Erro no treinamento do modelo

Olá, boa noite!
Alguém pode me ajudar com esse erro?

Epoch 1/5

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TypeError Traceback (most recent call last)

<ipython-input-118-2de532de76ee> in <module>
3 epochs = nb_epochs,
4 verbose = 1,
—-> 5 validation_split = 0.10)
6 ckpt_manager.save()

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
945 # In this case we have created variables on the first call, so we run the
946 # defunned version which is guaranteed to never create variables.
–> 947 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
948 elif self._stateful_fn is not None:
949 # Release the lock early so that multiple threads can perform the call

TypeError: ‘NoneType’ object is not callable

Estou utilizando construtor modelo do treinamento da aula.

class DCNN(tf.keras.Model):

def __init__(self,
vocab_size,
emb_dim=128,
nb_filters=50,
ffn_units=512,
nb_classes=2,
dropout_rate=0.1,
training=True,
name=”dcnn”):
super(DCNN, self).__init__(name=name)

self.embedding = layers.Embedding(vocab_size, emb_dim)

self.bigram = layers.Conv1D(filters=nb_filters, kernel_size=2, padding=’same’, activation=’relu’)

self.trigram = layers.Conv1D(filters=nb_filters, kernel_size=3, padding=’same’, activation=’relu’)

self.fourgram = layers.Conv1D(filters=nb_filters, kernel_size=4, padding=’same’, activation=’relu’)

self.pool = layers.GlobalMaxPool1D()

self.dense_1 = layers.Dense(units = ffn_units, activation = ‘relu’)
self.dropout = layers.Dropout(rate = dropout_rate)
if nb_classes == 3:
self.last_dense = layers.Dense(units = 1, activation = ‘sigmoid’)
else:
self.last_dense = layers.Dense(units = nb_classes, activation = ‘softmax’)

def call(self, inputs, training):
x = self.embedding(inputs)
x_1 = self.bigram(x)
x_1 = self.pool(x_1)
x_2 = self.trigram(x)
x_2 = self.pool(x_2)
x_3 = self.fourgram(x)
x_3 = self.pool(x_3)

merged = tf.concat([x_1, x_2, x_3], axis = -1) # (batch_size, 3 * nb_filters)
merged = self.dense_1(merged)
merged = self.dropout(merged, training)
output = self.last_dense(merged)

return output

<hr />

Código do treinamento

history = Dcnn.fit(train_inputs, train_labels,
batch_size = batch_size,
epochs = nb_epochs,
verbose = 1,
validation_split = 0.10)
ckpt_manager.save()