa = metrics.cohen_kappa_score(pred_test_conv, gt_test)
KAPPA_CONV.append(kappa)
OA_CONV.append(overall_acc_conv)
AA_CONV.append(average_acc_conv)
TRAINING_TIME_CONV.append(toc2 - tic2)
TESTING_TIME_CONV.append(toc3 - tic3)
ELEMENT_ACC_CONV[index_iter, :] = each_acc_conv
print("Overall Accuracy:", overall_acc_conv)
print("Confusion matrix:", confusion_matrix_conv)
print("Average Accuracy:", average_acc_conv)
print("Each Class Accuracies are listed as follows:")
for idx, acc in enumerate(each_acc_conv):
print("Class %d : %.3e" % (idx + 1, acc))
