Wsrtjtyk

I have a Feed forward Neural Network which classifies the MNIST data set.
For some reason the memory keeps approaching 99% no matter how big the batch size is.
I don't have anything which is increasing in size - every dynamic variable is getting overridden after the first epoch but even after epoch number 70 the memory keeps rising.

Im running it on 8GB memory, 2.8 ghZ intel i-5 (7th gen) quad core (Ubuntu 18.04)

batch_size =50 #Number of x's we pass through the net at each iteration

num_epochs =100 #Number of times the entire training data is through the    net



n_train = len(xtrain)

n_batch_train = n_train // batch_size

n_val =len(xval)

n_batch_val = n_val // batch_size



# loss/acc

train_acc,train_loss =,

val_acc,val_loss =,



test_acc,test_loss =,





#Get parameters from the net



par=



for i in range(len(layers)-1):

    par=par+list(net.L[i].parameters())







#Optimizer



optimizer = optim.Adam(par,lr=0.001)



#interval of x



get_slice = lambda i,size: range(i*size,(i+1)*size)





for e in range(num_epochs):

  curr_loss =0

  net.train()

  for i in range(n_batch_train):



    x_interval = get_slice(i,batch_size)

    slze = get_slice(i,batch_size)

    #Batchnorm

    bn = nn.BatchNorm1d(num_features = num_features)

    x_batch = bn(Variable(torch.from_numpy(xtrain[slze])))



    out = (net(x_batch)).double()

    target_batch = Variable(torch.from_numpy(ytrain[slze]).double())

    L=criterion(out,target_batch)



    #Update gradients

    optimizer.zero_grad()

    L.backward()

    optimizer.step()



    #Store training accuracy and loss

    train_acc.append(accuracy(target_batch, out).data)

    train_loss.append(L.data.numpy())



#### Validate ####

  net.eval()

  for j in range(n_batch_val):

    slze = get_slice(j,batch_size)

    val_batch = Variable(torch.from_numpy(xval[slze]))

    val_out = (net(bn(val_batch))).double()

    target_batch = Variable(torch.from_numpy(yval[slze]).double())



    #Store val acc and loss

    val_acc.append(accuracy(target_batch,val_out).data)