Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

add docstring for EnerFitting #1024

Merged
merged 2 commits into from
Aug 25, 2021
Merged

add docstring for EnerFitting #1024

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
f54e2ce
add docstring for `EnerFitting`
njzjz Aug 24, 2021
233b8cc
Apply suggestions from code review
njzjz Aug 25, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
43 changes: 36 additions & 7 deletions deepmd/fit/ener.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,17 +14,46 @@
from deepmd.env import GLOBAL_TF_FLOAT_PRECISION

class EnerFitting ():
"""Fitting the energy of the system. The force and the virial can also be trained.
r"""Fitting the energy of the system. The force and the virial can also be trained.

The potential energy :math:`E` is a fitting network function of the descriptor :math:`\mathcal{D}`:

.. math::
E(\mathcal{D}) = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)}
\circ \cdots \circ \mathcal{L}^{(1)} \circ \mathcal{L}^{(0)}

The first :math:`n` hidden layers :math:`\mathcal{L}^{(0)}, \cdots, \mathcal{L}^{(n-1)}` are given by

.. math::
\mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})=
\boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b})

where :math:`\mathbf{x} \in \mathbb{R}^{N_1}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}`
is the output vector. :math:`\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}` and
:math:`\mathbf{b} \in \mathbb{R}^{N_2}`$` are weights and biases, respectively,
both of which are trainable if `trainable[i]` is `True`. :math:`\boldsymbol{\phi}`
is the activation function.

The output layer :math:`\mathcal{L}^{(n)}` is given by

.. math::
\mathbf{y}=\mathcal{L}^{(n)}(\mathbf{x};\mathbf{w},\mathbf{b})=
\mathbf{x}^T\mathbf{w}+\mathbf{b}

where :math:`\mathbf{x} \in \mathbb{R}^{N_{n-1}}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}`
is the output scalar. :math:`\mathbf{w} \in \mathbb{R}^{N_{n-1}}` and
:math:`\mathbf{b} \in \mathbb{R}`$` are weights and bias, respectively,
both of which are trainable if `trainable[n]` is `True`.

Parameters
----------
descrpt
The descrptor
The descrptor :math:`\mathcal{D}`
neuron
Number of neurons in each hidden layer of the fitting net
Number of neurons :math:`N` in each hidden layer of the fitting net
resnet_dt
Time-step `dt` in the resnet construction:
y = x + dt * \phi (Wx + b)
:math:`y = x + dt * \phi (Wx + b)`
numb_fparam
Number of frame parameter
numb_aparam
Expand All @@ -35,14 +64,14 @@ class EnerFitting ():
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.
Suppose that we have :math:`N_l` hidden layers in the fitting net,
this list is of length :math:`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 in the embedding net. Supported options are {0}
The activation function :math:`\boldsymbol{\phi}` in the embedding net. Supported options are {0}
precision
The precision of the embedding net parameters. Supported options are {1}
uniform_seed
Expand Down