自然语言处理，语音处理、文本处理。语音识别(speech recognition)，让计算机能够“听懂”人类语音，语音的文字信息“提取”。
日本富国生命保险公司花170万美元安装人工智能系统，客户语言转换文本，分析词正面或负面。智能客服是人工能智能公司研究重点。循环神经网络(recurrent neural network,RNN)模型。
模型选择。每一个矩形是一个向量，箭头表示函数。最下面一行输入向量，最上面一行输出向量，中间一行RNN状态。一对一，没用RNN，如Vanilla模型，固定大小输入到固定大小输出(图像分类)。一对多，序列输出，图片描述，输入一张图片输出一段文字序列，CNN、RNN结合，图像、语言结合。多对一，序列输入，情感分析，输入一段文字，分类积极、消极情感，如淘宝商品评论分类，用LSTM。多对多，异步序列输入、序列输出，机器翻译，如RNN读取英文语句，以法语形式输出。多对多，同步序列输入、序列输出，视频分类，视频每帧打标记。中间RNN状态部分固定，可多次使用，不需对序列长度预先约束。Andrej Karpathy《The Unreasonable Effectiveness of Recurrent Neural Networks》。http://karpathy.github.io/2015/05/21/rnn-effectiveness/ 。自然语言处理，语音合成(文字生成语音)、语单识别、声纹识别(声纹鉴权)、文本处理(分词、情感分析、文本挖掘)。
英文数字语音识别。https://github.com/pannous/tensorflow-speech-recognition/blob/master/speech2text-tflearn.py 。20行Python代码创建超简单语音识别器。LSTM循环神经网络，TFLearn训练英文数字口语数据集。spoken numbers pcm数据集 http://pannous.net/spoken_numbers.tar 。多人阅读0~9数字英文音频，分男女声，一段音频(wav文件)只有一个数字对应英文声音。标识方法{数字}_人名_xxx。
定义输入数据，预处理数据。语音处理成矩阵形式。梅尔频率倒谱系数(Mel frequency cepstral coefficents, MFCC)特征向量。语音分帧、取对数、逆矩阵，生成MFCC代表语音特征。
定义网络模型。LSTM模型。
训练模型，并存储模型。
预测模型。任意输入一个语音文件，预测。
语音识别，可用在智能输入法、会议快速录入、语音控制系统、智能家居领域。
#!/usr/bin/env python
#!/usr/local/bin/python
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import tflearn
import speech_data
learning_rate = 0.0001
training_iters = 300000 # steps 迭代次数
batch_size = 64
width = 20 # mfcc features MFCC特征
height = 80 # (max) length of utterance 最大发音长度
classes = 10 # digits 数字类别
batch = word_batch = speech_data.mfcc_batch_generator(batch_size) # 生成每一批MFCC语音
X, Y = next(batch)
# train, test, _ = ,X
trainX, trainY = X, Y
testX, testY = X, Y #overfit for now
# Data preprocessing
# Sequence padding
# trainX = pad_sequences(trainX, maxlen=100, value=0.)
# testX = pad_sequences(testX, maxlen=100, value=0.)
# # Converting labels to binary vectors
# trainY = to_categorical(trainY, nb_classes=2)
# testY = to_categorical(testY, nb_classes=2)
# Network building
# LSTM模型
net = tflearn.input_data([None, width, height])
# net = tflearn.embedding(net, input_dim=10000, output_dim=128)
net = tflearn.lstm(net, 128, dropout=0.8)
net = tflearn.fully_connected(net, classes, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=learning_rate, loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net, tensorboard_verbose=0)
model.load("tflearn.lstm.model")
while 1: #training_iters
model.fit(trainX, trainY, n_epoch=100, validation_set=(testX, testY), show_metric=True,
batch_size=batch_size)
_y=model.predict(X)
model.save("tflearn.lstm.model")
print (_y)
print (y)
智能聊天机器人。未来方向“自然语言人机交互”。苹果Siri、微软Cortana和小冰、Google Now、百度度秘、亚马逊蓝牙音箱Amazon Echo内置语音助手Alexa、Facebook 语音助手M。通过和用户“语音机器人”对话，引导用户到对应服务。今后智能硬件、智能家居嵌入式应用。
智能聊天机器人3代技术。第一代特征工程，大量逻辑判断。第二代检索库，给定问题、聊天，从检索库找到与已有答案最匹配答案。第三代深度学习，seq2seq+Attention模型，大量训练，根据输入生成输出。
seq2seq+Attention模型原理、构建方法。翻译模型，把一个序列翻译成另一个序列。两个RNNLM，一个作编码器，一个解码器，组成RNN编码器-解码器。文本处理领域，常用编码器-解码器(encoder-decoder)框架。输入->编码器->语义编码C->解码器->输出。适合处理上下文(context)生成一个目标(target)通用处理模型。一个句子对<X,Y>，输入给定句子X，通过编码器-解码器框架生成目标句子Y。X、Y可以不同语言，机器翻译。X、Y是对话问句答句，聊天机器人。X、Y可以是图片和对应描述，看图说话。
X由x1､x2等单词序列组成，Y由y1､y2等单词序列组成。编码器编码输入X，生成中间语义编码C，解码器解码中间语义编码C，每个i时刻结合已生成y1､y2……yi-1历史信息生成Yi。生成句子每个词采用中间语义编码相同 C。短句子贴切，长句子不合语义。
实际实现聊天系统，编码器和解码器采用RNN模型、LSTM模型。句子长度超过30，LSTM模型效果急剧下降，引入Attention模型，长句子提升系统效果。Attention机制，人在做一件事情，专注做这件事，忽略周围其他事。源句子中对生成句子重要关键词权重提高，产生更准确应答。增加Attention模型编码器-解码器模型框架：输入->编码器->语义编码C1､C2､C3->解码器->输出Y1、Y2、Y3。中间语义编码Ci不断变化，产生更准确Yi。
最佳实践。https://github.com/suriyadeepan/easy_seq2seq ，依赖TensorFlow 0.12.1环境。康奈尔大学 Corpus数据集(Cornell Movie Dialogs Corpus) http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html 。600 部电影对白。
处理聊天数据。
先把数据集整理成“问”、“答”文件，生成.enc(问句)、.dec(答句)文件。test.dec #测试集答句，test.enc #测试集问句，train.dec #训练集答句，train.enc #训练集问句。
创建词汇表，问句、答句转换成对应id形式。词汇表文件2万个词汇。vocab20000.dec #答句词汇表，vocab20000.enc #问句词汇表。_GO、_EOS、_UNK、_PAD seq2seq模型特殊标记，填充标记对话。_GO标记对话开始。_EOS标记对话结束。_UNK标记未出现词汇表字符，替换稀有词汇。_PAD填充序列，保证批次序列长度相同。转换成ids文件，test.enc.ids20000､train.dec.ids20000､train.enc.ids20000。问句、答句转换ids文件，每行是一个问句或答句，每行每个id代表问句或答句对应位置词。
采用编码器-解码器框架训练。
定义训练参数。seq2seq.ini。
[strings]
# Mode : train, test, serve 模式
mode = train
train_enc = data/train.enc
train_dec = data/train.dec
test_enc = data/test.enc
test_dec = data/test.dec
# folder where checkpoints, vocabulary, temporary data will be stored
# 模型文件和词汇表存储路径
working_directory = working_dir/
[ints]
# vocabulary size
# 词汇表大小
# 20,000 is a reasonable size
enc_vocab_size = 20000
dec_vocab_size = 20000
# number of LSTM layers : 1/2/3
# LSTM层数
num_layers = 3
# typical options : 128, 256, 512, 1024 每层大小，可取值
layer_size = 256
# dataset size limit; typically none : no limit
max_train_data_size = 0
batch_size = 64
# steps per checkpoint
# 每多少次迭代存储一次模型
# Note : At a checkpoint, models parameters are saved, model is evaluated
# and results are printed
steps_per_checkpoint = 300
[floats]
learning_rate = 0.5 # 学习速率
learning_rate_decay_factor = 0.99 # 学习速率下降系数
max_gradient_norm = 5.0
定义网络模型 seq2seq。seq2seq_model.py。TensorFlow 0.12。定义seq2seq+Attention模型类，3个函数。《Grammar as a Foreign Language》 http://arxiv.org/abs/1412.7499 。初始化模型函数(__init__)、训练模型函数(step)、获取下一批次训练数据函数(get_batch)。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.models.rnn.translate import data_utils
class Seq2SeqModel(object):
def __init__(self, source_vocab_size, target_vocab_size, buckets, size,
num_layers, max_gradient_norm, batch_size, learning_rate,
learning_rate_decay_factor, use_lstm=False,
num_samples=512, forward_only=False):
""" 构建模型
Args: 参数
source_vocab_size: size of the source vocabulary. 问句词汇表大小
target_vocab_size: size of the target vocabulary.答句词汇表大小
buckets: a list of pairs (I, O), where I specifies maximum input length
that will be processed in that bucket, and O specifies maximum output
length. Training instances that have inputs longer than I or outputs
longer than O will be pushed to the next bucket and padded accordingly.
We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
其中I指定最大输入长度，O指定最大输出长度
size: number of units in each layer of the model.每层神经元数量
num_layers: number of layers in the model.模型层数
max_gradient_norm: gradients will be clipped to maximally this norm.梯度被削减到最大规范
batch_size: the size of the batches used during training;
the model construction is independent of batch_size, so it can be
changed after initialization if this is convenient, e.g., for decoding.批次大小。训练、预测批次大小，可不同
learning_rate: learning rate to start with.学习速率
learning_rate_decay_factor: decay learning rate by this much when needed.调整学习速率
use_lstm: if true, we use LSTM cells instead of GRU cells.使用LSTM 单元代替GRU单元
num_samples: number of samples for sampled softmax.使用softmax样本数
forward_only: if set, we do not construct the backward pass in the model.是否仅构建前向传播
"""
self.source_vocab_size = source_vocab_size
self.target_vocab_size = target_vocab_size
self.buckets = buckets
self.batch_size = batch_size
self.learning_rate = tf.Variable(float(learning_rate), trainable=False)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
# If we use sampled softmax, we need an output projection.
output_projection = None
softmax_loss_function = None
# Sampled softmax only makes sense if we sample less than vocabulary size.
# 如果样本量比词汇表量小，用抽样softmax
if num_samples > 0 and num_samples < self.target_vocab_size:
w = tf.get_variable("proj_w", [size, self.target_vocab_size])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [self.target_vocab_size])
output_projection = (w, b)
def sampled_loss(inputs, labels):
labels = tf.reshape(labels, [-1, 1])
return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, num_samples,
self.target_vocab_size)
softmax_loss_function = sampled_loss
# Create the internal multi-layer cell for our RNN.
# 构建RNN
single_cell = tf.nn.rnn_cell.GRUCell(size)
if use_lstm:
single_cell = tf.nn.rnn_cell.BasicLSTMCell(size)
cell = single_cell
cell = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=0.5)
if num_layers > 1:
cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers) # The seq2seq function: we use embedding for the input and attention.
# Attention模型
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return tf.nn.seq2seq.embedding_attention_seq2seq(
encoder_inputs, decoder_inputs, cell,
num_encoder_symbols=source_vocab_size,
num_decoder_symbols=target_vocab_size,
embedding_size=size,
output_projection=output_projection,
feed_previous=do_decode)
# Feeds for inputs.
# 给模型填充数据
self.encoder_inputs = []
self.decoder_inputs = []
self.target_weights = []
for i in xrange(buckets[-1][0]): # Last bucket is the biggest one.
self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="encoder{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
name="weight{0}".format(i)))
# Our targets are decoder inputs shifted by one.
# targets值是解码器偏移1位
targets = [self.decoder_inputs[i + 1]
for i in xrange(len(self.decoder_inputs) - 1)]
# Training outputs and losses.
# 训练模型输出
if forward_only:
self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs, targets,
self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True),
softmax_loss_function=softmax_loss_function)
# If we use output projection, we need to project outputs for decoding.
if output_projection is not None:
for b in xrange(len(buckets)):
self.outputs[b] = [
tf.matmul(output, output_projection[0]) + output_projection[1]
for output in self.outputs[b]
]
else:
self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs, targets,
self.target_weights, buckets,
lambda x, y: seq2seq_f(x, y, False),
softmax_loss_function=softmax_loss_function)
# Gradients and SGD update operation for training the model.
# 训练模型，更新梯度
params = tf.trainable_variables()
if not forward_only:
self.gradient_norms = []
self.updates = []
opt = tf.train.AdamOptimizer()
for b in xrange(len(buckets)):
gradients = tf.gradients(self.losses[b], params)
clipped_gradients, norm = tf.clip_by_global_norm(gradients,
max_gradient_norm)
self.gradient_norms.append(norm)
self.updates.append(opt.apply_gradients(
zip(clipped_gradients, params), global_step=self.global_step))
self.saver = tf.train.Saver(tf.global_variables())
def step(self, session, encoder_inputs, decoder_inputs, target_weights,
bucket_id, forward_only):
"""Run a step of the model feeding the given inputs.
定义运行模型的每一步
Args:
session: tensorflow session to use.
encoder_inputs: list of numpy int vectors to feed as encoder inputs.问句向量序列
decoder_inputs: list of numpy int vectors to feed as decoder inputs.答句向量序列
target_weights: list of numpy float vectors to feed as target weights.
bucket_id: which bucket of the model to use.输入bucket_id
forward_only: whether to do the backward step or only forward.是否只做前向传播
Returns:
A triple consisting of gradient norm (or None if we did not do backward),
average perplexity, and the outputs.
Raises:
ValueError: if length of encoder_inputs, decoder_inputs, or
target_weights disagrees with bucket size for the specified bucket_id.
"""
# Check if the sizes match.
encoder_size, decoder_size = self.buckets[bucket_id]
if len(encoder_inputs) != encoder_size:
raise ValueError("Encoder length must be equal to the one in bucket,"
" %d != %d." % (len(encoder_inputs), encoder_size))
if len(decoder_inputs) != decoder_size:
raise ValueError("Decoder length must be equal to the one in bucket,"
" %d != %d." % (len(decoder_inputs), decoder_size))
if len(target_weights) != decoder_size:
raise ValueError("Weights length must be equal to the one in bucket,"
" %d != %d." % (len(target_weights), decoder_size))
# Input feed: encoder inputs, decoder inputs, target_weights, as provided.
# 输入填充
input_feed = {}
for l in xrange(encoder_size):
input_feed[self.encoder_inputs[l].name] = encoder_inputs[l]
for l in xrange(decoder_size):
input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
input_feed[self.target_weights[l].name] = target_weights[l]
# Since our targets are decoder inputs shifted by one, we need one more.
last_target = self.decoder_inputs[decoder_size].name
input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)
# Output feed: depends on whether we do a backward step or not.
# 输出填充：与是否有后向传播有关
if not forward_only:
output_feed = [self.updates[bucket_id], # Update Op that does SGD.
self.gradient_norms[bucket_id], # Gradient norm.
self.losses[bucket_id]] # Loss for this batch.
else:
output_feed = [self.losses[bucket_id]] # Loss for this batch.
for l in xrange(decoder_size): # Output logits.
output_feed.append(self.outputs[bucket_id][l])
outputs = session.run(output_feed, input_feed)
if not forward_only:
return outputs[1], outputs[2], None # Gradient norm, loss, no outputs.有后向传播输出，梯度、损失值、None
else:
return None, outputs[0], outputs[1:] # No gradient norm, loss, outputs.仅有前向传播输出，None，损失值，None
def get_batch(self, data, bucket_id):
"""
从指定桶获取一个批次随机数据，在训练每步(step)使用
Args:参数
data: a tuple of size len(self.buckets) in which each element contains
lists of pairs of input and output data that we use to create a batch.长度为(self.buckets)元组，每个元素包含创建批次输入、输出数据对列表
bucket_id: integer, which bucket to get the batch for.整数，从哪个bucket获取批次
Returns:返回
The triple (encoder_inputs, decoder_inputs, target_weights) for
the constructed batch that has the proper format to call step(...) later.一个包含三项元组(encoder_inputs, decoder_inputs, target_weights)
"""
encoder_size, decoder_size = self.buckets[bucket_id]
encoder_inputs, decoder_inputs = [], []
# Get a random batch of encoder and decoder inputs from data,
# pad them if needed, reverse encoder inputs and add GO to decoder.
for _ in xrange(self.batch_size):
encoder_input, decoder_input = random.choice(data[bucket_id])
# Encoder inputs are padded and then reversed.
encoder_pad = [data_utils.PAD_ID] * (encoder_size - len(encoder_input))
encoder_inputs.append(list(reversed(encoder_input + encoder_pad)))
# Decoder inputs get an extra "GO" symbol, and are padded then.
decoder_pad_size = decoder_size - len(decoder_input) - 1
decoder_inputs.append([data_utils.GO_ID] + decoder_input +
[data_utils.PAD_ID] * decoder_pad_size)
# Now we create batch-major vectors from the data selected above.
batch_encoder_inputs, batch_decoder_inputs, batch_weights = [], [], []
# Batch encoder inputs are just re-indexed encoder_inputs.
for length_idx in xrange(encoder_size):
batch_encoder_inputs.append(
np.array([encoder_inputs[batch_idx][length_idx]
for batch_idx in xrange(self.batch_size)], dtype=np.int32))
# Batch decoder inputs are re-indexed decoder_inputs, we create weights.
for length_idx in xrange(decoder_size):
batch_decoder_inputs.append(
np.array([decoder_inputs[batch_idx][length_idx]
for batch_idx in xrange(self.batch_size)], dtype=np.int32))
# Create target_weights to be 0 for targets that are padding.
batch_weight = np.ones(self.batch_size, dtype=np.float32)
for batch_idx in xrange(self.batch_size):
# We set weight to 0 if the corresponding target is a PAD symbol.
# The corresponding target is decoder_input shifted by 1 forward.
if length_idx < decoder_size - 1:
target = decoder_inputs[batch_idx][length_idx + 1]
if length_idx == decoder_size - 1 or target == data_utils.PAD_ID:
batch_weight[batch_idx] = 0.0
batch_weights.append(batch_weight)
return batch_encoder_inputs, batch_decoder_inputs, batch_weights
训练模型。修改seq2seq.ini文件mode值“train”，execute.py训练。
验证模型。修改seq2seq.ini文件mode值“test”，execute.py测试。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import random
import sys
import time
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import data_utils
import seq2seq_model
try:
from ConfigParser import SafeConfigParser
except:
from configparser import SafeConfigParser # In Python 3, ConfigParser has been renamed to configparser for PEP 8 compliance.
gConfig = {}
def get_config(config_file='seq2seq.ini'):
parser = SafeConfigParser()
parser.read(config_file)
# get the ints, floats and strings
_conf_ints = [ (key, int(value)) for key,value in parser.items('ints') ]
_conf_floats = [ (key, float(value)) for key,value in parser.items('floats') ]
_conf_strings = [ (key, str(value)) for key,value in parser.items('strings') ]
return dict(_conf_ints + _conf_floats + _conf_strings)
# We use a number of buckets and pad to the closest one for efficiency.
# See seq2seq_model.Seq2SeqModel for details of how they work.
_buckets = [(5, 10), (10, 15), (20, 25), (40, 50)]
def read_data(source_path, target_path, max_size=None):
"""Read data from source and target files and put into buckets.
Args:
source_path: path to the files with token-ids for the source language.
target_path: path to the file with token-ids for the target language;
it must be aligned with the source file: n-th line contains the desired
output for n-th line from the source_path.
max_size: maximum number of lines to read, all other will be ignored;
if 0 or None, data files will be read completely (no limit).
Returns:
data_set: a list of length len(_buckets); data_set[n] contains a list of
(source, target) pairs read from the provided data files that fit
into the n-th bucket, i.e., such that len(source) < _buckets[n][0] and
len(target) < _buckets[n][1]; source and target are lists of token-ids.
"""
data_set = [[] for _ in _buckets]
with tf.gfile.GFile(source_path, mode="r") as source_file:
with tf.gfile.GFile(target_path, mode="r") as target_file:
source, target = source_file.readline(), target_file.readline()
counter = 0
while source and target and (not max_size or counter < max_size):
counter += 1
if counter % 100000 == 0:
print(" reading data line %d" % counter)
sys.stdout.flush()
source_ids = [int(x) for x in source.split()]
target_ids = [int(x) for x in target.split()]
target_ids.append(data_utils.EOS_ID)
for bucket_id, (source_size, target_size) in enumerate(_buckets):
if len(source_ids) < source_size and len(target_ids) < target_size:
data_set[bucket_id].append([source_ids, target_ids])
break
source, target = source_file.readline(), target_file.readline()
return data_set
def create_model(session, forward_only):
"""Create model and initialize or load parameters"""
model = seq2seq_model.Seq2SeqModel( gConfig['enc_vocab_size'], gConfig['dec_vocab_size'], _buckets, gConfig['layer_size'], gConfig['num_layers'], gConfig['max_gradient_norm'], gConfig['batch_size'], gConfig['learning_rate'], gConfig['learning_rate_decay_factor'], forward_only=forward_only)
if 'pretrained_model' in gConfig:
model.saver.restore(session,gConfig['pretrained_model'])
return model
ckpt = tf.train.get_checkpoint_state(gConfig['working_directory'])
if ckpt and ckpt.model_checkpoint_path:
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.global_variables_initializer())
return model
def train():
# prepare dataset
# 准备数据集
print("Preparing data in %s" % gConfig['working_directory'])
enc_train, dec_train, enc_dev, dec_dev, _, _ = data_utils.prepare_custom_data(gConfig['working_directory'],gConfig['train_enc'],gConfig['train_dec'],gConfig['test_enc'],gConfig['test_dec'],gConfig['enc_vocab_size'],gConfig['dec_vocab_size'])
# setup config to use BFC allocator
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
with tf.Session(config=config) as sess:
# Create model.
# 构建模型
print("Creating %d layers of %d units." % (gConfig['num_layers'], gConfig['layer_size']))
model = create_model(sess, False)
# Read data into buckets and compute their sizes.
# 把数据读入桶(bucket)中，计算桶大小
print ("Reading development and training data (limit: %d)."
% gConfig['max_train_data_size'])
dev_set = read_data(enc_dev, dec_dev)
train_set = read_data(enc_train, dec_train, gConfig['max_train_data_size'])
train_bucket_sizes = [len(train_set[b]) for b in xrange(len(_buckets))]
train_total_size = float(sum(train_bucket_sizes))
# A bucket scale is a list of increasing numbers from 0 to 1 that we'll use
# to select a bucket. Length of [scale[i], scale[i+1]] is proportional to
# the size if i-th training bucket, as used later.
train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))]
# This is the training loop.
# 开始训练循环
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
while True:
# Choose a bucket according to data distribution. We pick a random number
# in [0, 1] and use the corresponding interval in train_buckets_scale.
# 随机生成一个0-1数，在生成bucket_id中使用
random_number_01 = np.random.random_sample()
bucket_id = min([i for i in xrange(len(train_buckets_scale))
if train_buckets_scale[i] > random_number_01])
# Get a batch and make a step.
# 获取一个批次数据，进行一步训练
start_time = time.time()
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
train_set, bucket_id)
_, step_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, False)
step_time += (time.time() - start_time) / gConfig['steps_per_checkpoint']
loss += step_loss / gConfig['steps_per_checkpoint']
current_step += 1
# Once in a while, we save checkpoint, print statistics, and run evals.
# 保存检查点文件，打印统计数据
if current_step % gConfig['steps_per_checkpoint'] == 0:
# Print statistics for the previous epoch.
perplexity = math.exp(loss) if loss < 300 else float('inf')
print ("global step %d learning rate %.4f step-time %.2f perplexity "
"%.2f" % (model.global_step.eval(), model.learning_rate.eval(),
step_time, perplexity))
# Decrease learning rate if no improvement was seen over last 3 times.
# 如果损失值在最近3次内没有再降低，减小学习率
if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
# Save checkpoint and zero timer and loss.
# 保存检查点文件，计数器、损失值归零
checkpoint_path = os.path.join(gConfig['working_directory'], "seq2seq.ckpt")
model.saver.save(sess, checkpoint_path, global_step=model.global_step)
step_time, loss = 0.0, 0.0
# Run evals on development set and print their perplexity.
for bucket_id in xrange(len(_buckets)):
if len(dev_set[bucket_id]) == 0:
print(" eval: empty bucket %d" % (bucket_id))
continue
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
dev_set, bucket_id)
_, eval_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, True)
eval_ppx = math.exp(eval_loss) if eval_loss < 300 else float('inf')
print(" eval: bucket %d perplexity %.2f" % (bucket_id, eval_ppx))
sys.stdout.flush()
def decode():
with tf.Session() as sess:
# Create model and load parameters.
# 建立模型，定义超参数batch_size
model = create_model(sess, True)
model.batch_size = 1 # We decode one sentence at a time.一次只解码一个句子
# Load vocabularies.
# 加载词汇表文件
enc_vocab_path = os.path.join(gConfig['working_directory'],"vocab%d.enc" % gConfig['enc_vocab_size'])
dec_vocab_path = os.path.join(gConfig['working_directory'],"vocab%d.dec" % gConfig['dec_vocab_size'])
enc_vocab, _ = data_utils.initialize_vocabulary(enc_vocab_path)
_, rev_dec_vocab = data_utils.initialize_vocabulary(dec_vocab_path)
# Decode from standard input.
# 对标准输入句子解码
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
while sentence:
# Get token-ids for the input sentence.
# 得到输入句子的token-ids
token_ids = data_utils.sentence_to_token_ids(tf.compat.as_bytes(sentence), enc_vocab)
# Which bucket does it belong to?
# 计算token_ids属于哪个桶(bucket)
bucket_id = min([b for b in xrange(len(_buckets))
if _buckets[b][0] > len(token_ids)])
# Get a 1-element batch to feed the sentence to the model.
# 句子送入模型
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
{bucket_id: [(token_ids, [])]}, bucket_id)
# Get output logits for the sentence.
_, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, True)
# This is a greedy decoder - outputs are just argmaxes of output_logits.
# 贪心解码器，输出output_logits argmaxes
outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]
# If there is an EOS symbol in outputs, cut them at that point.
if data_utils.EOS_ID in outputs:
outputs = outputs[:outputs.index(data_utils.EOS_ID)]
# Print out French sentence corresponding to outputs.
# 打印与输出句子对应法语句子
print(" ".join([tf.compat.as_str(rev_dec_vocab[output]) for output in outputs]))
print("> ", end="")
sys.stdout.flush()
sentence = sys.stdin.readline()
def self_test():
"""Test the translation model."""
with tf.Session() as sess:
print("Self-test for neural translation model.")
# Create model with vocabularies of 10, 2 small buckets, 2 layers of 32.
model = seq2seq_model.Seq2SeqModel(10, 10, [(3, 3), (6, 6)], 32, 2,
5.0, 32, 0.3, 0.99, num_samples=8)
sess.run(tf.initialize_all_variables())
# Fake data set for both the (3, 3) and (6, 6) bucket.
data_set = ([([1, 1], [2, 2]), ([3, 3], [4]), ([5], [6])],
[([1, 1, 1, 1, 1], [2, 2, 2, 2, 2]), ([3, 3, 3], [5, 6])])
for _ in xrange(5): # Train the fake model for 5 steps.
bucket_id = random.choice([0, 1])
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
data_set, bucket_id)
model.step(sess, encoder_inputs, decoder_inputs, target_weights,
bucket_id, False)
def init_session(sess, conf='seq2seq.ini'):
global gConfig
gConfig = get_config(conf)
# Create model and load parameters.
model = create_model(sess, True)
model.batch_size = 1 # We decode one sentence at a time.
# Load vocabularies.
enc_vocab_path = os.path.join(gConfig['working_directory'],"vocab%d.enc" % gConfig['enc_vocab_size'])
dec_vocab_path = os.path.join(gConfig['working_directory'],"vocab%d.dec" % gConfig['dec_vocab_size'])
enc_vocab, _ = data_utils.initialize_vocabulary(enc_vocab_path)
_, rev_dec_vocab = data_utils.initialize_vocabulary(dec_vocab_path)
return sess, model, enc_vocab, rev_dec_vocab
def decode_line(sess, model, enc_vocab, rev_dec_vocab, sentence):
# Get token-ids for the input sentence.
token_ids = data_utils.sentence_to_token_ids(tf.compat.as_bytes(sentence), enc_vocab)
# Which bucket does it belong to?
bucket_id = min([b for b in xrange(len(_buckets)) if _buckets[b][0] > len(token_ids)])
# Get a 1-element batch to feed the sentence to the model.
encoder_inputs, decoder_inputs, target_weights = model.get_batch({bucket_id: [(token_ids, [])]}, bucket_id)
# Get output logits for the sentence.
_, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True)
# This is a greedy decoder - outputs are just argmaxes of output_logits.
outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]
# If there is an EOS symbol in outputs, cut them at that point.
if data_utils.EOS_ID in outputs:
outputs = outputs[:outputs.index(data_utils.EOS_ID)]
return " ".join([tf.compat.as_str(rev_dec_vocab[output]) for output in outputs])
if __name__ == '__main__':
if len(sys.argv) - 1:
gConfig = get_config(sys.argv[1])
else:
# get configuration from seq2seq.ini
gConfig = get_config()
print('\n>> Mode : %s\n' %(gConfig['mode']))
if gConfig['mode'] == 'train':
# start training
train()
elif gConfig['mode'] == 'test':
# interactive decode
decode()
else:
# wrong way to execute "serve"
# Use : >> python ui/app.py
# uses seq2seq_serve.ini as conf file
print('Serve Usage : >> python ui/app.py')
print('# uses seq2seq_serve.ini as conf file')
基于文字智能机器人，结合语音识别，产生直接对话机器人。系统架构：
人->语音识别(ASR)->自然语言理解(NLU)->对话管理->自然语言生成(NLG)->语音合成(TTS)->人。《中国人工智能学会通讯》2016年第6卷第1期。
图灵机器人公司，提高对话和语义准确度，提升中文语境智能程度。竹间智能科技，研究记忆、自学习情感机器人，机器人真正理解多模式多渠道信息，高度拟人化回应，最理想自然语言交流模式交流。腾讯公司，社交对话数据。微信，最庞大自然语言交流语料库，利用庞大真实数据，结合小程序成为所有服务入口。
参考资料：
《TensorFlow技术解析与实战》
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