Contents

# Two-Dimensional Convolution Backward Layer

The forward two-dimensional (2D) convolution layer applies a set of
nKernels
2D kernels
K
of size
m
3
x
m
4
to the input tensor
X
. The library supports four-dimensional input tensors
X
R
n
1
x
n
2
x
n
3
x
n
4
. Therefore, the following formula applies:
where
i
+
u
<
n
3
,
j
+
v
<
n
4
, and
r
is the kernel index.
For more details, see Forward 2D Convolution Layer.
The backward 2D convolution layer computes the derivatives of the objective function
E
:

## Problem Statement

Without loss of generality, let's assume that convolution kernels are applied to the last two dimensions.
Given:
• Four-dimensional tensor
G
R
n
1
x
nKernels
x
l
3
x
l
4
with the gradient from the preceding layer
• Four-dimensional tensor
K
R
nKernels
x
m
2
x
m
3
x
m
4
with kernel parameters/weights of kernels (convolutions)
For the above tensors:
• and
p
i
is the respective padding.
• nGroups
is defined as follows: let's assume that
n
2
is the group dimension in the input tensor for the forward 2D convolution layer. The output gradient tensor is split along this dimension into
nGroups
groups, and the input gradient tensor and weights tensor are split into
nGroups
groups along the
nKernels
dimension.
nKernels
and
n
2
must be multiples of
nGroups
.
The problem is to compute:
• Four-dimensional tensor
Z
R
n
1
x
n
2
x
n
3
x
n
4
such that:
• Values:
In the above formulas:
• s
3
and
s
4
are strides

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1

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Notice revision #20110804