Open MichealRay opened 5 years ago
input_2 (InputLayer) (None, 3, 256, 256) 0
conv2d_12 (Conv2D) (None, 32, 256, 256) 896 input_2[0][0]
max_pooling2d_3 (MaxPooling2D) (None, 32, 128, 128) 0 conv2d_12[0][0]
conv2d_13 (Conv2D) (None, 64, 128, 128) 18496 max_pooling2d_3[0][0]
max_pooling2d_4 (MaxPooling2D) (None, 64, 64, 64) 0 conv2d_13[0][0]
conv2d_14 (Conv2D) (None, 128, 64, 64) 73856 max_pooling2d_4[0][0]
up_sampling2d_3 (UpSampling2D) (None, 128, 128, 128 0 conv2d_14[0][0]
concatenate_3 (Concatenate) (None, 192, 128, 128 0 up_sampling2d_3[0][0] conv2d_13[0][0]
conv2d_15 (Conv2D) (None, 64, 128, 128) 110656 concatenate_3[0][0]
up_sampling2d_4 (UpSampling2D) (None, 64, 256, 256) 0 conv2d_15[0][0]
concatenate_4 (Concatenate) (None, 96, 256, 256) 0 up_sampling2d_4[0][0] conv2d_12[0][0]
conv2d_16 (Conv2D) (None, 32, 256, 256) 27680 concatenate_4[0][0]
Total params: 231,617 Trainable params: 231,617 Non-trainable params: 0 然后发现数据训练val_acc: 0.4705最高了，然后多次epoch都是0.4705没有进一步提高。 同时测试的结果mask输出都是1。感觉网络好像没有学习到有效的分割信息。 所以想问一下这个情况，会有哪些可能性？可以如何解决？
兄嘚咋解决的？我这个预测结果也全是1，预测正确率倒是挺高的
mepleleo
commented
5 years ago mepleleo
commented
5 years ago
@James Lee, hi，非常感谢你代码，学习了很多。经历了一点小挫折，最终还是跑通代码。作为简单的实验，我用gen_dataset.py 生成了2000个训练数据对，，然后精简了u-net的网络 Layer (type) Output Shape Param # Connected to
input_2 (InputLayer) (None, 3, 256, 256) 0
conv2d_12 (Conv2D) (None, 32, 256, 256) 896 input_2[0][0]
max_pooling2d_3 (MaxPooling2D) (None, 32, 128, 128) 0 conv2d_12[0][0]
conv2d_13 (Conv2D) (None, 64, 128, 128) 18496 max_pooling2d_3[0][0]
max_pooling2d_4 (MaxPooling2D) (None, 64, 64, 64) 0 conv2d_13[0][0]
conv2d_14 (Conv2D) (None, 128, 64, 64) 73856 max_pooling2d_4[0][0]
up_sampling2d_3 (UpSampling2D) (None, 128, 128, 128 0 conv2d_14[0][0]
concatenate_3 (Concatenate) (None, 192, 128, 128 0 up_sampling2d_3[0][0]
conv2d_13[0][0]
conv2d_15 (Conv2D) (None, 64, 128, 128) 110656 concatenate_3[0][0]
up_sampling2d_4 (UpSampling2D) (None, 64, 256, 256) 0 conv2d_15[0][0]
concatenate_4 (Concatenate) (None, 96, 256, 256) 0 up_sampling2d_4[0][0]
conv2d_12[0][0]
conv2d_16 (Conv2D) (None, 32, 256, 256) 27680 concatenate_4[0][0]
conv2d_17 (Conv2D) (None, 1, 256, 256) 33 conv2d_16[0][0]
Total params: 231,617 Trainable params: 231,617 Non-trainable params: 0 然后发现数据训练val_acc: 0.4705最高了，然后多次epoch都是0.4705没有进一步提高。 同时测试的结果mask输出都是1。感觉网络好像没有学习到有效的分割信息。 所以想问一下这个情况，会有哪些可能性？可以如何解决？