Hey there,
I just want to know, if I want to expand only one dimension in the middle for a tensor like [3, 5], both of those statements give me [3, 1, 5], which one is better?
y1 = np.random.randn(3, 5)
y1_exp = tf.expand_dims(y1, axis=1)
print(y1_exp.shape)
y2_exp = RepeatVector(1)(y1)
print(y2_exp.shape)
Yes both will give you the same output.
tf.expand_dims is a tensorflow built-in function that allows you to insert a new axis into a tensor at a specified position. It is the simplest and most straightforward method to achieve a desired result.
Whereas tf.keras.layers.RepeatVector is a keras layer that repeats the input n times.It is a bit more complex and may not be as intuitive as using tf.expand_dims .
Thank you!