AttributeError: 'list' object has no attribute 'compile' [closed]

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I am getting an error, I am not sure why. I have already tried this solution but it wasn't successful.

Code:

my_model = tf.keras.Sequential()([
        tf.keras.layers.Conv2D(16, kernel_size=(3, 3),
                      activation='relu'),
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01),
                               bias_regularizer=tf.keras.regularizers.l1(0.01)),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
        tf.keras.layers.Dropout(0.25),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    # Horovod: adjust learning rate based on number of GPUs.
    scaled_lr = 0.00001 * hvd.size()
    opt = tf.keras.optimizers.Adam(scaled_lr)
    # opt = tf.keras.optimizers.Adam(0.00001 * hvd.size())
    # Horovod: add Horovod DistributedOptimizer.
    opt = hvd.DistributedOptimizer(
        opt, backward_passes_per_step=1, average_aggregated_gradients=True)

    # Horovod: Specify `ex  perimental_run_tf_function=False` to ensure TensorFlow
    # uses hvd.DistributedOptimizer() to compute gradients.
    my_model.compile(loss=tf.losses.SparseCategoricalCrossentropy(),
                      optimizer=opt,
                      metrics=['accuracy'],
                      experimental_run_tf_function=False)

Error:

my_model.compile(loss=tf.losses.SparseCategoricalCrossentropy(),
AttributeError: 'list' object has no attribute 'compile'

Replace tf.keras.Sequential() with tf.keras.Sequential.

user11989081
– user11989081

2022-09-03 12:27:00 +00:00
Commented Sep 3, 2022 at 12:27 — user11989081
– user11989081, Commented Sep 3, 2022 at 12:27

Mohamed El Amine BELLEBNA · Accepted Answer · 2022-09-04 01:00:56Z

1

the problem is in the the first line of your code, you need to define the list of layers inside the Sequential as following :

my_model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(16, kernel_size=(3, 3),
                      activation='relu'),
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01),
                               bias_regularizer=tf.keras.regularizers.l1(0.01)),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
        tf.keras.layers.Dropout(0.25),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(10, activation='softmax')
    ])

answered Sep 4, 2022 at 1:00

Mohamed El Amine BELLEBNA

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