Multilayer Perceptron questions
I am working on a school project, designing a neural network (mlp),
I made it with a GUI so it can be interactive.
For all my neurons I am using SUM as GIN function,
the user can select the activation function for each layer.
I have a theoretical question:
- do I set the threshold,g and a - parameters individually for each neuron or for the entire layer?
neural-network perceptron
asked Nov 10 at 11:06
random_numbers
237
add a comment |
I am working on a school project, designing a neural network (mlp),
I made it with a GUI so it can be interactive.
For all my neurons I am using SUM as GIN function,
the user can select the activation function for each layer.
I have a theoretical question:
- do I set the threshold,g and a - parameters individually for each neuron or for the entire layer?
neural-network perceptron
asked Nov 10 at 11:06
random_numbers
237
add a comment |
I am working on a school project, designing a neural network (mlp),
I made it with a GUI so it can be interactive.
For all my neurons I am using SUM as GIN function,
the user can select the activation function for each layer.
I have a theoretical question:
- do I set the threshold,g and a - parameters individually for each neuron or for the entire layer?
neural-network perceptron
asked Nov 10 at 11:06
random_numbers
237
I am working on a school project, designing a neural network (mlp),
I made it with a GUI so it can be interactive.
For all my neurons I am using SUM as GIN function,
the user can select the activation function for each layer.
I have a theoretical question:
- do I set the threshold,g and a - parameters individually for each neuron or for the entire layer?
neural-network perceptron
neural-network perceptron
asked Nov 10 at 11:06
random_numbers
237
asked Nov 10 at 11:06
random_numbers
237
edited Nov 10 at 11:12
asked Nov 10 at 11:06
random_numbers
237
asked Nov 10 at 11:06
random_numbers
237
asked Nov 10 at 11:06
random_numbers
237
237
add a comment |
add a comment |
1 Answer
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Looks nice ! You can have 3 hidden layers, but you'll see with experimenting, you will rarely need that many layers. What is your training pattern ?
Answer to your question depends on your training pattern and purpose of input neurons.. when e.g. some input neuron has a different type of value, you could use another threshold function or different settings for parameters in neurons connected to that input neuron.
But in general, it is better to feed neural network input into seperate perceptrons. So, the answer is: in theory, you could preset individual properties of neurons.. but in practice of back-propagation learning, it is not needed. There are no "individual properties" of neurons, the weight values that result of your training cycles will differ every time. All initial weights can be set on a small random value, transfer threshold and learning rate are to be set per layer.
answered Nov 10 at 11:40
Goodies
41127
We haven`t learned yet about learning patterns , ohh wait we did , supervised learning, this project is more of a GUI thing and so the teacher can see that we understand how a neural network should work, In a later project we will have to implement a network to solve a problem. And we will only be learning about feed forward, no back propagation.
– random_numbers
Nov 10 at 11:44
In that case I would keep it simple. User should specify the number of layers, the size of layers and a maximum random initial weight value for all weights. Use a single treshold function for all neurons.. There is one thing: you will need to set 3 learning rates in above case: when using hidden layers, user should be able to set different learning rates for each layer.
– Goodies
Nov 10 at 11:48
add a comment |
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1 Answer
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1 Answer
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oldest
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Looks nice ! You can have 3 hidden layers, but you'll see with experimenting, you will rarely need that many layers. What is your training pattern ?
Answer to your question depends on your training pattern and purpose of input neurons.. when e.g. some input neuron has a different type of value, you could use another threshold function or different settings for parameters in neurons connected to that input neuron.
But in general, it is better to feed neural network input into seperate perceptrons. So, the answer is: in theory, you could preset individual properties of neurons.. but in practice of back-propagation learning, it is not needed. There are no "individual properties" of neurons, the weight values that result of your training cycles will differ every time. All initial weights can be set on a small random value, transfer threshold and learning rate are to be set per layer.
answered Nov 10 at 11:40
Goodies
41127
We haven`t learned yet about learning patterns , ohh wait we did , supervised learning, this project is more of a GUI thing and so the teacher can see that we understand how a neural network should work, In a later project we will have to implement a network to solve a problem. And we will only be learning about feed forward, no back propagation.
– random_numbers
Nov 10 at 11:44
In that case I would keep it simple. User should specify the number of layers, the size of layers and a maximum random initial weight value for all weights. Use a single treshold function for all neurons.. There is one thing: you will need to set 3 learning rates in above case: when using hidden layers, user should be able to set different learning rates for each layer.
– Goodies
Nov 10 at 11:48
add a comment |
Looks nice ! You can have 3 hidden layers, but you'll see with experimenting, you will rarely need that many layers. What is your training pattern ?
Answer to your question depends on your training pattern and purpose of input neurons.. when e.g. some input neuron has a different type of value, you could use another threshold function or different settings for parameters in neurons connected to that input neuron.
But in general, it is better to feed neural network input into seperate perceptrons. So, the answer is: in theory, you could preset individual properties of neurons.. but in practice of back-propagation learning, it is not needed. There are no "individual properties" of neurons, the weight values that result of your training cycles will differ every time. All initial weights can be set on a small random value, transfer threshold and learning rate are to be set per layer.
answered Nov 10 at 11:40
Goodies
41127
We haven`t learned yet about learning patterns , ohh wait we did , supervised learning, this project is more of a GUI thing and so the teacher can see that we understand how a neural network should work, In a later project we will have to implement a network to solve a problem. And we will only be learning about feed forward, no back propagation.
– random_numbers
Nov 10 at 11:44
In that case I would keep it simple. User should specify the number of layers, the size of layers and a maximum random initial weight value for all weights. Use a single treshold function for all neurons.. There is one thing: you will need to set 3 learning rates in above case: when using hidden layers, user should be able to set different learning rates for each layer.
– Goodies
Nov 10 at 11:48
add a comment |
Looks nice ! You can have 3 hidden layers, but you'll see with experimenting, you will rarely need that many layers. What is your training pattern ?
Answer to your question depends on your training pattern and purpose of input neurons.. when e.g. some input neuron has a different type of value, you could use another threshold function or different settings for parameters in neurons connected to that input neuron.
But in general, it is better to feed neural network input into seperate perceptrons. So, the answer is: in theory, you could preset individual properties of neurons.. but in practice of back-propagation learning, it is not needed. There are no "individual properties" of neurons, the weight values that result of your training cycles will differ every time. All initial weights can be set on a small random value, transfer threshold and learning rate are to be set per layer.
answered Nov 10 at 11:40
Goodies
41127
Looks nice ! You can have 3 hidden layers, but you'll see with experimenting, you will rarely need that many layers. What is your training pattern ?
Answer to your question depends on your training pattern and purpose of input neurons.. when e.g. some input neuron has a different type of value, you could use another threshold function or different settings for parameters in neurons connected to that input neuron.
But in general, it is better to feed neural network input into seperate perceptrons. So, the answer is: in theory, you could preset individual properties of neurons.. but in practice of back-propagation learning, it is not needed. There are no "individual properties" of neurons, the weight values that result of your training cycles will differ every time. All initial weights can be set on a small random value, transfer threshold and learning rate are to be set per layer.
answered Nov 10 at 11:40
Goodies
41127
edited Nov 10 at 11:46
answered Nov 10 at 11:40
Goodies
41127
answered Nov 10 at 11:40
Goodies
41127
answered Nov 10 at 11:40
Goodies
41127
41127
We haven`t learned yet about learning patterns , ohh wait we did , supervised learning, this project is more of a GUI thing and so the teacher can see that we understand how a neural network should work, In a later project we will have to implement a network to solve a problem. And we will only be learning about feed forward, no back propagation.
– random_numbers
Nov 10 at 11:44
In that case I would keep it simple. User should specify the number of layers, the size of layers and a maximum random initial weight value for all weights. Use a single treshold function for all neurons.. There is one thing: you will need to set 3 learning rates in above case: when using hidden layers, user should be able to set different learning rates for each layer.
– Goodies
Nov 10 at 11:48
add a comment |
We haven`t learned yet about learning patterns , ohh wait we did , supervised learning, this project is more of a GUI thing and so the teacher can see that we understand how a neural network should work, In a later project we will have to implement a network to solve a problem. And we will only be learning about feed forward, no back propagation.
– random_numbers
Nov 10 at 11:44
In that case I would keep it simple. User should specify the number of layers, the size of layers and a maximum random initial weight value for all weights. Use a single treshold function for all neurons.. There is one thing: you will need to set 3 learning rates in above case: when using hidden layers, user should be able to set different learning rates for each layer.
– Goodies
Nov 10 at 11:48
We haven`t learned yet about learning patterns , ohh wait we did , supervised learning, this project is more of a GUI thing and so the teacher can see that we understand how a neural network should work, In a later project we will have to implement a network to solve a problem. And we will only be learning about feed forward, no back propagation.
– random_numbers
Nov 10 at 11:44
We haven`t learned yet about learning patterns , ohh wait we did , supervised learning, this project is more of a GUI thing and so the teacher can see that we understand how a neural network should work, In a later project we will have to implement a network to solve a problem. And we will only be learning about feed forward, no back propagation.
– random_numbers
Nov 10 at 11:44
In that case I would keep it simple. User should specify the number of layers, the size of layers and a maximum random initial weight value for all weights. Use a single treshold function for all neurons.. There is one thing: you will need to set 3 learning rates in above case: when using hidden layers, user should be able to set different learning rates for each layer.
– Goodies
Nov 10 at 11:48
In that case I would keep it simple. User should specify the number of layers, the size of layers and a maximum random initial weight value for all weights. Use a single treshold function for all neurons.. There is one thing: you will need to set 3 learning rates in above case: when using hidden layers, user should be able to set different learning rates for each layer.
– Goodies
Nov 10 at 11:48
add a comment |
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