Transfer Learning in Natural Language Processing

    import json
    
    with open('vocab_en.json', 'r') as f:
    vocab_en = json.load(f)
    
    with open('vocab_zh.json', 'r') as f:
    vocab_zh = json.load(f)
    
    word_index_en = {w: i for i, w in enumerate(vocab_en)}
    word_index_zh = {w: i for i, w in enumerate(vocab_zh)}
    
      
      
      
      
      
      
      
      
      
    
    代码解读

    from keras import models
    
    # 创建源域的WE模型
    source_model = models.load_model('source_we_model.h5')
    
    # 只训练最后几层
    for layer in source_model.layers[:-3]:
    layer.trainable = False
    
    # 将模型编译
    source_model.compile(...) 
    
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import tensorflow as tf
    from keras.callbacks import ModelCheckpoint, EarlyStopping
    
    # 定义训练参数
    batch_size = 32
    epochs = 10
    steps_per_epoch = ceil(len(data) / batch_size)
    validation_steps = ceil(len(val_data) / batch_size)
    
    # 定义模型checkpoint回调函数
    checkpoint = ModelCheckpoint('target_we_model.{epoch:02d}-{val_loss:.2f}.h5',
                             save_weights_only=True, period=5)
    
    # 定义early stopping回调函数
    earlystopping = EarlyStopping(monitor='val_loss', patience=3)
    
    # 定义tensorboard callback函数
    tensorboard = TensorBoard(log_dir="logs/target")
    
    # 初始化目标域的训练数据生成器
    train_generator =... # 根据任务定义，这里省略
    
    # 初始化目标域的验证数据生成器
    val_generator =... # 根据任务定义，这里省略
    
    # 初始化目标域的训练环境
    strategy = tf.distribute.MirroredStrategy()
    with strategy.scope():
    target_model = create_target_model() # 根据任务定义创建目标域模型
    
    if os.path.exists('latest_target_weight'):
        print("Loading latest weights...")
        target_model.load_weights('latest_target_weight')
    
    target_model.compile(optimizer, loss, metrics=[accuracy])
    
    # 启动目标域的训练过程
    history = target_model.fit_generator(
            train_generator,
            steps_per_epoch=steps_per_epoch,
            epochs=epochs,
            validation_data=val_generator,
            validation_steps=validation_steps,
            callbacks=[checkpoint, earlystopping],
            verbose=1
        )
    
    print('Training complete.')
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    from nltk.translate.bleu_score import sentence_bleu
    from sklearn.metrics import classification_report
    
    def evaluate_model(test_set):
    """
    测试模型
    :param test_set: 测试数据集
    :return: BLEU score and EM score
    """
    
    preds = []
    trues = []
    
    for en_sent, zh_sent in test_set:
        src_seq = [word_index_en[w] for w in en_sent] + [0]*(max_length-len(en_sent))
    
        enc_out = encoder_model.predict([np.array([src_seq]), np.zeros((1, max_length)), np.zeros((1, max_length))])[0]
    
        tgt_seq = np.zeros((1, 1))
        decoded_sentence = ''
        while True:
            output_tokens, h, c = decoder_model.predict([tgt_seq] + states_value)
    
            sampled_token_index = np.argmax(output_tokens[0, -1, :])
            sampled_char = reverse_target_char_index[sampled_token_index]
            decoded_sentence += sampled_char
    
            if len(decoded_sentence) > max_decoder_seq_length or sampled_char == '\n':
                break
    
        pred_sent = [reverse_target_word_index[i] for i in dec_input_data[:, 0]]
    
        preds.append(pred_sent)
        trues.append([' '.join(zh_sent)])
    
    bleu_scores = []
    em_scores = []
    
    for true_sent, pred_sent in zip(trues, preds):
        bleu_scores.append(sentence_bleu([true_sent], pred_sent))
        em_scores.append(1 if pred_sent[-1][:-1]==true_sent else 0)
    
    return sum(bleu_scores)/len(bleu_scores), sum(em_scores)/len(em_scores)
    
    
    evaluate_model(test_set)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import numpy as np
    import pandas as pd
    
    def load_dataset(filename):
    data = pd.read_csv(filename).values
    X, y = data[:, :-1], data[:, -1].astype(int)
    return X, y
    
      
      
      
      
      
      
    
    代码解读

    from keras.applications import ResNet50
    
    # 获取源域的标签集
    num_classes = len(set(labels))
    
    # 初始化源域的预训练模型
    base_model = ResNet50(include_top=False, input_shape=(224, 224, 3))
    
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(1024, activation='relu')(x)
    predictions = Dense(num_classes, activation='softmax')(x)
    
    model = Model(inputs=base_model.input, outputs=predictions)
    
    # 对源域进行预训练
    model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
              metrics=['accuracy'])
    
    train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
    val_datagen = ImageDataGenerator(rescale=1./255)
    
    train_flow = train_datagen.flow_from_directory('/content/train/', target_size=(224, 224), color_mode='rgb', class_mode='categorical', shuffle=True)
    val_flow = val_datagen.flow_from_directory('/content/val/', target_size=(224, 224), color_mode='rgb', class_mode='categorical', shuffle=False)
    
    model.fit_generator(train_flow, epochs=10, validation_data=val_flow)
    
    model.save('pretrained_resnet.h5')
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def generate_data():
    inputs = ['[CLS]'] + list(text[:random.randint(0, 257)]+'.'+'\n' for text in texts)
    targets = labels + [label] * random.randint(0, 257)[:512-(len(texts)+1)]
    masks = [1] * len(inputs) + [0] * 512 - len(inputs)
    segment_ids = [0] * len(inputs) + [0] * 512 - len(inputs)
    
    assert len(inputs) <= 512 and len(targets) <= 512 and len(masks) <= 512 and len(segment_ids) <= 512
    
    return [inputs, targets, masks, segment_ids]
    
      
      
      
      
      
      
      
      
    
    代码解读

    import tensorflow as tf
    from keras.models import Sequential, Model
    from keras.layers import Input, Dropout, Dense, LSTM, Embedding, TimeDistributed, Lambda, Concatenate, Multiply, GRU, Bidirectional
    from keras.optimizers import Adam
    from keras.losses import categorical_crossentropy
    
    # 从预训练模型加载权重
    model = Sequential()
    model.add(InputLayer(input_shape=(512,), dtype='int32'))
    model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, mask_zero=True))
    model.add(Bidirectional(GRU(units=hidden_size//2, return_sequences=True)))
    model.add(Dropout(rate=drop_rate))
    model.add(Dense(units=num_classes, activation='sigmoid'))
    model.build()
    model.load_weights('pretrained_resnet.h5')
    
    # 添加目标域的训练网络层
    train_model = Model(inputs=model.input, outputs=model.get_layer(-2).output)
    
    opt = Adam(lr=learning_rate)
    train_model.compile(optimizer=opt,
                    loss={'dense_1': lambda y_true, y_pred: y_pred},
                    metrics=['accuracy'], experimental_run_tf_function=False)
    
    # 初始化目标域的训练数据生成器
    train_dataset = tf.data.Dataset.from_generator(lambda: generate_data(), output_types=(tf.int32, tf.int32, tf.float32, tf.int32)).repeat().batch(batch_size).prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
    
    # 启动目标域的训练过程
    history = train_model.fit(train_dataset, epochs=10, steps_per_epoch=steps_per_epoch, verbose=1)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    from sklearn.metrics import accuracy_score, precision_recall_fscore_support
    
    def evaluate_model(eval_data):
    """
    测试模型
    :param eval_data: 测试数据集
    :return: 准确率和F1 score
    """
    
    predictions = []
    
    for texts in eval_data:
        pred = model.predict([[tokenizer.convert_tokens_to_ids('[CLS]')]+tokenizer.encode(text)[1:] for text in texts]).argmax(axis=-1)
        predictions.extend(pred)
    
    acc = accuracy_score(labels, predictions)
    p, r, f1, _ = precision_recall_fscore_support(labels, predictions, average='weighted')
    
    return {'acc': acc, 'precision': p,'recall': r, 'f1': f1}
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读