Nowadays, the models of deep learning are increasingly used in various industrial applications. However, the storage space of the models is too large, which makes it quite difficult to apply to mobile devices. In order to solve this problem, a model compression algorithm based on optimal clustering is proposed in this paper. Firstly, the model parameters of fully connected layer in deep convolutional neural network are clustered according to the best clustering method. Then the cluster center of the parameters is selected as the representative of the original parameter matrix. At last, the parameters of the cluster center are transformed differently in the forward calculation of the model to achieve the effect of compressing the parameters of the model and ensured the accuracy of the model. The compression algorithm proposed here is compared with other model compression algorithms in several common deep learning models such as Alexnet, VGG16 and so on. The results show that the algorithm proposed in this paper can compress the memory of the model greatly and improve the accuracy.
KEYWORDS: Signal to noise ratio, Optical networks, Receivers, Transmitters, Critical dimension metrology, Networks, Multiplexing, Digital signal processing, Fiber optic communications
A method of using pilot-tone to monitor optical signal-to-noise ratio (OSNR) is proposed in the paper. High-order statistical moments of pilot component are utilized to evaluate the noise level of the transmitted optical signals. This method of OSNR monitoring has the advantage of insensitivity to chromatic dispersion (CD) and polarization mode dispersion (PMD). Simulations are carried out in optical Nyquist transmission system. It is shown that the method has 1 dB monitoring accuracy over a wide OSNR range from 5 dB to 25 dB.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.