Loading model fails: can only concatenate tuple

[sebplorenz]

Hi, I'm trying to save and load the model from this example: https://keras.io/examples/rl/deep_q_network_breakout/

Saving the model works. When I load the model I'm getting the following error:

Exception encountered: Exception encountered when calling Lambda.call().

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

Arguments received by Lambda.call():
  • args=('<KerasTensor shape=(None, 4, 84, 84), dtype=float32, sparse=False, name=input_layer>',)
  • kwargs={'mask': 'None'}

I've created a small script to reproduce:

import keras

def create_q_model():
    # Network defined by the Deepmind paper
    return keras.Sequential(
        [
            keras.layers.Lambda(
                lambda tensor: keras.ops.transpose(tensor, [0, 2, 3, 1]),
                output_shape=(84, 84, 4),
                input_shape=(4, 84, 84),
            ),
            # Convolutions on the frames on the screen
            keras.layers.Conv2D(32, 8, strides=4, activation="relu", input_shape=(4, 84, 84)),
            keras.layers.Conv2D(64, 4, strides=2, activation="relu"),
            keras.layers.Conv2D(64, 3, strides=1, activation="relu"),
            keras.layers.Flatten(),
            keras.layers.Dense(512, activation="relu"),
            keras.layers.Dense(4, activation="linear"),
        ]
    )

def save(model):
    keras.saving.save_model(model, './my_model.keras')

def load():
    return keras.models.load_model('my_model.keras', safe_mode=False)

model = create_q_model()
save(model)

# the following line fails
loaded_model = load()

[stylesaniswi]

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'}``

I have model which works on previous version of keras but due to gpu access constraints, I need to stick with keras 3x and tf 2.16 TBH ,I am naive . Help me resolve the problem. This is my model which i saved but while loading it gives problem:

`
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`