-- Nov 15 In-Class Exercise

For Image 1, we can use the feature map @BT@[[2,0,1,0],[0,2,0,1],[1,0,2,0],[0,1,0,2]]@BT@ For Image 2, we can use the feature map @BT@[[0,1,1,0],[1,1,2,1],[1,2,1,1],[0,1,1,0]]@BT@ Number of neurons = 16

-- Nov 15 In-Class Exercise

Image 1's resultant map <math>\begin{bmatrix} 2 & 0 & 1 & 0 \\ 0 & 2 & 0 & 1 \\ 1 & 0 & 2 & 0 \\ 0 & 1 & 0 & 2 \end{bmatrix} Image 2's resultant map \begin{bmatrix} 0 & 1 & 1 & 0 \\ 1 & 1 & 2 & 1 \\ 1 & 2 & 1 & 1 \\ 0 & 1 & 1 & 0 \end{bmatrix}</math> Number of neurons in the feature map = 4*4 = 16

-- Nov 15 In-Class Exercise

Image 1: Feature Map 2 0 1 0 0 2 0 1 1 0 2 0 0 1 0 2 Image 2: Feature Map 0 1 1 0 1 1 2 1 1 2 1 1 0 1 1 0 No. of Neurons = 16

-- Nov 15 In-Class Exercise

Ishan Shinde Feature Map Image 1 would be: 2 0 1 0 0 2 0 1 1 0 2 0 0 1 0 2 Feature Map Image 2 would be: 0 1 1 0 1 1 2 1 1 2 1 1 0 1 1 0 Total No. of Neurons = 4*4=16

-- Nov 15 In-Class Exercise

((resource:IMG_2472.jpg|Resource Description for IMG_2472.jpg))

-- Nov 15 In-Class Exercise

1st image (X) Feature map : 2 0 1 0 0 2 0 1 1 0 2 0 0 1 0 2 2nd Image Feature Map (+): 0 1 1 0 1 1 2 1 1 2 1 1 0 1 1 0 4x4 = 16 neurons