Which factor does NOT affect the accuracy of a deep neural network?

The factor that does not affect the accuracy of a deep neural network is pixel randomization. In the context of training a deep neural network, pixel randomization refers to techniques used in data augmentation or preprocessing, such as random cropping or shifts, which can help improve the robustness of a model by making it less sensitive to specific pixel arrangements. However, pixel randomization in itself does not directly contribute to the accuracy of the model.

In contrast, learning rate, network depth, and the number of training examples are fundamental parameters that significantly influence the performance and accuracy of a neural network. The learning rate, which dictates how much to adjust weights during training, can lead to faster convergence or divergence if set improperly. The network depth, meaning the number of layers in the architecture, can enable the network to learn more complex patterns and hierarchies in data, thus potentially improving accuracy. Lastly, the number of training examples is crucial, as having more data generally helps the model to generalize better and make accurate predictions by minimizing overfitting.

Therefore, while pixel randomization can aid in creating a more robust system, it is not a core factor determining the actual accuracy of the deep neural networking process itself.