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model.py
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"""
from https://github.com/ibalazevic/TuckER
"""
import numpy as np
import torch
from torch.nn.init import xavier_normal_
class TuckER(torch.nn.Module):
def __init__(self, d, d1, d2, **kwargs):
super(TuckER, self).__init__()
self.E = torch.nn.Embedding(len(d.entities), d1, padding_idx=0)
self.R = torch.nn.Embedding(len(d.relations), d2, padding_idx=0)
self.W = torch.nn.Parameter(
torch.tensor(np.random.uniform(-1, 1, (d2, d1, d1)),
dtype=torch.float,
device="cuda",
requires_grad=True))
self.input_dropout = torch.nn.Dropout(kwargs["input_dropout"])
self.hidden_dropout1 = torch.nn.Dropout(kwargs["hidden_dropout1"])
self.hidden_dropout2 = torch.nn.Dropout(kwargs["hidden_dropout2"])
if kwargs["loss"] == 'BCE':
self.loss = torch.nn.BCELoss()
elif kwargs["loss"] == 'CE':
self.loss = torch.nn.CrossEntropyLoss(reduction="sum")
self.klloss = torch.nn.KLDivLoss(reduction="sum")
self.bn0 = torch.nn.BatchNorm1d(d1)
self.bn1 = torch.nn.BatchNorm1d(d1)
def init(self):
xavier_normal_(self.E.weight.data)
xavier_normal_(self.R.weight.data)
def forward(self, e1_idx, r_idx, y=0):
e1 = self.E(e1_idx)
x = self.bn0(e1)
x = self.input_dropout(x)
x = x.view(-1, 1, e1.size(1))
r = self.R(r_idx)
W_mat = torch.mm(r, self.W.view(r.size(1), -1))
W_mat = W_mat.view(-1, e1.size(1), e1.size(1))
W_mat = self.hidden_dropout1(W_mat)
x = torch.bmm(x, W_mat)
x = x.view(-1, e1.size(1))
x = self.bn1(x)
x = self.hidden_dropout2(x)
x = torch.mm(x, self.E.weight.transpose(1, 0))
pred = torch.sigmoid(x)
return pred
def count_zero_weights_ent(self):
zeros = 0
p = 0
for param in self.E.weight:
p += param.size()[0]
zeros += param.numel() - param.nonzero().size(0)
return (zeros / p)
def count_zero_weights_rel(self):
zeros = 0
p = 0
for param in self.R.weight:
p += param.size()[0]
zeros += param.numel() - param.nonzero().size(0)
return (zeros / p)
# TODO: is a 3 way tensor!
def count_zero_weights_W(self):
zeros = 0
p = 0
for param in self.W:
p += param.size()[0]
zeros += param.numel() - param.nonzero().size(0)
return (zeros / p)
def count_negative_weights_ent(self):
neg = 0
p = 0
for param in self.E.weight:
if param is not None:
p += param.size()[0]
neg += torch.sum((param < 0)).data.item()
return (neg / p)
def count_negative_weights_rel(self):
neg = 0
p = 0
for param in self.R.weight:
if param is not None:
p += param.size()[0]
neg += torch.sum((param < 0)).data.item()
return (neg / p)
# TODO: is a 3 way tensor!
def count_negative_weights_W(self):
neg = 0
p = 0
for param in self.W:
if param is not None:
p += param.size()[0]
neg += torch.sum((param < 0)).data.item()
return (neg / p)
class RESCAL(torch.nn.Module):
def __init__(self, d, d1, d2, **kwargs):
super(RESCAL, self).__init__()
self.E = torch.nn.Embedding(len(d.entities), d1, padding_idx=0)
self.R = torch.nn.Embedding(len(d.relations), d1*d1, padding_idx=0)
self.input_dropout = torch.nn.Dropout(kwargs["input_dropout"])
self.hidden_dropout1 = torch.nn.Dropout(kwargs["hidden_dropout1"])
self.hidden_dropout2 = torch.nn.Dropout(kwargs["hidden_dropout2"])
if kwargs["loss"] == 'BCE':
self.loss = torch.nn.BCELoss()
elif kwargs["loss"] == 'CE':
self.loss = torch.nn.CrossEntropyLoss(reduction="sum")
self.klloss = torch.nn.KLDivLoss(reduction="sum")
self.bn0 = torch.nn.BatchNorm1d(d1)
self.bn1 = torch.nn.BatchNorm1d(d1)
def init(self):
xavier_normal_(self.E.weight.data)
xavier_normal_(self.R.weight.data)
def forward(self, e1_idx, r_idx, y=0):
e1 = self.E(e1_idx)
x = self.bn0(e1)
x = self.input_dropout(x)
x = x.view(-1, 1, e1.size(1))
r = self.R(r_idx)
W_mat = r.view(-1, e1.size(1), e1.size(1))
W_mat = self.hidden_dropout1(W_mat)
x = torch.bmm(x, W_mat)
x = x.view(-1, e1.size(1))
x = self.bn1(x)
x = self.hidden_dropout2(x)
x = torch.mm(x, self.E.weight.transpose(1, 0))
pred = torch.sigmoid(x)
return pred
def count_zero_weights_ent(self):
zeros = 0
p = 0
for param in self.E.weight:
p += param.size()[0]
zeros += param.numel() - param.nonzero().size(0)
return (zeros / p)
def count_zero_weights_rel(self):
zeros = 0
p = 0
for param in self.R.weight:
p += param.size()[0]
zeros += param.numel() - param.nonzero().size(0)
return (zeros / p)
# TODO: is a 3 way tensor!
def count_zero_weights_W(self):
zeros = 0
p = 0
for param in self.R.weight:
p += param.size()[0]
zeros += param.numel() - param.nonzero().size(0)
return (zeros / p)
def count_negative_weights_ent(self):
neg = 0
p = 0
for param in self.E.weight:
if param is not None:
p += param.size()[0]
neg += torch.sum((param < 0)).data.item()
return (neg / p)
def count_negative_weights_rel(self):
neg = 0
p = 0
for param in self.R.weight:
if param is not None:
p += param.size()[0]
neg += torch.sum((param < 0)).data.item()
return (neg / p)
# TODO: is a 3 way tensor!
def count_negative_weights_W(self):
neg = 0
p = 0
for param in self.R.weight:
if param is not None:
p += param.size()[0]
neg += torch.sum((param < 0)).data.item()
return (neg / p)