TensorFlow Keras(v2.2) model fit with multiple outputs and losses failed - tensorflow

Question

I want to use TensorFlow Keras(v2.2) model fit in mnist with multiple outputs and losses, but it failed.
My costume model will return a list [logits, embedding]. logits is 2D tensor [batch , 10] and embedding is also 2D tensor [batch, 64].
class MyModel(tf.keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.reshape = tf.keras.layers.Reshape((28, 28, 1))
self.conv2D1 = tf.keras.layers.Conv2D(filters=8, kernel_size=(3,3), strides=(1, 1), padding='same', activation='relu')
self.maxPool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding="same")
self.conv2D2 = tf.keras.layers.Conv2D(filters=8, kernel_size=(3,3), strides=(1, 1), padding='same', activation='relu')
self.maxPool2 = tf.keras.layers.MaxPooling2D(pool_size=2)
self.flatten = tf.keras.layers.Flatten(data_format="channels_last")
self.dropout = tf.keras.layers.Dropout(tf.compat.v1.placeholder_with_default(0.25, shape=[], name="dropout"))
self.dense1 = tf.keras.layers.Dense(64, activation=None)
self.dense2 = tf.keras.layers.Dense(10, activation=None)
def call(self, inputs, training):
x = self.reshape(inputs)
x = self.conv2D1(x)
x = self.maxPool1(x)
if training:
x = self.dropout(x)
x = self.conv2D2(x)
x = self.maxPool2(x)
if training:
x = self.dropout(x)
x = self.flatten(x)
x = self.dense1(x)
embedding = tf.math.l2_normalize(x, axis=1)
logits = self.dense2(embedding)
return [logits, embedding]
loss_0 is normal cross_entropy
def loss_0(y_true, y_pred):
loss_0 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred[0]))
loss_1 is triplet_semihard_loss
def loss_1(y_true, y_pred):
loss_1 = tfa.losses.triplet_semihard_loss(y_true=y_true, y_pred=y_pred[1], distance_metric="L2")
return loss_1
When I use model fit, I can only get logits tensor in each loss. I can't get embedding tensor. y_pred[0] and y_pred[1] is not work. Any suggestion?
model = MyModel()
model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-3), loss=[loss_0, loss_1], loss_weights=[0.1, 0.1])
history = model.fit(train_dataset, epochs=5)