if PERSIST:
pickle.dump(word_indices, open(best_model_word_indices(), 'wb'))
if FINAL:
print("\n > running in FINAL mode!\n")
training, testing = loader.load_final()
else:
training, validation, testing = loader.load_train_val_test()
if POST_MORTEM:
print("\n > running in Post-Mortem mode!\n")
gold_data = SemEvalDataLoader().get_gold(task=TASK)
gX = [obs[1] for obs in gold_data]
gy = [obs[0] for obs in gold_data]
gold = prepare_dataset(gX, gy, loader.pipeline, loader.y_one_hot)
validation = testing
testing = gold
FINAL = False
############################################################################
# NN MODEL
############################################################################
print("Building NN Model...")
nn_model = target_RNN(embeddings,
tweet_max_length=text_max_length,
aspect_max_length=target_max_length,
noise=0.2,
activity_l2=0.001,
drop_text_rnn_U=0.2,
if FINAL:
print("\n > running in FINAL mode!\n")
training, testing = loader.load_final()
else:
training, validation, testing = loader.load_train_val_test()
if SEMEVAL_GOLD:
print("\n > running in Post-Mortem mode!\n")
gold_data = SemEval2017Task6().get_gold_data_task_1()
gold_data = [v for k, v in sorted(gold_data.items())]
X = [x for hashtag in gold_data for x in hashtag[0]]
y = [x for hashtag in gold_data for x in hashtag[1]]
gold = prepare_dataset(X,
y,
loader.pipeline,
loader.y_one_hot,
y_as_is=loader.subtask == "2")
validation = testing
testing = gold
FINAL = False
print("Building NN Model...")
nn_model = humor_RNN(embeddings, text_length)
# nn_model = humor_CNN(embeddings, text_length)
# nn_model = humor_FFNN(embeddings, text_length)
plot(nn_model,
show_layer_names=True,
show_shapes=True,
to_file="model_task6_sub{}.png".format(TASK))