Importing weights into BNNS

I have setup a Neural Network using (Keras with a Tensorflow backend).I have exported the weights and have 84 weights and one bias. The NN has 84 inputs and one output.

I am trying to use the steps (by converting the code to Swift from Objective-C) in https://www.bignerdranch.com/blog/use-tensorflow-and-bnns-to-add-machine-learning-to-your-mac-or-ios-app/

My current code looks like this:

//: Playground - noun: a place where people can play

import Accelerate

//// //// CREATE MODEL ///

var OUT_COUNT = 1

var IN_COUNT = 84

var inVectorDescriptor = BNNSVectorDescriptor(size: IN_COUNT, data_type: BNNSDataTypeFloat32, data_scale: 0.0, data_bias: 0.0)

var outVectorDescriptor = BNNSVectorDescriptor(size: OUT_COUNT, data_type: BNNSDataTypeFloat32, data_scale: 0.0, data_bias: 0.0)

let weightsPath = Bundle.main.url(forResource: "weights", withExtension: "data")

let biasesPath = Bundle.main.url(forResource: "biases", withExtension: "data")

let activation = BNNSActivation(function: BNNSActivationFunctionIdentity, alpha: 0, beta: 0)

var weightsVector = Array<Float>(repeating:0.0, count:IN_COUNT*OUT_COUNT)

var biasesVector = Array<Float>(repeating:0.0, count:OUT_COUNT)

// reading biases

let biasesText = try String(contentsOf: biasesPath!, encoding: String.Encoding.utf8)

var biasesTextArr2 = biasesText.characters.split{$0 == ","}.map(String.init)

let floatsArrayB = biasesTextArr2.flatMap{ Float($0) }

biasesVector = floatsArrayB

// reading weights

let weightsText = try String(contentsOf: weightsPath!, encoding: String.Encoding.utf8)

let weightsTextArr = weightsText.characters.split{$0 == ","}.map(String.init)

let floatsArrayW = weightsTextArr.flatMap{ Float($0) }

weightsVector = floatsArrayW

var weightsVectorBNNS = BNNSLayerData(data: &weightsVector, data_type: BNNSDataTypeFloat32, data_scale: 0.0, data_bias: 0.0, data_table: nil)

var biasesVectorBNNS = BNNSLayerData(data: biasesVector, data_type: BNNSDataTypeFloat32, data_scale: 0.0, data_bias: 0.0, data_table: nil)

var parameters = BNNSFullyConnectedLayerParameters(in_size: IN_COUNT, out_size: OUT_COUNT, weights: weightsVectorBNNS, bias: biasesVectorBNNS, activation:activation)

let filter = BNNSFilterCreateFullyConnectedLayer(&inVectorDescriptor, &outVectorDescriptor, &parameters, nil)

and then to make a prediction:

let buffer = ....// data to be tested

var output = Array<Float>(repeating:0.0, count:OUT_COUNT)

let success = BNNSFilterApply(filter, buffer, &output);

/// softmax

let outputSM = output.map { 1/(1 + exp($0)) }

for (index,value) in output.enumerated() {

output[index] = log(1 + exp(value))

}

print(outputSM)

print(output)

The data in biases and weights are in csv format. The result shown is a nan and that is not what is supposed to come out. I should be getting a value between 0 and 1.

Hope someone can help.

Best,

Feras A.