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The practicality of this network

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@brjathu Hi, Thanks for your great work. I want to know whether this network is suitable for two-category image data, such as cat and dog images with shape (64,64,3) ? I train the model with my prevate medical image (two class), It happens the following strange loss, and the accuracy is always not improved. Can to give me some advices about it?

Epoch 46/500
107/106 [==============================] - 15s 137ms/step - loss: 0.6319 - capsnet_loss: 0.2150 - decoder_loss: 1.0422 - capsnet_acc: 0.4690 - val_loss: 0.6452 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0853 - val_capsnet_acc: 0.4788
Epoch 47/500
107/106 [==============================] - 15s 136ms/step - loss: 0.6323 - capsnet_loss: 0.2158 - decoder_loss: 1.0413 - capsnet_acc: 0.4670 - val_loss: 0.6449 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0846 - val_capsnet_acc: 0.4788
Epoch 48/500
107/106 [==============================] - 15s 138ms/step - loss: 0.6331 - capsnet_loss: 0.2150 - decoder_loss: 1.0453 - capsnet_acc: 0.4690 - val_loss: 0.6457 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0867 - val_capsnet_acc: 0.4788
Epoch 49/500
107/106 [==============================] - 15s 139ms/step - loss: 0.6330 - capsnet_loss: 0.2158 - decoder_loss: 1.0428 - capsnet_acc: 0.4670 - val_loss: 0.6446 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0838 - val_capsnet_acc: 0.4788
Epoch 50/500
107/106 [==============================] - 15s 136ms/step - loss: 0.6324 - capsnet_loss: 0.2158 - decoder_loss: 1.0415 - capsnet_acc: 0.4670 - val_loss: 0.6435 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0811 - val_capsnet_acc: 0.4788
Epoch 51/500
107/106 [==============================] - 15s 140ms/step - loss: 0.6295 - capsnet_loss: 0.2150 - decoder_loss: 1.0361 - capsnet_acc: 0.4690 - val_loss: 0.6431 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0802 - val_capsnet_acc: 0.4788
Epoch 52/500
107/106 [==============================] - 15s 138ms/step - loss: 0.6317 - capsnet_loss: 0.2167 - decoder_loss: 1.0377 - capsnet_acc: 0.4650 - val_loss: 0.6433 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0807 - val_capsnet_acc: 0.4788
Epoch 53/500
107/106 [==============================] - 15s 136ms/step - loss: 0.6274 - capsnet_loss: 0.2150 - decoder_loss: 1.0309 - capsnet_acc: 0.4690 - val_loss: 0.6460 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0874 - val_capsnet_acc: 0.4788
Epoch 54/500
107/106 [==============================] - 15s 138ms/step - loss: 0.6333 - capsnet_loss: 0.2175 - decoder_loss: 1.0395 - capsnet_acc: 0.4629 - val_loss: 0.6434 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0809 - val_capsnet_acc: 0.4788
Epoch 55/500
107/106 [==============================] - 15s 137ms/step - loss: 0.6316 - capsnet_loss: 0.2158 - decoder_loss: 1.0394 - capsnet_acc: 0.4670 - val_loss: 0.6433 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0806 - val_capsnet_acc: 0.4788
Epoch 56/500
107/106 [==============================] - 15s 136ms/step - loss: 0.6315 - capsnet_loss: 0.2150 - decoder_loss: 1.0412 - capsnet_acc: 0.4690 - val_loss: 0.6440 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0823 - val_capsnet_acc: 0.4788
Epoch 57/500
107/106 [==============================] - 15s 139ms/step - loss: 0.6295 - capsnet_loss: 0.2158 - decoder_loss: 1.0341 - capsnet_acc: 0.4670 - val_loss: 0.6434 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0808 - val_capsnet_acc: 0.4788
Epoch 58/500
107/106 [==============================] - 14s 135ms/step - loss: 0.6300 - capsnet_loss: 0.2158 - decoder_loss: 1.0355 - capsnet_acc: 0.4670 - val_loss: 0.6428 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0794 - val_capsnet_acc: 0.4788
Epoch 59/500
107/106 [==============================] - 15s 136ms/step - loss: 0.6305 - capsnet_loss: 0.2150 - decoder_loss: 1.0388 - capsnet_acc: 0.4690 - val_loss: 0.6430 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0800 - val_capsnet_acc: 0.4788
Epoch 60/500
107/106 [==============================] - 14s 134ms/step - loss: 0.6297 - capsnet_loss: 0.2158 - decoder_loss: 1.0347 - capsnet_acc: 0.4670 - val_loss: 0.6429 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0796 - val_capsnet_acc: 0.4788
Epoch 61/500
107/106 [==============================] - 15s 139ms/step - loss: 0.6319 - capsnet_loss: 0.2167 - decoder_loss: 1.0382 - capsnet_acc: 0.4650 - val_loss: 0.6451 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0852 - val_capsnet_acc: 0.4788
Epoch 62/500
107/106 [==============================] - 14s 135ms/step - loss: 0.6306 - capsnet_loss: 0.2158 - decoder_loss: 1.0368 - capsnet_acc: 0.4670 - val_loss: 0.6430 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0799 - val_capsnet_acc: 0.4788
Epoch 63/500
107/106 [==============================] - 15s 138ms/step - loss: 0.6267 - capsnet_loss: 0.2142 - decoder_loss: 1.0314 - capsnet_acc: 0.4710 - val_loss: 0.6425 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0787 - val_capsnet_acc: 0.4788
Epoch 64/500
107/106 [==============================] - 15s 137ms/step - loss: 0.6302 - capsnet_loss: 0.2167 - decoder_loss: 1.0339 - capsnet_acc: 0.4650 - val_loss: 0.6424 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0783 - val_capsnet_acc: 0.4788
Epoch 65/500
107/106 [==============================] - 15s 137ms/step - loss: 0.6321 - capsnet_loss: 0.2158 - decoder_loss: 1.0406 - capsnet_acc: 0.4670 - val_loss: 0.6425 - val_capsnet_loss: 0.2110 - val_decoder_loss: 1.0787 - val_capsnet_acc: 0.4788

Issue Analytics

State:
Created 4 years ago
Comments:7 (3 by maintainers)

Top GitHub Comments

2reactions

brjathucommented, Jul 4, 2019

Hi, extremely sorry for the delay in replying the post, as I have to reproduce the problem in my side.

Theoretically, It should work, but I also found that the accuracy is still around 50% and not converging. I will keep investigate this problem and keep you posted.

0reactions

amir-ghzcommented, May 8, 2022

Hi, is the PyTorch implementation finished? I would be grateful if you could share with me the PyTorch version.

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