Loading model fails: can only concatenate tuple about keras HOT 1 OPEN

can only concatenate tuple (not "TrackedList") to tuple

Arguments received by Lambda.call():
  • args=('<KerasTensor shape=(None, 48, 48, 1), dtype=float32, sparse=False, name=input_layer_4>',)
  • kwargs={'mask': 'None'}``

`
def create_model(norm, start_filters=32):
    ir069 = tf.keras.layers.Input(shape=(192, 192, 1)) 
    ir107 = tf.keras.layers.Input(shape=(192, 192, 1)) 
    lght = tf.keras.layers.Input(shape=(48, 48, 1))
    inputs = [ir069, ir107, lght]

    # Normalize inputs
    ir069_norm = tf.keras.layers.Lambda(lambda x, mu, scale: (x - mu) / scale,
                                        arguments={'mu': norm['ir069']['shift'], 'scale': norm['ir069']['scale']},
                                        output_shape=(192, 192, 1))(ir069)
    ir107_norm = tf.keras.layers.Lambda(lambda x, mu, scale: (x - mu) / scale,
                                        arguments={'mu': norm['ir107']['shift'], 'scale': norm['ir107']['scale']},
                                        output_shape=(192, 192, 1))(ir107)
    lght_norm = tf.keras.layers.Lambda(lambda x, mu, scale: (x - mu) / scale,
                                       arguments={'mu': norm['lght']['shift'], 'scale': norm['lght']['scale']},
                                       output_shape=(48, 48, 1))(lght)

    # Reshape lght into 192
    lght_res = tf.keras.layers.Lambda(lambda t: tf.image.resize(t, (192, 192)), output_shape=(192, 192, 1))(lght_norm)

    # Concatenate all inputs
    x_inp = tf.keras.layers.Concatenate(axis=-1)([ir069_norm, ir107_norm, lght_res])

    encoder0_pool, encoder0 = encoder_block(x_inp, start_filters)
    encoder1_pool, encoder1 = encoder_block(encoder0_pool, start_filters * 2)
    encoder2_pool, encoder2 = encoder_block(encoder1_pool, start_filters * 4)
    encoder3_pool, encoder3 = encoder_block(encoder2_pool, start_filters * 8)
    center = conv_block(encoder3_pool, start_filters * 32)
    decoder3 = decoder_block(center, encoder3, start_filters * 8)
    decoder2 = decoder_block(decoder3, encoder2, start_filters * 6)
    decoder1 = decoder_block(decoder2, encoder1, start_filters * 4)
    decoder0 = decoder_block(decoder1, encoder0, start_filters * 2)
    decoder00 = decoder_block(decoder0, None, start_filters)
    output = layers.Conv2D(1, (1, 1), padding='same', activation='linear', name='output_layer')(decoder00)

    
    return inputs , output`