以简单英文问答问题为例测试tensorflow1.4 tf.contrib.legacy_seq2seq中seq2seq文件的几个seq2seq接口

github:https://github.com/buyizhiyou/tf_seq2seq

测试 basic_rnn_seq2seq 的使用

 #-*-coding:utf8-*-

 __author="buyizhiyou"

 __date = "2018-7-30"

 import os

 import pdb

 import re

 from collections import Counter

 import matplotlib.pyplot as plt

 import tensorflow as tf

 from seq2seq import basic_rnn_seq2seq

 from utils import * 

 os.environ["CUDA_VISIBLE_DEVICES"] = ""#choose GPU 1

 input_batches = [

     ['Hi What is your name?', 'Nice to meet you!'],

     ['Which programming language do you use?', 'See you later.'],

     ['Where do you live?', 'What is your major?'],

     ['What do you want to drink?', 'What is your favorite beer?']]

 target_batches = [

     ['Hi this is Jaemin.', 'Nice to meet you too!'],

     ['I like Python.', 'Bye Bye.'],

     ['I live in Seoul, South Korea.', 'I study industrial engineering.'],

     ['Beer please!', 'Leffe brown!']]

 all_input_sentences = []

 for input_batch in input_batches:

     all_input_sentences.extend(input_batch)

 all_target_sentences = []

 for target_batch in target_batches:

     all_target_sentences.extend(target_batch)

 enc_vocab, enc_reverse_vocab, enc_vocab_size = build_vocab(all_input_sentences)#enc_vocab:word2idx,enc_reverse_vacab:idx2word,enc_vocab_size:26

 dec_vocab, dec_reverse_vocab, dec_vocab_size = build_vocab(all_target_sentences, is_target=True)##dec_vocab:word2idx,dec_reverse_vacab:idx2word,dec_vocab_size:28

 #hyperParameters

 n_epoch = 2000

 hidden_size = 50

 enc_emb_size = 20

 dec_emb_size = 21

 enc_sentence_length=10

 dec_sentence_length=11

 enc_inputs = tf.placeholder(tf.int32,shape=[None,enc_sentence_length],name='input_sentences')

 sequence_lengths = tf.placeholder(tf.int32,shape=[None],name='sentences_length')

 dec_inputs = tf.placeholder(tf.int32,shape=[None,dec_sentence_length+1],name='output_sentences')

 enc_inputs_t = tf.transpose(enc_inputs,perm=[1,0])

 dec_inputs_t = tf.transpose(dec_inputs,perm=[1,0])

 '''

 embedding

 '''

 enc_Wemb = tf.get_variable('enc_word_emb',initializer=tf.random_uniform([enc_vocab_size+1,enc_emb_size]))

 dec_Wemb = tf.get_variable('dec_word_emb',initializer=tf.random_uniform([dec_vocab_size+2,dec_emb_size]))

 enc_emb_inputs = tf.nn.embedding_lookup(enc_Wemb,enc_inputs_t)

 dec_emb_inputs = tf.nn.embedding_lookup(dec_Wemb,dec_inputs_t)

 # enc_emb_inputs:list(enc_sent_len) of tensor[batch_size x embedding_size]

 # Because `static_rnn` takes list inputs

 enc_emb_inputs = tf.unstack(enc_emb_inputs)

 dec_emb_inputs = tf.unstack(dec_emb_inputs)

 cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)

 dec_outputs,state = basic_rnn_seq2seq(enc_emb_inputs,dec_emb_inputs,cell)

 dec_outputs = tf.stack(dec_outputs)

 logits = tf.layers.dense(dec_outputs,units=dec_vocab_size+2,activation=tf.nn.relu)#fc层

 predictions = tf.argmax(logits,axis=2)

 predictions = tf.transpose(predictions,[1,0])

 # labels & logits: [dec_sentence_length+1 x batch_size x dec_vocab_size+2]

 labels = tf.one_hot(dec_inputs_t, dec_vocab_size+2)

 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(

     labels=labels, logits=logits))

 # training_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss)

 training_op = tf.train.RMSPropOptimizer(learning_rate=0.0001).minimize(loss)

 with tf.Session() as sess:

     sess.run(tf.global_variables_initializer())

     loss_history = []

     for epoch in range(n_epoch):

         all_preds = []

         epoch_loss = 0

         for input_batch,target_batch in zip(input_batches,target_batches):

             input_token_indices = []

             target_token_indices = []

             sentence_lengths = []

             for input_sent in input_batch:

                 input_sent,sent_len = sent2idx(input_sent,vocab=enc_vocab,max_sentence_length=enc_sentence_length)

                 input_token_indices.append(input_sent)

                 sentence_lengths.append(sent_len)

             for target_sent in target_batch:

                 target_token_indices.append(sent2idx(target_sent,vocab=dec_vocab,max_sentence_length=dec_sentence_length,is_target=True))

             batch_preds,batch_loss, _ = sess.run(

                 [predictions,loss, training_op],

                 feed_dict={

                     enc_inputs: input_token_indices,

                     sequence_lengths: sentence_lengths,

                     dec_inputs: target_token_indices

                 })

             loss_history.append(batch_loss)

             epoch_loss += batch_loss

             all_preds.append(batch_preds)

         # Logging every 400 epochs

         if epoch % 400 == 0:

             print('Epoch', epoch)

             for input_batch, target_batch, batch_preds in zip(input_batches, target_batches, all_preds):

                 for input_sent, target_sent, pred in zip(input_batch, target_batch, batch_preds):

                     print('\t', input_sent)

                     print('\t => ', idx2sent(pred, reverse_vocab=dec_reverse_vocab))

                     print('\tCorrent answer:', target_sent)

             print('\tepoch loss: {:.2f}\n'.format(epoch_loss))

 show_loss(loss_history)

测试　tied_rnn_seq2seq　的使用(该接口encoder和decoder共享参数)

 #-*-coding:utf8-*-

 __author="buyizhiyou"

 __date = "2018-7-30"

 import os

 import pdb

 import re

 from collections import Counter

 import matplotlib.pyplot as plt

 import tensorflow as tf

 from seq2seq import tied_rnn_seq2seq

 from utils import * 

 os.environ["CUDA_VISIBLE_DEVICES"] = ""#choose GPU 1

 input_batches = [

     ['Hi What is your name?', 'Nice to meet you!'],

     ['Which programming language do you use?', 'See you later.'],

     ['Where do you live?', 'What is your major?'],

     ['What do you want to drink?', 'What is your favorite beer?']]

 target_batches = [

     ['Hi this is Jaemin.', 'Nice to meet you too!'],

     ['I like Python.', 'Bye Bye.'],

     ['I live in Seoul, South Korea.', 'I study industrial engineering.'],

     ['Beer please!', 'Leffe brown!']]

 all_input_sentences = []

 for input_batch in input_batches:

     all_input_sentences.extend(input_batch)

 all_target_sentences = []

 for target_batch in target_batches:

     all_target_sentences.extend(target_batch)

 enc_vocab, enc_reverse_vocab, enc_vocab_size = build_vocab(all_input_sentences)#enc_vocab:word2idx,enc_reverse_vacab:idx2word,enc_vocab_size:26

 dec_vocab, dec_reverse_vocab, dec_vocab_size = build_vocab(all_target_sentences, is_target=True)##dec_vocab:word2idx,dec_reverse_vacab:idx2word,dec_vocab_size:28

 #hyperParameters

 n_epoch = 2000

 hidden_size = 50

 enc_emb_size = 20

 dec_emb_size = 20#must consistent with enc_emb_size for parameter sharing

 enc_sentence_length=10

 dec_sentence_length=11

 enc_inputs = tf.placeholder(tf.int32,shape=[None,enc_sentence_length],name='input_sentences')

 sequence_lengths = tf.placeholder(tf.int32,shape=[None],name='sentences_length')

 dec_inputs = tf.placeholder(tf.int32,shape=[None,dec_sentence_length+1],name='output_sentences')

 enc_inputs_t = tf.transpose(enc_inputs,perm=[1,0])

 dec_inputs_t = tf.transpose(dec_inputs,perm=[1,0])

 '''

 embedding

 '''

 enc_Wemb = tf.get_variable('enc_word_emb',initializer=tf.random_uniform([enc_vocab_size+1,enc_emb_size]))

 dec_Wemb = tf.get_variable('dec_word_emb',initializer=tf.random_uniform([dec_vocab_size+2,dec_emb_size]))

 enc_emb_inputs = tf.nn.embedding_lookup(enc_Wemb,enc_inputs_t)

 dec_emb_inputs = tf.nn.embedding_lookup(dec_Wemb,dec_inputs_t)

 # enc_emb_inputs:list(enc_sent_len) of tensor[batch_size x embedding_size]

 # Because `static_rnn` takes list inputs

 enc_emb_inputs = tf.unstack(enc_emb_inputs)

 dec_emb_inputs = tf.unstack(dec_emb_inputs)

 cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)

 dec_outputs,state = tied_rnn_seq2seq(enc_emb_inputs,dec_emb_inputs,cell)

 dec_outputs = tf.stack(dec_outputs)

 logits = tf.layers.dense(dec_outputs,units=dec_vocab_size+2,activation=tf.nn.relu)#fc层

 predictions = tf.argmax(logits,axis=2)

 predictions = tf.transpose(predictions,[1,0])

 # labels & logits: [dec_sentence_length+1 x batch_size x dec_vocab_size+2]

 labels = tf.one_hot(dec_inputs_t, dec_vocab_size+2)

 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(

     labels=labels, logits=logits))

 # training_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss)

 training_op = tf.train.RMSPropOptimizer(learning_rate=0.0001).minimize(loss)

 with tf.Session() as sess:

     sess.run(tf.global_variables_initializer())

     loss_history = []

     for epoch in range(n_epoch):

         all_preds = []

         epoch_loss = 0

         for input_batch,target_batch in zip(input_batches,target_batches):

             input_token_indices = []

             target_token_indices = []

             sentence_lengths = []

             for input_sent in input_batch:

                 input_sent,sent_len = sent2idx(input_sent,vocab=enc_vocab,max_sentence_length=enc_sentence_length)

                 input_token_indices.append(input_sent)

                 sentence_lengths.append(sent_len)

             for target_sent in target_batch:

                 target_token_indices.append(sent2idx(target_sent,vocab=dec_vocab,max_sentence_length=dec_sentence_length,is_target=True))

             batch_preds,batch_loss, _ = sess.run(

                 [predictions,loss, training_op],

                 feed_dict={

                     enc_inputs: input_token_indices,

                     sequence_lengths: sentence_lengths,

                     dec_inputs: target_token_indices

                 })

             loss_history.append(batch_loss)

             epoch_loss += batch_loss

             all_preds.append(batch_preds)

         # Logging every 400 epochs

         if epoch % 400 == 0:

             print('Epoch', epoch)

             for input_batch, target_batch, batch_preds in zip(input_batches, target_batches, all_preds):

                 for input_sent, target_sent, pred in zip(input_batch, target_batch, batch_preds):

                     print('\t', input_sent)

                     print('\t => ', idx2sent(pred, reverse_vocab=dec_reverse_vocab))

                     print('\tCorrent answer:', target_sent)

             print('\tepoch loss: {:.2f}\n'.format(epoch_loss))

 show_loss(loss_history)

测试　embedding_attention_seq2seq的使用

 #-*-coding:utf8-*-

 __author="buyizhiyou"

 __date = "2018-7-30"

 import os

 import pdb

 import re

 from collections import Counter

 import matplotlib.pyplot as plt

 import tensorflow as tf

 from seq2seq import embedding_attention_seq2seq

 from utils import * 

 os.environ["CUDA_VISIBLE_DEVICES"] = ""#choose GPU 1

 input_batches = [

     ['Hi What is your name?', 'Nice to meet you!'],

     ['Which programming language do you use?', 'See you later.'],

     ['Where do you live?', 'What is your major?'],

     ['What do you want to drink?', 'What is your favorite beer?']]

 target_batches = [

     ['Hi this is Jaemin.', 'Nice to meet you too!'],

     ['I like Python.', 'Bye Bye.'],

     ['I live in Seoul, South Korea.', 'I study industrial engineering.'],

     ['Beer please!', 'Leffe brown!']]

 all_input_sentences = []

 for input_batch in input_batches:

     all_input_sentences.extend(input_batch)

 all_target_sentences = []

 for target_batch in target_batches:

     all_target_sentences.extend(target_batch)

 enc_vocab, enc_reverse_vocab, enc_vocab_size = build_vocab(all_input_sentences)#enc_vocab:word2idx,enc_reverse_vacab:idx2word,enc_vocab_size:26

 dec_vocab, dec_reverse_vocab, dec_vocab_size = build_vocab(all_target_sentences, is_target=True)##dec_vocab:word2idx,dec_reverse_vacab:idx2word,dec_vocab_size:28

 #hyperParameters

 n_epoch = 2000

 hidden_size = 50

 enc_emb_size = 20

 dec_emb_size = 21

 enc_sentence_length=10

 dec_sentence_length=11

 enc_inputs = tf.placeholder(tf.int32,shape=[None,enc_sentence_length],name='input_sentences')

 sequence_lengths = tf.placeholder(tf.int32,shape=[None],name='sentences_length')

 dec_inputs = tf.placeholder(tf.int32,shape=[None,dec_sentence_length+1],name='output_sentences')

 enc_inputs_t = tf.transpose(enc_inputs,perm=[1,0])

 dec_inputs_t = tf.transpose(dec_inputs,perm=[1,0])

 labels = tf.one_hot(dec_inputs_t, dec_vocab_size+2)

 # labels & logits: [dec_sentence_length+1 x batch_size x dec_vocab_size+2]

 # enc_emb_inputs:list(enc_sent_len) of tensor[batch_size x embedding_size]

 # Because `static_rnn` takes list inputs

 enc_inputs_t = tf.unstack(enc_inputs_t)

 dec_inputs_t = tf.unstack(dec_inputs_t)

 cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)

 dec_outputs,state = embedding_attention_seq2seq(

                             encoder_inputs=enc_inputs_t,

                             decoder_inputs=dec_inputs_t,

                             cell=cell,

                             num_encoder_symbols=enc_vocab_size+1,

                             num_decoder_symbols=dec_vocab_size+2,

                             embedding_size=enc_emb_size,

                             output_projection=None,

                             feed_previous=True

 )

 logits = tf.stack(dec_outputs)

 predictions = tf.argmax(logits,axis=2)

 predictions = tf.transpose(predictions,[1,0])

 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(

     labels=labels, logits=logits))

 # training_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss)

 training_op = tf.train.RMSPropOptimizer(learning_rate=0.0001).minimize(loss)

 with tf.Session() as sess:

     sess.run(tf.global_variables_initializer())

     loss_history = []

     for epoch in range(n_epoch):

         all_preds = []

         epoch_loss = 0

         for input_batch,target_batch in zip(input_batches,target_batches):

             input_token_indices = []

             target_token_indices = []

             sentence_lengths = []

             for input_sent in input_batch:

                 input_sent,sent_len = sent2idx(input_sent,vocab=enc_vocab,max_sentence_length=enc_sentence_length)

                 input_token_indices.append(input_sent)

                 sentence_lengths.append(sent_len)

             for target_sent in target_batch:

                 target_token_indices.append(sent2idx(target_sent,vocab=dec_vocab,max_sentence_length=dec_sentence_length,is_target=True))

             batch_preds,batch_loss, _ = sess.run(

                 [predictions,loss, training_op],

                 feed_dict={

                     enc_inputs: input_token_indices,

                     sequence_lengths: sentence_lengths,

                     dec_inputs: target_token_indices

                 })

             loss_history.append(batch_loss)

             epoch_loss += batch_loss

             all_preds.append(batch_preds)

         # Logging every 400 epochs

         if epoch % 400 == 0:

             print('Epoch', epoch)

             for input_batch, target_batch, batch_preds in zip(input_batches, target_batches, all_preds):

                 for input_sent, target_sent, pred in zip(input_batch, target_batch, batch_preds):

                     print('\t', input_sent)

                     print('\t => ', idx2sent(pred, reverse_vocab=dec_reverse_vocab))

                     print('\tCorrent answer:', target_sent)

             print('\tepoch loss: {:.2f}\n'.format(epoch_loss))

 show_loss(loss_history)

测试embedding_seq2seq 的使用

 #-*-coding:utf8-*-

 __author="buyizhiyou"

 __date = "2018-7-30"

 '''

 测试embedding_rnn_seq2seq函数

 '''

 import os

 import pdb

 import re

 from collections import Counter

 import matplotlib.pyplot as plt

 import tensorflow as tf

 from seq2seq import embedding_rnn_seq2seq

 from utils import * 

 os.environ["CUDA_VISIBLE_DEVICES"] = ""#choose GPU 1

 input_batches = [

     ['Hi What is your name?', 'Nice to meet you!'],

     ['Which programming language do you use?', 'See you later.'],

     ['Where do you live?', 'What is your major?'],

     ['What do you want to drink?', 'What is your favorite beer?']]

 target_batches = [

     ['Hi this is Jaemin.', 'Nice to meet you too!'],

     ['I like Python.', 'Bye Bye.'],

     ['I live in Seoul, South Korea.', 'I study industrial engineering.'],

     ['Beer please!', 'Leffe brown!']]

 all_input_sentences = []

 for input_batch in input_batches:

     all_input_sentences.extend(input_batch)

 all_target_sentences = []

 for target_batch in target_batches:

     all_target_sentences.extend(target_batch)

 enc_vocab, enc_reverse_vocab, enc_vocab_size = build_vocab(all_input_sentences)#enc_vocab:word2idx,enc_reverse_vacab:idx2word,enc_vocab_size:26

 dec_vocab, dec_reverse_vocab, dec_vocab_size = build_vocab(all_target_sentences, is_target=True)##dec_vocab:word2idx,dec_reverse_vacab:idx2word,dec_vocab_size:28

 #hyperParameters

 n_epoch = 2000

 hidden_size = 50

 enc_emb_size = 20

 dec_emb_size = 21

 enc_sentence_length=10

 dec_sentence_length=11

 enc_inputs = tf.placeholder(tf.int32,shape=[None,enc_sentence_length],name='input_sentences')

 sequence_lengths = tf.placeholder(tf.int32,shape=[None],name='sentences_length')

 dec_inputs = tf.placeholder(tf.int32,shape=[None,dec_sentence_length+1],name='output_sentences')

 enc_inputs_t = tf.transpose(enc_inputs,perm=[1,0])

 dec_inputs_t = tf.transpose(dec_inputs,perm=[1,0])

 labels = tf.one_hot(dec_inputs_t, dec_vocab_size+2)

 # labels & logits: [dec_sentence_length+1 x batch_size x dec_vocab_size+2]

 # enc_emb_inputs:list(enc_sent_len) of tensor[batch_size x embedding_size]

 # Because `static_rnn` takes list inputs

 enc_inputs_t = tf.unstack(enc_inputs_t)

 dec_inputs_t = tf.unstack(dec_inputs_t)

 cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)

 dec_outputs,state = embedding_rnn_seq2seq(

                             encoder_inputs=enc_inputs_t,

                             decoder_inputs=dec_inputs_t,

                             cell=cell,

                             num_encoder_symbols=enc_vocab_size+1,

                             num_decoder_symbols=dec_vocab_size+2,

                             embedding_size=enc_emb_size,

                             output_projection=None,

                             feed_previous=True

 )

 logits = tf.stack(dec_outputs)

 predictions = tf.argmax(logits,axis=2)

 predictions = tf.transpose(predictions,[1,0])

 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(

     labels=labels, logits=logits))

 # training_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss)

 training_op = tf.train.RMSPropOptimizer(learning_rate=0.0001).minimize(loss)

 with tf.Session() as sess:

     sess.run(tf.global_variables_initializer())

     loss_history = []

     for epoch in range(n_epoch):

         all_preds = []

         epoch_loss = 0

         for input_batch,target_batch in zip(input_batches,target_batches):

             input_token_indices = []

             target_token_indices = []

             sentence_lengths = []

             for input_sent in input_batch:

                 input_sent,sent_len = sent2idx(input_sent,vocab=enc_vocab,max_sentence_length=enc_sentence_length)

                 input_token_indices.append(input_sent)

                 sentence_lengths.append(sent_len)

             for target_sent in target_batch:

                 target_token_indices.append(sent2idx(target_sent,vocab=dec_vocab,max_sentence_length=dec_sentence_length,is_target=True))

             batch_preds,batch_loss, _ = sess.run(

                 [predictions,loss, training_op],

                 feed_dict={

                     enc_inputs: input_token_indices,

                     sequence_lengths: sentence_lengths,

                     dec_inputs: target_token_indices

                 })

             loss_history.append(batch_loss)

             epoch_loss += batch_loss

             all_preds.append(batch_preds)

         # Logging every 400 epochs

         if epoch % 400 == 0:

             print('Epoch', epoch)

             for input_batch, target_batch, batch_preds in zip(input_batches, target_batches, all_preds):

                 for input_sent, target_sent, pred in zip(input_batch, target_batch, batch_preds):

                     print('\t', input_sent)

                     print('\t => ', idx2sent(pred, reverse_vocab=dec_reverse_vocab))

                     print('\tCorrent answer:', target_sent)

             print('\tepoch loss: {:.2f}\n'.format(epoch_loss))

 show_loss(loss_history)

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