How to compute an ANN ouptut manually knowing the ANN weights and biases?

I got a trained simple neural network named "net" with 12 input values, a single hidden layer with 9 nuerons, and single numeric output.

To get predicitons,

1) I used the trained "net" object as follows:

y = net(input)

2) I then tried to re-use the network manually as follows:

% Extract network weights and biases

w1 = net.IW{1}; %the input-to-hidden layer weights

w2 = net.LW{2}; %the hidden-to-output layer weights

b1 = net.b{1}; %the input-to-hidden layer bias

b2 = net.b{2}; %the hidden-to-output layer bias

% Check the activaition function type in the hidden and output layers

net.layers{1}.transferFcn % = tansig

net.layers{2}.transferFcn % = purelin

% Compute precition with a feed-forward pass through the net

L1_vals = tansig((wL1*input)+b1); % Layer #1 node values

y = purelin(wL2*L1_vals + b2); % Output node value (prediction)

The first option provides the correct predcition but the second "manual" option does not.

Did I miss anything in the manual computation?

Here are the complete network properites for reference:

net =

Neural Network

name: 'Feed-Forward Neural Network'

userdata: (your custom info)

dimensions:

numInputs: 1

numLayers: 2

numOutputs: 1

numInputDelays: 0

numLayerDelays: 0

numFeedbackDelays: 0

numWeightElements: 127

sampleTime: 1

connections:

biasConnect: [1; 1]

inputConnect: [1; 0]

layerConnect: [0 0; 1 0]

outputConnect: [0 1]

subobjects:

input: Equivalent to inputs{1}

output: Equivalent to outputs{2}

inputs: {1x1 cell array of 1 input}

layers: {2x1 cell array of 2 layers}

outputs: {1x2 cell array of 1 output}

biases: {2x1 cell array of 2 biases}

inputWeights: {2x1 cell array of 1 weight}

layerWeights: {2x2 cell array of 1 weight}

functions:

adaptFcn: 'adaptwb'

adaptParam: (none)

derivFcn: 'defaultderiv'

divideFcn: 'divideind'

divideParam: .trainInd, .valInd, .testInd

divideMode: 'sample'

initFcn: 'initlay'

performFcn: 'mse'

performParam: .regularization, .normalization

plotFcns: {'plotperform', plottrainstate, ploterrhist,

plotregression}

plotParams: {1x4 cell array of 4 params}

trainFcn: 'trainlm'

trainParam: .showWindow, .showCommandLine, .show, .epochs,

.time, .goal, .min_grad, .max_fail, .mu, .mu_dec,

.mu_inc, .mu_max

weight and bias values:

IW: {2x1 cell} containing 1 input weight matrix

LW: {2x2 cell} containing 1 layer weight matrix

b: {2x1 cell} containing 2 bias vectors