强制模型将单个输出作为一个元素的列表返回

Question

According to https://keras.io/models/model/ , when a model returns a multiple output, it is represented as a list of outputs.

It is possible to force the model to return a list even with a single output? By default when building the model, if there is only one output, it is returned

I am doing a generic utility and this inconsistency in the keras API (returning two output types) does not seem very elegant. I don't know what will the input be so I always assume multiple inputs/outputs.

Is it possible to return a list of one output? How can I overcome this limitation?

import tensorflow as tf
import numpy as np


def get_model1():
    in_layer = tf.keras.Input(
        name='IN',
        shape=(10), 
        dtype=tf.float32
    )
    out_layer = tf.keras.layers.Dense(
        name="OUT",
        units=1,
        activation=tf.keras.activations.sigmoid 
    )(in_layer)    
    return tf.keras.Model(
        name='Model_1',
        inputs=[in_layer],
        outputs=[out_layer]
    )

def get_model2():
    in_layer1 = tf.keras.Input(
        name='IN1',
        shape=(10),
        dtype=tf.float32
    )
    in_layer2 = tf.keras.Input(
        name='IN2',
        shape=(10),
        dtype=tf.float32
    )
    out_layer1 = tf.keras.layers.Dense(
        name="OUT1",
        units=1,
        activation=tf.keras.activations.sigmoid
    )(in_layer1)    
    out_layer2 = tf.keras.layers.Dense(
        name="OUT2",
        units=1,
        activation=tf.keras.activations.sigmoid
    )(in_layer2) 
    return tf.keras.Model(
        name='Model_1',
        inputs=[in_layer1,in_layer2],
        outputs=[out_layer1, out_layer2]
    )

m1 = get_model1()
m2 = get_model2()

print(m1.input_shape)
print(m2.input_shape)

print(m1.output_shape)
print(m2.output_shape)

This is the result.

(None, 10)
[(None, 10), (None, 10)]
(None, 1)
[(None, 1), (None, 1)]

What I expect is the first model to have a list as an input and as an output, the same behavior as the second model but with one element.

Answer 1

At the end I have used the keras internal function to_list

from tensorflow.python.keras.utils.generic_utils import to_list

to_list(model.predict(x))

This way I unify the expected output of my model by returning always a list and delegating the checks in the library that generates the inconsistency.