Can't load_model with error “Optimizer weight shape (256, 32) not compatible with provided weight shape (4,)”

I have 3 trained model file.

1. left_branch.h5
2. right_branch.h5
3. concat.h5

The model concat.h5 is fine-tuned by concatenating from the two pre-trained model as the initial model(left_branch.h5, right_branch.h5). While left_branch.h5 and right_branch.h5 model file can be load by function keras.models.load_model(), but I load the trained concat.h5 formatted model file, I get the error blew.

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 167, in load_model
    model.optimizer.set_weights(optimizer_weight_values)
  File "/usr/local/lib/python2.7/dist-packages/keras/optimizers.py", line 97, in set_weights
    'provided weight shape ' + str(w.shape))
Exception: Optimizer weight shape (256, 32) not compatible with provided weight shape (4,)

[dteoh]

I am using Keras 1.1.1 and am having the same problem. Deleting the optimizer weights as a workaround works for me.

Just in case someone needs to do the same, here’s the code:

import h5py
f = h5py.File('model_file.h5', 'r+')
del f['optimizer_weights']
f.close()

[oarriaga]

I had the same problem and what it seemed to work for me was to set the compile flag to False in the load_model function. Afterwards one can compile the model with the previously used optimizer.

model = load_model('my_model.hdf5', compile=False)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])