Creating Request with VNCoreMLRequest

First of all, assign "I'm investigating..." to resultLabel.text.

func recognizeImage(image: CIImage) {

        resultLabel.text = "I'm investigating..."
}

Assign the return value of VNCoreMLModel function to variable model.

func recognizeImage(image: CIImage) {

        resultLabel.text = "I'm investigating..."

        if let model = try? VNCoreMLModel(for: GoogLeNetPlaces().model) {

        }
}

Create the request with VNCoreMLRequest function and send the variable model we created above as parameter which is type model.

func recognizeImage(image: CIImage) {

        resultLabel.text = "I'm investigating..."

        if let model = try? VNCoreMLModel(for: GoogLeNetPlaces().model) {
                let request = VNCoreMLRequest(model: model, completionHandler: { (vnrequest, error) in

                })
        }
}

Assign the results of type VNClassificationObservation to results.

func recognizeImage(image: CIImage) {

        resultLabel.text = "I'm investigating..."

        if let model = try? VNCoreMLModel(for: GoogLeNetPlaces().model) {
                let request = VNCoreMLRequest(model: model, completionHandler: { (vnrequest, error) in
                        if let results = vnrequest.results as? [VNClassificationObservation] {

                        }
                })
        }
}

Assign the first result to topResult.

func recognizeImage(image: CIImage) {

        resultLabel.text = "I'm investigating..."

        if let model = try? VNCoreMLModel(for: GoogLeNetPlaces().model) {
                let request = VNCoreMLRequest(model: model, completionHandler: { (vnrequest, error) in
                        if let results = vnrequest.results as? [VNClassificationObservation] {
                                let topResult = results.first
                        }
                })
        }
}

Implement the DispatchQueue.main.async method that will work on the background to create an assumption.

func recognizeImage(image: CIImage) {

        resultLabel.text = "I'm investigating..."

        if let model = try? VNCoreMLModel(for: GoogLeNetPlaces().model) {
                let request = VNCoreMLRequest(model: model, completionHandler: { (vnrequest, error) in
                        if let results = vnrequest.results as? [VNClassificationObservation] {
                                let topResult = results.first
                                DispatchQueue.main.async {

                                }
                        }
                })
        }
}

Compute the confidence rate of the value stored in topResult and assign it to confidenceRate. And then, for display purposes resultLabel.text is used.

func recognizeImage(image: CIImage) {

        resultLabel.text = "I'm investigating..."

        if let model = try? VNCoreMLModel(for: GoogLeNetPlaces().model) {
                let request = VNCoreMLRequest(model: model, completionHandler: { (vnrequest, error) in
                        if let results = vnrequest.results as? [VNClassificationObservation] {
                                let topResult = results.first
                                DispatchQueue.main.async {
                                        let confidenceRate = (topResult?.confidence)! * 100
                                        self.resultLabel.text = "\(confidenceRate)% it's \(String(describing: topResult?.identifier))"
                                }
                        }
                })
        }
}