Keras compile() function takes very much time with custom loss function

Question

I'm implementing an MLP with Keras and a custom loss function. I notice model.compile() takes very much time: it seems doesn't end. The loss that I passed to the compile() function is custom. I'm also using another function that is used in the loss function.

This is my custom loss:

def get_top_one_probability(vector):
  return (K.exp(vector) / K.sum(K.exp(vector)))

def custom_loss(groups_id_count, tf_session):
  def listnet_loss(real_labels, predicted_labels):
    losses = tf.Variable([[0.0]], tf.float32)
    for group in groups_id_count:
      start_range = 0
      end_range = (start_range + group[1])
      batch_real_labels = real_labels[start_range:end_range]
      batch_predicted_labels = predicted_labels[start_range:end_range]
      loss = -K.sum(get_top_one_probability(batch_real_labels)) * tf.math.log(get_top_one_probability(batch_predicted_labels))
      losses = tf.concat([losses, loss], axis=0)
      start_range = end_range
    return K.mean(losses)
  return listnet_loss

And this is the MLP code:

mlp = keras.models.Sequential()

# add input layer
mlp.add(
    keras.layers.Dense(
        units=training_dataset.shape[1],
        input_shape = (training_dataset.shape[1], ),
        kernel_initializer='glorot_uniform',
        bias_initializer='zeros',
        activation='tanh') 
)
# add hidden layer
mlp.add(
    keras.layers.Dense(
        units=training_dataset.shape[1] + 10,
        input_shape = (training_dataset.shape[1] + 10,),
        kernel_initializer='glorot_uniform',
        bias_initializer='zeros',
        activation='relu')
    )
# add output layer
mlp.add(
    keras.layers.Dense(
        units=1,
        input_shape = (1, ),
        kernel_initializer='glorot_uniform',
        bias_initializer='zeros',
        activation='softmax')
    )

# define SGD optimizer
sgd_optimizer = keras.optimizers.SGD(
    lr=0.01, decay=0.01, momentum=0.9, nesterov=True
)
# compile model

print('Compiling model...\n')

mlp.compile(
    optimizer=sgd_optimizer,
    loss=custom_loss(groups_id_count, tf.compat.v1.Session())
)

mlp.summary() # print model settings


# Training
with tf.device('/GPU:0'):
  print('Start training')

  mlp.fit(training_dataset, training_dataset_labels, epochs=50, verbose=2, batch_size=training_dataset.shape[0], workers=10)

Why the compile() function takes very very much time? Thanks in advance