def makecsv(file, model, loadfile):
cuda = False
kwargs = {'num_workers': 1, 'pin_memory': True} if cuda else {}
data_loader = Loader(c.FILE_TRAIN_LABELED_AUG, c.FILE_TRAIN_UNLABELED, c.FILE_TEST, 'data/test-labeled.p', kwargs)
test_loader = data_loader.getTest()
test_actual = data_loader.getValidation()
label_predict = np.array([])
mnist_model = model
if loadfile:
mnist_model = torch.load(model)
correct = 0
for data, target in test_loader:
mnist_model.eval()
data, target = Variable(data, volatile=True), Variable(target)
output = mnist_model(data)
temp = output.data.max(1)[1]
pred = output.data.max(1)[1]
correct += pred.eq(target.data).cpu().sum()
label_predict = np.concatenate((label_predict, temp.numpy().reshape(-1)))
print('\nTest set: Accuracy: {}/{} ({:.0f}%)\n'.format(correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
predict_label = pd.DataFrame(label_predict, columns=['label'], dtype=int)
predict_label.reset_index(inplace=True)
predict_label.rename(columns={'index': 'ID'}, inplace=True)
filename = 'predictions/' + file + "-labeled.csv"
predict_label.to_csv(filename, index=False)
label_predict = np.array([])
correct = 0
for data, target in test_actual:
mnist_model.eval()
data, target = Variable(data, volatile=True), Variable(target)
output = mnist_model(data)
temp = output.data.max(1)[1]
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(target.data).cpu().sum()
label_predict = np.concatenate((label_predict, temp.numpy().reshape(-1)))
print('\nTest set: Accuracy: {}/{} ({:.0f}%)\n'.format(correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
predict_label = pd.DataFrame(label_predict, columns=['label'], dtype=int)
predict_label.reset_index(inplace=True)
predict_label.rename(columns={'index': 'ID'}, inplace=True)
filename = 'predictions/' + file + "-unlabeled.csv"
predict_label.to_csv(filename, index=False)
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
print(args)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
data_loader = Loader('data/train_labeled_aug.p', c.FILE_TRAIN_UNLABELED,
c.FILE_VALIDATION, c.FILE_TEST, kwargs)
train_loader = data_loader.getLabeledtrain()
unlabeled_train_loader = data_loader.getUnlabeledtrain()
valid_loader = data_loader.getValidation()
model = Ladder()
if args.cuda:
model.cuda()
l2loss = torch.nn.BCELoss(
) #torch.nn.L1Loss() # BCELoss : Pass through sigmoid
#l2_2 = torch.nn.L1Loss()
nllloss = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
def train_unlabeled(epoch):
model.train()
import pickle
import numpy as np
import pandas as pd
import torch.nn as nn
import constants as c
from dataloader import Loader
from torch.autograd import Variable
cuda = torch.cuda.is_available()
kwargs = {'num_workers': 1, 'pin_memory': True} if cuda else {}
data_loader = Loader(c.FILE_TRAIN_LABELED_AUG, c.FILE_TRAIN_UNLABELED, c.FILE_TEST, 'data/test-labeled.p', kwargs)
test_loader = data_loader.getTest()
test_actual = data_loader.getValidation()
label_predict = np.array([])
def callval(mnist_model, test_loader, test_actual, model, file):
label_predict = np.array([])
loadfile = True
if loadfile:
mnist = torch.load(model)
mnist_model.load_state_dict(mnist)
correct = 0
if torch.cuda.is_available():
mnist_model.cuda()
mnist_model.setsupervised(True)
