making a custom (tf.function) function 2d array

Question

I am trying to calculate a 2D array of a function of variables x, y as a tf.function. The function is fairly complicated and i want to make a 2d array of this function where x and y take a list of values (tf.linspace). Ive tried inputing the relevant arguments for such a function, here is what it looks like

@tf.function
def function_matrix(xi, xf, yi, yf, num , some_other_args):
    
    #part1
    M=np.zeros((num, num))
    xlist=tf.linspace(xi, xf, num)
    ylist=tf.linspace(yi, yf, num)
    
    #part2
    for x in range(num):
         for y in range(num):
             M[x,y]=some_complicated_function(xlist[x], ylist[y], some_other_args)     #this is also a @tf.function
    
    return (M)

The problem I'm encountering is that within a tf.function, if I try to access elements of an array like xlist[x], the result is a Tensor("strided_slice:0", shape=(), dtype=float64). So when passing this value in some_complicated_function, I get an error "setting an array element with a sequence". No such error occurs if function_matrix is not a tf.function. Could someone help with this? as to where I could be going wrong? Or any alternative way I could calculate the 2D matrix of a fairly complicated function? Any help would be appreciated, Thanks!

What I've tried: Part 1 runs fine, If I return xlist as the output of a function, I get a normal array, tf.Tensor( [the_array_here], shape=(num,), dtype=float64). Similarly if the the output is xlist[index], I get tf.Tensor( [the_element_here], shape=(), dtype=float64). But is I try to print xlist[index] from within the function, I get Tensor("strided_slice:0", shape=(), dtype=float64). So I am concluding that somehow tf is treating xlist[index] as placeholder of somekind. But I dont know why...

qmzp · Accepted Answer · 2023-08-03 18:30:38Z

3

Ooh nice question! tensorflow really doesn't like the for loops, it's python code that can't be automatically converted to a tensorflow graph representation. The way to implement this, is to generate the grid you want to operate on in a tensor. Let's say:

xlist=[1,2] # this is a tf.Tensor
ylist=[1,2] # this is a tf.Tensor

then, using tf.meshgrid, you should construct xylist:

xylist=[[1,1], [1,2], [2,1], [2,2]] # this is a tf.Tensor

and then use tf.map_fn to apply your function to each pair.

M = tf.map_fn(some_complicated_function, xylist)
M = tf.reshape(M, (...))

Note that if some_complicated_function contains any non tensorflow code (or code that cannot be automatically converted), like using numpy, pandas, pillow..., you can wrap it in a tf.py_function - but now that that kind of defeats the purpose of converting your function into a tf.function. (EDIT: I see now you say: # this is also a tf.function, which means you don't have to wrap it in a tf.py_function)

You can also include the extra_args by appending to each pair in xylist (yes each pair, even though they are constant).

TL;DR: use tf.map_fn instead of nested for loops.

edited Aug 3, 2023 at 18:30

qmzp

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answered Jan 4, 2021 at 10:56

Frederik Bode

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3 Comments

Lescurel Over a year ago

Good answer. I would suggest using tf.meshgrid instead of stack and concatenate though.

Frederik Bode Over a year ago

Thanks! I knew it existed but forgot the name :D included it

Lescurel Over a year ago

Also, for extra_args, if they don't change during the iteration, currying functools.partial or a lambda seems appropriate.

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