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evaluate_nn.py
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evaluate_nn.py
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import numpy as np
import torch
import torch.nn as nn
import utils
from utils import LogReg
hid_units = 512
xent = nn.CrossEntropyLoss()
cnt_wait = 0
def evaluate_pre(args, emb):
_, _, labels, idx_train, idx_val, idx_test = utils.load_data(args.dataset)
nb_classes = int(labels.shape[1])
labels = torch.FloatTensor(labels[np.newaxis])
emb = torch.FloatTensor(emb)
if torch.cuda.is_available():
emb = emb.cuda()
labels = labels.cuda()
train_embs = emb[idx_train]
test_embs = emb[idx_test]
train_lbls = torch.argmax(labels[0, idx_train], dim=1)
test_lbls = torch.argmax(labels[0, idx_test], dim=1)
evaluate_semi(train_embs=train_embs,
test_embs=test_embs,
train_lbls=train_lbls,
test_lbls=test_lbls,
nb_classes=nb_classes)
def evaluate_semi(train_embs, test_embs, train_lbls, test_lbls, nb_classes):
is_cuda = torch.cuda.is_available()
# torch.manual_seed(0)
# if is_cuda:
# torch.cuda.manual_seed(0)
hid_units = train_embs.shape[1]
tot = torch.zeros(1)
if is_cuda:
tot = tot.cuda()
accs = []
for _ in range(50):
log = LogReg(hid_units, nb_classes)
opt = torch.optim.Adam(log.parameters(), lr=0.01, weight_decay=0.0)
if is_cuda:
log.cuda()
pat_steps = 0
best_acc = torch.zeros(1)
if is_cuda:
best_acc = best_acc.cuda()
for _ in range(100):
log.train()
opt.zero_grad()
logits = log(train_embs)
loss = xent(logits, train_lbls)
loss.backward()
opt.step()
logits = log(test_embs)
preds = torch.argmax(logits, dim=1)
acc = torch.sum(preds == test_lbls).float() / test_lbls.shape[0]
accs.append(acc * 100)
print(acc)
tot += acc
print('Average accuracy:', tot / 50)
accs = torch.stack(accs)
print(accs.mean())
print(accs.std())