Conditional assignment of tensor values in TensorFlow

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Comparison operators such as greater than are available within TensorFlow API.

However, there is nothing equivalent to the concise NumPy syntax when it comes to manipulating the tensors directly. You have to make use of individual comparison, where and assign operators to perform the same action.

Equivalent code to your NumPy example is this:

import tensorflow as tf

a = tf.Variable( [1,2,3,1] )    
start_op = tf.global_variables_initializer()    
comparison = tf.equal( a, tf.constant( 1 ) )    
conditional_assignment_op = a.assign( tf.where (comparison, tf.zeros_like(a), a) )

with tf.Session() as session:
    # Equivalent to: a = np.array( [1, 2, 3, 1] )
    session.run( start_op )
    print( a.eval() )    
    # Equivalent to: a[a==1] = 0
    session.run( conditional_assignment_op )
    print( a.eval() )

# Output is:
# [1 2 3 1]
# [0 2 3 0]

The print statements are of course optional, they are just there to demonstrate the code is performing correctly.

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