TensorFlow uses the MSE loss to evaluate the closeness of network outputs and training data. The MSE loss is differentiable (as are the activation functions, 𝜑 𝑘 ) such that TensorFlow is able to adjust the weight and bias parameters [Eq. ( )] with respect to a computed gradient (using back propagation) to decrease the loss .