2nd_Convolution Layer

Mathematically, let's say you have:
- Input image or feature map from the previous layer: I (with dimensions Height x Width x Number of Channels).
- Filters: F (with dimensions Filter Height x Filter Width x Number of Input Channels x Number of Output Channels).

For each position (i, j) in the output feature map (let's call it O) of the second convolutional layer:
- Take a receptive field from the previous layer (I) that corresponds to the size of the filter. This receptive field is centered around the position (i, j).
- Perform an element-wise multiplication between the filter weights (F) and the values in the receptive field (I) at that position.
- Sum up the results of the element-wise multiplication to get a single value at position (i, j) in the output feature map.

Mathematically, this can be represented as:
O(i, j, k) = Σ Σ Σ Σ ( I(a, b, c) * F(p, q, c, k) )

Where:
O(i, j, k) is the value in the output feature map at position (i, j) for the k-th filter.
I(a, b, c) is the value in the input feature map at position (a, b) in channel c.
F(p, q, c, k) is the weight of the filter at position (p, q) for input channel c and output channel k.