타이타닉 model.fit 아래와 같이 에러납니다.

feature_columns = [] #빈 리스트안에 모두다 넣을거다.
feature_columns.append(   tf.feature_column.numeric_column('Fare')    )
feature_columns.append(   tf.feature_column.numeric_column('Parch')   )
feature_columns.append(   tf.feature_column.numeric_column('SibSp')   )
feature_columns.append(   tf.feature_column.numeric_column('Age')   )

Age = tf.feature_column.numeric_column('Age')#뭉퉁그려서 넣을거 (나이)
Age_bucket = tf.feature_column.bucketized_column(Age, boundaries=[10,20,30,40,50,60])
feature_columns.append(   Age_bucket   )

vocab = data['Sex'].unique()#원핫인코딩: 종류몇개없는 카테고리 (성별)
cat = tf.feature_column.sequence_categorical_column_with_vocabulary_list('Sex', vocab)
one_hot = tf.feature_column.indicator_column(cat)
feature_columns.append(   one_hot   )

vocab = data['Embarked'].unique()#원핫인코딩: (클래스등급)
cat = tf.feature_column.sequence_categorical_column_with_vocabulary_list('Embarked', vocab)
one_hot = tf.feature_column.indicator_column(cat)
feature_columns.append(   one_hot   )

vocab = data['Pclass'].unique()#원핫인코딩: (객실등급)
cat = tf.feature_column.sequence_categorical_column_with_vocabulary_list('Pclass', vocab)
one_hot = tf.feature_column.indicator_column(cat)
feature_columns.append(   one_hot   )

#embedding
vocab = data['Ticket'].unique()#임베딩: 종류가 넘 많은 카테고리(티켓이름)
cat = tf.feature_column.sequence_categorical_column_with_vocabulary_list('Ticket', vocab)
one_hot = tf.feature_column.embedding_column(cat, dimension = 9)#복잡한거 차원 만들어서 넣기
feature_columns.append(   one_hot   )

model = tf.keras.Sequential([
    tf.keras.layers.DenseFeatures(feature_columns),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(1, activation='sigmoid'),
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])
ds_batch = ds.batch(32)
model.fit(ds_batch, shuffle=True, epochs=20)



WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. 
Received: inputs={'PassengerId': <tf.Tensor 'IteratorGetNext:5' shape=(None,) dtype=int64>, 
'Pclass': <tf.Tensor 'IteratorGetNext:6' shape=(None,) dtype=int64>, 
'Name': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=string>, 
'Sex': <tf.Tensor 'IteratorGetNext:7' shape=(None,) dtype=string>, 
'Age': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=float64>, 
'SibSp': <tf.Tensor 'IteratorGetNext:8' shape=(None,) dtype=int64>, 
'Parch': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=int64>, 
'Ticket': <tf.Tensor 'IteratorGetNext:9' shape=(None,) dtype=string>, 
'Fare': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float64>, 
'Embarked': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>}. 
Consider rewriting this model with the Functional API.

feature_column에 넣지도 않은  'Name' 이 왜 나오는지 모르겠어요.
그리고 영상에서 Age 컬럼  두번이나 append되던데 맞나요?
잘 따라쳤는데... 어렵네용.
도와주세용~~~"