Slicing tensors in tensorflow using argmax

Question

I want to make a dynamic loss function in tensorflow. I want to calculate the energy of a signal's FFT, more specifically only a window of size 3 around the most dominant peak. I am unable to implement in TF, as it throws a lot of errors like Stride and InvalidArgumentError (see above for traceback): Expected begin, end, and strides to be 1D equal size tensors, but got shapes [1,64], [1,64], and [1] instead.

My code is this:

self.spec = tf.fft(self.signal)
self.spec_mag = tf.complex_abs(self.spec[:,1:33])
self.argm = tf.cast(tf.argmax(self.spec_mag, 1), dtype=tf.int32)
self.frac = tf.reduce_sum(self.spec_mag[self.argm-1:self.argm+2], 1)

Since I am computing batchwise of 64 and dimension of data as 64 too, the shape of self.signal is (64,64) . I wish to calculate only the AC components of the FFT. As the signal is real valued, only half the spectrum would do the job. Hence, the shape of self.spec_mag is (64,32) .

The max in this fft is located at self.argm which has a shape (64,1) .

Now I want to calculate the energy of 3 elements around the max peak via: self.spec_mag[self.argm-1:self.argm+2] .

However when I run the code and try to obtain the value of self.frac , I get thrown with multiple errors.

Answer 1

It seems like you were missing and index when accessing argm. Here is the fixed version of the 1, 64 version.

import tensorflow as tf
import numpy as np

x = np.random.rand(1, 64)
xt = tf.constant(value=x, dtype=tf.complex64)

signal = xt
print('signal', signal.shape)
print('signal', signal.eval())

spec = tf.fft(signal)
print('spec', spec.shape)
print('spec', spec.eval())

spec_mag = tf.abs(spec[:,1:33])
print('spec_mag', spec_mag.shape)
print('spec_mag', spec_mag.eval())

argm = tf.cast(tf.argmax(spec_mag, 1), dtype=tf.int32)
print('argm', argm.shape)
print('argm', argm.eval())

frac = tf.reduce_sum(spec_mag[0][(argm[0]-1):(argm[0]+2)], 0)
print('frac', frac.shape)
print('frac', frac.eval())

and here is the expanded version (batch, m, n)

import tensorflow as tf
import numpy as np

x = np.random.rand(1, 1, 64)
xt = tf.constant(value=x, dtype=tf.complex64)

signal = xt
print('signal', signal.shape)
print('signal', signal.eval())

spec = tf.fft(signal)
print('spec', spec.shape)
print('spec', spec.eval())

spec_mag = tf.abs(spec[:, :, 1:33])
print('spec_mag', spec_mag.shape)
print('spec_mag', spec_mag.eval())

argm = tf.cast(tf.argmax(spec_mag, 2), dtype=tf.int32)
print('argm', argm.shape)
print('argm', argm.eval())

frac = tf.reduce_sum(spec_mag[0][0][(argm[0][0]-1):(argm[0][0]+2)], 0)
print('frac', frac.shape)
print('frac', frac.eval())

you may want to fix function names since I edit this code at a newer version of tensorflow.

Answer 2

Tensorflow indexing uses tf.Tensor.getitem :

This operation extracts the specified region from the tensor. The notation is similar to NumPy with the restriction that currently only support basic indexing. That means that using a tensor as input is not currently allowed

So using tf.slice and tf.strided_slice is out of the question as well.

Whereas in tf.gather indices defines slices into the first dimension of Tensor , in tf.gather_nd , indices defines slices into the first N dimensions of the Tensor , where N = indices.shape[-1]

Since you wanted the 3 values around the max , I manually extract the first, second and third element using a list comprehension, followed be a tf.stack

import tensorflow as tf

signal = tf.placeholder(shape=(64, 64), dtype=tf.complex64)
spec = tf.fft(signal)
spec_mag = tf.abs(spec[:,1:33])
argm = tf.cast(tf.argmax(spec_mag, 1), dtype=tf.int32)

frac = tf.stack([tf.gather_nd(spec,tf.transpose(tf.stack(
             [tf.range(64), argm+i]))) for i in [-1, 0, 1]])

frac = tf.reduce_sum(frac, 1)

This will fail for the corner case where argm is the first or last element in the row, but it should be easy to resolve.