Linked Questions

36
votes
6answers
8k views

How to set the number of neurons and layers in neural networks

I am a beginner to neural networks and have had trouble grasping two concepts: How does one decide the number of middle layers a given neural network have? 1 vs. 10 or whatever. How does one decide ...
15
votes
3answers
12k views

Is there a person class in ImageNet? Are there any classes related to humans?

If I look at one of the many sources for the Imagenet classes on the Internet I cannot find a single class related to human beings (and no, harvestman is not someone who harvests, but it's what I knew ...
5
votes
3answers
4k views

Neural Network Performs Bad On MNIST

I've been struggling with Neural Networks for a while now. I get the math behind backpropagation. Still as reference I'm using the formulas from here. The Network learns XOR: Prediction After ...
6
votes
3answers
3k views

Are CNNs insensitive to rotations and shifts in images?

Can CNNs predict well if they are trained on canonical-like images but tested on a version of images that are little bit shifted? I tried it using ...
3
votes
1answer
8k views

what is filter and kernel_size? [closed]

For below line of code model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu', input_shape = (28,28,1))) Here, ...
0
votes
4answers
652 views

CNN'S are what?

I have a very fundamental question on what CNN'S actually are. I understand fully the training process as to take a bunch of images, start with random filters, convolve, activate, calculate loss, back ...
1
vote
2answers
3k views
2
votes
1answer
2k views

Choosing layer hyper-parameters of a CNN

Context: I'm building a CNN on MATLAB to classify wallpaper groups. I'm using the following network type. CONV -> ReLU -> POOL -> CONV -> ReLU -> POOL -> FC -> ...
7
votes
2answers
158 views

What's the point with neural networks if you can only predict linear test data?

So, I have tried all the different activation functions listed on https://keras.io/api/layers/activations/. I can indeed approximate any nonlinear function in the training range perfectly well - but ...
2
votes
3answers
332 views

How each layer of a neural net is responsible for one feature

Through my study of neural networks, I came across the idea that each layer of a neural network is responsible for recognizing one feature of the input data. For example, if we build a neural network ...
2
votes
2answers
386 views

Should the different layers of deep learning models have same size or they should be changed based on a rule

I see a lot of people varying the width of each layer in a deep neural network. ie. Input->50->100->150->Output. I'm curious what, if any, are the advantages of this structure over static layer widths ...
2
votes
1answer
218 views

Is color information only extracted in the first input layer of a convolutional neural network?

In a convolutional neural network (CNN), since the RGB values get multiplied in the first convolutional layer, does this mean that color is essentially only extracted in the very first layer? ...
1
vote
1answer
80 views

Handle Unbalanced data [closed]

I have a data-set with 2 target classes. In training dataset, the ratio of the 2 classes are 1:93 With my neural network, the current accuracy is 63%. I tried undersampling, oversampling, equal ...