Investigating http://koaning.io/homogenous-autoencoder-ui.html.

not_space_filling.py

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

from keras import backend
from keras.layers import Input, Dense
from keras.models import Model

# Autoencoder.
point_2d = Input(shape = (2, ))
h1 = Dense(50, activation = "sigmoid")(point_2d)
point_1d = Dense(1, activation = "sigmoid")(h1)
h2 = Dense(50, activation = "sigmoid")(point_1d)
point_2d_hat = Dense(2, activation = "sigmoid")(h2)

autoencoder = Model(point_2d, point_2d_hat)
autoencoder.compile(optimizer = "adadelta", loss = "binary_crossentropy")

X = np.random.uniform(size = (10000, 2))
epochs = 100
autoencoder.fit(X, X, epochs = epochs, shuffle = True)

get_2d = backend.function([point_1d], [point_2d_hat])
get_1d = backend.function([point_2d], [point_1d])

size = 1000
points_1d = np.arange(0, 1, 1 / size).reshape(size, 1)
points_2d = pd.DataFrame(get_2d([points_1d])[0], columns = ["x", "y"])
sns.regplot(x = "x", y = "y", data = points_2d, fit_reg = False)
plt.show()

points_1d_hat = get_1d([X])
sns.distplot(points_1d_hat)
plt.show()