Question

Compute the forward pass of the following part of a convolutional net: I -> Conv -> J -> Max pool -> K -> ReLU -> O ^ | W If I is the following image patch 1 3 2 4 6 4 4 8 3 1 0 2 2 1 4 3 9 1 4 7 2 3 9 2 and W is the following 3-by-3 filter matrix -1 0 1 -1 0 1 -1 0 1 Compute J, K and O. You are not doing any zero padding while doing the convolution. Assume stride size 1 for the convolution. Assume a 2-by-2 max pooling with stride 2. Write J, K and O below. Now Assume that ideal output IO = [7,10]. Also assume the loss as 0.5 times the square of Euclidean distance between IO and O. Compute back-propagation for O, K, J and W.

          Compute the forward pass of the following part of a convolutional net:
I -> Conv -> J -> Max pool -> K -> ReLU -> O
^
|
W
If I is the following image patch
1 3 2 4 6 4
4 8 3 1 0 2
2 1 4 3 9 1
4 7 2 3 9 2
and W is the following 3-by-3 filter matrix
-1 0 1
-1 0 1
-1 0 1
Compute J, K and O. You are not doing any zero padding while doing the convolution. Assume
stride size 1 for the convolution. Assume a 2-by-2 max pooling with stride 2. Write J, K and O
below.
Now Assume that ideal output IO = [7,10]. Also assume the loss as 0.5 times the square of
Euclidean distance between IO and O. Compute back-propagation for O, K, J and W.

Compute the forward pass of the following part of a convolutional net:
I -> Conv -> J -> Max pool -> K -> ReLU -> O
^
|
W
If I is the following image patch
1 3 2 4 6 4
4 8 3 1 0 2
2 1 4 3 9 1
4 7 2 3 9 2
and W is the following 3-by-3 filter matrix
-1 0 1
-1 0 1
-1 0 1
Compute J, K and O. You are not doing any zero padding while doing the convolution. Assume
stride size 1 for the convolution. Assume a 2-by-2 max pooling with stride 2. Write J, K and O
below.
Now Assume that ideal output IO = [7,10]. Also assume the loss as 0.5 times the square of
Euclidean distance between IO and O. Compute back-propagation for O, K, J and W.

Added by Francisco Jose S.

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