config.num_steps, shuffle=False): data_feed = { inputs: batch_X, targets: batch_Y, learning_rate: 1.0, keep_prob: 1.0 } predict = sess.run([prediction], data_feed) predict = predict[0] final_predict = np.append(final_predict, predict) final_predict = np.array(final_predict).reshape( len(final_predict), 1) print("0.5 threshold") classified = [utils.get_class(x, 0.5) for x in final_predict] f, _ = utils.get_score_and_confusion_matrix(labels, classified) print("Best threshold") best_threshold = utils.find_best_threshold( labels, final_predict) best_classified = [ utils.get_class(x, best_threshold) for x in final_predict ] best_f, _ = utils.get_score_and_confusion_matrix( labels, best_classified) if which_data == "training": train_score.append(round(sp.average(best_f) * 100, 2)) else: validation_score.append(round(sp.average(best_f) * 100, 2)) classified = [
config.batch_size, config.num_steps, shuffle=False): data_feed = {inputs: batch_X, keep_prob: 1.0} predict = sess.run([prediction], data_feed) predict = predict[0] final_predict = np.append(final_predict, predict) final_predict = np.array(final_predict).reshape( len(final_predict), 1) # print("0.5 threshold") # classified = [utils.get_class(x, 0.5) for x in final_predict] # f, _ = utils.get_score_and_confusion_matrix(labels, classified) # print("Best threshold") # best_threshold = utils.find_best_threshold(labels, final_predict) # best_classified = [utils.get_class(x, best_threshold) for x in final_predict] best_f, _ = utils.get_score_and_confusion_matrix( labels, final_predict) if which_data == "training": train_score.append(round(sp.average(best_f) * 100, 2)) else: validation_score.append(round(sp.average(best_f) * 100, 2)) # classified = [utils.get_class(x, best_threshold) for x in final_predict] # # Check in all the cases we correctly predict label is explosive 1, how many have the current labels 1 # current_check = [] # current_labels = reader.current_labels.values # for index, value in enumerate(classified): # if value + labels[index] == 2: # correctly recognition 1 # current = current_labels[index] # if current >= 1: # current_check.append(1)