import torch
from torch import nn
from torch.autograd import grad, Variable
from .backflow_kernel_base import BackFlowKernelBase
[docs]class BackFlowKernelFullyConnected(BackFlowKernelBase):
def __init__(self, mol, cuda):
"""Compute the back flow kernel, i.e. the function
f(rij) where rij is the distance between electron i and j
This kernel is used in the backflow transformation
.. math:
q_i = r_i + \\sum_{j\\neq i} f(r_{ij}) (r_i-r_j)
"""
super().__init__(mol, cuda)
self.fc1 = nn.Linear(1, 16, bias=False)
self.fc2 = nn.Linear(16, 1, bias=False)
self.nl_func = torch.nn.Sigmoid()
eps = 1E-0
self.fc1.weight.data *= eps
self.fc2.weight.data *= eps
def _backflow_kernel(self, ree):
"""Computes the kernel via autodiff
Args:
ree ([type]): [description]
Returns:
[type]: [description]
"""
original_shape = ree.shape
x = ree.reshape(-1, 1)
x = self.fc1(x)
x = self.nl_func(x)
x = self.fc2(x)
x = self.nl_func(x)
x = x.reshape(*original_shape)
return x