Centos 7

We applied deep convolutional GAN (DCGAN) to generate artificial brain images on the basis of a previously described procedure with minor modifications (7 ). The DCGAN generator consisted of a fully connected layer projecting input of a 100-dimensional uniform distribution to the following layers of 4 fractionally strided convolutions with filter sizes of 256, 128, 64, and 32 and a kernel size of 5 × 5. The rectified linear unit activation functions were used except for the output layer, which used the tanh function. Batch normalization was performed in each layer. As discriminators, leaky rectified linear unit functions were used in all layers with a slope of leak of 0.2. Batch normalization was performed except for the output layer. Training and testing were implemented using NVIDIA (R) CUDA 9.0, Google (R) TensorFlow 1.7.0, and Python 3.6, and performed with 35 epochs on a personal computer with CPU, Intel (R) Core i7-2600 CPU 3.4 GHz; memory, 8 Gbytes; GPU, NVIDIA (R) GeForce GTX 1080; OS, CentOS 7.4.