Is it possible to have differential weights as per input importance for a Neural Network
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I have a Computed Tomography data set where central slices are more important as the scan angle approaches 90 degrees. The information in the initial and last slices(0 degrees and 180 degrees) may assigned lesser weight as they contain lateral information. Could anyone please suggest me a way to assign weights as per importance of the input? Shall I focus on any specific hyper-parameter while training?
tensorflow
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I have a Computed Tomography data set where central slices are more important as the scan angle approaches 90 degrees. The information in the initial and last slices(0 degrees and 180 degrees) may assigned lesser weight as they contain lateral information. Could anyone please suggest me a way to assign weights as per importance of the input? Shall I focus on any specific hyper-parameter while training?
tensorflow
asked yesterday
user68817user68817
61
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I have a Computed Tomography data set where central slices are more important as the scan angle approaches 90 degrees. The information in the initial and last slices(0 degrees and 180 degrees) may assigned lesser weight as they contain lateral information. Could anyone please suggest me a way to assign weights as per importance of the input? Shall I focus on any specific hyper-parameter while training?
tensorflow
asked yesterday
user68817user68817
61
New contributor
user68817 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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I have a Computed Tomography data set where central slices are more important as the scan angle approaches 90 degrees. The information in the initial and last slices(0 degrees and 180 degrees) may assigned lesser weight as they contain lateral information. Could anyone please suggest me a way to assign weights as per importance of the input? Shall I focus on any specific hyper-parameter while training?
tensorflow
tensorflow
asked yesterday
user68817user68817
61
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Check out our Code of Conduct.
asked yesterday
user68817user68817
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asked yesterday
user68817user68817
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asked yesterday
user68817user68817
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asked yesterday
user68817user68817
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Yes! It is totally possible, generally weights are never same, they differ for different inputs.This is the first reason why they are called weights, as they associate weight to every input.
Preparing neural network parameters (weights and bias) using TensorFlow Variables in python:(assuming you have 3 inputs and want to assign them with .3,.1 and .8 weight respectively.)
weights = tensorflow.Variable(initial_value=[[.3],[.1],[.8]],dtype=tensorflow.float32)
bias = tensorflow.Variable(initial_value=[[1]], dtype=tensorflow.float32)
Happy to answer.
answered yesterday
Ankit AgrawalAnkit Agrawal
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1 Answer
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active
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1 Answer
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$begingroup$
Yes! It is totally possible, generally weights are never same, they differ for different inputs.This is the first reason why they are called weights, as they associate weight to every input.
Preparing neural network parameters (weights and bias) using TensorFlow Variables in python:(assuming you have 3 inputs and want to assign them with .3,.1 and .8 weight respectively.)
weights = tensorflow.Variable(initial_value=[[.3],[.1],[.8]],dtype=tensorflow.float32)
bias = tensorflow.Variable(initial_value=[[1]], dtype=tensorflow.float32)
Happy to answer.
answered yesterday
Ankit AgrawalAnkit Agrawal
264
New contributor
Ankit Agrawal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
Yes! It is totally possible, generally weights are never same, they differ for different inputs.This is the first reason why they are called weights, as they associate weight to every input.
Preparing neural network parameters (weights and bias) using TensorFlow Variables in python:(assuming you have 3 inputs and want to assign them with .3,.1 and .8 weight respectively.)
weights = tensorflow.Variable(initial_value=[[.3],[.1],[.8]],dtype=tensorflow.float32)
bias = tensorflow.Variable(initial_value=[[1]], dtype=tensorflow.float32)
Happy to answer.
answered yesterday
Ankit AgrawalAnkit Agrawal
264
New contributor
Ankit Agrawal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Yes! It is totally possible, generally weights are never same, they differ for different inputs.This is the first reason why they are called weights, as they associate weight to every input.
Preparing neural network parameters (weights and bias) using TensorFlow Variables in python:(assuming you have 3 inputs and want to assign them with .3,.1 and .8 weight respectively.)
weights = tensorflow.Variable(initial_value=[[.3],[.1],[.8]],dtype=tensorflow.float32)
bias = tensorflow.Variable(initial_value=[[1]], dtype=tensorflow.float32)
Happy to answer.
answered yesterday
Ankit AgrawalAnkit Agrawal
264
New contributor
Ankit Agrawal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Yes! It is totally possible, generally weights are never same, they differ for different inputs.This is the first reason why they are called weights, as they associate weight to every input.
Preparing neural network parameters (weights and bias) using TensorFlow Variables in python:(assuming you have 3 inputs and want to assign them with .3,.1 and .8 weight respectively.)
weights = tensorflow.Variable(initial_value=[[.3],[.1],[.8]],dtype=tensorflow.float32)
bias = tensorflow.Variable(initial_value=[[1]], dtype=tensorflow.float32)
Happy to answer.
answered yesterday
Ankit AgrawalAnkit Agrawal
264
New contributor
Ankit Agrawal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered yesterday
Ankit AgrawalAnkit Agrawal
264
New contributor
Ankit Agrawal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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answered yesterday
Ankit AgrawalAnkit Agrawal
264
answered yesterday
Ankit AgrawalAnkit Agrawal
264
264
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