I’d like to prepare variables to run a multi class classification.
Note I try replicating the model shown in this post, where @Oli , @tymokvo , @god_sp33d  and @Rojin contributed.
I have 10 variables split as (X_train,X_test) and 1 target variable split as (Y_train, Y_test).
My target/label is a 3 class (1,2,3)
I thought I would prepare them as follows:
> X_train = np.array(X_train, dtype=np.float32)
> X_test = np.array(X_test, dtype=np.float32)
> Y_train = np.array(Y_train, dtype=np.float32)
> y_test = np.array(y_test, dtype=np.float32)

and then convert them to tensor as follows:
train_x = torch.from_numpy(np.asanyarray(X_train)).requires_grad_()
train_y = torch.LongTensor(Y_train).long().requires_grad()
test_x = torch.from_numpy(np.asanyarray(X_test)).requires_grad_()
test_y = torch.LongTensor(y_test).long().requires_grad()

unfortunately, when running code above I get as result:

I would be very grateful if any one could shed some light on this.
Sincerely,