[1.1-custom_cnn_2d.ipynb] model.fit_generator() generates an error.

[leemgs]

I established Python 3.6.4 , Conda 3, Tensorflow 2.0, Keras 2.3.1 on ubuntu 18.04.
Then, I ran "ubuntu18.04$ pip install -r ./gtzan.keras/requirements.txt" command to avoid unexpected situations.

At first, I modified the below statement in 1.1-custom_cnn_2d.ipynb file.

def conv_block(x, n_filters, pool_size=(2, 2)):
    x = Conv2D(n_filters, (3, 3), strides=(1, 1), padding='same')(x)
    x = Activation('relu')(x)
    #x = MaxPooling2D(pool_size=pool_size, strides=pool_size)(x)    
    x = MaxPooling2D(pool_size=pool_size, strides=pool_size, padding='same')(x)    
    x = Dropout(0.25)(x)
    return x

However, when I run in the Jupyter notebook environment, I got the below error message in the model.fit_generator() statement. Could you give me a hint to fix this issue? Any hints will be helpful to me. :)

• code

hist = model.fit_generator(
    train_generator,
    steps_per_epoch=steps_per_epoch,
    validation_data=validation_generator,
    validation_steps=val_steps,
    epochs=150,
    verbose=1,
    callbacks=[reduceLROnPlat]
)

• debug message

----------------------------------------------------------
IndexError               Traceback (most recent call last)
<ipython-input-146-763fdcb20cdf> in <module>
      6     epochs=150,
      7     verbose=1,
----> 8     callbacks=[reduceLROnPlat]
      9 )

~/anaconda3/envs/python36/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py in new_func(*args, **kwargs)
    322               'in a future version' if date is None else ('after %s' % date),
    323               instructions)
--> 324       return func(*args, **kwargs)
    325     return tf_decorator.make_decorator(
    326         func, new_func, 'deprecated',

~/anaconda3/envs/python36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1304                          workers=1,
   1305                          use_multiprocessing=False,
-> 1306                          verbose=0):
   1307     """Evaluates the model on a data generator.
   1308 

~/anaconda3/envs/python36/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     _keras_api_gauge.get_cell('evaluate').set(True)
    818     self._assert_compile_was_called()
--> 819     self._check_call_args('evaluate')
    820 
    821     func = self._select_training_loop(x)

~/anaconda3/envs/python36/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)
    233         # TODO(b/139762795): Add step inference for when steps is None to
    234         # prevent end of sequence warning message.
--> 235         steps_per_epoch = training_data_adapter.get_size()
    236 
    237       # tf.print('{} on {} steps.'.format(ModeKeys.TRAIN, steps_per_epoch))

~/anaconda3/envs/python36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_training_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, steps_per_epoch, validation_split, validation_data, validation_steps, shuffle, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    591         class_weight=class_weights,
    592         batch_size=batch_size,
--> 593         check_steps=False,
    594         steps=steps)
    595   adapter = adapter_cls(

~/anaconda3/envs/python36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)

~/anaconda3/envs/python36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, standardize_function, shuffle, workers, use_multiprocessing, max_queue_size, **kwargs)

~/anaconda3/envs/python36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, standardize_function, workers, use_multiprocessing, max_queue_size, **kwargs)

~/anaconda3/envs/python36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/data_adapter.py in _peek_and_restore(x)

<ipython-input-38-647b4e41efc7> in __getitem__(self, index)
     17         # Apply data augmentation
     18         if not self.is_test:
---> 19             signals = self.__augment(signals)
     20         return signals, self.y[index*self.batch_size:(index+1)*self.batch_size]
     21 

<ipython-input-38-647b4e41efc7> in __augment(self, signals, hor_flip, random_cutout)
     34                 cols = np.random.randint(signal.shape[0], size=4)
     35                 signal[lines, :, :] = -80 # dB
---> 36                 signal[:, cols, :] = -80 # dB
     37 
     38             spectrograms.append(signal)

IndexError: index 58 is out of bounds for axis 1 with size 2