But, we developed the original cache server to improve end-to-end performance in a massive team of 1000+ developers in South Korea working on a single huge codebase. The problem was that the traffic on the central server was so high that the network link was saturated. Also, most of the traffic was data being downloaded.
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Python 3.6.10 \(\vert\)Anaconda, Inc. is used as a base coding environment for the CNN algorithm. More specifically, the CNN codes were written and run in the TensorFlow environment, which was installed onto the Anaconda. TensorFlow is an open-source and free library used for machine learning which can be utilized across a wide range of tasks and has a specific focus on training and deep neural networks. In terms of hardware, the programs are run on a CPU. The CPU used belongs to an ordinary Lenovo laptop ideapad CORE i7, 7th Gen. Because the number of training samples is not particularly large, we are able to use a CPU here. The training of the CNN is performed using the artificially yellowed images and their corresponding colored ones. 800 epochs are used with a batch size of 1 and a learning rate of 0.01. The images used are of varying size and in the format of jpg. To check if there is overfitting in the training process, the training and validation curves are examined (shown below). Visual examination of the outputs in the training and validation samples is also performed to ensure the colors look realistic. MATLAB R2020b, the package of mathematical software was also used for data preparation. The yellowing filter, converting the images into a suitable format to be read by the CNN, and all other evaluation calculations (such as computing Euclidean distance and spectral angle) are performed in MATLAB. 2ff7e9595c
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