How to use CNN for making predictions on non-image data?
You can use CNN on any data, but it's recommended to use CNN only on data that have spatial features (It might still work on data that doesn't have spatial features, see DuttaA's comment …
When training a CNN, what are the hyperparameters to tune first?
Firstly when you say an object detection CNN, there are a huge number of model architectures available. Considering that you have narrowed down on your model architecture a CNN will …
neural networks - Are fully connected layers necessary in a CNN ...
Aug 6, 2019 · A convolutional neural network (CNN) that does not have fully connected layers is called a fully convolutional network (FCN). See this answer for more info. An example of an …
convolutional neural networks - How do multiple filters in a CNN …
Aug 1, 2024 · In a CNN, each filter produces one feature map regardless of the number of input channels. For your example: Single channel input : The input image has 1 channel of size $ …
How do I handle large images when training a CNN?
Aug 31, 2017 · Commonly researches scale the images to a resonable size. But if that's not an option for you, you'll need to restrict your CNN. In addition to downsampling in early layers, I …
How are the kernels initialized in a convolutional neural network?
Sep 29, 2021 · I am currently learning about CNNs. I am confused about how filters (aka kernels) are initialized. Suppose that we have a $3 \\times 3$ kernel. How are the values of this filter …
Is there any difference between ConvNet and CNN?
Feb 15, 2021 · I don't believe there is any particular reason to choose one over the other: ConvNet is slightly easier to say out loud and CNN is slightly shorter to write, but there is …
How do CNNs handle inputs of different sizes and shapes?
Aug 29, 2021 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for …
neural networks - How to compute the number of weights of a …
Dec 14, 2019 · Calculating the number of parameters in a CNN is very straightforward. A CNN is composed of different filters , which are essentially 3d tensors. CNN weights are shared , …
neural networks - Which layer in a CNN consumes more training …
In general in any CNN the maximum time of training goes in the Back-Propagation of errors in the Fully Connected Layer (depends on the image size). Also the maximum memory is also …