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Use of MLP with one hidden layer and direct weights from input to output units

Input for individual perceptron in input layer in MLPNeural Network accuracy and loss guarantees?How to implement patternet in python as it is in matlab?Error in Neural NetworkAdvantages of Recurrent Neural Networks over basic Artificial Neural Networksinput and output to fully connected layerAdding the input layer - units with a decimalTraining Accuracy stuck in Kerashidden layer weights calculationOne Hot Encoding of Age

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One of the questions I saw online while reading about MLPs was - "Consider an MLP architecture with one hidden layer where there are also direct weights from the inputs directly to the output units. Explain when such a structure would be helpful and how it can be trained."

After some thinking, I felt that the use of such a configuration would be when the output must depend more on the current input than the history. However, I am not sure if my answer is right or how training a model with such a configuration can be done. Any help on this would be appreciated. Thanks!

neural-network perceptron mlp

share|improve this question

asked Apr 7 at 5:13

my_cse labmy_cse lab

11

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add a comment | 

0

0

$begingroup$

One of the questions I saw online while reading about MLPs was - "Consider an MLP architecture with one hidden layer where there are also direct weights from the inputs directly to the output units. Explain when such a structure would be helpful and how it can be trained."

After some thinking, I felt that the use of such a configuration would be when the output must depend more on the current input than the history. However, I am not sure if my answer is right or how training a model with such a configuration can be done. Any help on this would be appreciated. Thanks!

neural-network perceptron mlp

share|improve this question

asked Apr 7 at 5:13

my_cse labmy_cse lab

11

$endgroup$

add a comment | 

$begingroup$

One of the questions I saw online while reading about MLPs was - "Consider an MLP architecture with one hidden layer where there are also direct weights from the inputs directly to the output units. Explain when such a structure would be helpful and how it can be trained."

After some thinking, I felt that the use of such a configuration would be when the output must depend more on the current input than the history. However, I am not sure if my answer is right or how training a model with such a configuration can be done. Any help on this would be appreciated. Thanks!

neural-network perceptron mlp

share|improve this question

asked Apr 7 at 5:13

my_cse labmy_cse lab

11

$endgroup$

share|improve this question

asked Apr 7 at 5:13

my_cse labmy_cse lab

11

share|improve this question

asked Apr 7 at 5:13

my_cse labmy_cse lab

11

asked Apr 7 at 5:13

my_cse labmy_cse lab

11

asked Apr 7 at 5:13

my_cse labmy_cse lab

11