convolution - Is there a correct order of "conv2d", "batchnorm2d ...
May 29, 2023 · After investigating the structure of the official UNet architecture as proposed in the official paper I noticed a recurrent pattern of Conv2d->BatchNorm2d->ReLU (->MaxPool2d) …
Confusion about conversion of RGB image to grayscale image …
Aug 2, 2021 · Note the groups parameter of Conv2d, which affects how the channels are convolved. The default is 1, which means: At groups=1, all inputs are convolved to all outputs. …
How is the convolution layer is usually implemented in practice?
Joking apart, in PyTorch Conv2d is a layer that applies another low level function, conv2d, written in c++. Luckily enough, the guys from PyTorch wrote the general idea of how convolution is …
Why is the convolution layer called Conv2D?
Aug 21, 2020 · A 2D convolution is a convolution where the kernel has the same depth as the input, so, in theory, you do not need to specify the depth of the kernel, if you know the depth of …
computer vision - Is there a best practice for creating multiple ...
Oct 4, 2022 · With all the work being done on larger and larger images, I'd like to ask if a best practice(s) has arisen for allowing multiple convolutional layers on small image inputs? For …
What does 'input planes' mean in the phrase 'input signal/image ...
Jul 22, 2021 · Yes, it is a bit misleading. What it really means is input channels, so it would be: nn.Conv2d: Applies a 2D convolution over an input signal composed of several input channels. …
filters - How to calculate the number of parameters of a …
Mar 16, 2020 · I was recently asked at an interview to calculate the number of parameters for a convolutional layer. I am deeply ashamed to admit I didn't know how to do that, even though …
neural networks - Convolution layer, with biases too? - Artificial ...
Aug 16, 2023 · For example the TensforFlow Keras Conv2D layer has bias optional, but enabled by default. Making the bias a learnable "kernel" that adds separately would not do anything …
In a CNN, does each new filter have different weights for each …
The Conv2d filter is defined as a 3D-represented tensor, but it's indeed a collection of num_input_channels kernels applied laterally. The different kernels' layers' values are distinct …
convolutional neural networks - Is there any gain by lazy ...
Jul 22, 2021 · The basic layers for performing convolution operations 1, 2, 3 in PyTorch are nn.Conv1d: Applies a 1D convolution over an input signal composed of several input planes. …