def pretrain_actorCritic():
if USE_SAVED_PREPROCESSED_INPUT:
#sentences = pickle.load(open(PRETRAINING_PREPROCESSED_INPUT_FILE, 'r'))[:5000]
sentences =
else:
print("Loading data")
sentences = load_data(PRETRAINING_DATA_FILE)
print("Data loaded")
sentences = preprocess_text(sentences)[:5000]
print("shape of sentences", sentences.shape)
print("Training w2v model")
w2v_model = train_w2v_model(sentences)
print("w2v model trained")
token_sequences, output_sequences, token_to_index_dic = tokenize_and_pad_sentences(sentences)
index_to_word_dic = get_index_to_word_dic(token_to_index_dic)
token_sequences = np.asarray(token_sequences)
output_sequences = np.asarray(output_sequences)
print("input shape", token_sequences.shape)
#token_sequences = token_sequences[:1000, :]
#output_sequences[:1000, :]
output_sequences = [one_hot(seq, len(token_to_index_dic)) for seq in output_sequences]
print("Tokenization done. %d sequences" % len(token_sequences), "shape ", token_sequences.shape)
#token_to_index_dic = get_word_to_index_dic(w2v_model, token_sequences)
print("preprocessing done")
train_x, train_y, val_x, val_y, test_x, test_y = get_train_val_test_data(token_sequences, output_sequences)
autoencoder = Autoencoder(w2v_model, token_to_index_dic)
print("Creating NN model")
autoencoder.create_nn_model()
print("NN model created")
if LOAD_WEIGHTS:
print("Loading saved weights from %s" % PRETRAINING_ACTOR_WEIGHTS_FILE)
autoencoder.load_weights(PRETRAINING_ACTOR_WEIGHTS_FILE)
if TRAIN_ACTOR:
print("Training actor")
autoencoder.train(train_x, train_y, val_x, val_y)
if SAVE_WEIGHTS:
print("Saving actor weights")
autoencoder.save(PRETRAINING_ACTOR_WEIGHTS_FILE)
print("Predicting using actor")
output = autoencoder.predict(train_x)
for seq in output:
print(index_to_sentence(index_to_word_dic, [np.argmax(ele) for ele in seq]))
print("Initializing actorCritic")
actor_critic = ActorCriticAutoEncoder(w2v_model=w2v_model,
token_to_index_dic=token_to_index_dic,
actor=autoencoder.autoencoder)
print("Creating critic model")
actor_critic.create_critic_model()
print("Critic model created")
critic_train_x = output
critic_train_y = [one_hot(seq, len(token_to_index_dic)) for seq in train_x]
if TRAIN_CRITIC:
print("Training critic")
actor_critic.train_critic(critic_train_x, critic_train_y)
print("Critic trained")
if SAVE_WEIGHTS:
print("Saving critic")
actor_critic.save_critic(PRETRAINING_CRITIC_MODEL_FILE)
print("Critic saved")
def main_auto():
print("Loading w2v model")
w2v_model = get_w2v_model("/home/aries/Documents/Learning/DL/autoencoder/data/glove.6B/glove.6B.200d_gensim.txt")
print("w2v model loaded")
#print("Loading data")
#sentences = load_data(PRETRAINING_DATA_FILE)
#print("Data loaded")
if USE_SAVED_PREPROCESSED_INPUT:
sentences = pickle.load(open(PRETRAINING_PREPROCESSED_INPUT_FILE, 'r'))[:5000]
#else:
# sentences = preprocess_text(sentences)[:5000]
print("shape of sentences", sentences.shape)
token_sequences, output_sequences, token_to_index_dic = tokenize_and_pad_sentences(sentences)
index_to_word_dic = get_index_to_word_dic(token_to_index_dic)
token_sequences = np.asarray(token_sequences)
output_sequences = np.asarray(output_sequences)
print("input shape", token_sequences.shape)
#token_sequences = token_sequences[:1000, :]
#output_sequences[:1000, :]
output_sequences = [one_hot(seq, len(token_to_index_dic)) for seq in output_sequences]
print("Tokenization done. %d sequences" % len(token_sequences), "shape ", token_sequences.shape)
#token_to_index_dic = get_word_to_index_dic(w2v_model, token_sequences)
print("preprocessing done")
train_x, train_y, val_x, val_y, test_x, test_y = get_train_val_test_data(token_sequences, output_sequences)
autoencoder = Autoencoder(w2v_model, token_to_index_dic)
print("Creating NN model")
autoencoder.create_nn_model()
print("NN model created")
if LOAD_WEIGHTS:
print("Loading saved weights from %s" % PRETRAINING_ACTOR_WEIGHTS_FILE)
autoencoder.load_weights(PRETRAINING_ACTOR_WEIGHTS_FILE)
print("Training autoencoder")
if TRAIN_ACTOR:
autoencoder.train(train_x, train_y, val_x, val_y)
if SAVE_WEIGHTS:
autoencoder.save(PRETRAINING_ACTOR_WEIGHTS_FILE)
output = autoencoder.predict(test_x)
#print(test_x)
#test_x = [de_one_hot(ele) for ele in test_x]
print("Input")
for seq in test_x:
print(index_to_sentence(index_to_word_dic, seq))
print("\n")
print("########Generated##########")
print("\n")
for seq in output:
print(index_to_sentence(index_to_word_dic, [np.argmax(ele) for ele in seq]))
