tfcompute¶
Note tfcompute
is available as htf.tfcompute
Details
-
class
htf.tensorflowcompute.
tfcompute
(model)¶ The main class for applying
SimModel
to Hoomd simulation.-
attach
(nlist=None, r_cut=0, period=1, batch_size=None, train=False, save_output_period=None)¶ Attaches the TensorFlow instance to Hoomd. This method sets up TensorFlow and gets Hoomd ready to interact with it.
Parameters: - nlist (Hoomd nlist) – The Hoomd neighbor list that will be used as the TensorFlow input.
- r_cut (float) – Cutoff radius for neighbor listing.
- period (int) – How many Hoomd steps should pass before updating the TensorFlow model.
- batch_size (int) – The size of batches if we are using batching. Cannot be used if molecule-wise batching is active.
- train (bool) – Indicate if
train_on_batch
Keras model method should be called at each step with the labels being Hoomd forces. - save_output_period (int) – How often to save output from
model
. Each output is accessible after as attributesoutputs
as numpy arrays with a new axis at 0, representing each call. Note that if your model outputs forces or forces and virial, then these will not be present.
-
enable_mapped_nlist
(system, mapping_fxn)¶ Modifies existing snapshot to enable CG beads to be in simulation simultaneously with AA so that CG bead nlists can be accessed using hoomd’s accelerated nlist methods. This must be called in order to use
SimModel.mapped_nlist()
in a model.Warning
Hoomd re-orders positions to improve performance. Calling this will disable sorting to keep a specific ordering of positions necessary for CG mapping.
Parameters: - system (hoomd system) – hoomd system
- mapping_fxn (python callable) – a function whose signature is
f(positions, box)
where positions is anNx4
array of fine-grained positions and box is a list containing Lx, Ly, and Lz of the simulation box, and whose return value is anMx4
array of coarse-grained positions.
-
get_forces_array
()¶ Retrieve forces array as numpy array
-
get_nlist_array
()¶ Retrieve neighbor list array as numpy array
-
get_positions_array
()¶ Retrieve positions array as numpy array
-
get_virial_array
()¶ Retrieve virial array as numpy array
-
set_reference_forces
(*forces)¶ Sets the Hoomd reference forces to be used by TensorFlow.
This allows you to choose which forces are used as the label for training.
Parameters: forces – Hoomd force objects
-