Questions tagged [hidden-layers]
For questions about functioning, applications, structure and improvement of performance of hidden layers in a Neural Network.
33
questions
4
votes
1answer
58 views
Is it expected that adding an additional hidden layer to my 3-layer ANN reduces accuracy significantly?
I've been using several resources to implement my own artificial neural network package in C++.
Among some of the resources I've been using are
https://www.anotsorandomwalk.com/backpropagation-...
2
votes
1answer
105 views
Do all neurons in a layer have the same activation function?
I'm new to machine learning (so excuse my nomenclature), and not being a python developer, I decided to jump in at the deep (no pun intended) end writing my own framework in C++.
In my current design ...
3
votes
3answers
104 views
Convolutional Neural Network: does each filter in each convolution layer create a new image?
Say I have a CNN with this structure:
input = 1 image (say, 30x30 RGB pixels)
first convolution layer = 10 5x5 convolution filters
second convolution layer = 5 3x3 convolution filters
one dense layer ...
2
votes
2answers
66 views
What could be the problem when a neural network with four hidden layers with the sigmoid activation function is not learning?
I have a large set of data points describing mappings of binary vectors to real-valued outputs. I am using TensorFlow, and would like to train a model to predict these relationships. I used four ...
2
votes
2answers
59 views
What is hidden state exactly in LSTM and RNN?
I'm working on research rn using LSTM as an encoder decoder in hopes to make inferences. The reason we are using encoder decoder for this is because there is hopes that the hidden state given by the ...
5
votes
1answer
778 views
Maximum nodes in hidden layer
I have an idea to find the optimal number of hidden neurons required in a neural network but I'm not sure how accurate it is.
Assuming that it has only one hidden layer, it is a classification ...
2
votes
2answers
103 views
Why is there a sigmoid function in the hidden layer of a neural network? [duplicate]
I got this slide from CMU's lecture notes. The $x_i$s on the right are inputs and the $w_i$s are weights that get multiplied together then summed up at each hidden layer node. So I'm assuming this is ...
0
votes
2answers
55 views
How do we choose the activation function for each hidden node? [duplicate]
I am new to neural networks. I would like to use them as a fitting or forecasting method.
A simple NN model that does not contain hidden layers, that is, the input nodes are directly connected to the ...
0
votes
1answer
78 views
Neural Network to estimate distance
I built a three layer neural network (first is 1D Convolutional and the remaining two are Linear). It takes an input of 5 angles in radians, and outputs two numbers from 0 to 1, which are respectively ...
1
vote
2answers
52 views
Why is embedding important in NLP, and how does autoencoder work?
People say embedding is necessary in NLP because if using just the word indices, the efficiency is not high as similar words are supposed to be related to each other. However, I still don't truly get ...
0
votes
1answer
42 views
How many hidden layers are needed for this training data set
I'm trying to separate classes in 3D space, the data are as in the sketch below:
There are 3 classes: 0,1,2; and with the look into the sketch, it seems that I need 3 planes to separate the classes, ...
1
vote
0answers
28 views
How does a single neuron in hidden layer affect training accuracy
I'm currently a student learning about AI Networks. I've came across a statement in one of my Professor's books that a FFBP (Feed-Forward Back-Propagation) Neural Network with a single hidden layer ...
2
votes
1answer
63 views
Does the encoding of a restricted Boltzmann machine improve with more layers?
I'm using a Restricted Boltzmann Machine (RBM) as an autoencoder. For now, I use a simple architecture of two layers, the input (~100 nodes) and the output (3 nodes) layers. I'm thinking to add more ...
2
votes
0answers
20 views
Multi-field text input for LSTM
I'm using LSTM to categorize medium-sized pieces of text. Each item to be categorized has several free-form text fields, in addition to several categorical fields. What is the best approach to using ...
1
vote
1answer
56 views
How to chose dense layer size?
I am fine-tuning a VGG16 model on 20 classes with 500k images I was wondering how do you chose the size of the dense layer (the one before the prediction layer which has a size 20). I would prefer not ...
2
votes
1answer
38 views
Representation of hidden layer in neural network diagrams
Why is the representation of hidden layers made in terms of circles?
What is the difference between a circle and a box in diagrams of neural networks?
6
votes
3answers
105 views
To what does the number of hidden layers in a neural network correspond?
In a neural network, the number of neurons in the hidden layer corresponds to the complexity of the model generated to map the inputs to output(s). More neurons creates a more complex function (and ...
1
vote
1answer
203 views
Hidden state of the GRU
I'm having a hard time understanding how does the size of the hidden state affects GRU. For example in a concrete example lets say I want to lean a GRU to count. I'm gonna feed it fx 3 timestamps the ...
5
votes
1answer
101 views
How does a single hidden layer neuron affect output?
I'm learning about multilayer perceptrons, and I have a quick theory question in regards to hidden layer neurons.
I know we can use two hidden layers to solve a non-linearly seperable problem by ...
-1
votes
1answer
74 views
What ANN layer widths support the learning of digit recognition?
I have created an ANN in Python (without libs). On beginning, it had been learned in target of solve linear problems like distinguishing between negative and positive numbers, where the layer widths ...
4
votes
2answers
441 views
In feed forward neural networks, why don't we connect the output of each layer to all proceeding layers?
For example, hidden layer 1's outputs would be fed to the perceptrons in layer 2, 3, 4, ... etc.
Beyond computational power considerations, wouldn't this be better than only connecting layers 1 and 2,...
5
votes
1answer
1k views
Why number of hidden units in a layer are suggested to be in powers of 2?
It is suggested that the number of hidden units in a layer should be in powers of 2 because it helps converge faster. Is it a fact and if it is, how this helps the NN learn faster. Does it have to do ...
2
votes
0answers
111 views
Data prepared to linear regression. Can I use it with backpropagation?
I'm studying a Master's Degree in Artificial Intelligence and I need to learn how to use the Java Neural Network Simulator, JavaNNS, program.
In one practice I have to build a neural network to use ...
0
votes
2answers
75 views
Is there any common principle/ build algorithm for deep NN structure?
I started to study NN recently.
So I understand principles with which I should define input and output layers.
But I can't find any guide/directions how to build hidden layers: how many layers do I ...
3
votes
2answers
1k views
What type of neural network would be most feasible for playing a realtime game?
For implementing a neural network algorithm that can play air hockey, I had two ideas for input, and I'm trying to figure out which design would be most viable.
The output must be two analog values ...
3
votes
2answers
86 views
Different Suggestion for Estimating Number of layers in Neural Network
In this note Justin Domke says that
In practice, neural networks seem to usually find a reasonable solution when the number of layers is not too large, but find poor solutions when using more than, ...
1
vote
1answer
61 views
ANN Shape for Sale Prediction
The inputs (features) and expected output for my ANN are these:
Input 1: Product id (number, cast to double)
Input 2: Year in the past (1900..2017, cast to double)
Input 3: Month of year (1..12, cast ...
6
votes
1answer
319 views
How many nodes/hidden layers are required to solve a classification problem where the boundary is a sinusoidal function?
A single neuron is capable of forming a decision boundary between linearly seperable data. Is there any intuition as to how many, and in what configuration, would be necessary to correctly approximate ...
4
votes
1answer
966 views
Recurrent neural networks with hidden layer
In the lecture, there was a statement:
"Recurrent neural networks with multiple hidden layers are just a
special case that has some of the hidden to hidden connections
missing."
I understand ...
13
votes
2answers
233 views
Optimal number of layers in a neural network?
How to decide the optimum number of layers to be created while implementing a Neural Network (Feedforward, back propagation or RNN)?
8
votes
2answers
692 views
What's the difference between hyperbolic tangent and sigmoid neurons?
Two common activation functions used in deep learning are the hyperbolic tangent function and the sigmoid activation function. I understand that the hyperbolic tangent is just a rescaling and ...
10
votes
1answer
174 views
What kind of problems require more than 2 hidden layers?
I've read that the most of the problems can be solved with 1-2 hidden layers.
How do you know you need more than 2? For what kind of problems you would need them (give me an example)?
5
votes
2answers
2k views
What is the purpose of the hidden layers?
Why would anybody want to use "hidden layers"? How do they enhance the learning ability of the network in comparison to the network which doesn't have them (linear models)?
