Open model.py and find the load_weights function, roughly at line 2842, and modify the location as follows

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def load_weights(self, filepath, by_name=False, exclude=None):
“””Modified version of the correspoding Keras function with
the addition of multi-GPU support and the ability to exclude
some layers from loading.
exlude: list of layer names to excluce
“””
import h5py

#Modify the first place
#from keras.engine import topology
from keras.engine import saving

if exclude:
by_name = True

if h5py is None:
raise ImportError(‘`load_weights` requires h5py.’)
f = h5py.File(filepath, mode=’r’)
if ‘layer_names’ not in f.attrs and ‘model_weights’ in f:
f = f[‘model_weights’]

# In multi-GPU training, we wrap the model. Get layers
# of the inner model because they have the weights.
keras_model = self.keras_model
layers = keras_model.inner_model.layers if hasattr(keras_model, “inner_model”)\
else keras_model.layers

# Exclude some layers
if exclude:
layers = filter(lambda l: l.name not in exclude, layers)

if by_name:

#Modify the second place
#topology.load_weights_from_hdf5_group_by_name(f, layers)
saving.load_weights_from_hdf5_group_by_name(f, layers)
else:

#Modify the third place
#topology.load_weights_from_hdf5_group(f, layers)
saving.load_weights_from_hdf5_group(f, layers)
if hasattr(f, ‘close’):
f.close()

# Update the log directory
self.set_log_dir(filepath)

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After successful modification, save and restart, then run the code