API¶
-
class
deepmd.utils.data.
DataSets
(sys_path, set_prefix, seed=None, shuffle_test=True)¶ Outdated class for one data system. Not maintained anymore.
-
check_batch_size
(batch_size)¶
-
check_test_size
(test_size)¶
-
get_batch
(batch_size)¶ returned property prefector [4] in order: energy, force, virial, atom_ener
-
get_ener
()¶
-
get_natoms
()¶
-
get_natoms_2
(ntypes)¶
-
get_natoms_vec
(ntypes)¶
-
get_numb_set
()¶
-
get_set
(data, idx=None)¶
-
get_sys_numb_batch
(batch_size)¶
-
get_test
()¶ returned property prefector [4] in order: energy, force, virial, atom_ener
-
get_type_map
()¶
-
load_batch_set
(set_name)¶
-
load_data
(set_name, data_name, shape, is_necessary=True)¶
-
load_energy
(set_name, nframes, nvalues, energy_file, atom_energy_file)¶ return : coeff_ener, ener, coeff_atom_ener, atom_ener
-
load_set
(set_name, shuffle=True)¶
-
load_test_set
(set_name, shuffle_test)¶
-
load_type
(sys_path)¶
-
load_type_map
(sys_path)¶
-
numb_aparam
()¶
-
numb_fparam
()¶
-
reset_iter
()¶
-
set_numb_batch
(batch_size)¶
-
stats_energy
()¶
-
-
class
deepmd.utils.data.
DeepmdData
(sys_path: str, set_prefix: str = 'set', shuffle_test: bool = True, type_map: Optional[List[str]] = None, modifier=None, trn_all_set: bool = False)¶ Class for a data system. It loads data from hard disk, and mantains the data as a data_dict
-
add
(key: str, ndof: int, atomic: bool = False, must: bool = False, high_prec: bool = False, type_sel: Optional[List[int]] = None, repeat: int = 1)¶ Add a data item that to be loaded
- key
The key of the item. The corresponding data is stored in sys_path/set.*/key.npy
- ndof
The number of dof
- atomic
The item is an atomic property. If False, the size of the data should be nframes x ndof If True, the size of data should be nframes x natoms x ndof
- must
The data file sys_path/set.*/key.npy must exist. If must is False and the data file does not exist, the data_dict[find_key] is set to 0.0
- high_prec
Load the data and store in float64, otherwise in float32
- type_sel
Select certain type of atoms
- repeat
The data will be repeated repeat times.
-
avg
(key)¶ Return the average value of an item.
-
check_batch_size
(batch_size)¶ Check if the system can get a batch of data with batch_size frames.
-
check_test_size
(test_size)¶ Check if the system can get a test dataset with test_size frames.
-
get_atom_type
() → List[int]¶ Get atom types
-
get_batch
(batch_size: int) → dict¶ Get a batch of data with batch_size frames. The frames are randomly picked from the data system.
- batch_size
size of the batch
-
get_data_dict
() → dict¶ Get the data_dict
-
get_natoms
()¶ Get number of atoms
-
get_natoms_vec
(ntypes: int)¶ Get number of atoms and number of atoms in different types
- ntypes
Number of types (may be larger than the actual number of types in the system).
- natoms
natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
-
get_ntypes
() → int¶ Number of atom types in the system
-
get_numb_batch
(batch_size: int, set_idx: int) → int¶ Get the number of batches in a set.
-
get_numb_set
() → int¶ Get number of training sets
-
get_sys_numb_batch
(batch_size: int) → int¶ Get the number of batches in the data system.
-
get_test
(ntests: int = - 1) → dict¶ Get the test data with ntests frames.
- ntests
Size of the test data set. If ntests is -1, all test data will be get.
-
get_type_map
() → List[str]¶ Get the type map
-
reduce
(key_out: str, key_in: str)¶ Generate a new item from the reduction of another atom
- key_out
The name of the reduced item
- key_in
The name of the data item to be reduced
-
reset_get_batch
()¶
-
-
class
deepmd.infer.data_modifier.
DipoleChargeModifier
(model_name: str, model_charge_map: List[float], sys_charge_map: List[float], ewald_h: float = 1, ewald_beta: float = 1)¶ -
build_fv_graph
() → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for the force and virial inference.
-
eval
(coord: numpy.array, box: numpy.array, atype: numpy.array, eval_fv: bool = True) → Tuple[numpy.array, numpy.array, numpy.array]¶ Evaluate the modification
- coord
The coordinates of atoms
- box
The simulation region. PBC is assumed
- atype
The atom types
- eval_fv
Evaluate force and virial
- tot_e
The energy modification
- tot_f
The force modification
- tot_v
The virial modification
-
load_prefix
: str¶
-
modify_data
(data: dict) → None¶ Modify data.
- data
Internal data of DeepmdData. Be a dict, has the following keys - coord coordinates - box simulation box - type atom types - find_energy tells if data has energy - find_force tells if data has force - find_virial tells if data has virial - energy energy - force force - virial virial
-
-
class
deepmd.utils.data_system.
DataSystem
(systems, set_prefix, batch_size, test_size, rcut, run_opt=None)¶ Outdated class for the data systems. Not maintained anymore.
-
check_type_map_consistency
(type_map_list)¶
-
compute_energy_shift
()¶
-
format_name_length
(name, width)¶
-
get_batch
(sys_idx=None, sys_weights=None, style='prob_sys_size')¶
-
get_batch_size
()¶
-
get_nbatches
()¶
-
get_nsystems
()¶
-
get_ntypes
()¶
-
get_sys
(sys_idx)¶
-
get_test
(sys_idx=None)¶
-
get_type_map
()¶
-
numb_fparam
()¶
-
print_summary
()¶
-
process_sys_weights
(sys_weights)¶
-
-
class
deepmd.utils.data_system.
DeepmdDataSystem
(systems: List[str], batch_size: int, test_size: int, rcut: float, set_prefix: str = 'set', shuffle_test: bool = True, type_map: Optional[List[str]] = None, modifier=None, trn_all_set=False, sys_probs=None, auto_prob_style='prob_sys_size')¶ Class for manipulating many data systems. It is implemented with the help of DeepmdData
-
add
(key: str, ndof: int, atomic: bool = False, must: bool = False, high_prec: bool = False, type_sel: Optional[List[int]] = None, repeat: int = 1)¶ Add a data item that to be loaded
- key
The key of the item. The corresponding data is stored in sys_path/set.*/key.npy
- ndof
The number of dof
- atomic
The item is an atomic property. If False, the size of the data should be nframes x ndof If True, the size of data should be nframes x natoms x ndof
- must
The data file sys_path/set.*/key.npy must exist. If must is False and the data file does not exist, the data_dict[find_key] is set to 0.0
- high_prec
Load the data and store in float64, otherwise in float32
- type_sel
Select certain type of atoms
- repeat
The data will be repeated repeat times.
-
add_dict
(adict: dict) → None¶ Add items to the data system by a dict. adict should have items like adict[key] = {
‘ndof’: ndof, ‘atomic’: atomic, ‘must’: must, ‘high_prec’: high_prec, ‘type_sel’: type_sel, ‘repeat’: repeat,
} For the explaination of the keys see add
-
compute_energy_shift
(rcond=0.001, key='energy')¶
-
get_batch
(sys_idx: Optional[int] = None)¶ Get a batch of data from the data systems
- sys_idx: int
The index of system from which the batch is get. If sys_idx is not None, sys_probs and auto_prob_style are ignored If sys_idx is None, automatically determine the system according to sys_probs or auto_prob_style, see the following.
-
get_batch_size
() → int¶ Get the batch size
-
get_data_dict
(ii: int = 0) → dict¶
-
get_nbatches
() → int¶ Get the total number of batches
-
get_nsystems
() → int¶ Get the number of data systems
-
get_ntypes
() → int¶ Get the number of types
-
get_sys
(idx: int) → deepmd.utils.data.DeepmdData¶ Get a certain data system
-
get_sys_ntest
(sys_idx=None)¶ - Get number of tests for the currently selected system,
or one defined by sys_idx.
-
get_test
(sys_idx: Optional[int] = None, n_test: int = - 1)¶ Get test data from the the data systems.
- sys_idx
The test dat of system with index sys_idx will be returned. If is None, the currently selected system will be returned.
- n_test
Number of test data. If set to -1 all test data will be get.
-
get_type_map
() → List[str]¶ Get the type map
-
print_summary
(name)¶
-
reduce
(key_out, key_in)¶ Generate a new item from the reduction of another atom
- key_out
The name of the reduced item
- key_in
The name of the data item to be reduced
-
set_sys_probs
(sys_probs=None, auto_prob_style: str = 'prob_sys_size')¶
-
-
class
deepmd.infer.deep_eval.
DeepEval
(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)¶ Common methods for DeepPot, DeepWFC, DeepPolar, …
-
load_prefix
: str¶
-
make_natoms_vec
(atom_types: numpy.ndarray) → numpy.ndarray¶ Make the natom vector used by deepmd-kit.
- atom_types
The type of atoms
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
-
property
model_type
¶ Get type of model.
:type:str
-
property
model_version
¶ Get type of model.
:type:str
-
static
reverse_map
(vec: numpy.ndarray, imap: List[int]) → numpy.ndarray¶ Reverse mapping of a vector according to the index map
- vec
Input vector. Be of shape [nframes, natoms, -1]
- imap
Index map. Be of shape [natoms]
- vec_out
Reverse mapped vector.
-
static
sort_input
(coord: numpy.array, atom_type: numpy.array, sel_atoms: Optional[List[int]] = None)¶ Sort atoms in the system according their types.
- coord
The coordinates of atoms. Should be of shape [nframes, natoms, 3]
- atom_type
The type of atoms Should be of shape [natoms]
- sel_atom
The selected atoms by type
- coord_out
The coordinates after sorting
- atom_type_out
The atom types after sorting
- idx_map
The index mapping from the input to the output. For example coord_out = coord[:,idx_map,:]
- sel_atom_type
Only output if sel_atoms is not None The sorted selected atom types
- sel_idx_map
Only output if sel_atoms is not None The index mapping from the selected atoms to sorted selected atoms.
-
-
class
deepmd.infer.deep_polar.
DeepGlobalPolar
(model_file: str, load_prefix: str = 'load', default_tf_graph: bool = False)¶ Constructor.
- model_filestr
The name of the frozen model file.
- load_prefix: str
The prefix in the load computational graph
- default_tf_graphbool
If uses the default tf graph, otherwise build a new tf graph for evaluation
-
eval
(coords: numpy.array, cells: numpy.array, atom_types: List[int], atomic: bool = True, fparam: Optional[numpy.array] = None, aparam: Optional[numpy.array] = None, efield: Optional[numpy.array] = None) → numpy.array¶ Evaluate the model.
- coords
The coordinates of atoms. The array should be of size nframes x natoms x 3
- cells
The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
- atom_types
The atom types The list should contain natoms ints
- atomic
Calculate the atomic energy and virial
- fparam
Not used in this model
- aparam
Not used in this model
- efield
Not used in this model
- tensor
The returned tensor If atomic == False then of size nframes x variable_dof else of size nframes x natoms x variable_dof
-
get_dim_aparam
() → int¶ Unsupported in this model.
-
get_dim_fparam
() → int¶ Unsupported in this model.
-
load_prefix
: str¶
-
class
deepmd.infer.deep_polar.
DeepPolar
(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)¶ Constructor.
- model_filePath
The name of the frozen model file.
- load_prefix: str
The prefix in the load computational graph
- default_tf_graphbool
If uses the default tf graph, otherwise build a new tf graph for evaluation
For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!
-
get_dim_aparam
() → int¶ Unsupported in this model.
-
get_dim_fparam
() → int¶ Unsupported in this model.
-
load_prefix
: str¶
-
class
deepmd.infer.deep_pot.
DeepPot
(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)¶ Constructor.
- model_filePath
The name of the frozen model file.
- load_prefix: str
The prefix in the load computational graph
- default_tf_graphbool
If uses the default tf graph, otherwise build a new tf graph for evaluation
For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!
-
eval
(coords: numpy.array, cells: numpy.array, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.array] = None, aparam: Optional[numpy.array] = None, efield: Optional[numpy.array] = None) → Tuple[numpy.ndarray, …]¶ Evaluate the energy, force and virial by using this DP.
- coords
The coordinates of atoms. The array should be of size nframes x natoms x 3
- cells
The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
- atom_types
The atom types The list should contain natoms ints
- atomic
Calculate the atomic energy and virial
- fparam
The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.
- aparam
The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.
- efield
The external field on atoms. The array should be of size nframes x natoms x 3
- energy
The system energy.
- force
The force on each atom
- virial
The virial
- atom_energy
The atomic energy. Only returned when atomic == True
- atom_virial
The atomic virial. Only returned when atomic == True
-
get_dim_aparam
() → int¶ Get the number (dimension) of atomic parameters of this DP.
-
get_dim_fparam
() → int¶ Get the number (dimension) of frame parameters of this DP.
-
get_ntypes
() → int¶ Get the number of atom types of this model.
-
get_rcut
() → float¶ Get the cut-off radius of this model.
-
get_sel_type
() → List[int]¶ Unsupported in this model.
-
get_type_map
() → List[int]¶ Get the type map (element name of the atom types) of this model.
-
load_prefix
: str¶
-
class
deepmd.infer.deep_wfc.
DeepWFC
(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)¶ Constructor.
- model_filePath
The name of the frozen model file.
- load_prefix: str
The prefix in the load computational graph
- default_tf_graphbool
If uses the default tf graph, otherwise build a new tf graph for evaluation
For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!
-
get_dim_aparam
() → int¶ Unsupported in this model.
-
get_dim_fparam
() → int¶ Unsupported in this model.
-
load_prefix
: str¶
-
class
deepmd.descriptor.loc_frame.
DescrptLocFrame
(rcut: float, sel_a: List[int], sel_r: List[int], axis_rule: List[int])¶ -
build
(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for the descriptor
- coord_
The coordinate of atoms
- atype_
The type of atoms
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- mesh
For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
- input_dict
Dictionary for additional inputs
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- descriptor
The output descriptor
-
compute_input_stats
(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None¶ Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.
- data_coord
The coordinates. Can be generated by deepmd.model.make_stat_input
- data_box
The box. Can be generated by deepmd.model.make_stat_input
- data_atype
The atom types. Can be generated by deepmd.model.make_stat_input
- natoms_vec
The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
- mesh
The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
- input_dict
Dictionary for additional input
-
get_dim_out
() → int¶ Returns the output dimension of this descriptor
-
get_nlist
() → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]]¶ - nlist
Neighbor list
- rij
The relative distance between the neighbor and the center atom.
- sel_a
The number of neighbors with full information
- sel_r
The number of neighbors with only radial information
-
get_ntypes
() → int¶ Returns the number of atom types
-
get_rcut
() → float¶ Returns the cut-off radisu
-
get_rot_mat
() → tensorflow.python.framework.ops.Tensor¶ Get rotational matrix
-
prod_force_virial
(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor]¶ Compute force and virial
- atom_ener
The atomic energy
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- force
The force on atoms
- virial
The total virial
- atom_virial
The atomic virial
-
-
class
deepmd.descriptor.se_a.
DescrptSeA
¶ -
build
(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for the descriptor
- coord_
The coordinate of atoms
- atype_
The type of atoms
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- mesh
For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
- input_dict
Dictionary for additional inputs
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- descriptor
The output descriptor
-
compute_input_stats
(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None¶ Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.
- data_coord
The coordinates. Can be generated by deepmd.model.make_stat_input
- data_box
The box. Can be generated by deepmd.model.make_stat_input
- data_atype
The atom types. Can be generated by deepmd.model.make_stat_input
- natoms_vec
The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
- mesh
The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
- input_dict
Dictionary for additional input
-
enable_compression
(min_nbor_dist: float, model_file: str = 'frozon_model.pb', table_extrapolate: float = 5, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = - 1) → None¶ Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.
- min_nbor_dist
The nearest distance between atoms
- model_file
The original frozen model, which will be compressed by the program
- table_extrapolate
The scale of model extrapolation
- table_stride_1
The uniform stride of the first table
- table_stride_2
The uniform stride of the second table
- check_frequency
The overflow check frequency
-
get_dim_out
() → int¶ Returns the output dimension of this descriptor
-
get_dim_rot_mat_1
() → int¶ Returns the first dimension of the rotation matrix. The rotation is of shape dim_1 x 3
-
get_nlist
() → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]]¶ - nlist
Neighbor list
- rij
The relative distance between the neighbor and the center atom.
- sel_a
The number of neighbors with full information
- sel_r
The number of neighbors with only radial information
-
get_ntypes
() → int¶ Returns the number of atom types
-
get_rcut
() → float¶ Returns the cut-off radius
-
get_rot_mat
() → tensorflow.python.framework.ops.Tensor¶ Get rotational matrix
-
prod_force_virial
(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor]¶ Compute force and virial
- atom_ener
The atomic energy
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- force
The force on atoms
- virial
The total virial
- atom_virial
The atomic virial
-
-
class
deepmd.descriptor.se_r.
DescrptSeR
¶ -
build
(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for the descriptor
- coord_
The coordinate of atoms
- atype_
The type of atoms
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- mesh
For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
- input_dict
Dictionary for additional inputs
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- descriptor
The output descriptor
-
compute_input_stats
(data_coord, data_box, data_atype, natoms_vec, mesh, input_dict)¶ Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.
- data_coord
The coordinates. Can be generated by deepmd.model.make_stat_input
- data_box
The box. Can be generated by deepmd.model.make_stat_input
- data_atype
The atom types. Can be generated by deepmd.model.make_stat_input
- natoms_vec
The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
- mesh
The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
- input_dict
Dictionary for additional input
-
get_dim_out
()¶ Returns the output dimension of this descriptor
-
get_nlist
()¶ - nlist
Neighbor list
- rij
The relative distance between the neighbor and the center atom.
- sel_a
The number of neighbors with full information
- sel_r
The number of neighbors with only radial information
-
get_ntypes
()¶ Returns the number of atom types
-
get_rcut
()¶ Returns the cut-off radisu
-
prod_force_virial
(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor]¶ Compute force and virial
- atom_ener
The atomic energy
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- force
The force on atoms
- virial
The total virial
- atom_virial
The atomic virial
-
-
class
deepmd.descriptor.se_ar.
DescrptSeAR
(jdata)¶ -
build
(coord_, atype_, natoms, box, mesh, input_dict, suffix='', reuse=None)¶
-
compute_input_stats
(data_coord, data_box, data_atype, natoms_vec, mesh, input_dict)¶
-
get_dim_out
()¶
-
get_nlist_a
()¶
-
get_nlist_r
()¶
-
get_ntypes
()¶
-
get_rcut
()¶
-
prod_force_virial
(atom_ener, natoms)¶
-
-
class
deepmd.descriptor.se_a_ebd.
DescrptSeAEbd
(rcut: float, rcut_smth: float, sel: List[str], neuron: List[int] = [24, 48, 96], axis_neuron: int = 8, resnet_dt: bool = False, trainable: bool = True, seed: int = 1, type_one_side: bool = True, type_nchanl: int = 2, type_nlayer: int = 1, numb_aparam: int = 0, set_davg_zero: bool = False, activation_function: str = 'tanh', precision: str = 'default', exclude_types: List[int] = [])¶ -
build
(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for the descriptor
- coord_
The coordinate of atoms
- atype_
The type of atoms
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- mesh
For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
- input_dict
Dictionary for additional inputs
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- descriptor
The output descriptor
-
-
class
deepmd.descriptor.se_t.
DescrptSeT
¶ -
build
(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for the descriptor
- coord_
The coordinate of atoms
- atype_
The type of atoms
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- mesh
For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
- input_dict
Dictionary for additional inputs
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- descriptor
The output descriptor
-
compute_input_stats
(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None¶ Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.
- data_coord
The coordinates. Can be generated by deepmd.model.make_stat_input
- data_box
The box. Can be generated by deepmd.model.make_stat_input
- data_atype
The atom types. Can be generated by deepmd.model.make_stat_input
- natoms_vec
The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
- mesh
The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
- input_dict
Dictionary for additional input
-
get_dim_out
() → int¶ Returns the output dimension of this descriptor
-
get_nlist
() → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]]¶ - nlist
Neighbor list
- rij
The relative distance between the neighbor and the center atom.
- sel_a
The number of neighbors with full information
- sel_r
The number of neighbors with only radial information
-
get_ntypes
() → int¶ Returns the number of atom types
-
get_rcut
() → float¶ Returns the cut-off radisu
-
prod_force_virial
(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor]¶ Compute force and virial
- atom_ener
The atomic energy
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- force
The force on atoms
- virial
The total virial
- atom_virial
The atomic virial
-
-
class
deepmd.descriptor.se_a_ef.
DescrptSeAEf
¶ -
build
(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for the descriptor
- coord_
The coordinate of atoms
- atype_
The type of atoms
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- mesh
For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
- input_dict
Dictionary for additional inputs. Should have ‘efield’.
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- descriptor
The output descriptor
-
compute_input_stats
(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None¶ Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.
- data_coord
The coordinates. Can be generated by deepmd.model.make_stat_input
- data_box
The box. Can be generated by deepmd.model.make_stat_input
- data_atype
The atom types. Can be generated by deepmd.model.make_stat_input
- natoms_vec
The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
- mesh
The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
- input_dict
Dictionary for additional input
-
get_dim_out
() → int¶ Returns the output dimension of this descriptor
-
get_dim_rot_mat_1
() → int¶ Returns the first dimension of the rotation matrix. The rotation is of shape dim_1 x 3
-
get_nlist
() → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]]¶ - nlist
Neighbor list
- rij
The relative distance between the neighbor and the center atom.
- sel_a
The number of neighbors with full information
- sel_r
The number of neighbors with only radial information
-
get_ntypes
() → int¶ Returns the number of atom types
-
get_rcut
() → float¶ Returns the cut-off radisu
-
get_rot_mat
() → tensorflow.python.framework.ops.Tensor¶ Get rotational matrix
-
prod_force_virial
(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor]¶ Compute force and virial
- atom_ener
The atomic energy
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- force
The force on atoms
- virial
The total virial
- atom_virial
The atomic virial
-
-
class
deepmd.descriptor.se_a_ef.
DescrptSeAEfLower
(op, rcut: float, rcut_smth: float, sel: List[str], neuron: List[int] = [24, 48, 96], axis_neuron: int = 8, resnet_dt: bool = False, trainable: bool = True, seed: int = 1, type_one_side: bool = True, exclude_types: List[int] = [], set_davg_zero: bool = False, activation_function: str = 'tanh', precision: str = 'default')¶ Helper class for implementing DescrptSeAEf
-
build
(coord_, atype_, natoms, box_, mesh, input_dict, suffix='', reuse=None)¶ Build the computational graph for the descriptor
- coord_
The coordinate of atoms
- atype_
The type of atoms
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- mesh
For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
- input_dict
Dictionary for additional inputs
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- descriptor
The output descriptor
-
compute_input_stats
(data_coord, data_box, data_atype, natoms_vec, mesh, input_dict)¶ Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.
- data_coord
The coordinates. Can be generated by deepmd.model.make_stat_input
- data_box
The box. Can be generated by deepmd.model.make_stat_input
- data_atype
The atom types. Can be generated by deepmd.model.make_stat_input
- natoms_vec
The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
- mesh
The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
- input_dict
Dictionary for additional input
-
-
class
deepmd.descriptor.hybrid.
DescrptHybrid
(descrpt_list: list)¶ -
build
(coord_: tensorflow.python.framework.ops.Tensor, atype_: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, box_: tensorflow.python.framework.ops.Tensor, mesh: tensorflow.python.framework.ops.Tensor, input_dict: dict, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for the descriptor
- coord_
The coordinate of atoms
- atype_
The type of atoms
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- mesh
For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
- input_dict
Dictionary for additional inputs
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- descriptor
The output descriptor
-
compute_input_stats
(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict) → None¶ Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.
- data_coord
The coordinates. Can be generated by deepmd.model.make_stat_input
- data_box
The box. Can be generated by deepmd.model.make_stat_input
- data_atype
The atom types. Can be generated by deepmd.model.make_stat_input
- natoms_vec
The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.model.make_stat_input
- mesh
The mesh for neighbor searching. Can be generated by deepmd.model.make_stat_input
- input_dict
Dictionary for additional input
-
get_dim_out
() → int¶ Returns the output dimension of this descriptor
-
get_nlist_i
(ii: int) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, List[int], List[int]]¶ - iiint
Get the neighbor information of the ii-th descriptor
- nlist
Neighbor list
- rij
The relative distance between the neighbor and the center atom.
- sel_a
The number of neighbors with full information
- sel_r
The number of neighbors with only radial information
-
get_ntypes
() → int¶ Returns the number of atom types
-
get_rcut
() → float¶ Returns the cut-off radius
-
prod_force_virial
(atom_ener: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor]¶ Compute force and virial
- atom_ener
The atomic energy
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- force
The force on atoms
- virial
The total virial
- atom_virial
The atomic virial
-
-
class
deepmd.infer.ewald_recp.
EwaldRecp
(hh, beta)¶ Evaluate the reciprocal part of the Ewald sum
-
eval
(coord: numpy.array, charge: numpy.array, box: numpy.array) → Tuple[numpy.array, numpy.array, numpy.array]¶ Evaluate
- coord
The coordinates of atoms
- charge
The atomic charge
- box
The simulation region. PBC is assumed
- e
The energy
- f
The force
- v
The virial
-
-
class
deepmd.fit.ener.
EnerFitting
¶ -
build
(inputs: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, input_dict: dict = {}, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for fitting net
- inputs
The input descriptor
- input_dict
Additional dict for inputs. if numb_fparam > 0, should have input_dict[‘fparam’] if numb_aparam > 0, should have input_dict[‘aparam’]
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- ener
The system energy
-
compute_input_stats
(all_stat: dict, protection: float = 0.01) → None¶ Compute the input statistics
Parameters: all_stat
if numb_fparam > 0 must have all_stat[‘fparam’] if numb_aparam > 0 must have all_stat[‘aparam’] can be prepared by model.make_stat_input
- protection
Divided-by-zero protection
-
compute_output_stats
(all_stat: dict) → None¶ Compute the ouput statistics
- all_stat
must have the following components: all_stat[‘energy’] of shape n_sys x n_batch x n_frame can be prepared by model.make_stat_input
-
get_numb_aparam
() → int¶ Get the number of atomic parameters
-
get_numb_fparam
() → int¶ Get the number of frame parameters
-
-
class
deepmd.fit.dipole.
DipoleFittingSeA
¶ Fit the atomic dipole with descriptor se_a
-
build
(input_d: tensorflow.python.framework.ops.Tensor, rot_mat: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, reuse: Optional[bool] = None, suffix: str = '') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for fitting net
- input_d
The input descriptor
- rot_mat
The rotation matrix from the descriptor.
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- dipole
The atomic dipole.
-
get_out_size
() → int¶ Get the output size. Should be 3
-
get_sel_type
() → int¶ Get selected type
-
-
class
deepmd.fit.polar.
GlobalPolarFittingSeA
¶ Fit the system polarizability with descriptor se_a
-
build
(input_d, rot_mat, natoms, reuse=None, suffix='') → tensorflow.python.framework.ops.Tensor¶ Build the computational graph for fitting net
- input_d
The input descriptor
- rot_mat
The rotation matrix from the descriptor.
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- polar
The system polarizability
-
get_out_size
() → int¶ Get the output size. Should be 9
-
get_sel_type
() → int¶ Get selected atom types
-
-
class
deepmd.fit.polar.
PolarFittingLocFrame
(jdata, descrpt)¶ Fitting polarizability with local frame descriptor. not supported anymore.
-
build
(input_d, rot_mat, natoms, reuse=None, suffix='')¶
-
get_out_size
()¶
-
get_sel_type
()¶
-
-
class
deepmd.fit.polar.
PolarFittingSeA
¶ Fit the atomic polarizability with descriptor se_a
-
build
(input_d: tensorflow.python.framework.ops.Tensor, rot_mat: tensorflow.python.framework.ops.Tensor, natoms: tensorflow.python.framework.ops.Tensor, reuse: Optional[bool] = None, suffix: str = '')¶ Build the computational graph for fitting net
- input_d
The input descriptor
- rot_mat
The rotation matrix from the descriptor.
- natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- atomic_polar
The atomic polarizability
-
compute_input_stats
(all_stat, protection=0.01)¶ Compute the input statistics
Parameters: all_stat
Dictionary of inputs. can be prepared by model.make_stat_input
- protection
Divided-by-zero protection
-
get_out_size
() → int¶ Get the output size. Should be 9
-
get_sel_type
() → List[int]¶ Get selected atom types
-
-
class
deepmd.fit.wfc.
WFCFitting
(jdata, descrpt)¶ Fitting Wannier function centers (WFCs) with local frame descriptor. Not supported anymore.
-
build
(input_d, rot_mat, natoms, reuse=None, suffix='')¶
-
get_out_size
()¶
-
get_sel_type
()¶
-
get_wfc_numb
()¶
-
-
deepmd.utils.network.
embedding_net
(xx, network_size, precision, activation_fn=<function tanh>, resnet_dt=False, name_suffix='', stddev=1.0, bavg=0.0, seed=None, trainable=True)¶ - xxTensor
Input tensor of shape [-1,1]
- network_size: list of int
Size of the embedding network. For example [16,32,64]
- precision:
Precision of network weights. For example, tf.float64
- activation_fn:
Activation function
- resnet_dt: boolean
Using time-step in the ResNet construction
- name_suffix: str
The name suffix append to each variable.
- stddev: float
Standard deviation of initializing network parameters
- bavg: float
Mean of network intial bias
- seed: int
Random seed for initializing network parameters
- trainable: boolean
If the netowk is trainable
-
deepmd.utils.network.
one_layer
(inputs, outputs_size, activation_fn=<function tanh>, precision=tf.float64, stddev=1.0, bavg=0.0, name='linear', reuse=None, seed=None, use_timestep=False, trainable=True, useBN=False)¶
-
deepmd.utils.network.
variable_summaries
(var: tensorflow.python.ops.variables.VariableV1, name: str)¶ Attach a lot of summaries to a Tensor (for TensorBoard visualization).
- vartf.Variable
[description]
- namestr
variable name
-
class
deepmd.utils.learning_rate.
LearningRateExp
(start_lr: float, stop_lr: float = 5e-08, decay_steps: int = 5000, decay_rate: float = 0.95)¶ The exponentially decaying learning rate.
The learning rate at step t is given by
lr(t) = start_lr * decay_rate ^ ( t / decay_steps )
-
build
(global_step: tensorflow.python.framework.ops.Tensor, stop_step: Optional[int] = None) → tensorflow.python.framework.ops.Tensor¶ Build the learning rate
- global_step
The tf Tensor prividing the global training step
- stop_step
The stop step. If provided, the decay_rate will be determined automatically and overwritten.
- learning_rate
The learning rate
-
start_lr
() → float¶ Get the start lr
-
value
(step: int) → float¶ Get the lr at a certain step
-
Get local GPU resources from CUDA_VISIBLE_DEVICES enviroment variable.
-
deepmd.cluster.local.
get_resource
() → Tuple[str, List[str], Optional[List[int]]]¶ Get local resources: nodename, nodelist, and gpus.
- Tuple[str, List[str], Optional[List[int]]]
nodename, nodelist, and gpus
MOdule to get resources on SLURM cluster.
References¶
https://github.com/deepsense-ai/tensorflow_on_slurm ####
-
deepmd.cluster.slurm.
get_resource
() → Tuple[str, List[str], Optional[List[int]]]¶ Get SLURM resources: nodename, nodelist, and gpus.
- Tuple[str, List[str], Optional[List[int]]]
nodename, nodelist, and gpus
- RuntimeError
if number of nodes could not be retrieved
- ValueError
list of nodes is not of the same length sa number of nodes
- ValueError
if current nodename is not found in node list
-
class
deepmd.loss.ener.
EnerDipoleLoss
(starter_learning_rate: float, start_pref_e: float = 0.1, limit_pref_e: float = 1.0, start_pref_ed: float = 1.0, limit_pref_ed: float = 1.0)¶ -
build
(learning_rate, natoms, model_dict, label_dict, suffix)¶
-
eval
(sess, feed_dict, natoms)¶
-
static
print_header
()¶
-
print_on_training
(tb_writer, cur_batch, sess, natoms, feed_dict_test, feed_dict_batch)¶
-
-
class
deepmd.loss.ener.
EnerStdLoss
(starter_learning_rate: float, start_pref_e: float = 0.02, limit_pref_e: float = 1.0, start_pref_f: float = 1000, limit_pref_f: float = 1.0, start_pref_v: float = 0.0, limit_pref_v: float = 0.0, start_pref_ae: float = 0.0, limit_pref_ae: float = 0.0, start_pref_pf: float = 0.0, limit_pref_pf: float = 0.0, relative_f: Optional[float] = None)¶ Standard loss function for DP models
-
build
(learning_rate, natoms, model_dict, label_dict, suffix)¶
-
eval
(sess, feed_dict, natoms)¶
-
print_header
()¶
-
print_on_training
(tb_writer, cur_batch, sess, natoms, feed_dict_test, feed_dict_batch)¶
-
-
class
deepmd.loss.tensor.
TensorLoss
(jdata, **kwarg)¶ Loss function for tensorial properties.
-
build
(learning_rate, natoms, model_dict, label_dict, suffix)¶
-
eval
(sess, feed_dict, natoms)¶
-
print_header
()¶
-
print_on_training
(tb_writer, cur_batch, sess, natoms, feed_dict_test, feed_dict_batch)¶
-
-
class
deepmd.model.ener.
EnerModel
(descrpt, fitting, typeebd=None, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01, use_srtab: Optional[str] = None, smin_alpha: Optional[float] = None, sw_rmin: Optional[float] = None, sw_rmax: Optional[float] = None)¶ -
build
(coord_, atype_, natoms, box, mesh, input_dict, suffix='', reuse=None)¶
-
data_stat
(data)¶
-
get_ntypes
()¶
-
get_rcut
()¶
-
get_type_map
()¶
-
model_type
= 'ener'¶
-
-
class
deepmd.model.tensor.
DipoleModel
(descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)¶
-
class
deepmd.model.tensor.
GlobalPolarModel
(descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)¶
-
class
deepmd.model.tensor.
PolarModel
(descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)¶
-
class
deepmd.model.tensor.
TensorModel
(tensor_name: str, descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)¶ -
build
(coord_, atype_, natoms, box, mesh, input_dict, suffix='', reuse=None)¶
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data_stat
(data)¶
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get_ntypes
()¶
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get_out_size
()¶
-
get_rcut
()¶
-
get_sel_type
()¶
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get_type_map
()¶
-
-
class
deepmd.model.tensor.
WFCModel
(descrpt, fitting, type_map: Optional[List[str]] = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)¶
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class
deepmd.train.trainer.
DPTrainer
(jdata, run_opt)¶ -
build
(data, stop_batch=0)¶
-
get_evaluation_results
(batch_list)¶
-
get_feed_dict
(batch, is_training)¶
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get_global_step
()¶
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static
print_header
(fp, train_results, valid_results)¶
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static
print_on_training
(fp, train_results, valid_results, cur_batch, cur_lr)¶
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train
(train_data, valid_data=None)¶
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valid_on_the_fly
(fp, train_batches, valid_batches, print_header=False)¶
-