[GitHub] [incubator-mxnet] JoseLucasgt opened a new issue #15639: My first neural network what's the problem (mxnet, Gluon, Multilayer Perceptron)

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JoseLucasgt opened a new issue #15639: My first neural network what's the problem (mxnet, Gluon, Multilayer Perceptron)
URL: https://github.com/apache/incubator-mxnet/issues/15639
 
 
   Hello, I have made my first neural network but I think It's completely wrong and I don't understand how exactly, this is my code:
   
   NUM_LABELS = 2    # The number of labels.
   file_train = open('Youtube_ex.csv')
   file_test = open('Train_Youtube_ex.csv')
   
   test_data,test_labels = extract_data(file_test)
   test_data_nd = nd.array(test_data)
   train_data,train_labels = extract_data(file_train)
   train_data_nd = nd.array(train_data)
   
   dataset = ArrayDataset(train_data, train_labels)
   dataloader = DataLoader(dataset, batch_size=30, shuffle=True, num_workers=2)  
   datasetvalidation = ArrayDataset(test_data, test_labels)
   data_validation = DataLoader(datasetvalidation, batch_size=30, shuffle=True, num_workers=2)
   
   batch_size = 30
   net = nn.Sequential()
   net.add(nn.Dense(256, activation='relu'),
           nn.Dense(2))
   net.initialize(init.Normal(sigma=0.01))
   
   softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss(sparse_label=False)
   trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})
   
   def acc(output, label):
       # output: (batch, num_output) float32 ndarray
       # label: (batch, ) int32 ndarray
       return (output.argmax(axis=1) ==
               label.astype('float32').argmax(axis=1)).mean().asscalar()
   
   for epoch in range(10):
       train_loss, train_acc, valid_acc = 0., 0., 0.
       tic = time.time()
       for data, label in dataloader:
           with autograd.record():
               output = net(data)
               loss = softmax_cross_entropy(output, label)
           loss.backward()
           # update parameters
           trainer.step(batch_size)
           # calculate training metrics
           train_loss += loss.mean().asscalar()
           train_acc += acc(output, label)
       # calculate validation accuracy  
       for data, label in data_validation:
           valid_acc += acc(net(data), label)
       print("Epoch %d: loss %.3f, train acc %.3f, test acc %.3f, in %.1f sec" % (
               epoch, train_loss/len(data_validation), train_acc/len(data_validation),
               valid_acc/len(data), time.time()-tic))
   
   And this is the result:
   Epoch 0: loss 0.142, train acc 0.166, test acc 2.660, in 0.1 sec
   Epoch 1: loss 0.142, train acc 0.134, test acc 2.703, in 0.1 sec
   Epoch 2: loss 0.142, train acc 0.181, test acc 2.703, in 0.1 sec
   Epoch 3: loss 0.142, train acc 0.150, test acc 2.730, in 0.1 sec
   Epoch 4: loss 0.142, train acc 0.179, test acc 2.740, in 0.1 sec
   Epoch 5: loss 0.142, train acc 0.175, test acc 2.753, in 0.1 sec
   Epoch 6: loss 0.142, train acc 0.202, test acc 2.730, in 0.1 sec
   Epoch 7: loss 0.142, train acc 0.179, test acc 2.743, in 0.1 sec
   Epoch 8: loss 0.142, train acc 0.183, test acc 2.763, in 0.1 sec
   Epoch 9: loss 0.142, train acc 0.169, test acc 2.757, in 0.1 sec
   
   I think it's wrong because my loss is always the same. I'm trying to use a .csv data set with 2 classes. I also don't know how exactly to normalize the data, if I have to do this with all my dataset including labels in on hot encode or not and if there is a function to do it. On the internet, most of the tutorials are with image datasets and with functions to use in image classification problems.
   If someone can give me some direction.

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