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- convolution - Is there a correct order of conv2d, batchnorm2d . . .
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)->Conv2d->BatchNorm2d->ReLU (->MaxPool2d) for the encoder part but I have also came across other implementations of a custom UNet where this order is different like Conv2d
- Why is the convolution layer called Conv2D?
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 the input I don't know which library you are referring to (although you tagged your post with TensorFlow and Keras), but, in TensorFlow, you only need to specify the width and height of the kernel in the Conv2D class
- How do I optimize the number of filters in a convolution layer?
If we have a dataset of 32x32 images, we could start with a Conv2D layer, filter of 3x3 and stride of 1x1 Therefore the maximum times this filter would be able to fit into the 32 x 32 images would be 30 times e g newImageX * newImageY newImageX = (imageX – filterX + 1) newImageY = (imageY – filterY + 1)
- neural networks - How to use a conv2d layer after a flatten . . .
So, I was wondering if I used a pretrained model (EfficientNet for example) if I want to change the _fc attribute and use conv2d in it, how can I recover a 2D structure? Because the pretrained model flattens it just before _fc for example, the pretrained model outputs a flattened feature vector of 1280 elements what I did is the following:
- In a CNN, does each new filter have different weights for each input . . .
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 and learned distinctly too
- convolutional neural networks - Is there any gain by lazy . . .
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 nn Conv2d: Applies a 2D convolution over an input signal composed of several input planes nn Conv3d: Applies a 3D convolution over an input signal composed of several input planes Along with them, there are lazy versions 1, 2
- convolutional neural networks - How can I have the same input and . . .
Conv2D using 3x3 kernels will also lose 2pixels, although I'm puzzled that it doesn't seem to happen in the downsampling steps Intuitively, padding the original images with enough border pixels to compensate for the pixel loss due to the various layers would be the simplest solution
- How can an MLP be implemented with convolutional layers?
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