def fit(self, X, y, quiet=True):
for epoch in range(100):
running_loss = 0
_skf = StratifiedKFold(n_splits=10, shuffle=True)
for more, less in _skf.split(X, y):
# Get data and labels
inputs = torch.from_numpy(X[more])
labels = torch.FloatTensor(np.array([y[more]]).T * 1.0)
# Make sure positive and negative samples have the same number
inputs = inputs[:torch.sum(labels).type(torch.int16) * 2]
labels = labels[:torch.sum(labels).type(torch.int16) * 2]
# wrap them in Variable
inputs = Variable(inputs.cuda())
labels = Variable(labels.cuda())
# zero the parameter gradients
self.optimizer.zero_grad()
# forward + backward + optimize
outputs = self.net(inputs)
loss = self.criterion(outputs, labels)
loss.backward()
self.optimizer.step()
running_loss += loss.data
self.scheduler.step()
if not quiet:
if epoch % 10 == 0:
print(f'Epoch {epoch}: {running_loss}')
def predict(self, X):
inputs = torch.from_numpy(X)
inputs = Variable(inputs.cuda())
return self.net(inputs)
import sys
import pickle
# Computing -------------------------------------
import numpy as np
from sklearn.model_selection import StratifiedKFold
# Local tools -----------------------------------
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'tools')) # noqa
from MEG_worker import MEG_Worker
# EEG net ---------------------------------------
import torchsummary
from eegnet import torch, nn, EEGNet, optim, Variable, evaluate
net = EEGNet().cuda()
print(net.forward(Variable(torch.Tensor(np.random.rand(1, 1, 61,
272)).cuda())))
torchsummary.summary(net, input_size=(1, 61, 272))
# Init parameters -------------------------------
# Init results_folder
RESULTS_FOLDER = os.path.join('.', 'results_eegnet')
# Make sure RESULTS_FOLDER is not a file
assert (not os.path.isfile(RESULTS_FOLDER))
# Mkdir if RESULTS_FOLDER does not exist
if not os.path.exists(RESULTS_FOLDER):
os.mkdir(RESULTS_FOLDER)
# %%