class DeepNeuralNetwork {

    ...

    private func layersForward(X: Matrix) -> Matrix {
        var output = X
        for layer in layers {
            output = layer.forward(X: output)
        }
        return output
    }

    private func layersBackward(y: Matrix) {
        let m = Double(y.shape.columns)
        for layer in layers.reversed() {
            layer.backward(m: m, y: layer === layers.last ? y : nil)
        }
    }

    private func layersUpdate() {
        for layer in layers {
            layer.update(learningRate: learningRate)
        }
    }

    private func cost(yHat: Matrix, y: Matrix) -> Double {
        let logprobs = yHat.log() * y
        let cost = -logprobs.sum()
        return cost
    }
}