Consider a CNN that is made up of just one convolution layer consisting of just one...
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Consider a CNN that is made up of just one convolution layer consisting of just one filter F of shape 2 x 2 followed by a max-pooling layer of shape 2 × 2. The input image I is of shape 3 x 3. The output of the CNN is calculated as output= Pool (ReLU (Conv(I))) where ReLU is the rectified linear activation function given by: ReLU (x) = max(0, x) Also assume that the stride for the convolution and pool layers is 1 and no padding is applied. For the following values of the image I and weights of the filter F, compute the value of the output of the CNN: I = F || 504 380 1 02 -2 0 0] 3 Consider a CNN that is made up of just one convolution layer consisting of just one filter F of shape 2 x 2 followed by a max-pooling layer of shape 2 × 2. The input image I is of shape 3 x 3. The output of the CNN is calculated as output= Pool (ReLU (Conv(I))) where ReLU is the rectified linear activation function given by: ReLU (x) = max(0, x) Also assume that the stride for the convolution and pool layers is 1 and no padding is applied. For the following values of the image I and weights of the filter F, compute the value of the output of the CNN: I = F || 504 380 1 02 -2 0 0] 3
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