Hello,
i habe an autoencoder model which i want to visualize but the internals of the model architecture are not visual.

latent_dim = 64
class Autoencoder(Model):
def init(self, latent_dim):
super(Autoencoder, self).init()
self.latent_dim = latent_dim
self.encoder = tf.keras.Sequential([
layers.Flatten(),
layers.Dense(latent_dim, activation=‘relu’),
])
self.decoder = tf.keras.Sequential([
layers.Dense(784, activation=‘sigmoid’),
layers.Reshape((28, 28))
])
def call(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return decoded
autoencoder = Autoencoder(latent_dim)
plot_model(autoencoder)


Why is the model architecture not visual?
Thanks
Christoph