class Model():
def __init__(self,
num_epochs=5,
num_classes=10,
batch_size=100,
learning_rate=0.001):
self.num_epochs = num_epochs
self.num_classes = num_classes
self.batch_size = batch_size
self.learning_rate = learning_rate
self.model = ConvNet(num_classes)
# Loss and optimizer
self.criterion = nn.CrossEntropyLoss()
self.optimizer = torch.optim.Adam(self.model.parameters(),
lr=learning_rate)
def train(self, train_loader):
total_step = len(train_loader)
for epoch in range(self.num_epochs):
for i, (images, labels) in enumerate(train_loader):
# Forward pass
outputs = self.model(images)
loss = self.criterion(outputs, labels)
# Backward and optimize
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if (i + 1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(
epoch + 1, self.num_epochs, i + 1, total_step,
loss.item()))
def eval(self, test_loader):
self.model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
outputs = self.model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
def save(self):
# Save the model checkpoint
torch.save(self.model.state_dict(), 'model.ckpt')
quot = int(len(training_files) / num_images)
print('quot:', quot)
#Neural Network declarations
num_epochs = 2
num_classes = 2
batch_size = 64 #TO BE CHANGED
learning_rate = 0.001
#Neural Net framework
model = ConvNet()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
'min',
factor=0.9)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
for j in range(0, quot):
input_tensor_training = []
block_labels_training = []
print('This is the ', j + 1, ' set of , ', num_images)
if (j > 0):
model = ConvNet()
model.cuda(cuda0)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
checkpoint = torch.load('/home/sharan/model_1.pth')