F1_score = esk.get_F1_score(y_true, y_pred_binary)
print('######################### end ####################################')
# 保存结果
AUC_prec_rec_list.append(AUC_prec_rec)
accuracy_list.append(accuracy)
specificity_list.append(specificity)
sensitivity_list.append(sensitivity)
precision_list.append(precision)
jaccard_index_list.append(jaccard_index)
F1_score_list.append(F1_score)
#1、评估数据存进列表中
all_list = [AUC_ROC_list, AUC_prec_rec_list, accuracy_list, specificity_list \
, sensitivity_list, precision_list, jaccard_index_list, F1_score_list]
name_list = ['AUC_ROC', 'AUC_prec_rec', 'accuracy', 'specificity',
'sensitivity', 'precision', 'jaccard_index', 'F1_score']
# 2、panda保存所有图片的评估值到CSV文件
esk.save_all_pics_value(name_list, all_list,save_list_csv)
#3、panda保存平均值到CSV文件
esk.save_mean_value(name_list, all_list,save_mean_csv)
#4、结束
dk.stop_threads(coord,threads)
def evaluate_by_sklearn_model():
with tf.Session(config=session_config) as sess:
# 入口
train_x, train_y = create_inputs(is_train)
train_y = tf.reshape(train_y, labels_shape)
x = tf.placeholder(tf.float32, shape=input_shape)
y = tf.placeholder(tf.float32, shape=labels_shape)
# 构建网络
prediction, end_points = model(images=x,
is_train=True,
size=input_shape,
l2_reg=0.0001)
prediction = tf.reshape(prediction, labels_shape)
# 打印模型结构
dk.print_model_struct(end_points)
# 初始化变量
coord, threads = dk.init_variables_and_start_thread(sess)
# 恢复model
saver = dk.restore_model(sess, ckpt, restore_model=restore_model)
# 显示参数量
dk.show_parament_numbers()
# 列表初始化
AUC_ROC_list,AUC_prec_rec_list,accuracy_list,specificity_list,sensitivity_list,\
precision_list,jaccard_index_list,F1_score_list,all_list,mean_list\
=[],[],[],[],[],[],[],[],[],[]
# 测试loop
start_epoch = 0
index = 0
for epoch_n in range(start_epoch, test_opoch):
for n_batch in range(n_batch_test):
batch_x, batch_y = sess.run([train_x,
train_y]) # 取出一个batchsize的图片。
batch_x = batch_x / 255.0
# 3、预测输出
since = time.time()
predict = sess.run(prediction, feed_dict={x: batch_x})
seconds = time.time() - since
# 4、预测图转二值化图（非0即1）,加了这步性能指标会下降2%左右
predict[predict >= 0.5] = 1
predict[predict < 0.5] = 0
# 5、把batch图片分割一张张图片
batch_pre_list = np.split(predict, batch_size, axis=0)
pre_pics_list = np.squeeze(batch_pre_list, axis=0)
for label, pre in zip(batch_y, pre_pics_list):
index += 1
y_scores = pre.reshape(-1, 1)
y_true = label.reshape(-1, 1)
print(
'######################### start ####################################'
)
# 1、画ROC曲线
AUC_ROC = esk.plot_roc_curve(y_true,
y_scores,
save_plot_curve_dir,
curve_name=str(index))
AUC_ROC_list.append(AUC_ROC)
#2、画P_R-curve曲线
AUC_prec_rec = esk.plot_precision_recall_curve(
y_true,
y_scores,
save_plot_curve_dir,
curve_name=str(index))
#3、Confusion matrix
y_pred_binary = esk.convert_to_binary(
shape=y_scores.shape[0], y_scores=y_scores)
accuracy, specificity, sensitivity, precision \
= esk.plot_confusion_matrix(y_true, y_pred_binary)
#4、Jaccard similarity index
jaccard_index = esk.get_jaccard_index(
y_true, y_pred_binary)
#5、F1 score
F1_score = esk.get_F1_score(y_true, y_pred_binary)
print(
'######################### end ####################################'
)
# 保存结果
AUC_prec_rec_list.append(AUC_prec_rec)
accuracy_list.append(accuracy)
specificity_list.append(specificity)
sensitivity_list.append(sensitivity)
precision_list.append(precision)
jaccard_index_list.append(jaccard_index)
F1_score_list.append(F1_score)
#1、评估数据存进列表中
all_list = [AUC_ROC_list, AUC_prec_rec_list, accuracy_list, specificity_list \
, sensitivity_list, precision_list, jaccard_index_list, F1_score_list]
name_list = [
'AUC_ROC', 'AUC_prec_rec', 'accuracy', 'specificity',
'sensitivity', 'precision', 'jaccard_index', 'F1_score'
]
# 2、panda保存所有图片的评估值到CSV文件
esk.save_all_pics_value(name_list, all_list, save_list_csv)
#3、panda保存平均值到CSV文件
esk.save_mean_value(name_list, all_list, save_mean_csv)
#4、结束
dk.stop_threads(coord, threads)