def __init__(self,
DIM,
batchsize_para,
optimizee,
train_steps,
retain_graph_flag=False,
reset_theta=False,
reset_function_from_IID_distirbution=True):
self.optimizee = optimizee
self.beta = 1
self.train_steps = train_steps
self.retain_graph_flag = retain_graph_flag
self.reset_theta = reset_theta
self.reset_function_from_IID_distirbution = reset_function_from_IID_distirbution
self.state = None
self.DIM = DIM
self.batchsize_para = batchsize_para
self.retraction = Retraction(1)
for parameters in optimizee.parameters():
print(torch.sum(parameters))
self.M = torch.randn(self.batchsize_para, self.DIM, self.DIM)
for i in range(self.batchsize_para):
self.M[i] = torch.eye(self.DIM)
self.M = self.M.cuda()
self.M.requires_grad = True
self.P_tangent = torch.zeros(self.batchsize_para, self.DIM,
self.DIM).cuda()
def __init__(self,
opt,
DIM,
outputDIM,
batchsize_para,
optimizee,
train_steps,
retain_graph_flag=False,
reset_theta=False,
reset_function_from_IID_distirbution=True):
self.criterion = ContrastiveLoss().cuda()
self.retraction = Retraction(1)
self.optimizee = optimizee
self.opt = opt
self.beta = 1
self.eiglayer1 = EigLayer()
self.mexp = M_Exp()
self.train_steps = train_steps
#self.num_roll=num_roll
self.retain_graph_flag = retain_graph_flag
self.reset_theta = reset_theta
self.reset_function_from_IID_distirbution = reset_function_from_IID_distirbution
self.state = None
self.DIM = DIM
self.outputDIM = outputDIM
self.batchsize_para = batchsize_para
self.global_loss_graph = 0 # global loss for optimizing LSTM
self.losses = [] # KEEP each loss of all epoches
for parameters in optimizee.parameters():
print(torch.sum(parameters))
self.M = torch.randn(self.batchsize_para, self.DIM,
self.outputDIM).cuda()
for i in range(self.batchsize_para):
'''
U = torch.empty(self.DIM, self.DIM)
nn.init.orthogonal_(U)
D=torch.abs(torch.diag(torch.rand(self.DIM)+0.6))
self.M[i]=(U.mm(D)).mm(U.t())
#self.M[i]=self.M[i]+0.001*torch.trace(self.M[i])*torch.eye(self.DIM)
'''
nn.init.orthogonal_(self.M[i])
self.M = self.M.cuda()
self.M.requires_grad = True
import torch
import torch.nn as nn
from torch.autograd import Variable
from timeit import default_timer as timer
import time
import math, random
import numpy as np
from losses.LOSS import ContrastiveLoss
from ReplyBuffer import ReplayBuffer
from retraction import Retraction
retraction = Retraction(1)
grad_list = []
def print_grad(grad):
grad_list.append(grad)
def nll_loss(data, label):
n = data.shape[0]
L = 0
for i in range(n):
L = L + torch.abs(data[i][label[i]])
return L
def f(inputs, target, M):
loss_function = torch.nn.CrossEntropyLoss(reduction='sum')