Given a Convolution Layer with 8 filters, a filter size of 6, a stride of 2, and a padding of 1. For an input feature map of 32 x 32 x 32, what is the output dimensionality after applying the Convolution Layer to the input?
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Output dimensionality after applying the Convolution
Full Answer Section
- Output Size Calculation:
We can use the following formula to calculate the output size after applying the convolution with stride:
Output size = (Input size - Filter size + 2 * Padding) / Stride + 1
Applying the formula:
Width (or Height) of output = (34 - 6 + 2 * 1) / 2 + 1 = 15 + 1 = 16
- Depth of Output:
The depth of the output remains the same as the number of filters used in the convolution, which is 8.
Therefore, the output dimensionality of the convolution layer is 16 x 16 x 8.
Sample Answer
Here's how to calculate the output dimensionality of the convolution layer:
- Effective Input Size:
Since padding is 1, for each dimension of the input (width and height), we add (2 * padding) to account for the padding added on either side. So, the effective input size becomes:
Width = Height = 32 + (2 * 1) = 34
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