deepmd.tf.fit.ener
Module Contents
Classes
Fitting the energy of the system. The force and the virial can also be trained. |
Attributes
- class deepmd.tf.fit.ener.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.