cat = Pipeline([
    ('onehot', OneHotEncoder(sparse=False, handle_unknown='ignore'))
])

num = Pipeline([
    ('scaler', StandardScaler(with_mean=False))
])

transformer = ColumnTransformer([
    ('cat', cat, df.select_dtypes(include='object').columns),
    ('num', num, df.select_dtypes(include=np.number).columns[:-1])
])


transformer.fit(x_train)

y.unique()

output: array([ 6, 10, 15, 11, 19, 9, 12, 14, 16, 5, 8, 17, 18, 13, 20, 7, 0,
4])

transformer.transform(x_train).shape

output: (316, 57)

   model = keras.Sequential([
    layers.Dense(7, activation='relu'),
    layers.Dropout(0.2),
    layers.Dense(7, activation='relu'),
    layers.Dense(4, activation='relu'),
    layers.Dropout(0.2),
    layers.Dense(4, activation='relu'),
    layers.Dense(2, activation='softmax'),
])

model.compile(
    loss='sparse_categorical_crossentropy',
    optimizer='adam',
    metrics=['accuracy']
)

model.fit(transformer.transform(x_train), y_train, epochs=15)

And i get error like
ValueError: Shapes (None, 1) and (None, 2) are incompatible

what can i do to solve this?