ValueError: You are trying to load a weight file containing 428 layers into a model with 114 layers.

I have trained a segmentation model, the backbone is densenet201 with pretrained model, the decoder is FPN But, when I load saved weight for predicting,raise error as below:

Traceback (most recent call last):
  File "test.py", line 62, in <module>
    main()
  File "test.py", line 57, in main
    apply_predict()
  File "test.py", line 27, in apply_predict
    model.load_weights("models/densenet201_fpn_w.h5")
  File "/home/jiangd/.conda/envs/tf110/lib/python3.5/site-packages/keras/engine/network.py", line 1166, in load_weights
    f, self.layers, reshape=reshape)
  File "/home/jiangd/.conda/envs/tf110/lib/python3.5/site-packages/keras/engine/saving.py", line 1030, in load_weights_from_hdf5_group
    str(len(filtered_layers)) + ' layers.')
ValueError: You are trying to load a weight file containing 428 layers into a model with 114 layers.

[qubvel]

Now I am working on adopting all models for tf.keras (you can check in tf.keras branch), after that I am going to make some tutorials with training/inference

[qubvel]

To load model use:

import segmentation_models # important!
from keras.models import load_model

model = load_model('path/model.h5')

Hope this helps.