any-to-normal.py

import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras import backend as K
from visdom import Visdom


B = 1000
N = 10
X = numpy.random.rand(B, N)


model = Sequential()
model.add(Dense(N, input_shape=(N,)))
model.add(Dense(N))


def kl(dummy, q):
    m = K.mean(q, axis=0)  # across the mini-batch
    v = K.maximum(K.mean((q - m) ** 2, axis=0), K.epsilon())
    return 0.5 * (-N + K.sum(m ** 2 + v - K.log(v)))


opt = Adam(clipnorm=0.9)
model.compile(loss=kl, optimizer=opt)
model.fit(X, X, epochs=100)

# test & plot
Y = model.predict(X)
plot = Visdom()
plot.histogram(Y[:, 0], opts={'title': 'Y'})