（Transfer Learning）迁移学习在IMDB上训练情感分析模型

1. 背景

有些场景下，开始的时候数据量很小，如果我们用一个几千条数据训练一个全新的深度机器学习的文本分类模型，效果不会很好。这个时候你有两种选择，1.用传统的机器学习训练，2.利用迁移学习在一个预训练的模型上训练。本博客教你怎么用tensorflow Hub和keras 在少量的数据上训练一个文本分类模型。

2. 实践

2.1. 下载IMDB 数据集，参考下面博客。

Imdb影评的数据集介绍与下载_imdb影评数据集-CSDN博客

2.2.  预处理数据

替换掉imdb目录 （imdb_raw_data_dir）. 创建dataset目录。

import numpy as np
import os as os
 
import re
from sklearn.model_selection import train_test_split
 
vocab_size = 30000
maxlen = 200
imdb_raw_data_dir = "/Users/harry/Documents/apps/ml/aclImdb"
save_dir = "dataset"
 
def get_data(datapath =r'D:\train_data\aclImdb\aclImdb\train' ):
    pos_files = os.listdir(datapath + '/pos')
    neg_files = os.listdir(datapath + '/neg')
    print(len(pos_files))
    print(len(neg_files))
 
    pos_all = []
    neg_all = []
    for pf, nf in zip(pos_files, neg_files):
        with open(datapath + '/pos' + '/' + pf, encoding='utf-8') as f:
            s = f.read()
            s = process(s)
            pos_all.append(s)
        with open(datapath + '/neg' + '/' + nf, encoding='utf-8') as f:
 
            s = f.read()
            s = process(s)
            neg_all.append(s)
    print(len(pos_all))
    # print(pos_all[0])
 
    print(len(neg_all))
 
    X_orig= np.array(pos_all + neg_all)
    # print(X_orig)
    Y_orig = np.array([1 for _ in range(len(pos_all))] + [0 for _ in range(len(neg_all))])
    print("X_orig:", X_orig.shape)
    print("Y_orig:", Y_orig.shape)
 
    return X_orig, Y_orig
 
def generate_dataset():
    X_orig, Y_orig = get_data(imdb_raw_data_dir + r'/train')
    X_orig_test, Y_orig_test = get_data(imdb_raw_data_dir + r'/test')
    X_orig = np.concatenate([X_orig, X_orig_test])
    Y_orig = np.concatenate([Y_orig, Y_orig_test])
    X = X_orig
    Y = Y_orig
 
    np.random.seed = 1
    random_indexs = np.random.permutation(len(X))
    X = X[random_indexs]
    Y = Y[random_indexs]
    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)
    print("X_train:", X_train.shape)
    print("y_train:", y_train.shape)
    print("X_test:", X_test.shape)
    print("y_test:", y_test.shape)
    np.savez(save_dir + '/train_test', X_train=X_train, y_train=y_train, X_test= X_test, y_test=y_test )
 
 
def rm_tags(text):
    re_tag = re.compile(r'<[^>]+>')
    return re_tag.sub(' ', text)
 
def clean_str(string):
    string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
    string = re.sub(r"\'s", " \'s", string)  # it's -> it 's
    string = re.sub(r"\'ve", " \'ve", string) # I've -> I 've
    string = re.sub(r"n\'t", " n\'t", string) # doesn't -> does n't
    string = re.sub(r"\'re", " \'re", string) # you're -> you are
    string = re.sub(r"\'d", " \'d", string)  # you'd -> you 'd
    string = re.sub(r"\'ll", " \'ll", string) # you'll -> you 'll
    string = re.sub(r"\'m", " \'m", string) # I'm -> I 'm
    string = re.sub(r",", " , ", string)
    string = re.sub(r"!", " ! ", string)
    string = re.sub(r"\(", " \( ", string)
    string = re.sub(r"\)", " \) ", string)
    string = re.sub(r"\?", " \? ", string)
    string = re.sub(r"\s{2,}", " ", string)
    return string.strip().lower()
 
def process(text):
    text = clean_str(text)
    text = rm_tags(text)
    #text = text.lower()
    return  text
 
if __name__ == '__main__':
 
    generate_dataset()
 
 

执行完后，产生train_test.npz 文件

2.3.  训练模型

1. 取数据集

def get_dataset_to_train():
 
    train_test = np.load('dataset/train_test.npz', allow_pickle=True)
    x_train =  train_test['X_train']
    y_train = train_test['y_train']
    x_test =  train_test['X_test']
    y_test = train_test['y_test']
 
    return x_train, y_train, x_test, y_test

2. 创建模型

基于nnlm-en-dim50/2 预训练的文本嵌入向量，在模型外面加了两层全连接。

def get_model():
    hub_layer = hub.KerasLayer(embedding_url, input_shape=[], dtype=tf.string, trainable=True)
    # Build the model
    model = Sequential([
        hub_layer,
        Dense(16, activation='relu'),
        Dropout(0.5),
        Dense(2, activation='softmax')
    ])
 
    print(model.summary())
 
    model.compile(optimizer=keras.optimizers.Adam(),
                  loss=keras.losses.SparseCategoricalCrossentropy(),
                  metrics=[keras.metrics.SparseCategoricalAccuracy()])
 
    return model

还可以使用来自 TFHub 的许多其他预训练文本嵌入向量：

• google/nnlm-en-dim128/2 - 基于与 google/nnlm-en-dim50/2 相同的数据并使用相同的 NNLM 架构进行训练，但具有更大的嵌入向量维度。更大维度的嵌入向量可以改进您的任务，但可能需要更长的时间来训练您的模型。
• google/nnlm-en-dim128-with-normalization/2 - 与 google/nnlm-en-dim128/2 相同，但具有额外的文本归一化，例如移除标点符号。如果您的任务中的文本包含附加字符或标点符号，这会有所帮助。
• google/universal-sentence-encoder/4 - 一个可产生 512 维嵌入向量的更大模型，使用深度平均网络 (DAN) 编码器训练。

还有很多！在 TFHub 上查找更多文本嵌入向量模型。

3. 评估你的模型

def evaluate_model(test_data, test_labels):
    model = load_trained_model()
    # Evaluate the model
    results = model.evaluate(test_data, test_labels, verbose=2)
 
    print("Test accuracy:", results[1])
 
 
def load_trained_model():
    # model = get_model()
    # model.load_weights('./models/model_new1.h5')
 
    model = tf.keras.models.load_model('models_pb')
 
    return model

4. 测试几个例子

def predict(real_data):
    model  = load_trained_model()
    probabilities = model.predict([real_data]);
    print("probabilities :",probabilities)
    result =  get_label(probabilities)
    return result
 
 
def get_label(probabilities):
 
    index = np.argmax(probabilities[0])
    print("index :" + str(index))
 
    result_str =  index_dic.get(str(index))
    # result_str = list(index_dic.keys())[list(index_dic.values()).index(index)]
 
    return result_str
 
 
def predict_my_module():
 
    # review = "I don't like it"
    # review = "this is bad movie "
    # review = "This is good movie"
    review = " this is terrible movie"
    # review = "This isn‘t great movie"
    # review = "i think this is bad movie"
    # review = "I'm not very disappoint for this movie"
    # review = "I'm not very disappoint for this movie"
    # review = "I am very happy for this movie"
    #neg:0 postive:1
    s = predict(review)
    print(s)
 
if __name__ == '__main__':
    x_train, y_train, x_test, y_test = get_dataset_to_train()
    model = get_model()
    model = train(model, x_train, y_train, x_test, y_test)
    evaluate_model(x_test, y_test)
    predict_my_module()

完整代码

import numpy as np
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, Dropout
import keras as keras
from keras.callbacks import EarlyStopping, ModelCheckpoint
import tensorflow_hub as hub
 
embedding_url = "https://tfhub.dev/google/nnlm-en-dim50/2"
 
index_dic = {"0":"negative", "1": "positive"}
 
def get_dataset_to_train():
 
    train_test = np.load('dataset/train_test.npz', allow_pickle=True)
    x_train =  train_test['X_train']
    y_train = train_test['y_train']
    x_test =  train_test['X_test']
    y_test = train_test['y_test']
 
    return x_train, y_train, x_test, y_test
 
 
def get_model():
    hub_layer = hub.KerasLayer(embedding_url, input_shape=[], dtype=tf.string, trainable=True)
    # Build the model
    model = Sequential([
        hub_layer,
        Dense(16, activation='relu'),
        Dropout(0.5),
        Dense(2, activation='softmax')
    ])
 
    print(model.summary())
 
    model.compile(optimizer=keras.optimizers.Adam(),
                  loss=keras.losses.SparseCategoricalCrossentropy(),
                  metrics=[keras.metrics.SparseCategoricalAccuracy()])
 
 
    return model
 
def train(model , train_data, train_labels, test_data, test_labels):
    # train_data, train_labels, test_data, test_labels = get_dataset_to_train()
    train_data = [tf.compat.as_str(tf.compat.as_bytes(str(x))) for x in train_data]
    test_data = [tf.compat.as_str(tf.compat.as_bytes(str(x))) for x in test_data]
 
    train_data = np.asarray(train_data)  # Convert to numpy array
    test_data = np.asarray(test_data)  # Convert to numpy array
    print(train_data.shape, test_data.shape)
 
    early_stop = EarlyStopping(monitor='val_sparse_categorical_accuracy', patience=4, mode='max', verbose=1)
    # 定义ModelCheckpoint回调函数
    # checkpoint = ModelCheckpoint( './models/model_new1.h5', monitor='val_sparse_categorical_accuracy', save_best_only=True,
    #                              mode='max', verbose=1)
 
    checkpoint_pb = ModelCheckpoint(filepath="./models_pb/",  monitor='val_sparse_categorical_accuracy', save_weights_only=False, save_best_only=True)
 
    history = model.fit(train_data[:2000], train_labels[:2000], epochs=45, batch_size=45, validation_data=(test_data, test_labels), shuffle=True,
                        verbose=1, callbacks=[early_stop, checkpoint_pb])
    print("history", history)
 
    return model
 
 
def evaluate_model(test_data, test_labels):
    model = load_trained_model()
    # Evaluate the model
    results = model.evaluate(test_data, test_labels, verbose=2)
 
    print("Test accuracy:", results[1])
 
 
def predict(real_data):
    model  = load_trained_model()
    probabilities = model.predict([real_data]);
    print("probabilities :",probabilities)
    result =  get_label(probabilities)
    return result
 
 
def get_label(probabilities):
 
    index = np.argmax(probabilities[0])
    print("index :" + str(index))
 
    result_str =  index_dic.get(str(index))
    # result_str = list(index_dic.keys())[list(index_dic.values()).index(index)]
 
    return result_str
 
def load_trained_model():
    # model = get_model()
    # model.load_weights('./models/model_new1.h5')
 
    model = tf.keras.models.load_model('models_pb')
 
    return model
 
def predict_my_module():
 
    # review = "I don't like it"
    # review = "this is bad movie "
    # review = "This is good movie"
    review = " this is terrible movie"
    # review = "This isn‘t great movie"
    # review = "i think this is bad movie"
    # review = "I'm not very disappoint for this movie"
    # review = "I'm not very disappoint for this movie"
    # review = "I am very happy for this movie"
    #neg:0 postive:1
    s = predict(review)
    print(s)
 
 
if __name__ == '__main__':
    x_train, y_train, x_test, y_test = get_dataset_to_train()
    model = get_model()
    model = train(model, x_train, y_train, x_test, y_test)
    evaluate_model(x_test, y_test)
    predict_my_module()