human
Solve the following python programming problem given the following code:
# Callback allowing to display the progression of the training task.
class ProgressTracker(keras.callbacks.Callback):

  def __init__(self, num_epochs, step=5):
    self.num_epochs = num_epochs
    self.current_epoch = 0.
    self.step = step
    self.last_percentage_report = 0

  def on_epoch_end(self, batch, logs={}):
    self.current_epoch += 1.
    training_percentage = int(self.current_epoch * 100.0 / self.num_epochs)
    if training_percentage - self.last_percentage_report >= self.step:
      print('Training ' + str(
          training_percentage) + '% complete. Training loss: ' + str(
              logs.get('loss')) + ' | Validation loss: ' + str(
                  logs.get('val_loss')))
      self.last_percentage_report = training_percentage

reduce_lr_callback = keras.callbacks.ReduceLROnPlateau(
    monitor='val_loss',
    factor=0.5,
    patience=10,
    verbose=0,
    mode='auto',
    min_delta=0.0001,
    cooldown=0,
    min_lr=0)

# google internal 1
# Everything is now in place to train.
EPOCHS = 100
pt = ProgressTracker(EPOCHS)
history = model.fit(
    data,
    target,
    epochs=EPOCHS,
    validation_split=0.2,
    verbose=0,
    batch_size=32,
    callbacks=[reduce_lr_callback, pt])

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.ylim([0, 1])
plt.legend(['loss', 'val loss'], loc='upper left')
plt.xlabel('Train epoch')
_ = plt.ylabel('Error [mean square distance]')
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Training
At this point, everything is in place to start training the neural network!