CODEWITHSUNDEEP
tensorflow
Sungsu Lim
Sungsu Lim

Reputation: 13

Clipping(Filtering) tf.placeholder values

I want to change my tf.placeholder values such that:

values < SmallConstant is set to 0.

It's not exactly clipping, so I can't use: tf.clip_by_value()

I tried the suggestion in Conditional assignment of tensor values in TensorFlow, and this is what I have so far:

x = tf.placeholder(tf.float32, None)
condition = tf.less(x, tf.constant(SmallConst))
tf.assign(x, tf.where(condition, tf.zeros_like(x), x))

However, on running this, I get an error saying

AttributeError: 'Tensor' object has no attribute 'assign'

It seems tf.assign() can be done on tf.Variable but not on tf.placeholder.

Is there any other way I can do this?

Thank you!

Upvotes: 0

Views: 235

Answers (1)

Multihunter
Multihunter

Reputation: 5918

Yes, it's even easier than you think:

x = tf.placeholder(tf.float32, None)
# create a bool tensor the same shape as x
condition = x < SmallConst 

# create tensor same shape as x, with values greater than SmallConst set to 0
to_remove = x*tf.to_float(condition)

# set all values of x less than SmallConst to 0
x_clipped = x - to_remove

I'd normally just put that into one line like:

x_clipped = x - x*tf.to_float(x < small_const)

note: using tf.to_float on a tensor of type bool will give you 0.0s in place of Falses and 1.0s in place of Trues

Additional information for cleaner code:

The numerical operators (e.g. <, >=, +, - etc, but not ==) are overloaded for tensorflow tensors such that you can use native python variables with tensors to get a new tensor that is the result of that operation. So tf.constant() is actually fairly rarely actually needed. Example of this in action:

a = tf.placeholder(tf.int32)
b = a + 1
c = a > 0
print(b) # gives "<tf.Tensor 'add:0' shape=<unknown> dtype=int32>"
print(c) # gives "<tf.Tensor 'Greater:0' shape=<unknown> dtype=bool>"
sess.run(b, {a: 1}) # gives scalar int32 numpy array with value 2
sess.run(c, {a: 1}) # gives scalar bool numpy array with value True

This is also true of numpy.

tf.assign() only works on Variables because it will

Update 'ref' by assigning 'value' to it.

Tensors in tensorflow are immutable. The result of any operation on a tensor is another tensor, but the original tensor will never change. Variables, however are mutable, and you change their value with tf.assign()

Upvotes: 1

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