{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CS 20 : TensorFlow for Deep Learning Research\n",
"## Lecture 12 : Seq2Seq with Attention\n",
"Simple example for Seq2Seq (Machine Translation) by Encoder RNN and Decoder RNN.\n",
"\n",
"### Seq2Seq by Encoder RNN and Decoder RNN\n",
"- Creating the **data pipeline** with `tf.data`\n",
"- Preprocessing word sequences (variable input sequence length) using `padding technique` by `user function (pad_seq)`\n",
"- Using `tf.nn.embedding_lookup` for getting vector of tokens (eg. word, character)\n",
"- Training **many to many classification** with `tf.contrib.seq2seq.sequence_loss`\n",
"- Masking unvalid token with `tf.sequence_mask`\n",
"- Using `tf.contrib.seq2seq.dynamic_decode`\n",
"- Training with `tf.contrib.seq2seq.TrainingHelper`\n",
"- Translating with `tf.contrib.seq2seq.GreedyEmbeddingHelper`\n",
"- Creating the model as **Class**\n",
"- Reference\n",
" - https://github.com/golbin/TensorFlow-Tutorials/blob/master/10%20-%20RNN/03%20-%20Seq2Seq.py\n",
" - https://github.com/HiJiGOO/tf_nmt_tutorial\n",
" - https://github.com/hccho2/RNN-Tutorial\n",
" - https://www.tensorflow.org/tutorials/seq2seq\n",
" - https://github.com/j-min/tf_tutorial_plus/tree/master/RNN_seq2seq/contrib_seq2seq\n",
" - https://gist.github.com/ilblackdragon/c92066d9d38b236a21d5a7b729a10f12"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.10.0\n"
]
}
],
"source": [
"import os, sys\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"import string\n",
"from pprint import pprint\n",
"%matplotlib inline\n",
"\n",
"s2s = tf.contrib.seq2seq\n",
"print(tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare example data "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"sources = [['I', 'feel', 'hungry'],\n",
" ['tensorflow', 'is', 'very', 'difficult'],\n",
" ['tensorflow', 'is', 'a', 'framework', 'for', 'deep', 'learning'],\n",
" ['tensorflow', 'is', 'very', 'fast', 'changing']]\n",
"targets = [['나는', '배가', '고프다'],\n",
" ['텐서플로우는', '매우', '어렵다'],\n",
" ['텐서플로우는', '딥러닝을', '위한', '프레임워크이다'],\n",
" ['텐서플로우는', '매우', '빠르게', '변화한다']]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'<pad>': 0, 'I': 1, 'a': 2, 'changing': 3, 'deep': 4, 'difficult': 5, 'fast': 6, 'feel': 7, 'for': 8, 'framework': 9, 'hungry': 10, 'is': 11, 'learning': 12, 'tensorflow': 13, 'very': 14}\n",
"15\n"
]
}
],
"source": [
"# word dic for sentences\n",
"source_words = []\n",
"for elm in sources:\n",
" source_words += elm\n",
"source_words = list(set(source_words))\n",
"source_words.sort()\n",
"source_words = ['<pad>'] + source_words\n",
"\n",
"source_dic = {word : idx for idx, word in enumerate(source_words)}\n",
"print(source_dic)\n",
"print(len(source_dic))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: '<pad>',\n",
" 1: 'I',\n",
" 2: 'a',\n",
" 3: 'changing',\n",
" 4: 'deep',\n",
" 5: 'difficult',\n",
" 6: 'fast',\n",
" 7: 'feel',\n",
" 8: 'for',\n",
" 9: 'framework',\n",
" 10: 'hungry',\n",
" 11: 'is',\n",
" 12: 'learning',\n",
" 13: 'tensorflow',\n",
" 14: 'very'}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source_idx_dic = {elm[1] : elm[0] for elm in source_dic.items()}\n",
"source_idx_dic"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'<pad>': 0, '<start>': 1, '<end>': 2, '고프다': 3, '나는': 4, '딥러닝을': 5, '매우': 6, '배가': 7, '변화한다': 8, '빠르게': 9, '어렵다': 10, '위한': 11, '텐서플로우는': 12, '프레임워크이다': 13}\n",
"14\n"
]
}
],
"source": [
"# word dic for translations\n",
"target_words = []\n",
"for elm in targets:\n",
" target_words += elm\n",
"target_words = list(set(target_words))\n",
"target_words.sort()\n",
"target_words = ['<pad>']+ ['<start>'] + ['<end>'] + \\\n",
" target_words # 번역문의 시작과 끝을 알리는 'start', 'end' token 추가\n",
"\n",
"target_dic = {word : idx for idx, word in enumerate(target_words)}\n",
"print(target_dic)\n",
"print(len(target_dic))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: '<pad>',\n",
" 1: '<start>',\n",
" 2: '<end>',\n",
" 3: '고프다',\n",
" 4: '나는',\n",
" 5: '딥러닝을',\n",
" 6: '매우',\n",
" 7: '배가',\n",
" 8: '변화한다',\n",
" 9: '빠르게',\n",
" 10: '어렵다',\n",
" 11: '위한',\n",
" 12: '텐서플로우는',\n",
" 13: '프레임워크이다'}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_idx_dic = {elm[1] : elm[0] for elm in target_dic.items()}\n",
"target_idx_dic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create pad_seq function for sentences"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def pad_seq_enc(sequences, max_len, dic):\n",
" seq_len = []\n",
" seq_indices = []\n",
" for seq in sequences:\n",
" seq_len.append(len(seq))\n",
" seq_idx = [dic.get(word) for word in seq]\n",
" seq_idx += (max_len - len(seq_idx)) * [dic.get('<pad>')] \n",
" seq_indices.append(seq_idx) \n",
" return seq_len, seq_indices"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def pad_seq_dec(sequences, max_len, dic):\n",
" seq_input_len = []\n",
" seq_input_indices = []\n",
" seq_target_indices = []\n",
" \n",
" # for decoder input\n",
" for seq in sequences:\n",
" seq_input_idx = [dic.get('<start>')] + [dic.get(token) for token in seq]\n",
" seq_input_len.append(len(seq_input_idx))\n",
" seq_input_idx += (max_len - len(seq_input_idx)) * [dic.get('<pad>')] \n",
" seq_input_indices.append(seq_input_idx)\n",
" \n",
" # for decoder output\n",
" for seq in sequences:\n",
" seq_target_idx = [dic.get(token) for token in seq] + [dic.get('<end>')]\n",
" seq_target_idx += (max_len - len(seq_target_idx)) * [dic.get('<pad>')]\n",
" seq_target_indices.append(seq_target_idx)\n",
" \n",
" return seq_input_len, seq_input_indices, seq_target_indices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pre-process example data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 4, 7, 5] (4, 10)\n"
]
}
],
"source": [
"# for encoder\n",
"source_max_len = 10\n",
"X_length, X_indices = pad_seq_enc(sequences = sources, max_len = source_max_len, dic = source_dic)\n",
"print(X_length, np.shape(X_indices))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4, 4, 5, 5]\n",
"[[1, 4, 7, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [1, 12, 6, 10, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [1, 12, 5, 11, 13, 0, 0, 0, 0, 0, 0, 0],\n",
" [1, 12, 6, 9, 8, 0, 0, 0, 0, 0, 0, 0]]\n",
"[[4, 7, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [12, 6, 10, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [12, 5, 11, 13, 2, 0, 0, 0, 0, 0, 0, 0],\n",
" [12, 6, 9, 8, 2, 0, 0, 0, 0, 0, 0, 0]]\n"
]
}
],
"source": [
"# for decoder\n",
"target_max_len = 12\n",
"y_length, y_input_indices, y_target_indices = pad_seq_dec(sequences = targets, max_len = target_max_len,\n",
" dic = target_dic)\n",
"pprint(y_length)\n",
"pprint(y_input_indices)\n",
"pprint(y_target_indices)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define SimpleNMT\n",
"Encoder RNN, Decoder RNN"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class SimpleNMT:\n",
" def __init__(self, s_len, s_indices, t_len, t_input_indices, t_output_indices,\n",
" t_max_len = target_max_len, s_dic = source_dic, t_dic = target_dic,\n",
" n_of_classes = len(target_dic), hidden_dim = 16):\n",
" \n",
" with tf.variable_scope('input_layer'):\n",
" # s : source, t : target\n",
" self._s_len = s_len\n",
" self._s_indices = s_indices\n",
" self._t_len = t_len\n",
" self._t_input_indices = t_input_indices\n",
" self._t_output_indices = t_output_indices\n",
" self._s_dic = s_dic\n",
" self._t_dic = t_dic\n",
" self._t_max_len = target_max_len\n",
" \n",
" s_embeddings = tf.eye(num_rows = len(self._s_dic), dtype = tf.float32)\n",
" s_embeddings = tf.get_variable(name = 's_embeddings', initializer = s_embeddings,\n",
" trainable = False)\n",
" s_batch = tf.nn.embedding_lookup(params = s_embeddings, ids = self._s_indices)\n",
" \n",
" with tf.variable_scope('encoder'):\n",
" \n",
" enc_cell = tf.contrib.rnn.BasicRNNCell(num_units = hidden_dim, activation = tf.nn.tanh)\n",
" _, enc_state = tf.nn.dynamic_rnn(cell = enc_cell, inputs = s_batch,\n",
" sequence_length = self._s_len, dtype = tf.float32)\n",
" \n",
" with tf.variable_scope('pipe'):\n",
" \n",
" t_embeddings = tf.eye(num_rows = len(self._t_dic))\n",
" t_embeddings = tf.get_variable(name = 't_embeddings',\n",
" initializer = t_embeddings,\n",
" trainable = False)\n",
" t_batch = tf.nn.embedding_lookup(params = t_embeddings, ids = self._t_input_indices)\n",
" \n",
" batch_size = tf.reduce_sum(tf.ones_like(tensor = self._s_len, dtype = tf.int32))\n",
" tr_tokens = tf.tile(input = [self._t_max_len], multiples = [batch_size])\n",
" trans_tokens = tf.tile(input = [self._t_dic.get('<start>')], multiples = [batch_size])\n",
" \n",
" with tf.variable_scope('decoder'):\n",
" \n",
" dec_cell = tf.contrib.rnn.BasicRNNCell(num_units = hidden_dim, activation = tf.nn.tanh)\n",
" output_layer = tf.layers.Dense(units = n_of_classes,\n",
" kernel_initializer = \\\n",
" tf.contrib.layers.xavier_initializer(uniform = False))\n",
" \n",
" with tf.variable_scope('training'):\n",
" \n",
" tr_helper = s2s.TrainingHelper(inputs = t_batch,\n",
" sequence_length = tr_tokens)\n",
" tr_decoder = s2s.BasicDecoder(cell = dec_cell, helper = tr_helper,\n",
" initial_state = enc_state, output_layer = output_layer)\n",
" self._tr_outputs,_,_ = s2s.dynamic_decode(decoder = tr_decoder,\n",
" impute_finished = True,\n",
" maximum_iterations = self._t_max_len)\n",
" \n",
" with tf.variable_scope('translation'):\n",
" \n",
" trans_helper = s2s.GreedyEmbeddingHelper(embedding = t_embeddings,\n",
" start_tokens = trans_tokens,\n",
" end_token = self._t_dic.get('<end>'))\n",
" trans_decoder = s2s.BasicDecoder(cell = dec_cell, helper = trans_helper,\n",
" initial_state = enc_state, output_layer = output_layer)\n",
" self._trans_outputs, _, _ = s2s.dynamic_decode(decoder = trans_decoder,\n",
" impute_finished = True,\n",
" maximum_iterations = self._t_max_len * 2)\n",
" \n",
" with tf.variable_scope('seq2seq_loss'):\n",
" \n",
" masking = tf.sequence_mask(lengths = self._t_len,\n",
" maxlen = self._t_max_len, dtype = tf.float32)\n",
" self.__seq2seq_loss = s2s.sequence_loss(logits = self._tr_outputs.rnn_output,\n",
" targets = self._t_output_indices,\n",
" weights = masking)\n",
" \n",
" def translate(self, sess, s_len, s_indices):\n",
" feed_translation = {self._s_len : s_len, self._s_indices : s_indices}\n",
" return sess.run(self._trans_outputs.sample_id, feed_dict = feed_translation)\n",
" \n",
" @property\n",
" def loss(self):\n",
" return self.__seq2seq_loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a model of SimpleNMT"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n"
]
}
],
"source": [
"# hyper-parameter#\n",
"lr = .003\n",
"epochs = 200\n",
"batch_size = 2\n",
"total_step = int(np.shape(X_indices)[0] / batch_size)\n",
"print(total_step)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<BatchDataset shapes: ((?,), (?, 10), (?,), (?, 12), (?, 12)), types: (tf.int32, tf.int32, tf.int32, tf.int32, tf.int32)>\n"
]
}
],
"source": [
"## create data pipeline with tf.data\n",
"tr_dataset = tf.data.Dataset.from_tensor_slices((X_length, X_indices, y_length, y_input_indices, y_target_indices))\n",
"tr_dataset = tr_dataset.shuffle(buffer_size = 20)\n",
"tr_dataset = tr_dataset.batch(batch_size = batch_size)\n",
"tr_iterator = tr_dataset.make_initializable_iterator()\n",
"print(tr_dataset)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"X_length_mb, X_indices_mb, y_length_mb, y_input_indices_mb, y_target_indices_mb = tr_iterator.get_next()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"sim_nmt = SimpleNMT(s_len = X_length_mb, s_indices = X_indices_mb,\n",
" t_len = y_length_mb, t_input_indices = y_input_indices_mb,\n",
" t_output_indices = y_target_indices_mb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creat training op and train model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"## create training op\n",
"opt = tf.train.AdamOptimizer(learning_rate = lr)\n",
"training_op = opt.minimize(loss = sim_nmt.loss)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch : 10, tr_loss : 2.119\n",
"epoch : 20, tr_loss : 1.529\n",
"epoch : 30, tr_loss : 1.046\n",
"epoch : 40, tr_loss : 0.705\n",
"epoch : 50, tr_loss : 0.496\n",
"epoch : 60, tr_loss : 0.357\n",
"epoch : 70, tr_loss : 0.262\n",
"epoch : 80, tr_loss : 0.195\n",
"epoch : 90, tr_loss : 0.149\n",
"epoch : 100, tr_loss : 0.118\n",
"epoch : 110, tr_loss : 0.095\n",
"epoch : 120, tr_loss : 0.080\n",
"epoch : 130, tr_loss : 0.067\n",
"epoch : 140, tr_loss : 0.058\n",
"epoch : 150, tr_loss : 0.051\n",
"epoch : 160, tr_loss : 0.045\n",
"epoch : 170, tr_loss : 0.040\n",
"epoch : 180, tr_loss : 0.036\n",
"epoch : 190, tr_loss : 0.032\n",
"epoch : 200, tr_loss : 0.029\n"
]
}
],
"source": [
"sess = tf.Session()\n",
"sess.run(tf.global_variables_initializer())\n",
"\n",
"tr_loss_hist = []\n",
"\n",
"for epoch in range(epochs):\n",
" avg_tr_loss = 0\n",
" tr_step = 0\n",
" \n",
" sess.run(tr_iterator.initializer)\n",
" try:\n",
" while True:\n",
" _, tr_loss = sess.run(fetches = [training_op, sim_nmt.loss])\n",
" avg_tr_loss += tr_loss\n",
" tr_step += 1\n",
" \n",
" except tf.errors.OutOfRangeError:\n",
" pass\n",
" \n",
" avg_tr_loss /= tr_step\n",
" tr_loss_hist.append(avg_tr_loss)\n",
" if (epoch + 1) % 10 == 0:\n",
" print('epoch : {:3}, tr_loss : {:.3f}'.format(epoch + 1, avg_tr_loss))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 4, 7, 3, 2, 0],\n",
" [12, 6, 10, 2, 0],\n",
" [12, 5, 11, 13, 2],\n",
" [12, 6, 9, 8, 2]], dtype=int32)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yhat = sim_nmt.translate(sess = sess, s_len = X_length, s_indices = X_indices)\n",
"yhat"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['나는', '배가', '고프다', '<end>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']\n",
"['텐서플로우는', '매우', '어렵다', '<end>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']\n",
"['텐서플로우는', '딥러닝을', '위한', '프레임워크이다', '<end>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']\n",
"['텐서플로우는', '매우', '빠르게', '변화한다', '<end>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']\n"
]
}
],
"source": [
"# 원래 문장\n",
"originals = list(map(lambda elm : [target_idx_dic.get(idx) for idx in elm], y_target_indices))\n",
"for original in originals:\n",
" print(original)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['나는', '배가', '고프다', '<end>', '<pad>']\n",
"['텐서플로우는', '매우', '어렵다', '<end>', '<pad>']\n",
"['텐서플로우는', '딥러닝을', '위한', '프레임워크이다', '<end>']\n",
"['텐서플로우는', '매우', '빠르게', '변화한다', '<end>']\n"
]
}
],
"source": [
"# 한글 넣은 번역문장\n",
"translations = list(map(lambda elm : [target_idx_dic.get(idx) for idx in elm], yhat))\n",
"for translation in translations:\n",
" print(translation)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
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