X5675 processors

Before training, all the kernels in the neural network are initialized using a uniform distribution of near-zero values, and the offsets are initialized to zero. Log squared residual (log((y-y′)^{2 (link)})) between the neural network output and the manual annotation is used as the loss function. Since there is a pooling layer in the network, the manual annotation is shrunk by 2 to match the network output. A L1 weight decay of 10⁻⁵ is used for regularization of the training process. No significant overfitting is observed, likely because the high noise level in the CryoET images also serves as a strong regularization factor. To optimize the kernels, we use stochastic gradient descent with a batch size of 20. By default, the neural network is trained for 20 iterations. The learning rate is set to 0.01 in the first iteration and decreased by 10% after each iteration. The training process can be performed on either a GPU or in parallel on multiple CPUs (~10x slower on our testing machine). Training each feature typically takes under 10 minutes on a current generation GPU, and the resulting network can be used for any tomogram of the same cell-type collected under similar conditions. A workstation with 96GB of RAM, 2× Intel X5675 processors for a total of 12 compute cores, and an Nvidia GTX 1080 GPU was used for all testing.