Questions tagged [weights]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
2
votes
0answers
33 views

How efficient is SCAWI weight initialization method?

I'm currently in the middle of a project (for my thesis) constructing a deep neural network. Since I'm still in the research part, I'm trying to find various ways and techniques to initialize weights. ...
1
vote
1answer
38 views

Should the range and initial values of weights and biases be adjusted to fit input and output data?

As a routine (in typical everyday tasks) of a data scientist, should they usually decide about weights and biases range and initial values as a function of which data they are planning to insert as ...
2
votes
1answer
80 views

Which hyperparameters in neural network are accesible to users adjustment

I am new to Neural Networks and my questions are still very basic. I know that most of neural networks allow and even ask user to chose hyper-parameters like: amount of hidden layers amount of ...
4
votes
2answers
71 views

What is the goal of weight initialization in neural networks?

This is a simple question. I know the weights in a neural network can be initialized in many different ways like: random uniform distribution, normal distribution, and Xavier initialization. But what ...
1
vote
1answer
59 views

What are some examples of functions that machine learning models compute?

My simple understanding of AI is that it is based on a mathematical model of a problem. If I understood correctly, the model is a polynomial equation and its weights are calculated by training the ...
1
vote
1answer
46 views

When would bias regularisation and activation regularisation be necessary?

For Keras on TensorFlow, a layer class constructor comes with these: kernel_regularizer=... bias_regularizer=... ...
1
vote
0answers
33 views

LSTM - MAPE Loss Function gives Better Results when Data is De-Scaled before Loss Calculation

I am building an LSTM for predicting a price chart. MAPE resulted in the best loss function compared to ...
2
votes
1answer
54 views

Is better to spend parameters on weights or bias?

If a neural network has a limited number of neuron parameters to find, -let's say only 1000 parameters-, it is generally better to spend the parameters on weights or neuron bias? For example, if each ...
1
vote
2answers
72 views

Backpropagation of neural nets with shared weight

I am trying to understand the mathematics behind the forward and backward propagation of neural nets. To make myself more comfortable, I am testing myself with an arbitrarily chosen neural network. ...
1
vote
2answers
79 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 ...
1
vote
0answers
37 views

Is having binary randomized unchanging neural network weights a good idea?

I am creating a neural network to experiment with, and I was wondering: If I have weights randomly initialized to be either 1 or 0 for each neuron, and then I made it so that the weights cannot be ...
2
votes
1answer
30 views

How are weights for weighted x-entropy loss on imbalanced data calculated?

I am trying to build a classifier which should be trained with the cross entropy loss. The training data is highly class-imbalanced. To tackle this, I've gone through the advice of the tensorflow docs ...
1
vote
1answer
30 views

Why I have a different number of terms in word2vec and TFIDF? How I can fix it?

I need multiply the weigths of terms in TFIDF matrix by the word-embeddings of word2vec matrix but I can't do it because each matrix have a different number of terms. I am using the same corpus for ...
1
vote
0answers
29 views

Function to update weights in back-propagation

I am trying to wrap my head around how weights get updated during back propagation. I've been going through a school book and I have the following setup for an ANN with 1 hidden layer, a couple of ...
3
votes
1answer
37 views

How are newer weight initialization techniques better than zero or random initialization?

How do newer weight initialization techniques (He, Xavier, etc) improve results over zero or random initialization of weights in a neural network? Is there any mathematical evidence behind this?
1
vote
0answers
35 views

Interpretability of feature weights from Gaussian process classifier

Suppose I trained a Gaussian process classifier with a linear kernel (using GPML toolbox) and got some feature weights for each input feature. My question is then: Does it/When does it make sense ...
3
votes
1answer
132 views

How does the neural-network know how to tweak weights for a specific neuron?

I know backpropagation uses cost and gradient descent to tweak the weights to minimize the cost. But how does it know which weights to give more weight to in the first place? Is there something inside ...
1
vote
1answer
46 views

Classification with deeplearning : clean start vs continue training

I trained some weights to identify apples and oranges (using YOLOv3). If I want to be able to identify peaches, which approach is usually recommended: Start clean and train the 3 classes. Train ...
1
vote
1answer
43 views

How to plot Loss Landscape with more than 2 weights in the network

For a single neuron with 2 weights, I can plot the loss landscape and it looks like this (OR data, sigmoid activation, MAE loss): But, when the neuron accepts more inputs, which means more than 2 ...