I have built an ANN using keras library.
# Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train_us, X_test_us, y_train_us, y_test_us = train_test_split(X, y, test_size = 0.2, random_state = 0) # Feature Scaling from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() sc_Y = StandardScaler() X_train = sc_X.fit_transform(X_train_us) X_test = sc_X.transform(X_test_us) y_train = sc_Y.fit_transform(y_train_us) y_test = sc_Y.transform(y_test_us) # Importing the Keras libraries and packages from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasRegressor #from keras.layers import Dropout def base_model(): # Initialising the ANN regressor = Sequential() # Adding the input layer and the first hidden layer regressor.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11)) # Adding the second hidden layer regressor.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu')) # Adding the output layer regressor.add(Dense(units = 1, kernel_initializer = 'uniform')) # Compiling the ANN regressor.compile(optimizer = 'adam', loss = 'mse', metrics = ['mae']) return regressor # Fitting the ANN to the Training set regressor = KerasRegressor(build_fn=base_model, epochs=500, batch_size=32) regressor.fit(X_train,y_train)
After training the model on this data set, I have saved the model as json and weights as .h5 file. I have used this model for a while. Now I have a new set of data available, and wish to train the model using this data set.
So I have loaded the model and its weights. Now when I train the model on the new data, my new data should be scaled using new scalar objects or the previous used scalar objects are to be used? I am confused about this part. Can some one help me understand?