Re-scaling outputs from a Keras model back to original scale

Question

I'm new to neural nets (just a disclaimer).

I have a regression problem of predicting the strength of concrete, based on 8 features. What I've done first, is rescaled the data using min-max normalization:

# Normalize data between 0 and 1
from sklearn.preprocessing import MinMaxScaler

min_max = MinMaxScaler()
dataframe2 = pd.DataFrame(min_max.fit_transform(dataframe), columns = dataframe.columns)

then converted the dataframe into numpy array and split it into X_train, y_train, X_test, y_test. Now here is the Keras code for the network itself:

from keras.models import Sequential
from keras.layers import Dense, Activation

#Set the params of the Neural Network
batch_size = 64
num_of_epochs = 40
hidden_layer_size = 256

model = Sequential()
model.add(Dense(hidden_layer_size, input_shape=(8, )))
model.add(Activation('relu'))
model.add(Dense(hidden_layer_size))
model.add(Activation('relu'))
model.add(Dense(hidden_layer_size))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('linear'))


model.compile(loss='mean_squared_error', # using the mean squared error function
              optimizer='adam', # using the Adam optimiser
              metrics=['mae', 'mse']) # reporting the accuracy with mean absolute error and mean squared error

model.fit(X_train, y_train, # Train the model using the training set...
          batch_size=batch_size, epochs=num_of_epochs,
          verbose=0, validation_split=0.1)

# All predictions in one array
predictions = model.predict(X_test)

Questions:

1. predictions array will have all the values in the scaled format (between 0 and 1), but obviously I would need the predictions to be in their real values. How can I rescale those outputs back to the real values?

2. Is Min-Max or Z-Score standardization more appropriate for regression problems? What about this 'Batch-Normalization'?

Thank you,

Answer 1

As per the doc, the MinMaxScaler class has an inverse_transform method which does what you want:

inverse_transform(X): Undo the scaling of X according to feature_range.