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pg_update.py
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pg_update.py
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import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from itertools import count
import encoder_decoder
from corpus_utils import tokenize_sentence, LanguageIndex
from data import BEGIN_TAG, END_TAG
from utils import load_trained_model, max_length
import data
import random
from sklearn.metrics.pairwise import cosine_similarity
import os
from embedding_utils import get_embedding_dim, get_GloVe_embeddings
import time
EPISODES = 1000
BATCH_SIZE = 64
TOP_K = 4
USE_GLOVE = True
EMBEDDING_DIM = get_embedding_dim(USE_GLOVE)
UNITS = 512
def main():
tf.enable_eager_execution()
questions1, answers1 = data.load_conv_text()
# questions2, answers2 = data.load_opensubtitles_text()
questions = list(questions1)
answers = list(answers1)
inp_lang, targ_lang = LanguageIndex(questions), LanguageIndex(answers)
input_tensor = [[inp_lang.word2idx[token]
for token in tokenize_sentence(question)] for question in questions]
target_tensor = [[targ_lang.word2idx[token]
for token in tokenize_sentence(answer)] for answer in answers]
max_length_inp, max_length_tar = max_length(
input_tensor), max_length(target_tensor)
input_tensor = tf.keras.preprocessing.sequence.pad_sequences(input_tensor,
maxlen=max_length_inp,
padding='post')
target_tensor = tf.keras.preprocessing.sequence.pad_sequences(target_tensor,
maxlen=max_length_tar,
padding='post')
BUFFER_SIZE = len(input_tensor)
dataset = tf.data.Dataset.from_tensor_slices(
(input_tensor, target_tensor)).shuffle(BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
model: encoder_decoder.Seq2Seq = load_trained_model(
BATCH_SIZE, EMBEDDING_DIM, UNITS, tf.train.AdamOptimizer())
# sentimental_words = ["absolutely","abundant","accept","acclaimed","accomplishment","achievement","action","active","activist","acumen","adjust","admire","adopt","adorable","adored","adventure","affirmation","affirmative","affluent","agree","airy","alive","alliance","ally","alter","amaze","amity","animated","answer","appreciation","approve","aptitude","artistic","assertive","astonish","astounding","astute","attractive","authentic","basic","beaming","beautiful","believe","benefactor","benefit","bighearted","blessed","bliss","bloom","bountiful","bounty","brave","bright","brilliant","bubbly","bunch","burgeon","calm","care","celebrate","certain","change","character","charitable","charming","cheer","cherish","clarity","classy","clean","clever","closeness","commend","companionship","complete","comradeship","confident","connect","connected","constant","content","conviction","copious","core","coupled","courageous","creative","cuddle","cultivate","cure","curious","cute","dazzling","delight","direct","discover","distinguished","divine","donate","each","day","eager","earnest","easy","ecstasy","effervescent","efficient","effortless","electrifying","elegance","embrace","encompassing","encourage","endorse","energized","energy","enjoy","enormous","enthuse","enthusiastic","entirely","essence","established","esteem","everyday","everyone","excited","exciting","exhilarating","expand","explore","express","exquisite","exultant","faith","familiar","family","famous","feat","fit","flourish","fortunate","fortune","freedom","fresh","friendship","full","funny","gather","generous","genius","genuine","give","glad","glow","good","gorgeous","grace","graceful","gratitude","green","grin","group","grow","handsome","happy","harmony","healed","healing","healthful","healthy","heart","hearty","heavenly","helpful","here","highest","good","hold","holy","honest","honor","hug","i","affirm","i","allow","i","am","willing","i","am.","i","can","i","choose","i","create","i","follow","i","know","i","know,","without","a","doubt","i","make","i","realize","i","take","action","i","trust","idea","ideal","imaginative","increase","incredible","independent","ingenious","innate","innovate","inspire","instantaneous","instinct","intellectual","intelligence","intuitive","inventive","joined","jovial","joy","jubilation","keen","key","kind","kiss","knowledge","laugh","leader","learn","legendary","let","go","light","lively","love","loveliness","lucidity","lucrative","luminous","maintain","marvelous","master","meaningful","meditate","mend","metamorphosis","mind-blowing","miracle","mission","modify","motivate","moving","natural","nature","nourish","nourished","novel","now","nurture","nutritious","one","open","openhanded","optimistic","paradise","party","peace","perfect","phenomenon","pleasure","plenteous","plentiful","plenty","plethora","poise","polish","popular","positive","powerful","prepared","pretty","principle","productive","project","prominent","prosperous","protect","proud","purpose","quest","quick","quiet","ready","recognize","refinement","refresh","rejoice","rejuvenate","relax","reliance","rely","remarkable","renew","renowned","replenish","resolution","resound","resources","respect","restore","revere","revolutionize","rewarding","rich","robust","rousing","safe","secure","see","sensation","serenity","shift","shine","show","silence","simple","sincerity","smart","smile","smooth","solution","soul","sparkling","spirit","spirited","spiritual","splendid","spontaneous","still","stir","strong","style","success","sunny","support","sure","surprise","sustain","synchronized","team","thankful","therapeutic","thorough","thrilled","thrive","today","together","tranquil","transform","triumph","trust","truth","unity","unusual","unwavering","upbeat","value","vary","venerate","venture","very","vibrant","victory","vigorous","vision","visualize","vital","vivacious","voyage","wealthy","welcome","well","whole","wholesome","willing","wonder","wonderful","wondrous","xanadu","yes","yippee","young","youth","youthful","zeal","zest","zing","zip"]
sentimental_words = ["good", "excellent", "well"]
targ_lang_embd = get_GloVe_embeddings(targ_lang.vocab, EMBEDDING_DIM)
sentimental_words_embd = get_GloVe_embeddings(
sentimental_words, EMBEDDING_DIM)
sim_scores = np.dot(sentimental_words_embd, np.transpose(targ_lang_embd))
print(sim_scores.shape)
#max_prob_ids = np.argmax(sim_scores, axis=1)
# print(max_prob_ids)
# print(targ_lang.word2idx)
# print(targ_lang.idx2word(max_prob_ids[1]))
optimizer = tf.train.AdamOptimizer()
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer, seq2seq=model)
for episode in range(EPISODES):
# Start of Episode
start = time.time()
total_loss = 0
for (batch, (inp, targ)) in enumerate(dataset):
with tf.GradientTape() as tape:
hidden = tf.zeros((BATCH_SIZE, UNITS))
enc_hidden = model.encoder(inp, hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims(
[targ_lang.word2idx[BEGIN_TAG]] * BATCH_SIZE, 1)
loss = 0 # loss for decoder
pg_loss = 0 # loss for semantic
result = ''
for t in range(1, targ.shape[1]):
actions = []
probs = []
rewards = []
predictions, dec_hidden = model.decoder(
dec_input, dec_hidden)
'''
predicted_id = tf.argmax(predictions[0]).numpy()
if targ_lang.idx2word[predicted_id] == END_TAG:
print("result: ", result)
else:
result += ' ' + targ_lang.idx2word[predicted_id]
'''
# using teacher forcing
dec_input = tf.expand_dims(targ[:, t], 1)
for ps in predictions:
# action = tf.distributions.Categorical(ps).sample(1)[0]
top_k_indices = tf.nn.top_k(ps, TOP_K).indices.numpy()
action = np.random.choice(top_k_indices, 1)[0]
actions.append(action)
prob = ps.numpy()[action]
probs.append(prob)
reward = np.max(sim_scores[1:, action])
print(targ_lang.idx2word[action], reward)
# print(targ_lang.idx2word[action], reward)
rewards.append(reward)
# normalize reward
reward_mean = np.mean(rewards)
reward_std = np.std(rewards)
norm_rewards = [(r - reward_mean) /
reward_std for r in rewards]
if targ_lang.idx2word[actions[0]] == END_TAG:
print("result: ", result)
else:
result += ' ' + targ_lang.idx2word[actions[0]]
onehot_labels = tf.keras.utils.to_categorical(
y=actions, num_classes=len(targ_lang.word2idx))
norm_rewards = tf.convert_to_tensor(
norm_rewards, dtype="float32")
# print(onehot_labels.shape)
# print(predictions.shape)
loss += model.loss_function(targ[:, t], predictions)
# print("------")
# print(loss)
# print(probs)
#pg_loss_cross = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=onehot_labels, logits=targ[:, t]))
pg_loss_cross = model.loss_function(
targ[:, t], )
# pg_loss_cross = tf.reduce_mean(
# pg_loss_cross * norm_rewards)
pg_loss_cross = tf.reduce_mean(
pg_loss_cross * rewards)
# print(pg_loss_cross)
# print("------")
# print(pg_loss_cross)
pg_loss += pg_loss_cross
# End of Episode
# Update policy
batch_loss = ((loss + pg_loss) / int(targ.shape[1]))
total_loss += batch_loss
variables = model.encoder.variables + model.decoder.variables
gradients = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(gradients, variables))
if batch % 10 == 0:
print('batch {} training loss {:.4f}'.format(
batch, total_loss.numpy()))
# saving (checkpoint) the model every 100 epochs
#if (episode + 1) % 100 == 0:
#checkpoint.save(file_prefix=checkpoint_prefix)
print('Time taken for {} episode {} sec\n'.format(
episode, time.time() - start))
main()