I have used the MinMax normalization in order to normalize my dataset, both features and label. My question is, it’s correct to normalize also the label? If yes, how can I denormalize the output of the neural network (the one that I predict with the test set that is normalized)?
I can’t upload the dataset, but it is composed by 18 features and 1 label. It is a regression task, the features and the label are physical quantities.
So the problem is that the y_train_pred and y_test_pred are between 0 and 1. How can I predict the “real value”?
The code:
dataset = pd.read_csv('DataSet.csv', decimal=',', delimiter = ";")
label = dataset.iloc[:,-1]
features = dataset.drop(columns = ['Label'])
features = features[best_features]
X_train1, X_test1, y_train1, y_test1 = train_test_split(features, label, test_size = 0.25, random_state = 1, shuffle = True)
y_test2 = y_test1.to_frame()
y_train2 = y_train1.to_frame()
scaler1 = preprocessing.MinMaxScaler()
scaler2 = preprocessing.MinMaxScaler()
X_train = scaler1.fit_transform(X_train1)
X_test = scaler2.fit_transform(X_test1)
scaler3 = preprocessing.MinMaxScaler()
scaler4 = preprocessing.MinMaxScaler()
y_train = scaler3.fit_transform(y_train2)
y_test = scaler4.fit_transform(y_test2)
optimizer = tf.keras.optimizers.Adamax(lr=0.001)
model = Sequential()
model.add(Dense(80, input_shape = (X_train.shape[1],), activation = 'relu',kernel_initializer='random_normal'))
model.add(Dropout(0.15))
model.add(Dense(120, activation = 'relu',kernel_initializer='random_normal'))
model.add(Dropout(0.15))
model.add(Dense(80, activation = 'relu',kernel_initializer='random_normal'))
model.add(Dense(1,activation = 'linear'))
model.compile(loss = 'mse', optimizer = optimizer, metrics = ['mse'])
history = model.fit(X_train, y_train, epochs = 300,
validation_split = 0.1, shuffle=False, batch_size=120
)
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)
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Answer
You should denormalize so you can get real world predictions to your neural network, rather than a number between 0-1
The min – max normalization is defined by:
z = (x - min)/(max - min)
With z being the normalized value, x being the label value, max being the max x value, and min being the min x value. So if we have z, min, and max we can resolve for x as follows:
x = z(max - min) + min
Thus before you normalize your data, define variables for the max and min value for the label if it is continuous. Then after you get your pred values, you can use the following function:
y_max_pre_normalize = max(label)
y_min_pre_normalize = min(label)
def denormalize(y):
final_value = y(y_max_pre_normalize - y_min_pre_normalize) + y_min_pre_normalize
return final_value
And apply this function to your y_test/y_pred to get the corresponding value.