CODEWITHSUNDEEP
pythontensorflow
Geoffrey Anderson
Geoffrey Anderson

Reputation: 1574

How to do tf.not_equal() on sparse tensor?

I get TypeError: Failed to convert object. Is there some way to do tf.not_equal() or equivalent on a sparse tensor? It must stay sparse; conversion to dense not permitted.

Upvotes: 0

Views: 2200

Answers (2)

DomJack
DomJack

Reputation: 4183

I think you'll have to operate on the indices/values independently.

import numpy as np
import tensorflow as tf


def sparse_not_equal(sparse_tensor, value):
    indices = sparse_tensor.indices
    values = sparse_tensor.values
    condition = tf.squeeze(tf.where(tf.not_equal(values, value)), axis=-1)
    indices = tf.gather(indices, condition)
    values = tf.ones(shape=(tf.shape(indices)[0],), dtype=tf.bool)
    return tf.SparseTensor(
      indices,
      values,
      sparse_tensor.dense_shape)


def get_sparse():
    vals = tf.constant([2, 3, 4, 2])
    indices = tf.constant(np.array([[1], [4], [5], [10]]))
    dense_shape = [16]
    return tf.SparseTensor(indices, vals, dense_shape)


sparse_tensor = get_sparse()
sparse_filtered = sparse_not_equal(sparse_tensor, 2)


with tf.Session() as sess:
    s = sess.run(sparse_filtered)

print(s)

Upvotes: 0

Peter Szoldan
Peter Szoldan

Reputation: 4868

Supposing you want to compare two sparse tensors, and you have numbers in them, I think it's easiest to subtract one from the other and keep resulting non-zero values as "True" with tf.sparse_retain(). DomJack's answer only works if you want to compare a sparse tensor to a constant, but that's much easier with tf.sparse_retain() like the function sparse_not_equal_to_constant() below. (Please note this is not an accurate not_equal operation because it only tests existing values for inequality. Since the non-listed elements of a sparse tensor are zero, if the constant we're comparing to is not itself zero, then the rest of the matrix should also be marked as not equal. That's best done when converting back to dense, with the default_value parameter, but considering where the matrix had values to start with.) Tested code for comparing two sparse tensors, including function to compare it to constant:

import tensorflow as tf
import numpy as np

def sparse_not_equal_to_constant( s, c ):
    a = tf.sparse_retain( s, tf.not_equal( c, s.values ) )
    return tf.SparseTensor( a.indices, tf.ones_like( a.values, dtype = tf.bool ), dense_shape = s.dense_shape )

def sparse_not_equal( a, b ):
    neg_b = tf.SparseTensor( b.indices, -b.values, dense_shape = b.dense_shape )
    difference = tf.sparse_add( a, neg_b )
    return sparse_not_equal_to_constant( difference, 0.0 )

# test data
a = tf.SparseTensor( [ [ 0, 0 ], [ 1, 4 ], [ 2, 3 ] ], [ 5.0, 6, 7 ], dense_shape = ( 5, 5 ) )
b = tf.SparseTensor( [ [ 0, 0 ], [ 0, 2 ], [ 2, 3 ] ], [ 5.0, 6, 2 ], dense_shape = ( 5, 5 ) )
e = sparse_not_equal( a, b )
f = tf.sparse_tensor_to_dense( e, default_value = False )

with tf.Session() as sess:
    print( sess.run( f ) )

Outputs:

[[False False True False False]
[False False False False True]
[False False False True False]
[False False False False False]
[False False False False False]]

as expected.

Upvotes: 1

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