deepmd.tf.fit
Submodules
Package Contents
Classes
Fit the atomic dipole with descriptor se_a. | |
Fitting the density of states (DOS) of the system. | |
Fitting the energy of the system. The force and the virial can also be trained. | |
A class to remove type from input arguments. | |
Fit the system polarizability with descriptor se_a. | |
Fit the atomic polarizability with descriptor se_a. |
- class deepmd.tf.fit.DipoleFittingSeA(ntypes: int, dim_descrpt: int, embedding_width: int, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, sel_type: List[int] | None = None, seed: int | None = None, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False, mixed_types: bool = False, **kwargs)[source]
Bases:
deepmd.tf.fit.fitting.FittingFit the atomic dipole with descriptor se_a.
- Parameters:
- ntypes
The ntypes of the descrptor \(\mathcal{D}\)
- dim_descrpt
The dimension of the descrptor \(\mathcal{D}\)
- embedding_width
The rotation matrix dimension of the descrptor \(\mathcal{D}\)
- neuron
List[int] Number of neurons in each hidden layer of the fitting net
- resnet_dtbool
Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
- sel_type
List[int] The atom types selected to have an atomic dipole prediction. If is None, all atoms are selected.
- seed
int Random seed for initializing the network parameters.
- activation_function
str The activation function in the embedding net. Supported options are “relu”, “tanh”, “none”, “linear”, “softplus”, “sigmoid”, “relu6”, “gelu”, “gelu_tf”.
- precision
str The precision of the embedding net parameters. Supported options are “float32”, “default”, “float16”, “float64”.
- uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
- mixed_typesbool
If true, use a uniform fitting net for all atom types, otherwise use different fitting nets for different atom types.
- build(input_d: deepmd.tf.env.tf.Tensor, rot_mat: deepmd.tf.env.tf.Tensor, natoms: deepmd.tf.env.tf.Tensor, input_dict: dict | None = None, reuse: bool | None = None, suffix: str = '') deepmd.tf.env.tf.Tensor[source]
Build the computational graph for fitting net.
- Parameters:
- 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
- input_dict
Additional dict for inputs.
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- Returns:
dipoleThe atomic dipole.
- init_variables(graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, suffix: str = '') None[source]
Init the fitting net variables with the given dict.
- enable_mixed_precision(mixed_prec: dict | None = None) None[source]
Reveive the mixed precision setting.
- Parameters:
- mixed_prec
The mixed precision setting used in the embedding net
- get_loss(loss: dict, lr) deepmd.tf.loss.loss.Loss[source]
Get the loss function.
- Parameters:
- loss
dict the loss dict
- lr
LearningRateExp the learning rate
- loss
- Returns:
Lossthe loss function
- class deepmd.tf.fit.DOSFitting(ntypes: int, dim_descrpt: int, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, numb_dos: int = 300, rcond: float | None = None, trainable: List[bool] | None = None, seed: int | None = None, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False, layer_name: List[str | None] | None = None, use_aparam_as_mask: bool = False, mixed_types: bool = False, **kwargs)[source]
Bases:
deepmd.tf.fit.fitting.FittingFitting the density of states (DOS) of the system. The energy should be shifted by the fermi level.
- Parameters:
- ntypes
The ntypes of the descrptor \(\mathcal{D}\)
- dim_descrpt
The dimension of the descrptor \(\mathcal{D}\)
- neuron
Number of neurons \(N\) in each hidden layer of the fitting net
- resnet_dt
Time-step dt in the resnet construction: \(y = x + dt * \phi (Wx + b)\)
- numb_fparam
Number of frame parameter
- numb_aparam
Number of atomic parameter
- ! numb_dos (added)
Number of gridpoints on which the DOS is evaluated (NEDOS in VASP)
- rcond
The condition number for the regression of atomic energy.
- trainable
If the weights of fitting net are trainable. Suppose that we have \(N_l\) hidden layers in the fitting net, this list is of length \(N_l + 1\), specifying if the hidden layers and the output layer are trainable.
- seed
Random seed for initializing the network parameters.
- activation_function
The activation function \(\boldsymbol{\phi}\) in the embedding net. Supported options are “relu”, “tanh”, “none”, “linear”, “softplus”, “sigmoid”, “relu6”, “gelu”, “gelu_tf”.
- precision
The precision of the embedding net parameters. Supported options are “float32”, “default”, “float16”, “float64”.
- uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
- layer_name
list[Optional[str]],optional The name of the each layer. If two layers, either in the same fitting or different fittings, have the same name, they will share the same neural network parameters.
- use_aparam_as_mask: bool, optional
If True, the atomic parameters will be used as a mask that determines the atom is real/virtual. And the aparam will not be used as the atomic parameters for embedding.
- mixed_typesbool
If true, use a uniform fitting net for all atom types, otherwise use different fitting nets for different atom types.
- compute_output_stats(all_stat: dict, mixed_type: bool = False) None[source]
Compute the ouput statistics.
- Parameters:
- all_stat
must have the following components: all_stat[‘dos’] of shape n_sys x n_batch x n_frame x numb_dos can be prepared by model.make_stat_input
- mixed_type
Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.
- compute_input_stats(all_stat: dict, protection: float = 0.01) None[source]
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
- _build_lower(start_index, natoms, inputs, fparam=None, aparam=None, bias_dos=0.0, type_suffix='', suffix='', reuse=None)[source]
- build(inputs: deepmd.tf.env.tf.Tensor, natoms: deepmd.tf.env.tf.Tensor, input_dict: dict | None = None, reuse: bool | None = None, suffix: str = '') deepmd.tf.env.tf.Tensor[source]
Build the computational graph for fitting net.
- Parameters:
- 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
- Returns:
enerThe system energy
- init_variables(graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, suffix: str = '') None[source]
Init the fitting net variables with the given dict.
- enable_mixed_precision(mixed_prec: dict | None = None) None[source]
Reveive the mixed precision setting.
- Parameters:
- mixed_prec
The mixed precision setting used in the embedding net
- get_loss(loss: dict, lr) deepmd.tf.loss.loss.Loss[source]
Get the loss function.
- Parameters:
- loss
dict the loss dict
- lr
LearningRateExp the learning rate
- loss
- Returns:
Lossthe loss function
- class deepmd.tf.fit.EnerFitting(ntypes: int, dim_descrpt: int, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, rcond: float | None = None, tot_ener_zero: bool = False, trainable: List[bool] | None = None, seed: int | None = None, atom_ener: List[float] = [], activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False, layer_name: List[str | None] | None = None, use_aparam_as_mask: bool = False, spin: deepmd.tf.utils.spin.Spin | None = None, mixed_types: bool = False, **kwargs)[source]
Bases:
deepmd.tf.fit.fitting.FittingFitting the energy of the system. The force and the virial can also be trained.
The potential energy \(E\) is a fitting network function of the descriptor \(\mathcal{D}\):
\[E(\mathcal{D}) = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)} \circ \cdots \circ \mathcal{L}^{(1)} \circ \mathcal{L}^{(0)}\]The first \(n\) hidden layers \(\mathcal{L}^{(0)}, \cdots, \mathcal{L}^{(n-1)}\) are given by
\[\mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})= \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b})\]where \(\mathbf{x} \in \mathbb{R}^{N_1}\) is the input vector and \(\mathbf{y} \in \mathbb{R}^{N_2}\) is the output vector. \(\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}\) and \(\mathbf{b} \in \mathbb{R}^{N_2}\) are weights and biases, respectively, both of which are trainable if trainable[i] is True. \(\boldsymbol{\phi}\) is the activation function.
The output layer \(\mathcal{L}^{(n)}\) is given by
\[\mathbf{y}=\mathcal{L}^{(n)}(\mathbf{x};\mathbf{w},\mathbf{b})= \mathbf{x}^T\mathbf{w}+\mathbf{b}\]where \(\mathbf{x} \in \mathbb{R}^{N_{n-1}}\) is the input vector and \(\mathbf{y} \in \mathbb{R}\) is the output scalar. \(\mathbf{w} \in \mathbb{R}^{N_{n-1}}\) and \(\mathbf{b} \in \mathbb{R}\) are weights and bias, respectively, both of which are trainable if trainable[n] is True.
- Parameters:
- ntypes
The ntypes of the descrptor \(\mathcal{D}\)
- dim_descrpt
The dimension of the descrptor \(\mathcal{D}\)
- neuron
Number of neurons \(N\) in each hidden layer of the fitting net
- resnet_dt
Time-step dt in the resnet construction: \(y = x + dt * \phi (Wx + b)\)
- numb_fparam
Number of frame parameter
- numb_aparam
Number of atomic parameter
- rcond
The condition number for the regression of atomic energy.
- tot_ener_zero
Force the total energy to zero. Useful for the charge fitting.
- trainable
If the weights of fitting net are trainable. Suppose that we have \(N_l\) hidden layers in the fitting net, this list is of length \(N_l + 1\), specifying if the hidden layers and the output layer are trainable.
- seed
Random seed for initializing the network parameters.
- atom_ener
Specifying atomic energy contribution in vacuum. The set_davg_zero key in the descrptor should be set.
- activation_function
The activation function \(\boldsymbol{\phi}\) in the embedding net. Supported options are “relu”, “tanh”, “none”, “linear”, “softplus”, “sigmoid”, “relu6”, “gelu”, “gelu_tf”.
- precision
The precision of the embedding net parameters. Supported options are “float32”, “default”, “float16”, “float64”.
- uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
- layer_name
list[Optional[str]],optional The name of the each layer. If two layers, either in the same fitting or different fittings, have the same name, they will share the same neural network parameters.
- use_aparam_as_mask: bool, optional
If True, the atomic parameters will be used as a mask that determines the atom is real/virtual. And the aparam will not be used as the atomic parameters for embedding.
- mixed_typesbool
If true, use a uniform fitting net for all atom types, otherwise use different fitting nets for different atom types.
- compute_output_stats(all_stat: dict, mixed_type: bool = False) None[source]
Compute the ouput statistics.
- Parameters:
- 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
- mixed_type
Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.
- compute_input_stats(all_stat: dict, protection: float = 0.01) None[source]
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
- _build_lower(start_index, natoms, inputs, fparam=None, aparam=None, bias_atom_e=0.0, type_suffix='', suffix='', reuse=None)[source]
- build(inputs: deepmd.tf.env.tf.Tensor, natoms: deepmd.tf.env.tf.Tensor, input_dict: dict | None = None, reuse: bool | None = None, suffix: str = '') deepmd.tf.env.tf.Tensor[source]
Build the computational graph for fitting net.
- Parameters:
- 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
- Returns:
enerThe system energy
- init_variables(graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, suffix: str = '') None[source]
Init the fitting net variables with the given dict.
- change_energy_bias(data, frozen_model, origin_type_map, full_type_map, bias_adjust_mode='change-by-statistic', ntest=10) None[source]
- enable_mixed_precision(mixed_prec: dict | None = None) None[source]
Reveive the mixed precision setting.
- Parameters:
- mixed_prec
The mixed precision setting used in the embedding net
- get_loss(loss: dict, lr) deepmd.tf.loss.loss.Loss[source]
Get the loss function.
- Parameters:
- loss
dict The loss function parameters.
- lr
LearningRateExp The learning rate.
- loss
- Returns:
LossThe loss function.
- class deepmd.tf.fit.Fitting[source]
Bases:
deepmd.tf.utils.PluginVariant,make_plugin_registry('fitting')A class to remove type from input arguments.
- property precision: deepmd.tf.env.tf.DType
Precision of fitting network.
- abstract init_variables(graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, suffix: str = '') None[source]
Init the fitting net variables with the given dict.
- Parameters:
Notes
This method is called by others when the fitting supported initialization from the given variables.
- abstract get_loss(loss: dict, lr) deepmd.tf.loss.loss.Loss[source]
Get the loss function.
- Parameters:
- loss
dict the loss dict
- lr
LearningRateExp the learning rate
- loss
- Returns:
Lossthe loss function
- classmethod deserialize(data: dict, suffix: str = '') Fitting[source]
Deserialize the fitting.
There is no suffix in a native DP model, but it is important for the TF backend.
- abstract serialize(suffix: str = '') dict[source]
Serialize the fitting.
There is no suffix in a native DP model, but it is important for the TF backend.
- serialize_network(ntypes: int, ndim: int, in_dim: int, neuron: List[int], activation_function: str, resnet_dt: bool, variables: dict, out_dim: int | None = 1, suffix: str = '') dict[source]
Serialize network.
- Parameters:
- ntypes
int The number of types
- ndim
int The dimension of elements
- in_dim
int The input dimension
- neuron
List[int] The neuron list
- activation_function
str The activation function
- resnet_dtbool
Whether to use resnet
- variables
dict The input variables
- suffix
str,optional The suffix of the scope
- out_dim
int,optional The output dimension
- ntypes
- Returns:
dictThe converted network data
- class deepmd.tf.fit.GlobalPolarFittingSeA(descrpt: deepmd.tf.env.tf.Tensor, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, sel_type: List[int] | None = None, fit_diag: bool = True, scale: List[float] | None = None, diag_shift: List[float] | None = None, seed: int | None = None, activation_function: str = 'tanh', precision: str = 'default')[source]
Fit the system polarizability with descriptor se_a.
- Parameters:
- descrpt
tf.Tensor The descrptor
- neuron
List[int] Number of neurons in each hidden layer of the fitting net
- resnet_dtbool
Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
- sel_type
List[int] The atom types selected to have an atomic polarizability prediction
- fit_diagbool
Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.
- scale
List[float] The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]
- diag_shift
List[float] The diagonal part of the polarizability matrix of type i will be shifted by diag_shift[i]. The shift operation is carried out after scale.
- seed
int Random seed for initializing the network parameters.
- activation_function
str The activation function in the embedding net. Supported options are “relu”, “tanh”, “none”, “linear”, “softplus”, “sigmoid”, “relu6”, “gelu”, “gelu_tf”.
- precision
str The precision of the embedding net parameters. Supported options are “float32”, “default”, “float16”, “float64”.
- descrpt
- build(input_d, rot_mat, natoms, input_dict: dict | None = None, reuse=None, suffix='') deepmd.tf.env.tf.Tensor[source]
Build the computational graph for fitting net.
- Parameters:
- 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
- input_dict
Additional dict for inputs.
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- Returns:
polarThe system polarizability
- init_variables(graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, suffix: str = '') None[source]
Init the fitting net variables with the given dict.
- enable_mixed_precision(mixed_prec: dict | None = None) None[source]
Reveive the mixed precision setting.
- Parameters:
- mixed_prec
The mixed precision setting used in the embedding net
- get_loss(loss: dict, lr) deepmd.tf.loss.loss.Loss[source]
Get the loss function.
- Parameters:
- loss
dict the loss dict
- lr
LearningRateExp the learning rate
- loss
- Returns:
Lossthe loss function
- class deepmd.tf.fit.PolarFittingSeA(ntypes: int, dim_descrpt: int, embedding_width: int, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, sel_type: List[int] | None = None, fit_diag: bool = True, scale: List[float] | None = None, shift_diag: bool = True, seed: int | None = None, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False, mixed_types: bool = False, **kwargs)[source]
Bases:
deepmd.tf.fit.fitting.FittingFit the atomic polarizability with descriptor se_a.
- Parameters:
- ntypes
The ntypes of the descrptor \(\mathcal{D}\)
- dim_descrpt
The dimension of the descrptor \(\mathcal{D}\)
- embedding_width
The rotation matrix dimension of the descrptor \(\mathcal{D}\)
- neuron
List[int] Number of neurons in each hidden layer of the fitting net
- resnet_dtbool
Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
- sel_type
List[int] The atom types selected to have an atomic polarizability prediction. If is None, all atoms are selected.
- fit_diagbool
Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.
- scale
List[float] The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]
- diag_shift
List[float] The diagonal part of the polarizability matrix of type i will be shifted by diag_shift[i]. The shift operation is carried out after scale.
- seed
int Random seed for initializing the network parameters.
- activation_function
str The activation function in the embedding net. Supported options are “relu”, “tanh”, “none”, “linear”, “softplus”, “sigmoid”, “relu6”, “gelu”, “gelu_tf”.
- precision
str The precision of the embedding net parameters. Supported options are “float32”, “default”, “float16”, “float64”.
- uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
- mixed_typesbool
If true, use a uniform fitting net for all atom types, otherwise use different fitting nets for different atom types.
- compute_output_stats(all_stat)[source]
Compute the output statistics.
- Parameters:
- all_stat
Dictionary of inputs. can be prepared by model.make_stat_input
- build(input_d: deepmd.tf.env.tf.Tensor, rot_mat: deepmd.tf.env.tf.Tensor, natoms: deepmd.tf.env.tf.Tensor, input_dict: dict | None = None, reuse: bool | None = None, suffix: str = '')[source]
Build the computational graph for fitting net.
- Parameters:
- 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
- input_dict
Additional dict for inputs.
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- Returns:
atomic_polarThe atomic polarizability
- init_variables(graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, suffix: str = '') None[source]
Init the fitting net variables with the given dict.
- enable_mixed_precision(mixed_prec: dict | None = None) None[source]
Reveive the mixed precision setting.
- Parameters:
- mixed_prec
The mixed precision setting used in the embedding net
- get_loss(loss: dict, lr) deepmd.tf.loss.loss.Loss[source]
Get the loss function.