Questions tagged [hidden-layers]
For questions about functioning, applications, structure and improvement of performance of hidden layers in a Neural Network.
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questions
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Why does Batch Normalization work?
Adding BatchNorm layers improves training time and makes the whole deep
model more stable. That's an experimental fact that is widely used in machine
learning practice.
My question is - why does it ...
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0answers
16 views
PyTorch: How to deal with hidden states of an LSTM?
I have a time series in which each date is correlated with the preview one, and base on that I am trying to predict action 1 and action 2. But the problem is that I am not sure how to deal with the <...
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1answer
51 views
What's the purpose of layers without biases?
I noticed that the TensorFlow library includes a use_bias parameter for the Dense layer, which is set to ...
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1answer
41 views
If neurons performed the operation of an entire layer, would that make the neural network more effective?
(I have a very primitive understanding of neural networks, so please forgive the lack of technicality here.)
I am used to seeing a neuron in a neural network as something that-
Takes the inputs and ...
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1answer
234 views
Can the hidden layer prior to the ouput layer have less hidden units than the output layer?
I attended an introductory class about neural network and I had a question regarding how to choose the number of hidden units per hidden layer.
I remember that the Professor saying that there is no ...
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1answer
93 views
When are multiple hidden layers necessary?
I know that my question probably seems like being asked many times, but Ill try
to be more speciffic:
Limitations to my question:
I am NOT asking about convolutional neural networks, so please, try ...
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1answer
59 views
Why does the output shape of a Dense layer contain a batch size?
I understand that the batch size is the number of examples you pass into the neural network (NN). If the batch size is 10, it means you feed the NN 10 examples at once.
Assuming I have an NN with a ...
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1answer
80 views
How to determine the number of hidden layers and units of a deep auto-encoder?
I am using a deep autoencoder for my problem. However, the way I choose the number of hidden layers and hidden units in a hidden layer is still based on my feeling.
The size of the model that ...
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1answer
64 views
What is the exact structure within the nodes of a hidden layer? [closed]
I've been reading on neural networks, but for me, seems like the easiest way for me to learn is seeing some code. I am curious about what is the exact structure within a node of a hidden layer and ...
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1answer
91 views
Number of LSTM layers needed to learn a certain number of sequences
Theoretically, number of units for a LSTM layer is the number of hidden states or the max length of sequences as per my practice.
For example, in Keras:
...
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1answer
33 views
How to work on different models for a given problem?
I am working on the MNIST data on my own. The idea is to use different values for the number of hidden layers, number of nodes in a given layer, etc. How do you organize these things while you are ...
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2answers
101 views
How are non-linear surfaces formed in the training of a neural network?
Desperate trying to understand something for couple of weeks. All those questions are actually one big question.Please help me. Time-codes and screens in my question refer to this great(IMHO) 3d ...
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1answer
78 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-...
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1answer
1k 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 ...
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3answers
1k 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 ...
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2answers
84 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 ...
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2answers
4k views
What exactly is a hidden state in an LSTM and RNN?
I'm working on a project, where we use an encoder-decoder architecture. We decided to use an LSTM for both the encoder and decoder due to its hidden states. In my specific case, the hidden state of ...
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1answer
1k views
Is this idea to calculate the required number of hidden neurons for a single hidden layer neural network correct?
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 1 hidden layer, it is a classification problem ...
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2answers
637 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 ...
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2answers
276 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 ...
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2answers
118 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 ...
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1answer
62 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, ...
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1answer
46 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 ...
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1answer
85 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 ...
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0answers
39 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 ...
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2answers
2k 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 ...
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1answer
64 views
Why are the nodes (or neurons) in neural networks depicted as circles?
Why are the nodes (or neurons) in neural networks depicted as circles?
What is the difference between a circle and a box in diagrams of neural networks?
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3answers
175 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 ...
2
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1answer
513 views
How do I choose the size of the hidden state of a GRU?
I'm trying to understand how the size of the hidden state affects the GRU.
For example, suppose I want to make a GRU count. I'm gonna feed it with three numbers, and I expect it to predict the ...
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1answer
137 views
How does a single hidden layer affect output? [duplicate]
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 separable problem by ...
7
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1answer
593 views
Why aren't there neural networks that connect the output of each layer to all next layers?
Why aren't there neural networks that connect the output of each layer to all next layers?
For example, the output of layer 1 would be fed to the input of layers 2, 3, 4, etc. Beyond computational ...
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2answers
3k views
Why should the number of neurons in a hidden layer be a power of 2?
I have read somewhere on the web (I lost the reference) that the number of units (or neurons) in a hidden layer should be a power of 2 because it helps the learning algorithm to converge faster.
Is ...
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0answers
140 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 ...
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1answer
97 views
Are there some guidelines for designing the architecture of neural networks? [duplicate]
I started to study neural networks recently. I understand how I should define the input and output layers. But I can't find any guidelines on how to build hidden layers. More concretely, for each ...
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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 ...
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3answers
103 views
Are these statements about the performance of neural networks as a function of the number of hidden layers contradictory?
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, ...
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1answer
78 views
How many layers and neurons on the layers does a neural network need for for sales 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
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1answer
332 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 ...
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1answer
1k views
How can I understand this statement about RNNs and hidden layers?
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 ...
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2answers
286 views
How do I decide the optimal number of layers for a neural network?
How do I decide the optimal number of layers for a neural network (feedforward or recurrent)?
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2answers
2k 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 ...
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1answer
216 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)?
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4answers
3k 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)?
