tensorflow CNN with complex features and labels? - tensorflow

Question

I recently found a paper where they used a CNN with complex 2D-feature-maps as an input. However, there Network also outputs a complex output vector. They used Keras with tensorflow backend.
Here is the link: https://arxiv.org/pdf/1802.04479.pdf
I asked myself if it is possible to build complex Deep Neural Networks like CNNs with tensorflow. As far as i know it is not possible. Did i missed something?
There are other related questions which adresses the same problem with no answer: Complex convolution in tensorflow
when building a realy senseless model with real number in and output all works correct:
import tensorflow as tf
from numpy import random, empty
n = 10
feature_vec_real = random.rand(1,n)
X_real = tf.placeholder(tf.float64,feature_vec_real.shape)
def model(x):
out = tf.layers.dense(
inputs=x,
units=2
)
return out
model_output = model(X_real)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
output = sess.run(model_output,feed_dict={X_real:feature_vec_real})
but when using complex inputs:
import tensorflow as tf
from numpy import random, empty
n = 10
feature_vec_complex = empty(shape=(1,n),dtype=complex)
feature_vec_complex.real = random.rand(1,n)
feature_vec_complex.imag = random.rand(1,n)
X_complex = tf.placeholder(tf.complex128,feature_vec_complex.shape)
def complex_model(x):
out = tf.layers.dense(
inputs=x,
units=2
)
return out
model_output = complex_model(X_complex)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
output = sess.run(model_output,feed_dict={X_complex:feature_vec_complex})
i get the following error:
ValueError: An initializer for variable dense_7/kernel of is required
So what is the correct way to initialize the weights of the dense kernel when having complex inputs?
I know there is the possibility to handle complex numbers as two different layers in the network. But this is not what i want.
Thanks for your help!