Question

我想使用注意力网络从眼底图像中进行视盘分割。该模型的体系结构选自“ https://github.com/lixiaolei1982/Keras-Implementation-of-U-Net-R2U-Net-Attention-U-Net-Attention-R2U-Net.-”（感谢，致-lixiaolei1982）。当我跟踪训练和验证损失时，它减少了（见下图），但是训练损失在20个历元后接近0。在前20个时间段内，分割后的图像是完全黑色或完全白色。我试图将预测图像标准化。但这是一样的。谁能帮我解决这个问题？损失函数的作用是导致输出图像完全变黑吗？下面是使用注意力网训练图像的代码

import os
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np 
import matplotlib.pyplot as plt
from tensorflow.keras.backend import flatten
from skimage.exposure import equalize_hist as clhe
import network as new_model

batch_size = 32
no_epochs = 20
img_height, img_width, img_num_channels = 512, 512, 3

mdl1 = "att_r2unet"

m1 = new_model.att_r2_unet(img_height, img_width, n_label=1)

def preprocess_im(imgs):
    """Make input image values lie between -1 and 1."""
    #imgs = clhe(imgs)
    out_imgs = imgs - np.max(imgs)/2.
    out_imgs /= np.max(imgs)/2.
    return out_imgs

##

seg_train_gen = ImageDataGenerator(preprocessing_function=preprocess_im)
seg_train_x = seg_train_gen.flow_from_directory(directory="../train_im",target_size=(512,512),batch_size=batch_size,color_mode="rgb",class_mode="sparse",shuffle=True,seed=30)
seg_train_y = seg_train_gen.flow_from_directory(directory="../train_gt",target_size=(512,512),batch_size=batch_size,color_mode="grayscale",class_mode="sparse",shuffle=True,seed=30)
seg_val_x = seg_train_gen.flow_from_directory(directory="../val_im",target_size=(512,512),batch_size=batch_size,color_mode="rgb",class_mode="sparse",shuffle=True,seed=30)
seg_val_y = seg_train_gen.flow_from_directory(directory="../val_gt",target_size=(512,512),batch_size=batch_size,color_mode="grayscale",class_mode="sparse",shuffle=True,seed=30)

b_iter = int(np.ceil(seg_train_x.n / batch_size))

hist = {'m1_loss': [],'m1_vloss': []}

epoch = 0
for e in range(epoch, no_epochs):
l1 = list(np.zeros(b_iter))  
    for it in range(b_iter):
        x_batch,_ = seg_train_x.next() 
        y_batch, _ = seg_train_y.next()
        loss1 = m1.train_on_batch(x_batch, y_batch)
        l1.append(loss1[0])
    xval,_ = seg_val_x.next()
    yval,_ = seg_val_y.next()
    loss2a = m1.evaluate(xval,yval, batch_size=batch_size, verbose=1) 
    print('Epoch %d / %d tr_loss %.6f val_loss %.6f, ' % (e + 1, no_epochs, np.mean(l1), loss2a[0]))


    hist['m1_vloss'].append(loss2a[0])
 
    # Save best model
    if e > epoch+1:

        Eopt1 = np.min(hist['m1_vloss'][:-1])

        if hist['m1_vloss'][-1] < Eopt1:
            m1.save((save_dir+mdl1+'_best_model.h5'),overwrite=True)
            m1.save_weights((save_dir+mdl1+'_best_weights.h5'),overwrite=True)

    # save intermediate to folder results every 2 epochs
    if e % 2 == 0:

        x_plt = (xval[0] - xval[0].min()) / (xval[0].max() - xval[0].min())

        ypred = m1.predict(xval)
        fix, ax = plt.subplots(1,3, figsize=(10,10))
        ax[0].imshow(x_plt)
        ax[1].imshow(yval[0, :, :, 0], cmap='gray')
        ax[2].imshow(ypred[0, :, :, 0], cmap='gray')
        plt.savefig((save_dir+mdl1+'e_' + str(e) + '.jpg'))
        plt.close()
  
print("Completed training...")

我尝试添加批处理规范化，并且还将激活功能从Sigmoid修改为relu到最后一层。也尝试过更改优化器。但是这些都没有帮助。实际上，有时在不修改体系结构的情况下损失是负的。

下面是训练过程中保存的样本输出（左边是输入图像，中间是地面实况，右边是预测输出）谢谢您的时间。

Answer 1

您可以检查学习率，使用的优化器类型。如果结果图像是全白或全黑，则可能是您的数据集类别不平衡。

某些班级压倒了另一个班级。您必须平衡数据集或使用更好的损失函数，例如 iou、骰子或焦点损失。

Keras-tf 2.x中注意r2unet网络的实现

1 个答案: