How to reshape a tensor with multiple `None` dimensions?

Question

I encountered a problem to reshape an intermediate 4D tensorflow tensor X to a 3D tensor Y, where

• X is of shape ( batch_size, nb_rows, nb_cols, nb_filters )
• Y is of shape ( batch_size, nb_rows*nb_cols, nb_filters )
• batch_size = None

Of course, when nb_rows and nb_cols are known integers, I can reshape X without any problem. However, in my application I need to deal with the case

nb_rows = nb_cols = None

What should I do? I tried Y = tf.reshape( X, (-1, -1, nb_filters)) but it clearly fails to work.

For me, this operation is deterministic because it always squeezes the two middle axes into a single one while keeping the first axis and the last axis unchanged. Can anyone help me?

Answer 1

In this case you can access to the dynamic shape of X through tf.shape(X):

shape = [tf.shape(X)[k] for k in range(4)]
Y = tf.reshape(X, [shape[0], shape[1]*shape[2], shape[3]])