#print(header) raw_data = np.column_stack((SpamEmails, EnronEmails)).T print("DEBUG::raw_data:") print(raw_data) encoder.process(raw_data, max_cells) X, y = encoder.encode_data(raw_data, header, maxlen) # build classifier model model = Classifier.generate_transfer_model(maxlen, max_cells, category_count_prior, category_count, checkpoint, checkpoint_dir, activation='sigmoid') #Classifier.load_weights(checkpoint, None, model, checkpoint_dir) model_compile = lambda m: m.compile( loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy']) model_compile(model) #y = model.predict(X) # discard empty column edge case # y[np.all(frame.isnull(),axis=0)]=0 #result = encoder.reverse_label_encode(y,p_threshold) ### FINISHED LABELING COMBINED DATA AS CATEGORICAL/ORDINAL #print("The predicted classes and probabilities are respectively:") #print(result)