def train(trainloader):
"""
Performs training and evaluation of CNN model.
NOTE: You should the model on the whole test set each eval_freq iterations.
"""
# YOUR TRAINING CODE GOES HERE
n_channels = 3
n_classes = 5
cnn = CNN(n_channels, n_classes)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cnn.to(device)
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=LEARNING_RATE_DEFAULT)
losses = []
accuracies = []
for epoch in range(MAX_EPOCHS_DEFAULT):
timestart = time.time()
running_loss = 0.0
for step, (batch_x, batch_y) in enumerate(trainloader):
# zero the parameter gradients
optimizer.zero_grad()
# Forward + Backward + Optimize
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
outputs = cnn(batch_x)
loss = criterion(outputs, batch_y)
loss.backward()
optimizer.step()
running_loss += loss.item()
if step % EVAL_FREQ_DEFAULT == EVAL_FREQ_DEFAULT - 1:
print('[epoch: %d, step: %5d] loss: %.4f' %
(epoch, step, running_loss / EVAL_FREQ_DEFAULT))
losses.append(running_loss / EVAL_FREQ_DEFAULT)
running_loss = 0.0
accu = accuracy(outputs, batch_y)
accuracies.append(accu)
print('Accuracy on the %d train images: %.3f %%' %
(batch_y.size(0), accu))
break
print('epoch %d cost %3f sec' % (epoch, time.time() - timestart))
print('---Finished Training---')
return cnn, losses, accuracies
######################################################
# LOAD DATA
######################################################
train_loader = get_train_loader(
'/scratch/ehd255/ssl_data_96/supervised/train/', batch_size=32)
# unsup_loader = get_unsup_loader('/scratch/ehd255/ssl_data_96/unsupervised/', batch_size=32)
test_loader = get_test_loader('/scratch/ehd255/ssl_data_96/supervised/val/',
batch_size=32)
######################################################
# BUILD MODEL
######################################################
model = CNN()
model.to(device)
######################################################
# TRAIN MODEL
######################################################
def train(num_epochs=10):
model.train()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
for epoch in range(num_epochs):
for batch_idx, (data, target) in enumerate(train_loader):
