RuntimeError                              Traceback (most recent call last)
<timed eval> in <module>

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    817         max_queue_size=max_queue_size,
    818         workers=workers,
--> 819         use_multiprocessing=use_multiprocessing)
    820 
    821   def evaluate(self,

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    395                       total_epochs=1)
    396                   cbks.make_logs(model, epoch_logs, eval_result, ModeKeys.TEST,
--> 397                                  prefix='val_')
    398 
    399     return model.history

/usr/lib/python3.6/contextlib.py in __exit__(self, type, value, traceback)
     86         if type is None:
     87             try:
---> 88                 next(self.gen)
     89             except StopIteration:
     90                 return False

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in on_epoch(self, epoch, mode)
    769       if mode == ModeKeys.TRAIN:
    770         # Epochs only apply to `fit`.
--> 771         self.callbacks.on_epoch_end(epoch, epoch_logs)
    772       self.progbar.on_epoch_end(epoch, epoch_logs)
    773 

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/callbacks.py in on_epoch_end(self, epoch, logs)
    300     logs = logs or {}
    301     for callback in self.callbacks:
--> 302       callback.on_epoch_end(epoch, logs)
    303 
    304   def on_train_batch_begin(self, batch, logs=None):

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/callbacks.py in on_epoch_end(self, epoch, logs)
    990           self._save_model(epoch=epoch, logs=logs)
    991       else:
--> 992         self._save_model(epoch=epoch, logs=logs)
    993     if self.model._in_multi_worker_mode():
    994       # For multi-worker training, back up the weights and current training

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/callbacks.py in _save_model(self, epoch, logs)
   1038             self.model.save_weights(filepath, overwrite=True)
   1039           else:
-> 1040             self.model.save(filepath, overwrite=True)
   1041 
   1042         self._maybe_remove_file()

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/network.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options)
   1006     """
   1007     save.save_model(self, filepath, overwrite, include_optimizer, save_format,
-> 1008                     signatures, options)
   1009 
   1010   def save_weights(self, filepath, overwrite=True, save_format=None):

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options)
    110           'or using `save_weights`.')
    111     hdf5_format.save_model_to_hdf5(
--> 112         model, filepath, overwrite, include_optimizer)
    113   else:
    114     saved_model_save.save(model, filepath, overwrite, include_optimizer,

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py in save_model_to_hdf5(model, filepath, overwrite, include_optimizer)
    107     model_weights_group = f.create_group('model_weights')
    108     model_layers = model.layers
--> 109     save_weights_to_hdf5_group(model_weights_group, model_layers)
    110 
    111     # TODO(b/128683857): Add integration tests between tf.keras and external

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py in save_weights_to_hdf5_group(f, layers)
    629     save_attributes_to_hdf5_group(g, 'weight_names', weight_names)
    630     for name, val in zip(weight_names, weight_values):
--> 631       param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
    632       if not val.shape:
    633         # scalar

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/h5py/_hl/group.py in create_dataset(self, name, shape, dtype, data, **kwds)
    137             dset = dataset.Dataset(dsid)
    138             if name is not None:
--> 139                 self[name] = dset
    140             return dset
    141 

~/workspace/understanding-unets/venv/lib/python3.6/site-packages/h5py/_hl/group.py in __setitem__(self, name, obj)
    371 
    372             if isinstance(obj, HLObject):
--> 373                 h5o.link(obj.id, self.id, name, lcpl=lcpl, lapl=self._lapl)
    374 
    375             elif isinstance(obj, SoftLink):

h5py/_objects.pyx in h5py._objects.with_phil.wrapper()

h5py/_objects.pyx in h5py._objects.with_phil.wrapper()

h5py/h5o.pyx in h5py.h5o.link()

RuntimeError: Unable to create link (name already exists)

T_{j,k}^{n+1} = T_{j,k}^{n+1} (1 + |alpha_{j,k}^{n}| / T_{j,k}^{n})