Reccurent neural network

Question

I watched the this video on YouTube and copied the code. Why it does not work in M12? I mean net[inData] and rnnLayerForward[param, inData, state] don't give the same result? What change since then and how can I fix it? Slides can be downloaded here.

    rnnTimeStep[params_Association, input_, state_] := 
      Tanh[params["StateWeights"].state + params["InputWeights"].input + 
        params["Biases"]];
    rnnLayerForward[params_Association, input_, state_] := 
     Rest@FoldList[rnnTimeStep[params, #2, #1] &, state, input]

sequenceLength = 3;
featureSize = 2;
rnnStateSize = 1;
inData = RandomReal[1, {sequenceLength, featureSize}];
state = ConstantArray[0, rnnStateSize];

net = NetInitialize@
  BasicRecurrentLayer[rnnStateSize, "Input" -> Dimensions@inData]

param = Normal@NetExtract[net, "Arrays"]

net[inData]

rnnLayerForward[param, inData, state]

Answer 1

rnnTimeStep[params_Association, input_, state_] := 
  Tanh[params["StateWeights"].state + params["InputWeights"].input + params["Biases"]];

rnnLayerForward[params_Association, input_, state_] := 
 Rest@FoldList[rnnTimeStep[params, #2, #1] &, state, input]

sequenceLength = 3;
featureSize = 2;
rnnStateSize = 1;
inData = RandomReal[1, {sequenceLength, featureSize}];
state = ConstantArray[0, rnnStateSize];

net = NetInitialize@BasicRecurrentLayer[rnnStateSize, "Input" -> Dimensions@inData];

param = Normal[#] & /@ NetExtract[net, "Arrays"];

net[inData]

{{-0.85818}, {-0.987016}, {-0.941039}}

rnnLayerForward[param, inData, state]

{{-0.85818}, {-0.987016}, {-0.941039}}