03_03_vae_digits_train: TypeError: unsupported format string passed to numpy.ndarray.__format__

[chc1129]

03_03_vae_digits_train Error

Error 発生個所

vae.train(     
    x_train
    , batch_size = BATCH_SIZE
    , epochs = EPOCHS
    , run_folder = RUN_FOLDER
    , print_every_n_batches = PRINT_EVERY_N_BATCHES
    , initial_epoch = INITIAL_EPOCH
)

Error Message

Epoch 1/200
   2/1875 [..............................] - ETA: 53s - loss: 230.6028 - reconstruction_loss: 230.6014 - kl_loss: 0.0014WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.173996). Check your callbacks.
1875/1875 [==============================] - ETA: 0s - loss: 58.3136 - reconstruction_loss: 55.0746 - kl_loss: 3.2390- ETA: 3s - los

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-11-a0cdb3ff19b5> in <module>
      5     , run_folder = RUN_FOLDER
      6     , print_every_n_batches = PRINT_EVERY_N_BATCHES
----> 7     , initial_epoch = INITIAL_EPOCH
      8 )

~\work\Generative_Deep_Learning\GDL_code\models\VAE.py in train(self, x_train, batch_size, epochs, run_folder, print_every_n_batches, initial_epoch, lr_decay)
    224             , epochs = epochs
    225             , initial_epoch = initial_epoch
--> 226             , callbacks = callbacks_list
    227         )
    228 

~\anaconda3\envs\generative\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
     64   def _method_wrapper(self, *args, **kwargs):
     65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
---> 66       return method(self, *args, **kwargs)
     67 
     68     # Running inside `run_distribute_coordinator` already.

~\anaconda3\envs\generative\lib\site-packages\tensorflow\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_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
    874           epoch_logs.update(val_logs)
    875 
--> 876         callbacks.on_epoch_end(epoch, epoch_logs)
    877         if self.stop_training:
    878           break

~\anaconda3\envs\generative\lib\site-packages\tensorflow\python\keras\callbacks.py in on_epoch_end(self, epoch, logs)
    363     logs = self._process_logs(logs)
    364     for callback in self.callbacks:
--> 365       callback.on_epoch_end(epoch, logs)
    366 
    367   def on_train_batch_begin(self, batch, logs=None):

~\anaconda3\envs\generative\lib\site-packages\tensorflow\python\keras\callbacks.py in on_epoch_end(self, epoch, logs)
   1175           self._save_model(epoch=epoch, logs=logs)
   1176       else:
-> 1177         self._save_model(epoch=epoch, logs=logs)
   1178     if self.model._in_multi_worker_mode():
   1179       # For multi-worker training, back up the weights and current training

~\anaconda3\envs\generative\lib\site-packages\tensorflow\python\keras\callbacks.py in _save_model(self, epoch, logs)
   1194                   int) or self.epochs_since_last_save >= self.period:
   1195       self.epochs_since_last_save = 0
-> 1196       filepath = self._get_file_path(epoch, logs)
   1197 
   1198       try:

~\anaconda3\envs\generative\lib\site-packages\tensorflow\python\keras\callbacks.py in _get_file_path(self, epoch, logs)
   1242         # `{mape:.2f}`. A mismatch between logged metrics and the path's
   1243         # placeholders can cause formatting to fail.
-> 1244         return self.filepath.format(epoch=epoch + 1, **logs)
   1245       except KeyError as e:
   1246         raise KeyError('Failed to format this callback filepath: "{}". '

TypeError: unsupported format string passed to numpy.ndarray.__format__

[chc1129]

chg code
GDL_code/models/VAE.py

Line:213

         custom_callback = CustomCallback(run_folder, print_every_n_batches, initial_epoch, self)
         lr_sched = step_decay_schedule(initial_lr=self.learning_rate, decay_factor=lr_decay, step_size=1)

-        checkpoint_filepath=os.path.join(run_folder, "weights/weights-{epoch:03d}-{loss:.2f}.h5")
+        checkpoint_filepath=os.path.join(run_folder, "weights/weights.h5")
         checkpoint1 = ModelCheckpoint(checkpoint_filepath, save_weights_only = True, verbose=1)
         checkpoint2 = ModelCheckpoint(os.path.join(run_folder, 'weights/weights.h5'), save_weights_only = True, verbose=1)

[chc1129]

上で修正された!
答えを書いてくれた人 thx!!!!

org issue
davidADSP/GDL_code#73

solusion
davidADSP/GDL_code#73 (comment)

[karaage0703]

@chc1129
Google Colab上で動くノートブックを作っています。よろしければ参考まで。
「生成Deep Learning」は久しぶりにワクワクするAI関係の本だった

I made google colab notebook of this repository scripts.
Refere to below link.
https://github.com/karaage0703/GDL_code/tree/karaage/colab