ValueError: All layers added to a Sequential model should have unique names

Question

I am trying to train a VAE model for music generation. When I run the following program, it shows an error:

All layers added to a Sequential model should have unique names. Name "" is already the name of a layer in this model. Update the name argument to pass a unique name.

tensorflow version is 2.3

class Resnet1DBlock(tf.keras.Model):
    def __init__(self, kernel_size, filters, type = 'encode', prefix = ''):
        super(Resnet1DBlock, self).__init__(name = '')
    
        if type == 'encode':
            self.conv1a = layers.Conv1D(filters, kernel_size, 2, padding = "same", \
                                        name = prefix + 'conv1a')
            self.conv1b = layers.Conv1D(filters, kernel_size, 1, padding = "same", \
                                        name = prefix + 'conv1b')
            self.norm1a = tfa.layers.InstanceNormalization(name =  prefix + 'norm1a')
            self.norm1b = tfa.layers.InstanceNormalization(name =  prefix + 'norm1b')
        elif type == 'decode':
            self.conv1a = layers.Conv1DTranspose(filters, kernel_size, 1, padding = "same", \
                                                name =  prefix + 'conv1a')
            self.conv1b = layers.Conv1DTranspose(filters, kernel_size, 1, padding = "same", \
                                                name =  prefix + 'conv1b')
            self.norm1a = tf.keras.layers.BatchNormalization(name =  prefix + 'norm1a')
            self.norm1b = tf.keras.layers.BatchNormalization(name =  prefix + 'norm1b')
        else:
            return None

    def call(self, input_tensor):
        x = tf.nn.relu(input_tensor)
        x = self.conv1a(x)
        x = self.norm1a(x)
        x = layers.LeakyReLU(0.4)(x)

        x = self.conv1b(x)
        x = self.norm1b(x)
        x = layers.LeakyReLU(0.4)(x)

        x += input_tensor
        return tf.nn.relu(x)
    
class CVAE(tf.keras.Model):

    def __init__(self, latent_dim):
        super(CVAE, self).__init__()
        self.latent_dim = latent_dim
        self.encoder = tf.keras.Sequential(
            [
                tf.keras.layers.InputLayer(input_shape = (1, 90001), name = 'input_encoder'),

                layers.Conv1D(64, 1, 2, name = 'conv1_layer1'),
                Resnet1DBlock(64, 1, 'encode', prefix = 'res1_'),
                layers.Conv1D(128, 1, 2, name = 'conv1_layer2'),
                Resnet1DBlock(128, 1, 'encode', prefix = 'res2_'),
                layers.Conv1D(128, 1, 2, name = 'conv1_layer3'),
                Resnet1DBlock(128, 1, 'encode', prefix = 'res3_'),
                layers.Conv1D(256, 1, 2, name = 'conv1_layer4'),
                Resnet1DBlock(256, 1, 'encode', prefix = 'res4_'),

                layers.Flatten(name = 'flatten'),
                layers.Dense(latent_dim + latent_dim, name = 'dense'),
            ]
        )
        self.decoder = tf.keras.Sequential(
            [
                tf.keras.layers.InputLayer(input_shape = (latent_dim,), name = 'input_decoder'),
                layers.Reshape(target_shape = (1, latent_dim)),
                Resnet1DBlock(512, 1, 'decode', prefix = 'res1_'),
                layers.Conv1DTranspose(512, 1, 1, name = 'Conv1Trans_Layer1'),
                Resnet1DBlock(256, 1, 'decode', prefix = 'res2_'),
                layers.Conv1DTranspose(256, 1, 1, name = 'Conv1Trans_Layer2'),
                Resnet1DBlock(128, 1, 'decode', prefix = 'res3_'),
                layers.Conv1DTranspose(128, 1, 1, name = 'Conv1Trans_Layer3'),
                Resnet1DBlock(64, 1, 'decode', prefix = 'res4_'),
                layers.Conv1DTranspose(64, 1, 1, name = 'Conv1Trans_Layer4'),
                layers.Conv1DTranspose(90001, 1, 1, name = 'Conv1Trans_Layer5')
            ]
        )

optimizer = tf.keras.optimizers.Adam(0.0003, beta_1 = 0.9, beta_2 = 0.999, epsilon = 1e-08)

random_vector_for_generation = tf.random.normal(shape = [num_examples_to_generate, latent_dim])
model = CVAE(latent_dim)

I'm so confused, I have clearly named all the network layers. (I'm a novice)

Answer 1

you should not send a default name to initialize the superclass, so if you replace "super(Resnet1DBlock, self).init(name = '')" with "super(Resnet1DBlock, self).init()" it would be run successfully.

good luck