net.to(device)
print(net)
softmax = nn.Softmax()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.1)
loss_list = []
train_acc_list = []
test_acc_list = []
pred_temp = []
true_temp = []
for epoch in range(EPOCH):
net.train()
running_loss = 0
total = train_size
correct = 0
for step, images_labels in enumerate(train_loader):
inputs, labels = images_labels
inputs, labels = inputs.type(
torch.FloatTensor).to(device), labels.type(
torch.LongTensor).to(device)
outputs = net(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
dataiter = iter(train_loader)
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
#=============Defining Training Parameters and type of Device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = Net(out_fea=len(classes))
model = model.train()
model = model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
step = 0
loss_train = []
loss_val = []
min_loss = 100
patience = 5
training_loss_store = []
validation_loss_store = []
writer = SummaryWriter('writer')