model_res4_SS_BN = res4_model_ss()
model_res4_SS_BN.load_weights(best_weights_RES_path_ss4)
pred_test_res4 = model_res4_SS_BN.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_res4)
gt_test = gt[test_indices] - 1
overall_acc_res4 = metrics.accuracy_score(pred_test_res4,
gt_test[:-VAL_SIZE])
confusion_matrix_res4 = metrics.confusion_matrix(pred_test_res4,
gt_test[:-VAL_SIZE])
each_acc_res4, average_acc_res4 = averageAccuracy.AA_andEachClassAccuracy(
confusion_matrix_res4)
kappa = metrics.cohen_kappa_score(pred_test_res4, gt_test[:-VAL_SIZE])
KAPPA_RES_SS4.append(kappa)
OA_RES_SS4.append(overall_acc_res4)
AA_RES_SS4.append(average_acc_res4)
#TRAINING_TIME_RES_SS4.append(toc6 - tic6)
#TESTING_TIME_RES_SS4.append(toc7 - tic7)
ELEMENT_ACC_RES_SS4[index_iter, :] = each_acc_res4
print("3D RESNET_SS4 without BN training finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats_assess(
KAPPA_RES_SS4, OA_RES_SS4, AA_RES_SS4, ELEMENT_ACC_RES_SS4, CATEGORY,
'/home/zilong/SSRN/records/IN_test_SS_10.txt',
'/home/zilong/SSRN/records/IN_test_SS_element_10.txt')
'f1score: %s' % (f1score))
except ZeroDivisionError:
print('precision: %s' % 0, 'recall: %s' % 0)
mcm = metrics.multilabel_confusion_matrix(pred_test, gt_test[:-VAL_SIZE])
# precision = metrics.precision_score(pred_test, gt_test[:-VAL_SIZE])
# recall = metrics.recall_score(pred_test, gt_test[:-VAL_SIZE])
# f1score = metrics.f1_score(pred_test, gt_test[:-VAL_SIZE])
KAPPA_3D_DenseNet.append(kappa)
OA_3D_DenseNet.append(overall_acc)
AA_3D_DenseNet.append(average_acc)
# TRAINING_TIME_3D_DenseNet.append(toc6 - tic6)
# TESTING_TIME_3D_DenseNet.append(toc7 - tic7)
ELEMENT_ACC_3D_DenseNet[index_iter, :] = each_acc
# PRECISION_3D_DenseNet.append(precision)
# RECALL_3D_DenseNet.append(recall)
# F1SCORE_3D_DenseNet.append(f1score)
print("3D DenseNet finished.")
print("# %d Iteration" % (index_iter + 1))
# print(counter)
modelStatsRecord.outputStats_assess(
KAPPA_3D_DenseNet, OA_3D_DenseNet, AA_3D_DenseNet, ELEMENT_ACC_3D_DenseNet,
CATEGORY, 'D:/RGCSA/records/'
'IN_test_RGCSA_3_1_6_100_1.txt', 'D:/RGCSA/records/'
'IN_test_element_RGCSA_3_1_6_100_1.txt')
model_res4_SS_BN.load_weights(best_weights_RES_path_ss4)
pred_test_res4 = model_res4_SS_BN.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_res4)
gt_test = gt[test_indices] - 1
overall_acc_res4 = metrics.accuracy_score(pred_test_res4,
gt_test[:-VAL_SIZE])
confusion_matrix_res4 = metrics.confusion_matrix(pred_test_res4,
gt_test[:-VAL_SIZE])
each_acc_res4, average_acc_res4 = averageAccuracy.AA_andEachClassAccuracy(
confusion_matrix_res4)
kappa = metrics.cohen_kappa_score(pred_test_res4, gt_test[:-VAL_SIZE])
KAPPA_RES_SS4.append(kappa)
OA_RES_SS4.append(overall_acc_res4)
AA_RES_SS4.append(average_acc_res4)
#TRAINING_TIME_RES_SS4.append(toc6 - tic6)
#TESTING_TIME_RES_SS4.append(toc7 - tic7)
ELEMENT_ACC_RES_SS4[index_iter, :] = each_acc_res4
print("3D RESNET_SS4 without BN training finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats_assess(
KAPPA_RES_SS4, OA_RES_SS4, AA_RES_SS4, ELEMENT_ACC_RES_SS4, CATEGORY,
'./drive/My Drive/Residual_HSI-Colab/SSRN/records/UP_test_SS_10.txt',
'./drive/My Drive/Residual_HSI-Colab/SSRN/records/UP_test_SS_element_10.txt'
)
# SS Residual Network 4 with BN
model_MSDNet = model_MSDNet()
# 直接加载训练权重
model_MSDNet.load_weights(best_weights_MSDNet_path)
pred_test = model_MSDNet.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_MSDNet.append(kappa)
OA_3D_MSDNet.append(overall_acc)
AA_3D_MSDNet.append(average_acc)
ELEMENT_ACC_3D_MSDNet[index_iter, :] = each_acc
print("3D MSDNet finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats_assess(
KAPPA_3D_MSDNet, OA_3D_MSDNet, AA_3D_MSDNet, ELEMENT_ACC_3D_MSDNet,
CATEGORY,
'F:/transfer code/Tensorflow Learning/SKNet/records-ksc-19-712/IN_test_3D.txt',
'F:/transfer code/Tensorflow Learning/SKNet/records-ksc-19-712/IN_test_element_3D.txt'
)
model_ResNeXt.load_weights(best_weights_ResNeXt_path)
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)
counter = 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))
# print(counter)
modelStatsRecord.outputStats_assess(
KAPPA_3D_ResNeXt, OA_3D_ResNeXt, AA_3D_ResNeXt, ELEMENT_ACC_3D_ResNeXt,
CATEGORY, 'D:/3D-ResNeXt-master/records/UP_test_3D_ResNeXt_4_1_5_60_1.txt',
'D:/3D-ResNeXt-master/records/UP_test_element_3D_ResNeXt_4_1_5_60_1.txt')
# 直接加载训练权重
model_densenet.load_weights(best_weights_DenseNet_path)
pred_test = model_densenet.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_DenseNet.append(kappa)
OA_3D_DenseNet.append(overall_acc)
AA_3D_DenseNet.append(average_acc)
# TRAINING_TIME_3D_DenseNet.append(toc6 - tic6)
# TESTING_TIME_3D_DenseNet.append(toc7 - tic7)
ELEMENT_ACC_3D_DenseNet[index_iter, :] = each_acc
print("3D DenseNet finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats_assess(
KAPPA_3D_DenseNet, OA_3D_DenseNet, AA_3D_DenseNet, ELEMENT_ACC_3D_DenseNet,
CATEGORY,
'D:/Tensorflow Learning/3D-DenseNet-Hyperspectral/records-in-no-bottlenck-layer-6-24/IN_test_3D.txt',
'D:/Tensorflow Learning/3D-DenseNet-Hyperspectral/records-in-no-bottlenck-layer-6-24/IN_test_element_3D.txt'
)
# SS Residual Network 4 with BN
model_MSDNet = model_MSDNet()
# 直接加载训练权重
model_MSDNet.load_weights(best_weights_MSDNet_path)
pred_test = model_MSDNet.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_MSDNet.append(kappa)
OA_3D_MSDNet.append(overall_acc)
AA_3D_MSDNet.append(average_acc)
ELEMENT_ACC_3D_MSDNet[index_iter, :] = each_acc
print("3D MSDNet finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats_assess(
KAPPA_3D_MSDNet, OA_3D_MSDNet, AA_3D_MSDNet, ELEMENT_ACC_3D_MSDNet,
CATEGORY,
'F:/transfer code/Tensorflow Learning/3D-MSDNet/records-botswana-13-514-5/IN_test_3D.txt',
'F:/transfer code/Tensorflow Learning/3D-MSDNet/records-botswana-13-514-5/IN_test_element_3D.txt'
)
model_densenet = model_DenseNet()
model_densenet.load_weights(best_weights_DenseNet_path)
pred_test = model_densenet.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_DenseNet.append(kappa)
OA_3D_DenseNet.append(overall_acc)
AA_3D_DenseNet.append(average_acc)
# TRAINING_TIME_3D_DenseNet.append(toc6 - tic6)
# TESTING_TIME_3D_DenseNet.append(toc7 - tic7)
ELEMENT_ACC_3D_DenseNet[index_iter, :] = each_acc
print("3D DenseNet finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats_assess(
KAPPA_3D_DenseNet, OA_3D_DenseNet, AA_3D_DenseNet, ELEMENT_ACC_3D_DenseNet,
CATEGORY,
'F:/transfer code/Tensorflow Learning/3D-MSDNet/records-up-13-217-7-1/UP_test_3D.txt',
'F:/transfer code/Tensorflow Learning/3D-MSDNet/records-up-13-217-7-1/UP_test_element_3D.txt'
)
x_test = x_test_all[:-VAL_SIZE]
y_test = y_test[:-VAL_SIZE]
model_HSICNNet = model_HSICNNet()
model_HSICNNet.load_weights(best_weights_HSICNNet_path)
pred_test = model_HSICNNet.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_HSICNNet.append(kappa)
OA_3D_HSICNNet.append(overall_acc)
AA_3D_HSICNNet.append(average_acc)
# TRAINING_TIME_3D_HSICNNet.append(toc6 - tic6)
# TESTING_TIME_3D_HSICNNet.append(toc7 - tic7)
ELEMENT_ACC_3D_HSICNNet[index_iter, :] = each_acc
print("3D HSICNNet finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats_assess(KAPPA_3D_HSICNNet, OA_3D_HSICNNet, AA_3D_HSICNNet,
ELEMENT_ACC_3D_HSICNNet, CATEGORY,
'F:/transfer code/Tensorflow Learning/SKNet/records-up-hy-15-514/UP_test_3D.txt',
'F:/transfer code/Tensorflow Learning/SKNet/records-up-hy-15-514/UP_test_element_3D.txt')
n = len(confusion_matrix)
for i in range(len(confusion_matrix[0])):
rowsum, colsum = sum(confusion_matrix[i]), sum(confusion_matrix[r][i]
for r in range(n))
try:
precision = (confusion_matrix[i][i] / float(colsum))
recall = (confusion_matrix[i][i] / float(rowsum))
f1score = (2 * precision * recall) / (precision + recall)
print('precision: %s' % (precision), 'recall: %s' % (recall),
'f1score: %s' % (f1score))
except ZeroDivisionError:
print('precision: %s' % 0, 'recall: %s' % 0)
KAPPA_3D_DenseNet.append(kappa)
OA_3D_DenseNet.append(overall_acc)
AA_3D_DenseNet.append(average_acc)
# TRAINING_TIME_3D_DenseNet.append(toc6 - tic6)
# TESTING_TIME_3D_DenseNet.append(toc7 - tic7)
ELEMENT_ACC_3D_DenseNet[index_iter, :] = each_acc
print("3D DenseNet finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats_assess(
KAPPA_3D_DenseNet, OA_3D_DenseNet, AA_3D_DenseNet, ELEMENT_ACC_3D_DenseNet,
CATEGORY,
'D:/3D-TwoStep-Network/records/KSC_test_ResNeXt_TwoStepAttention_5_1_4_60_1.txt',
'D:/3D-TwoStep-Network/records/KSC_test_element_ResNeXt_TwoStepAttention_5_1_4_60_1.txt'
)
model_densenet = model_DenseNet()
model_densenet.load_weights(best_weights_DenseNet_path)
pred_test = model_densenet.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_DenseNet.append(kappa)
OA_3D_DenseNet.append(overall_acc)
AA_3D_DenseNet.append(average_acc)
# TRAINING_TIME_3D_DenseNet.append(toc6 - tic6)
# TESTING_TIME_3D_DenseNet.append(toc7 - tic7)
ELEMENT_ACC_3D_DenseNet[index_iter, :] = each_acc
print("3D DenseNet finished.")
print("# %d Iteration" % (index_iter + 1))
modelStatsRecord.outputStats_assess(
KAPPA_3D_DenseNet, OA_3D_DenseNet, AA_3D_DenseNet, ELEMENT_ACC_3D_DenseNet,
CATEGORY,
'D:/Tensorflow Learning/3D-DenseNet-Hyperspectral/records-up-3dcnn/UP_test_3D.txt',
'D:/Tensorflow Learning/3D-DenseNet-Hyperspectral/records-up-3dcnn/UP_test_element_3D.txt'
)
