44 lines
1.4 KiB
Julia
44 lines
1.4 KiB
Julia
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function roughNN(w, x)
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#
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# v = roughNN( w , x )
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#
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# returns the falue of the function v = f( x ) as currently estimated by
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# a small NN with 1 input, 1 output, 3 hidden layers of 5 nodes each, and
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# tanh activation function.
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#
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# Input:
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#
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# - w is the [ 76 x 1 ] real vector containing the weights of the NN,
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# i.e., w is made as follows:
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# [ 1 .. 5 ] are the [ 5 x 1 ] weigths of the first layer
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# [ 6 .. 10 ] are the [ 5 x 1 ] biases of the first layer
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# [ 11 .. 35 ] are the [ 5 x 5 ] weigths of the second layer
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# [ 36 .. 40 ] are the [ 5 x 1 ] biases of the second layer
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# [ 41 .. 65 ] are the [ 5 x 5 ] weigths of the third layer
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# [ 66 .. 70 ] are the [ 5 x 1 ] biases of the third layer
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# [ 71 .. 75 ] are the [ 5 x 1 ] weigths of the fourth (output) layer
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# [ 76 ] is the [ 1 x 1 ] bias of the fourth (output) layer
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#
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# - x is the real scalar containing the input of f()
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#
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# Output:
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#
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# - v (real, scalar): v = f( x ) as estimated by the NN with weights w
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#
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#{
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# =======================================
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# Author: Antonio Frangioni
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# Date: 28-08-22
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# Version 1.00
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# Copyright Antonio Frangioni
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# =======================================
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#}
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g = tanh( (ones(5, 1) * x ) .* w( 1 : 5 ) + w( 6:10 ) )
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g = tanh( reshape( w( 11:35 ) , (5, 5) ) * g + w( 36:40 ) )
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g = tanh( reshape( w( 41:65 ) , (5, 5) ) * g + w( 66:70 ) )
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v = g' * w( 71:75 ) + w( 76 )
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return v
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end
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