@vmirly1 Thank you. How can I save model after each epoch?

here is the snippet which I put the save at the end.

```
criterion = nn.NLLLoss()
#optimizer = optim.Adam(model.parameters(), lr=0.00001, betas=(0.9, 0.999), eps=1e-08) # 1e-3
optimizer = optim.Adam(model.parameters(), lr=0.00001) # 1e-3
def train_valid_model():
num_epochs=150
since = time.time()
out_loss = open("history_loss_exp5.txt", "w")
out_acc = open("history_acc_exp5.txt", "w")
losses=[]
ACCes =[]
#losses = {}
for epoch in range(num_epochs): # loop over the dataset multiple times
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 30)
# Each epoch has a training and validation phase
for phase in ['train', 'valid']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
train_loss = 0.0
total_train = 0
correct_train = 0
#iterate over data
for t_image, mask, image_paths, target_paths in dataLoaders[phase]:
# get the inputs
t_image = t_image.to(device)
mask = mask.to(device)
# zeroes the gradient buffers of all parameters
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(t_image)
_, predicted = torch.max(outputs.data, 1)
loss = criterion(outputs, mask) # calculate the loss
# backward + optimize only if in training phase
if phase == 'train':
loss.backward() # back propagation
optimizer.step() # update gradients
# accuracy
train_loss += loss.item()
total_train += mask.nelement() # number of pixel in the batch
correct_train += predicted.eq(mask.data).sum().item() # sum all precited pixel values
epoch_loss = train_loss / len(dataLoaders[phase].dataset)
#losses[phase] = epoch_loss
losses.append(epoch_loss)
epoch_acc = 100 * correct_train / total_train
ACCes.append(epoch_acc)
print('{} Loss: {:.4f} {} Acc: {:.4f}'.format(phase, epoch_loss, phase, epoch_acc))
out_loss.write('{} {} Loss: {:.4f}\n'.format(epoch, phase, epoch_loss))
out_acc.write('{} {} ACC: {:.4f}\n'.format(epoch, phase, epoch_acc))
#numpy.savetxt('loss.csv', (losses), "%.4f", header= 'loss', comments='', delimiter = ",")
#numpy.savetxt('ACC.csv', (ACCes), "%.4f", header= 'accuracy', comments='', delimiter = ",")
numpy.savetxt('loss_acc_exp5.csv', numpy.c_[losses, ACCes], fmt=['%.4f', 'd'], header= "loss, acc", comments='', delimiter = ",")
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
torch.save(model.state_dict(), 'train_valid_exp4.pth')
```