My first neural network what's the problem (mxnet, Gluon, Multilayer Perceptron)

[JoseLucasgt]

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.

[JoseLucasgt]

Also, how can I test my artificial intelligence (giving some data and having the answer to know if it is correctly trained)?
My classes are in numbers and in the code I do a one hot encode but I dont know how to identify my labels with names after.

[keerthanvasist]

@mxnet-label-bot add [question]

[zachgk]

For questions, please submit them on the MXNet discussion forum (https://discuss.mxnet.io) where it will get a wider audience and allow others to learn as well.