According to the official documentation of tf.keras.layers.Conv3D
5+D tensor with shape: batch_shape + (channels, conv_dim1, conv_dim2, conv_dim3) if data_format='channels_first' or 5+D tensor with shape: batch_shape + (conv_dim1, conv_dim2, conv_dim3, channels) if data_format='channels_last'
. Now the whole idea around channels and batch shape makes sense, but will changing the general order of (conv_dim1, conv_dim2,conv_dim2) as (x,y,z) to say (z,x,y) affect the performance.
Does Conv3D worry about order of x-y-z dimension ?
I was training a U-net segmentation model and upon changing the order of axis I saw difference in performance. (x,y,z) order converges faster as compared to (y,x,z).
I just wanted to make sure what's the correct way..