New functionalities
- Utility functions for converting Segmentor output (coordinates and classes) to files readable by packages such as Atomic Simulation Environment, VESTA, etc.
- Optional time-dependent learning rate. For example,
# We are going to start with a constant learning rate, then after 600 iterations we begin
# linearly decreasing it over the next 200 iterations, and keep constant afterwards
lr_t = np.ones(800) * 1e-3
lr_t[600:800] = np.linspace(1e-3, 1e-4, 200)
model.fit(images, labels, images_test, labels_test, # training data
training_cycles=1000, compute_accuracy=True, # basic training parameters
swa=True, lr_scheduler=lr_t # advanced training parameters
)Other changes
- Added new examples (Graph analysis and Im2Spec) and expanded explanations in the markdown parts for the old ones
- Improved (slightly) documentation
