Remove the channel axis while preserving the batch

Implement drop_channel(x) for a tensor with shape (batch, 1, features). Return the same values with shape (batch, features). Preserve both the batch and feature axes, including when either has length one.

Why the starter fails

Calling squeeze() without a dimension removes every axis of length one:

input shape    squeeze() result    required result
(4, 1, 3)      (4, 3)             (4, 3)
(1, 1, 3)      (3,)               (1, 3)
(2, 1, 1)      (2,)               (2, 1)
(1, 1, 1)      ()                 (1, 1)

The starter works only when both batch and features are greater than one. A small final batch can expose the missing batch axis, but a single feature can cause the same kind of mistake.

Remove only the channel axis

squeeze(dim=1) removes axis 1 when its length is one and leaves every other axis in place. Here, axis 1 is the channel axis you want to remove.

Use an explicit dimension when you need to preserve other length-one axes. The argument-free form is useful when removing all such axes is intentional.

Give each tensor axis a meaning

A tensor's shape tells you how many entries each axis has. It does not tell you what those entries represent. For example, (batch, features) and (features, batch) use different axis orders. Name the axes before choosing an operation.

A reduction combines entries along an axis, for example by summing or averaging. Averaging the features of a (B, D) tensor gives one number per example, with shape (B,). Keeping the reduced axis gives shape (B, 1) instead.

For [[2, 4, 6], [1, 3, 5]], the row means with the axis kept are [[4], [3]]. Subtracting them from the input gives [[-2, 0, 2], [-2, 0, 2]]: each row has been centered using its own mean.

Reshape and transpose do different jobs

A reshape regroups elements using the operation's traversal order. A transpose changes the axis order. For the usual row-major reshape, [[1, 2, 3], [4, 5, 6]] reshaped to (3, 2) becomes [[1, 2], [3, 4], [5, 6]]. Transposing the original instead gives [[1, 4], [2, 5], [3, 6]].

Both results have shape (3, 2), but their entries mean different things. Check values as well as shapes. Unequal axis sizes make many axis mistakes easier to spot than square examples do.

Concatenate along an axis or stack on a new one

Concatenation extends an existing axis. Stacking creates a new axis. For two arrays of shape (2, 3), concatenating on axis zero gives (4, 3). Stacking on a new leading axis gives (2, 2, 3).