How best to implement a matrix mask operation in tensorflow?

Question

I had a case where I needed to fill some holes (missing data) in an image processing application in tensorflow. The 'holes' are easy to locate as they are zeros and the good data is not zeros. I wanted to fill the holes with random data. This is quite easy to do using python numpy but doing it in tensorflow requires some work. I came up with a solution and wanted to see if there is a better or more efficient way to do the same thing. I understand that tensorflow does not yet support the more advanced numpy type indexing yet but there is a function tf.gather_nd() that seems promising for this. However, I could not tell from the documentation how to us it for what I wanted to do. I would appreciate answers that improve on what I did or especially if someone can show me how to do it using tf.gather_nd(). Also, tf.boolean_mask() does not work for what I am trying to do because it does not allow you to use the output as an index. In python what I am trying to do:

a = np.ones((2,2))
a[0,0]=a[0,1] = 0
mask = a == 0
a[mask] = np.random.random_sample(a.shape)[mask]
print('new a = ', a)

What I ended up doing in Tensorflow to achieve same thing (skipping filling the array steps)

zeros = tf.zeros(tf.shape(a))  
mask = tf.greater(a,zeros)
mask_n = tf.equal(a,zeros)
mask = tf.cast(mask,tf.float32)
mask_n = tf.cast(mask_n,tf.float32
r = tf.random_uniform(tf.shape(a),minval = 0.0,maxval=1.0,dtype=tf.float32)
r_add = tf.multiply(mask_n,r)
targets = tf.add(tf.multiply(mask,a),r_add)

Answer 1

I think these three lines might do what you want. First, you make a mask. Then, you create the random data. Finally, fill in the masked values with the random data.

mask = tf.equal(a, 0.0)
r = tf.random_uniform(tf.shape(a), minval = 0.0,maxval=1.0,dtype=tf.float32)
targets = tf.where(mask, r, a)