Simple plot for plotting the latent space of a VAE... could be used for anything because it has a TSNE function in there to reduce dimensionality to appropriate plotting size.

plot_latent_space

from sklearn.manifold import TSNE
from mpl_toolkits.mplot3d import Axes3D
%matplotlib qt
from IPython import display
import matplotlib.cm as cmx
import matplotlib.colors as colors

def get_cmap(N):
    '''Returns a function that maps each index in 0, 1, ... N-1 to a distinct 
    RGB color.'''
    color_norm  = colors.Normalize(vmin=0, vmax=N-1)
    scalar_map = cmx.ScalarMappable(norm=color_norm, cmap='hsv') 
    def map_index_to_rgb_color(index):
        return scalar_map.to_rgba(index)
    return map_index_to_rgb_color

def plot_latent_space(x_batch, y_batch, iteration=None, dim=2):
    
    model = TSNE(n_components=dim, random_state=0, perplexity=50, learning_rate=500, n_iter=200)
    z_mu = model.fit_transform(mu.eval(feed_dict={X: x_batch}))
    n_classes = len(list(set(np.argmax(y_batch, 1))))
    cmap = get_cmap(n_classes)
    fig = plt.figure(2, figsize=(8,8))
    
    if dim is 3:
        for i in list(set(np.argmax(y_batch, 1))):
            bx = fig.add_subplot(111, projection='3d')

            index = np.where(np.argmax(y_batch, 1) == i)
            xs = z_mu[index, 0]
            ys = z_mu[index, 1:]
            zs = z_mu[index, 2]
            bx.scatter(xs, ys, zs,c=cmap(i), label=str(i))
    else:
        for i in list(set(np.argmax(y_batch, 1))):
            bx = fig.add_subplot(111)
            index = np.where(np.argmax(y_batch, 1) == i)
            xs = z_mu[index, 0]
            ys = z_mu[index, 1]
            bx.scatter(xs, ys, c=cmap(i), label=str(i))

    bx.set_xlabel('X Label')
    bx.set_ylabel('Y Label')
    bx.legend()
    bx.set_title('Truth')
    if iteration is None:
        plt.savefig('latent_space.png')
    else:
        plt.savefig('latent_space' + str(iteration) + '.png')
    plt.show()