AI 实战:垃圾分类系列(三)之快速搭建垃圾分类智能问答机器人

代码模型 kira ⋅ 于 2个月前 ⋅ 362 阅读
来源:作者:szZack CSDN

前言

快速搭建垃圾分类智能问答机器人:使用深度学习方法搭建算法框架,力求简洁、快速、有效。

2019上海市生活垃圾按照以下标准分类!:http://sh.bendibao.com/zffw/2019225/202535.shtm

上海生活垃圾分类标准及投放要求:https://www.sohu.com/a/163450869\_688983

算法框架

垂直领域搭建问答系统的框架很多,这里使用最简单的方法:把垃圾分类的问题分成8大类,每个类别给一个随机设定的回答。

  • 算法框架一:
    1、word2vec + TextCNN 搭建模型
    2、类别问答映射表做回答

  • 算法框架二:
    1、word2index + TextCNN 搭建模型
    2、类别问答映射表做回答

  • 两种方法的优缺点对比:
    1、方法一在语义上面比较优势大,但对训练语料小(百级、千级)的时候模型效果不太好
    2、方法二在较小语料时,模型收敛更好,但对于未知词识别差

代码实战

模型训练:train.py

import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helper as data_helpers
from text_cnn import TextCNN
import math
from tensorflow.contrib import learn

# Parameters

# Data loading params
tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")
tf.flags.DEFINE_string("train_data_file", "./data/train_data.txt", "Data source for the positive data.")
tf.flags.DEFINE_string("train_label_data_file", "", "Data source for the label data.")
tf.flags.DEFINE_string("w2v_file", "./data/word2vec.bin", "w2v_file path")
#tf.flags.DEFINE_string("w2v_file", "", "w2v_file path")
#w2v_file非空表示使用词向量做embedding,否则用vocabulary方法做embedding

# Model Hyperparameters
tf.flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "2, 3, 4", "Comma-separated filter sizes (default: '3, 4, 5')")
tf.flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)")

# Training parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 200, "Number of training epochs (default: 200)")
tf.flags.DEFINE_integer("evaluate_every", 20, "Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 20, "Save model after this many steps (default: 100)")
tf.flags.DEFINE_integer("num_checkpoints", 3, "Number of checkpoints to store (default: 5)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
    print("{}={}".format(attr.upper(), value))
print("")

max_words_length = 10

def load_data(w2v_model):
    """Loads starter word-vectors and train/dev/test data."""
    # Load the starter word vectors
    print("Loading data...")
    x_text, y = data_helpers.load_data_and_labels(FLAGS.train_data_file)

    x = []
    vocab_size = 0
    if(w2v_model is None):
      vocab_processor = learn.preprocessing.VocabularyProcessor(max_words_length)
      x = np.array(list(vocab_processor.fit_transform(x_text)))
      vocab_size = len(vocab_processor.vocabulary_)

      # out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", str(int(time.time()))))
      vocab_processor.save("./data/vocab.pkl")
      print( 'save "./data/vocab.pkl"')
    else:
      x = data_helpers.get_text_idx(x_text, w2v_model.vocab_hash, max_words_length)
      vocab_size = len(w2v_model.vocab_hash)
      print('use w2v .bin')

    np.random.seed(10)
    shuffle_indices = np.random.permutation(np.arange(len(y)))
    x_shuffled = x[shuffle_indices]
    y_shuffled = y[shuffle_indices]

    dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
    x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
    y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]

    return x_train, x_dev, y_train, y_dev, vocab_size

def train(w2v_model):
    # Training
    x_train, x_dev, y_train, y_dev, vocab_size= load_data(w2v_model)
    with tf.Graph().as_default():
        session_conf = tf.ConfigProto(
          allow_soft_placement=FLAGS.allow_soft_placement, 
          log_device_placement=FLAGS.log_device_placement)
        sess = tf.Session(config=session_conf)
        with sess.as_default():
            cnn = TextCNN(
                w2v_model, 
                sequence_length=x_train.shape[1], 
                num_classes=y_train.shape[1], 
                vocab_size=vocab_size, 
                embedding_size=FLAGS.embedding_dim, 
                filter_sizes=list(map(int, FLAGS.filter_sizes.split(", "))), 
                num_filters=FLAGS.num_filters, 
                l2_reg_lambda=FLAGS.l2_reg_lambda)

            # Define Training procedure
            global_step = tf.Variable(0, name="global_step", trainable=False)
            optimizer = tf.train.AdamOptimizer(1e-3)
            grads_and_vars = optimizer.compute_gradients(cnn.loss)
            train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)

            # Keep track of gradient values and sparsity (optional)
            grad_summaries = []
            for g, v in grads_and_vars:
                if g is not None:
                    grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g)
                    sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
                    grad_summaries.append(grad_hist_summary)
                    grad_summaries.append(sparsity_summary)
            grad_summaries_merged = tf.summary.merge(grad_summaries)

            # Output directory for models and summaries
            timestamp = str(int(time.time()))
            out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs"))
            print("Writing to {}\n".format(out_dir))

            # Summaries for loss and accuracy
            loss_summary = tf.summary.scalar("loss", cnn.loss)
            acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)

            # Train Summaries
            train_summary_op = tf.summary.merge([loss_summary, acc_summary, grad_summaries_merged])
            train_summary_dir = os.path.join(out_dir, "summaries", "train")
            train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)

            # Dev summaries
            dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
            dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
            dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)

            # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
            checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
            checkpoint_prefix = os.path.join(checkpoint_dir, "model")
            if not os.path.exists(checkpoint_dir):
                os.makedirs(checkpoint_dir)
            saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints)

            # Write vocabulary
            # vocab_processor.save(os.path.join(out_dir, "vocab"))

            # Initialize all variables
            sess.run(tf.global_variables_initializer())

            def train_step(x_batch, y_batch):
                """
                A single training step
                """
                feed_dict = {
                  cnn.input_x: x_batch, 
                  cnn.input_y: y_batch, 
                  cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
                }
                # _, step, summaries, loss, accuracy, (w, idx) = sess.run(
                #     [train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy, cnn.get_w2v_W()], 
                #     feed_dict)
                _, step, summaries, loss, accuracy = sess.run(
                    [train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy], 
                    feed_dict)

                time_str = datetime.datetime.now().isoformat()
                print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
                # print w[:2], idx[:2]
                train_summary_writer.add_summary(summaries, step)

            def dev_step(x_batch, y_batch, writer=None):
                """
                Evaluates model on a dev set
                """
                feed_dict = {
                  cnn.input_x: x_batch, 
                  cnn.input_y: y_batch, 
                  cnn.dropout_keep_prob: 1.0
                }
                step, summaries, loss, accuracy = sess.run(
                    [global_step, dev_summary_op, cnn.loss, cnn.accuracy], 
                    feed_dict)
                time_str = datetime.datetime.now().isoformat()
                print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
                if writer:
                    writer.add_summary(summaries, step)

            # Generate batches
            batches = data_helpers.batch_iter(
                list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)

            def dev_test():
                batches_dev = data_helpers.batch_iter(list(zip(x_dev, y_dev)), FLAGS.batch_size, 1)
                for batch_dev in batches_dev:
                    x_batch_dev, y_batch_dev = zip(*batch_dev)
                    dev_step(x_batch_dev, y_batch_dev, writer=dev_summary_writer)

            # Training loop. For each batch...
            for batch in batches:
                x_batch, y_batch = zip(*batch)
                train_step(x_batch, y_batch)
                current_step = tf.train.global_step(sess, global_step)
                # Training loop. For each batch...
                if current_step % FLAGS.evaluate_every == 0:
                    print("\nEvaluation:")
                    dev_test()

                if current_step % FLAGS.checkpoint_every == 0:
                    path = saver.save(sess, checkpoint_prefix, global_step=current_step)
                    print("Saved model checkpoint to {}\n".format(path))

if __name__ == "__main__":  
    w2v_wr = data_helpers.w2v_wrapper(FLAGS.w2v_file)
    train(w2v_wr.model)

模型评估:eval.py

import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helper as data_helpers
from text_cnn import TextCNN
from tensorflow.contrib import learn
import csv

# Parameters

# Data Parameters
tf.flags.DEFINE_string("valid_data_file", "./data/valid_data.txt", "Data source for the positive data.")
#tf.flags.DEFINE_string("w2v_file", "./data/word2vec.bin", "w2v_file path")
tf.flags.DEFINE_string("w2v_file", "", "w2v_file path")
#w2v_file非空表示使用词向量做embedding,否则用vocabulary方法做embedding

# Eval Parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_string("checkpoint_dir", "./runs/checkpoints/", "Checkpoint directory from training run")
tf.flags.DEFINE_boolean("eval_train", True, "Evaluate on all training data")

# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
    print("{}={}".format(attr.upper(), value))
print("")

max_words_length = 10

def load_data(w2v_model):
    """Loads starter word-vectors and train/dev/test data."""
    # Load the starter word vectors
    print("Loading data...")
    x_text, y_test = data_helpers.load_data_and_labels(FLAGS.valid_data_file)
    y_test = np.argmax(y_test, axis=1)

    if w2v_model is not None:
        x = data_helpers.get_text_idx(x_text, w2v_model.vocab_hash, max_words_length)
    else:
        vocab = learn.preprocessing.VocabularyProcessor.restore('./data/vocab.pkl')
        x = data_helpers.get_text_idx(x_text, vocab.vocabulary_._mapping, max_words_length)

    return x,y_test

def eval(w2v_model):
    # Evaluation
    checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
    graph = tf.Graph()
    with graph.as_default():
        session_conf = tf.ConfigProto(
          allow_soft_placement=FLAGS.allow_soft_placement,
          log_device_placement=FLAGS.log_device_placement)
        sess = tf.Session(config=session_conf)
        with sess.as_default():
            # Load the saved meta graph and restore variables
            saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
            saver.restore(sess, checkpoint_file)

            # Get the placeholders from the graph by name
            input_x = graph.get_operation_by_name("input_x").outputs[0]

            dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]

            # Tensors we want to evaluate
            predictions = graph.get_operation_by_name("output/predictions").outputs[0]

            x_test, y_test = load_data(w2v_model)

            # Generate batches for one epoch
            batches = data_helpers.batch_iter(list(x_test), FLAGS.batch_size, 1, shuffle=False)

            # Collect the predictions here
            all_predictions = []

            for x_test_batch in batches:
                batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
                all_predictions = np.concatenate([all_predictions, batch_predictions])

    # Print accuracy if y_test is defined
    if y_test is not None:
        correct_predictions = float(sum(all_predictions == y_test))
        print("Total number of test examples: {}".format(len(y_test)))
        print("Accuracy: {:g}".format(correct_predictions/float(len(y_test))))

    # Save the evaluation to a csv
    predictions_human_readable = np.column_stack(all_predictions)
    out_path = os.path.join(FLAGS.checkpoint_dir, "..", "prediction.csv")
    print("Saving evaluation to {0}".format(out_path))
    with open(out_path, 'w') as f:
        csv.writer(f).writerows(predictions_human_readable)

if __name__ == "__main__":
    w2v_wr = data_helpers.w2v_wrapper(FLAGS.w2v_file)
    eval(w2v_wr.model)

模型推理:predict.py

import tensorflow as tf
import numpy as np
import os, sys, random
import data_helper as data_helpers
import jieba
from tensorflow.contrib import learn

# Parameters

# Data Parameters
#tf.flags.DEFINE_string("w2v_file", "./data/word2vec.bin", "w2v_file path")
tf.flags.DEFINE_string("w2v_file", "", "w2v_file path")
#w2v_file非空表示使用词向量做embedding,否则用vocabulary方法做embedding

# Eval Parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_string("checkpoint_dir", "./runs/checkpoints/", "Checkpoint directory from training run")

# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()

max_words_length = 10 #输入文本的最大词个数

class RefuseClassification():

    def __init__(self):

        self.w2v_wr = data_helpers.w2v_wrapper(FLAGS.w2v_file)#加载词向量
        self.init_model()
        self.answer_map = {0: ['可回收垃圾', '是可回收垃圾哦', '可回收垃圾哟!', '这个是可回收垃圾。'], \
                        1: ['有害垃圾', '是有害垃圾哦', '有害垃圾哟!', '这个是有害垃圾。'], \
                        2: ['湿垃圾', '湿垃圾是哦', '湿垃圾哟!', '这个是湿垃圾。'], \
                        3: ['干垃圾', '是干垃圾哦', '干垃圾哟!', '这个是干垃圾。'], \
                        4:['您好!', '客官好!', '你好。', '您好,您可以问我一些垃圾分类的问题额。'], \
                        5:['再见!', '8', '88', '拜拜。'], \
                        6:['您可以问我上海垃圾分类的问题。'], \
                        7:['目前上海垃圾分为四大类:可回收垃圾、有害垃圾、湿垃圾、干垃圾。']}
        self.vocab = {}

    def deal_data(self, text):

        words = jieba.cut(text)
        #print('words', list(words))
        words = list(words)[:max_words_length]
        x_text = [' '.join(words)]

        if self.w2v_wr.model is None:
            if len(self.vocab) == 0:
                self.vocab = learn.preprocessing.VocabularyProcessor.restore('./data/vocab.pkl')

            x = data_helpers.get_text_idx(x_text, self.vocab.vocabulary_._mapping, max_words_length)

        else:
            x = data_helpers.get_text_idx(x_text, self.w2v_wr.model.vocab_hash, max_words_length)

        return x

    def init_model(self):

        checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
        graph = tf.Graph()
        with graph.as_default():
            session_conf = tf.ConfigProto(
                              allow_soft_placement=FLAGS.allow_soft_placement, 
                              log_device_placement=FLAGS.log_device_placement)
            self.sess = tf.Session(config=session_conf)
            self.sess.as_default()
            # Load the saved meta graph and restore variables
            saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
            saver.restore(self.sess, checkpoint_file)

            # Get the placeholders from the graph by name
            self.input_x = graph.get_operation_by_name("input_x").outputs[0]

            self.dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]

            # Tensors we want to evaluate
            self.predictions = graph.get_operation_by_name("output/predictions").outputs[0]

    def predict(self, text):

        if len(text) > 60:
            return '您的输入有点长哦!'

        x_test = self.deal_data(text)
        predictions = self.sess.run(self.predictions, {self.input_x: x_test, self.dropout_keep_prob: 1.0})

        refuse_text = self.answer_map[predictions[0]]
        answer = refuse_text[random.randint(0, len(refuse_text)-1)]

        return answer

if __name__ == "__main__":
    if len(sys.argv) == 2:
        test = RefuseClassification()
        res = test.predict(sys.argv[1])
        print('classify:', res)

模型训练

  • 词向量方法

    训练: 200个epoch,loss 0.829977, acc 0.686275
    评估:0.791946

  • 词汇索引方法

    训练:200个epoch,loss 0.657236, acc 0.862745
    评估:Accuracy: 0.852349

测试

    python src/predict.py '猪肉饺子是什么垃圾?'
    结果:
        classify: 湿垃圾

    python src/predict.py 猪骨 头是啥哪类
    结果:
        classify: 干垃圾哟!

    python src/predict.py 你会什么啊
    结果:
        classify: 您可以问我上海垃圾分类的问题。

    python src/predict.py hello
    结果:
        classify: 客官好!
    python src/predict.py 猪肉粉条是什么垃圾
    结果:
        classify: 湿垃圾是哦

但也存在问题:
    python src/predict.py 你好 !
    结果:
        classify: 可回收垃圾

注:由于训练数据只有500多条语料,比较少,对于4-7类的问题识别准确率不高;但对于0-3类问题还是比较准的。

本文仅是提供一种快速搭建垂直领域问答系统的一种简易方法,关于准确率这块就不再优化了。。。

说明

  • 环境要求

    python: 3.x
    tensorflow: 1.x
    jieba
    word2vec

  • 数据标注

    • 标注类别:
      0:可回收垃圾
      1:有害垃圾
      2:湿垃圾
      3:干垃圾
      4:问好
      5:再见
      6:功能
      7:垃圾种类
  • 模型训练

    python src/train.py

    训练数据:./data/train_data.txt

  • 模型评估

    python src/eval.py

    测试数据:./data/vilid_data.txt

  • 单句测试

    python src/predict.py ‘猪肉饺子是什么垃圾?’

    输出结果:
    回答: 湿垃圾

  • 修改回答

    修改 src/predict.py 中的 self.answer_map 的参数

  • 修改标注

    若是增加了问题类别,需修改 src/data_helper.py 中的 num_classfication

    修改 ./data/train_data.txt 作为训练集;
    ./data/vilid_data.txt 中,作为测试数据集

  • embedding方法选择

    修改 train.py, eval.py, predict.py 中的:
    tf.flags.DEFINE_string(“w2v_file”, “”, “w2v_file path”)
    w2v_file非空表示使用词向量做embedding,否则用vocabulary方法做embedding


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