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)