| --version |
None |
show program's version number and exit |
| --trainset |
None |
Training set. |
| --from_trained |
str |
Train from an existing network |
| --nnet |
str |
Trained network input (nnet_save_xx.hdf) |
| --nettag |
str |
Tag of the output neural net file. Will use the tag of good particles in training set by default. |
| --learnrate |
float |
Learning rate |
| --niter |
int |
Training iterations |
| --ncopy |
int |
Number of copies for each particle |
| --batch |
int |
Batch size for the stochastic gradient descent. Default is 20. |
| --nkernel |
str |
Number of kernels for each layer, from input to output. The number of kernels in the last layer must be 1. |
| --ksize |
str |
Width of kernels of each layer, the numbers must be odd. Note the number of layers should be the same as the nkernel option. |
| --poolsz |
str |
Pooling size for each layer. Note the number of layers should be the same as the nkernel option. |
| --trainout |
None |
Output the result of the training set |
| --training |
None |
Doing training |
| --tomograms |
str |
Tomograms input. |
| --applying |
None |
Applying the neural network on tomograms |
| --outtag |
str |
Tag of the segmentation output. When left empty, the segmentation will be saved to 'segmentations/<tomogram name>__<neural network tag>_seg.hdf'. When set, the output will be written to 'segmentations/<tomogram name>__<outtag>.hdf' |
| --threads |
int |
Number of thread to use when applying neural net on test images. Not used during trainning |
| --ppid |
int |
Set the PID of the parent process, used for cross platform PPID |
| --device |
str |
For Convnet training only. Pick a device to use. chose from cpu, gpu, or gpuX (X=0,1,...) when multiple gpus are available. default is cpu |