Can neural network weights be greater than 1?

Can neural network weights be greater than 1?

Weights can take those values. Especially when you’re propagating a large number of iterations; the connections that need to be ‘heavy’, get ‘heavier’. There are plenty examples showing neural networks with weights larger than 1.

Can neural network have more than one output?

Neural network models can be configured for multi-output regression tasks.

Can the sigmoid function be greater than 1?

Sigmoid functions most often show a return value (y axis) in the range 0 to 1. Another commonly used range is from −1 to 1. A wide variety of sigmoid functions including the logistic and hyperbolic tangent functions have been used as the activation function of artificial neurons.

What happens if you initialize the weights of a neural network to zero?

Initializing all the weights with zeros leads the neurons to learn the same features during training. Thus, both neurons will evolve symmetrically throughout training, effectively preventing different neurons from learning different things.

What is multi output regression?

Multioutput regression are regression problems that involve predicting two or more numerical values given an input example. An example might be to predict a coordinate given an input, e.g. predicting x and y values. The problem of multioutput regression in machine learning.

What is multi output classification?

3. Multiclass-multioutput classification. Multiclass-multioutput classification (also known as multitask classification) is a classification task which labels each sample with a set of non-binary properties. Both the number of properties and the number of classes per property is greater than 2.

What is the output of sigmoid function for an input with dynamic range 0 1 ]?

Sigmoid: It is also called as a Binary classifier or Logistic Activation function because function always pick value either 0(False) or 1 (True). The sigmoid function produces similar results to step function in that the output is between 0 and 1.

Is Softmax a sigmoid?

Softmax is used for multi-classification in the Logistic Regression model, whereas Sigmoid is used for binary classification in the Logistic Regression model. This is how the Softmax function looks like this: This is similar to the Sigmoid function. This is main reason why the Softmax is cool.

How to make a neural network output more than 1?

If you want the output greater than 1, you can set the activation function to give 0 till some threshold and after that just output the value you desire or just avoid the activation function. I am not really sure why one would want the network to output large values.

Why are my output neurons only getting values between 0-1?

Seems you have used either pure linear or sigmoid activation function in the output layer neurons, which is why you are getting values between 0 and 1.

How to limit the range of values a neural network can predict?

If you want to set no limit on the values your network can predict, then you can run the network’s output through a function that maps the [-1,1] range to all real numbers…like arctanh (x)! As long as you do this during training your network will adjust its weights to accommodate this.

Can a neural network predict the price of a house?

Case in point, Adam Geitgey gives as an example usage, a house price prediction system where given a data set containing No. of bedrooms, Sq. feet, Neighborhood and Sale price you can train a neural network to be able to predict the price of a house. However he stops short of actually implementing a possible solution in code.