Hi everyone,
I’m currently working on my thesis and am trying to implement the conv2dtranspose
function from Keras in C. To do this, I need a deeper understanding of the underlying mathematical operations involved.
In my CNN architecture, I have a specific layer that performs a transformation from an 8x8x1024 input matrix to a 16x16x512 output matrix. I understand that the stride parameter set to 2 is responsible for the increased output height and width (16x16). However, I’m struggling to grasp how the number of filters (n_filters) influences this process.
In my current example, the layer utilizes 512 filters, resulting in a final channel depth of 512 in the output. I’m particularly interested in a detailed explanation of how each element in the resulting matrix is calculated, specifically how the filter count plays a role in this computation.
Any insights or explanations regarding the impact of filter count on the output in transposed convolution would be greatly appreciated. Thanks in advance for your help!
Hi @Nugg3t_BisCuiT ,
Welcome to TensorFlow Forum ,
Number of Filters (n_filters) and Its Role:

The number of filters (n_filters) in a convolutional layer determines the depth or number of feature maps in the output volume. Each filter essentially acts as a separate detector, learning to identify specific features in the input.

In your case, with n_filters=512, the output has a depth of 512. This means there are 512 independent feature maps, each capturing different aspects of the input.

Each filter produces one channel in the output.

The convolution transpose operation with stride 2 increases the spatial dimensions from 8x8 to 16x16.

Each element in the output matrix is calculated through the dot product of the filter with the corresponding region in the input matrix, followed by summing up these products.
Please Refer this guide for detail Explanantion .
Thank You !
when i started i took me a while to realise that convolutions always take the full depth of the input tensor.
so if you have a 3D tensor, like an image of (256,256,3), and a convolution with a window of 4 by 4, then the depth dimension is also 3, as it is in the input. The number of weights of that kernel then is 4x4x3 (plus 1 bias.)
The cubes that multiply (2 of 4x4x3) will yield 4x4x1. Here you can see that the image’s channels are merged.
The n of filters is the number of those 4x4x3 cubes.