In recent days, Artificial Neural Network (ANN) can be applied to a vast
majority of fields including business, medicine, engineering, etc. The most
popular areas where ANN is employed nowadays are pattern and sequence
recognition, novelty detection, character recognition, regression analysis,
speech recognition, image compression, stock market prediction, Electronic
nose, security, loan applications, data processing, robotics, and control. The
benefits associated with its broad applications leads to increasing popularity
of ANN in the era of 21st Century. ANN confers many benefits such as organic
learning, nonlinear data processing, fault tolerance, and self-repairing
compared to other conventional approaches. The primary objective of this paper
is to analyze the influence of the hidden layers of a neural network over the
overall performance of the network. To demonstrate this influence, we applied
neural network with different layers on the MNIST dataset. Also, another goal
is to observe the variations of accuracies of ANN for different numbers of
hidden layers and epochs and to compare and contrast among them.

本文目的是分析神经网络的隐藏层数对网络性能的影响，为了展示这种影响，我们在 MNIST 数据集上应用了不同层数的神经网络，并观察不同层数和不同迭代次数下 ANN 精度的变化以进行比较和对比。

使用神经网络算法研究和观察手写数字识别在不同隐藏层数和纪元下的准确性变化

Study and Observation of the Variations of Accuracies for Handwritten  Digits Recognition with Various Hidden Layers and Epochs using Neural Network  Algorithm

Currently, deep neural networks are the state of the art on problems such as
speech recognition and computer vision. In this extended abstract, we show that
shallow feed-forward networks can learn the complex functions previously
learned by deep nets and achieve accuracies previously only achievable with
deep models. Moreover, in some cases the shallow neural nets can learn these
deep functions using a total number of parameters similar to the original deep
model. We evaluate our method on the TIMIT phoneme recognition task and are
able to train shallow fully-connected nets that perform similarly to complex,
well-engineered, deep convolutional architectures. Our success in training
shallow neural nets to mimic deeper models suggests that there probably exist
better algorithms for training shallow feed-forward nets than those currently
available.

该研究表明，浅层前馈神经网络可以学习先前由深层网络学习的复杂函数，并且可以达到仅用深度模型可以实现的准确度，在 TIMIT 语音识别任务中，没有经过复杂设计的浅层神经网络能够表现类似于深度卷积网络，并且成功地训练浅层神经网络模仿更深层次模型的方法表明，可能存在比当前更好的训练浅层前馈网络的算法。