CODEWITHSUNDEEP
pythonneural-networktensorflowdeep-learning
user3480922
user3480922

Reputation: 574

How to update a subset of 2D tensor in Tensorflow?

I want to update an index in a 2D tensor with value 0. So data is a 2D tensor whose 2nd row 2nd column index value is to be replaced by 0. However, I am getting a type error. Can anyone help me with it?

TypeError: Input 'ref' of 'ScatterUpdate' Op requires l-value input

data = tf.Variable([[1,2,3,4,5], [6,7,8,9,0], [1,2,3,4,5]])
data2 = tf.reshape(data, [-1])
sparse_update = tf.scatter_update(data2, tf.constant([7]), tf.constant([0]))
#data = tf.reshape(data, [N,S])
init_op = tf.initialize_all_variables()

sess = tf.Session()
sess.run([init_op])
print "Values before:", sess.run([data])
#sess.run([updated_data_subset])
print "Values after:", sess.run([sparse_update])

Upvotes: 4

Views: 7274

Answers (3)

user2585501
user2585501

Reputation: 606

This is the function I use to modify a subset (row/column) of a 2D tensor in Tensorflow 2:

#note if updatedValue isVector, updatedValue should be provided in 2D format
def modifyTensorRowColumn(a, isRow, index, updatedValue, isVector):
    
    if(not isRow):
        a = tf.transpose(a)
        if(isVector):
            updatedValue = tf.transpose(updatedValue)
    
    if(index == 0):
        if(isVector):
            values = [updatedValue, a[index+1:]]
        else:
            values = [[updatedValue], a[index+1:]]
    elif(index == a.shape[0]-1):
        if(isVector):
            values = [a[:index], updatedValue]
        else:
            values = [a[:index], [updatedValue]]
    else:
        if(isVector):
            values = [a[:index], updatedValue, a[index+1:]]
        else:
            values = [a[:index], [updatedValue], a[index+1:]]
            
    a = tf.concat(axis=0, values=values)
            
    if(not isRow):
        a = tf.transpose(a)
        
    return a

Upvotes: 0

Yaroslav Bulatov
Yaroslav Bulatov

Reputation: 57893

Scatter update only works on variables. Instead try this pattern.

Tensorflow version < 1.0: a = tf.concat(0, [a[:i], [updated_value], a[i+1:]])

Tensorflow version >= 1.0: a = tf.concat(axis=0, values=[a[:i], [updated_value], a[i+1:]])

Upvotes: 11

Da Tong
Da Tong

Reputation: 2026

tf.scatter_update could only be applied to Variable type. data in your code IS a Variable, while data2 IS NOT, because the return type of tf.reshape is Tensor.

Solution:

for tensorflow after v1.0

data = tf.Variable([[1,2,3,4,5], [6,7,8,9,0], [1,2,3,4,5]])
row = tf.gather(data, 2)
new_row = tf.concat([row[:2], tf.constant([0]), row[3:]], axis=0)
sparse_update = tf.scatter_update(data, tf.constant(2), new_row)

for tensorflow before v1.0

data = tf.Variable([[1,2,3,4,5], [6,7,8,9,0], [1,2,3,4,5]])
row = tf.gather(data, 2)
new_row = tf.concat(0, [row[:2], tf.constant([0]), row[3:]])
sparse_update = tf.scatter_update(data, tf.constant(2), new_row)

Upvotes: 3

Related Questions