PerfectCustomPapers | Output dimensionality after applying the Convolution

Output dimensionality after applying the Convolution

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?

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

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.

This question has been answered.

Get Answer

No More Worries!

Paper Formatting

No Lateness!

Our Guarantees

Output dimensionality after applying the Convolution

Sample Answer

Full Answer Section

This question has been answered.

Satisfaction Guaranteed

Why Choose Us