What's the difference between using tf.expand_dims and tf.newaxis in Tensorflow?

Question

Hi I am new to Tensorflow.
I want to change the dimention of Tensor, and I found 3 types of method to implement this, like below:

a = tf.constant([[1,2,3],[4,5,6]]) # shape (2,3)

# change dimention of a to (2,3,1)
b = tf.expand_dims(a,2)   # shape(2,3,1)
c = a[:,:,tf.newaxis]     # shape(2,3,1)
d = tf.reshape(a,(2,3,1)) # shape(2,3,1)

Is there any difference among the 3 methods, e.g. in terms of performance?
Which method should I use?

Answer 1

There is no real difference between the three, but sometimes one or the other may be more convenient:

• tf.expand_dims(a, 2): Convenient when you want to add one dimension and its index is variable (for example the result of another TensorFlow operation, or some function parameter). Depending on your style you may find it more readable, since it clearly expresses the intention of adding a dimension.
• a[:,:,tf.newaxis]: Personally I use this a lot because I find it readable (maybe because I'm used to it from NumPy), although not in every case. Especially convenient if you want to add multiple dimensions (instead of calling tf.expand_dims multiple times). Also (obviously) if you want to take a slice and add new dimensions at the same time. However it is not usable with variable axis indices, and if you have many dimensions tf.expand_dims may be less confusing.
• tf.reshape(a,(2,3,1)): Personally I rarely or never use this to just add a dimension, because it requires me to know and specify all (or all but one) the remaining dimension sizes, and also it may be misleading when reading the code. However, if I need to reshape and add a dimension, I usually do it in the same operation.