Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
filters filter_of_dis = 16 filter_of_decgen = 16 filter_of_encoder = 16 def sampling(args): mean, logsigma = args epsilon = K.random_normal(shape=(K.shape(mean)[0], latent_dim), mean=0., stddev=1.0) return mean + K.exp(logsigma / 2) * epsilon def vae_loss(X , output , E_mean, E_logsigma): # compute