sample_latent.py 文件源码

def visualize_latent_rep(args, model, x_latent):
    print("pca_on=%r pca_comp=%d tsne_comp=%d tsne_perplexity=%f tsne_lr=%f" % (
        args.use_pca,
        args.pca_components,
        args.tsne_components,
        args.tsne_perplexity,
        args.tsne_lr
    ))

    if args.use_pca:
        pca = PCA(n_components = args.pca_components)
        x_latent = pca.fit_transform(x_latent)

    figure(figsize=(6, 6))
    scatter(x_latent[:, 0], x_latent[:, 1], marker='.')
    show()

    tsne = TSNE(n_components = args.tsne_components,
                perplexity = args.tsne_perplexity,
                learning_rate = args.tsne_lr,
                n_iter = args.tsne_iterations,
                verbose = 4)
    x_latent_proj = tsne.fit_transform(x_latent)
    del x_latent

    figure(figsize=(6, 6))
    scatter(x_latent_proj[:, 0], x_latent_proj[:, 1], marker='.')
    show()