Question

我使用keras 1.2.2使用张量流1.4.0后端。

我使用的是unet架构，我有708个650x650像素和6个chanel的图像。我用镜像和旋转增加了数据集，共计4248个图像。

我有2个课程，我的损失功能就是这个：

def jaccard_coef_loss(y_true, y_pred):
    smooth = 1e-12
    intersection = K.sum(y_true * y_pred, axis=[0, -1, -2])
    sum_ = K.sum(y_true + y_pred, axis=[0, -1, -2])
    jac = (intersection + smooth) / (sum_ - intersection + smooth)
    return 1 - K.mean(jac)

我的优化器：

optimizer = SGD(lr=0.01, momentum=0.9, nesterov=True)

我的验证集约占图像总数的30％，batch_size为4，shuffle设置为True。该模型遍历每个时代的每个训练图像。预定了200个时期，但如果10个时期的验证集没有改进，则学习将停止。

以下是最后时期的培训日志

Epoch 10/200
4248/4248 [==============================] - 3192s - loss: 0.1388 - acc: 0.0868 - jaccard_coef: 0.8612 - jaccard_coef_int: 0.8613 - val_loss: 0.2957 - val_acc: 0.0536 - val_jaccard_coef: 0.7043 - val_jaccard_coef_int: 0.7043
Epoch 11/200
4248/4248 [==============================] - 3167s - loss: 0.1375 - acc: 0.0901 - jaccard_coef: 0.8625 - jaccard_coef_int: 0.8626 - val_loss: 0.2968 - val_acc: 0.0632 - val_jaccard_coef: 0.7032 - val_jaccard_coef_int: 0.7033
Epoch 12/200
4248/4248 [==============================] - 3272s - loss: 0.1964 - acc: 0.1084 - jaccard_coef: 0.8036 - jaccard_coef_int: 0.8037 - val_loss: 1.0000 - val_acc: 0.5066 - val_jaccard_coef: 1.2793e-15 - val_jaccard_coef_int: 4.7833e-18
Epoch 13/200
4248/4248 [==============================] - 3112s - loss: 1.0000 - acc: 0.5089 - jaccard_coef: 4.6290e-15 - jaccard_coef_int: 5.5532e-18 - val_loss: 1.0000 - val_acc: 0.5066 - val_jaccard_coef: 1.2659e-15 - val_jaccard_coef_int: 4.7833e-18
Epoch 14/200
4248/4248 [==============================] - 2032s - loss: 1.0000 - acc: 0.5089 - jaccard_coef: 2.5857e-15 - jaccard_coef_int: 5.1207e-18 - val_loss: 1.0000 - val_acc: 0.5066 - val_jaccard_coef: 1.2659e-15 - val_jaccard_coef_int: 4.7833e-18
Epoch 15/200
4248/4248 [==============================] - 2260s - loss: 1.0000 - acc: 0.5089 - jaccard_coef: 2.6600e-15 - jaccard_coef_int: 5.0932e-18 - val_loss: 1.0000 - val_acc: 0.5066 - val_jaccard_coef: 1.2659e-15 - val_jaccard_coef_int: 4.7833e-18
Epoch 16/200
4248/4248 [==============================] - 2914s - loss: 1.0000 - acc: 0.5089 - jaccard_coef: 2.3220e-15 - jaccard_coef_int: 4.8916e-18 - val_loss: 1.0000 - val_acc: 0.5066 - val_jaccard_coef: 1.2659e-15 - val_jaccard_coef_int: 4.7833e-18
Epoch 17/200
4248/4248 [==============================] - 2928s - loss: 1.0000 - acc: 0.5089 - jaccard_coef: 2.6034e-15 - jaccard_coef_int: 6.3645e-18 - val_loss: 1.0000 - val_acc: 0.5066 - val_jaccard_coef: 1.2659e-15 - val_jaccard_coef_int: 4.7833e-18
Epoch 18/200
4248/4248 [==============================] - 2738s - loss: 1.0000 - acc: 0.5089 - jaccard_coef: 2.3913e-15 - jaccard_coef_int: 4.7182e-18 - val_loss: 1.0000 - val_acc: 0.5066 - val_jaccard_coef: 1.2659e-15 - val_jaccard_coef_int: 4.7833e-18
Epoch 19/200
4248/4248 [==============================] - 2922s - loss: 1.0000 - acc: 0.5089 - jaccard_coef: 6.2745e-15 - jaccard_coef_int: 5.0041e-18 - val_loss: 1.0000 - val_acc: 0.5066 - val_jaccard_coef: 1.2659e-15 - val_jaccard_coef_int: 4.7833e-18

我不知道12和13世纪之间发生了什么。这是我的错还是有一个已知的错误可以通过升级到更新版本的keras / tf来修复？

Answer 1

您的优化过程似乎有所不同：可能您有非常大的渐变，导致您的模型预测垃圾。尝试将学习率降低到0.001并从第12次迭代中恢复

在训练CNN进行图像分割时，我的损失怎么会突然增加？

1 个答案: