plt.plot(epochs, loss, 'bo', label='Training loss') plt.plot(epochs, val_loss, 'b', label='Validation loss') plt.title('Training and validation loss {}'.format( datasets_helper.get_dataset_name())) plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend() plt.savefig( results_saver.get_plot_path(datasets_helper.get_dataset_name(), "loss")) plt.clf() acc = history.history['acc'] val_acc = history.history['val_acc'] plt.plot(epochs, acc, 'bo', label='Training acc') plt.plot(epochs, val_acc, 'b', label='Validation acc') plt.title('Training and validation accuracy {}'.format( datasets_helper.get_dataset_name())) plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend() plt.savefig( results_saver.get_plot_path(datasets_helper.get_dataset_name(), "acc")) plt.clf() results_saver.add_log("Finished testing dataset {}".format( datasets_helper.get_dataset_name())) results_saver.write_2D_list("results", results) results_saver.end_logging()
neural_lda_out = NeuralTopicMatrix(weight_out, reverse_word_map, num_of_topics, tokenizer) #neural_lda_combined = NeuralTopicMatrix(combined_weight, reverse_word_map,num_of_topics,tokenizer) test_model(documents, labels, neural_lda_in, log_writer, 'neural_lda_in') test_model(documents, labels, neural_lda_out, log_writer, 'neural_lda_out') #test_model(documents, labels, neural_lda_combined, log_writer,'neural_lda_combined') try: measureCoherence(topic_words_in_max, log_writer, model.dictionary, documents, 'neural_in_max', dataset_helper.get_dataset_name()) except Exception as exception: print(exception) #measureCoherence(topic_words_in_min,log_writer,model.dictionary,documents,'neural_in_min',dataset_helper.get_dataset_name()) try: measureCoherence(topic_words_out_max, log_writer, model.dictionary, documents, 'neural_out_max', dataset_helper.get_dataset_name()) except Exception as exception: print(exception) #measureCoherence(topic_words_out_min,log_writer,model.dictionary,documents,'neural_out_min',dataset_helper.get_dataset_name()) #measureCoherence(topic_words_combined, log_writer, model.dictionary, documents, 'neural_combined', dataset_helper.get_dataset_name()) #plot_clustering_chart(neural_lda_out,False,documents,log_writer,'neural_topic_out',dataset_helper.get_dataset_name(),dataset_helper.get_num_of_topics()) #plot_clustering_chart(neural_lda_in,False,documents,log_writer,'neural_topic_in',dataset_helper.get_dataset_name(),dataset_helper.get_num_of_topics()) #plot_clustering_chart(neural_lda_combined,False,documents,log_writer,'neural_topic_combined',dataset_helper.get_dataset_name()) log_writer.end_logging() #print(topic_words)