我在将数据输入到嵌入层时看到了两种填充方式。

例如：

考虑两个句子：

word1 = “我是一个爱狗的人。”

word2 = “Krishni和Pradeepa都喜欢猫。”

word1_int = [1,2,3,4,5,6]

word2_int = [7,8,9,10,11,12,13]

将两个词填充到长度 = 8

填充方法1（在开头添加0）

word1_int = [0,0,1,2,3,4,5,6]

word2_int = [0,7,8,9,10,11,12,13]

填充方法2（在结尾添加0）

word1_int = [1,2,3,4,5,6,0,0]

word2_int = [7,8,9,10,11,12,13,0]

我正在使用20个新闻组数据集进行在线分类，目前我使用的是第一种方法来填充我的文本。

问题：在我的实现中，使用第一种方法相较于第二种方法有什么优势吗？

提前感谢您！

我的代码如下所示：

from collections import Counterimport tensorflow as tffrom sklearn.datasets import fetch_20newsgroupsimport matplotlib as mpltmplt.use('agg') # Must be before importing matplotlib.pyplot or pylab!import matplotlib.pyplot as pltfrom string import punctuationfrom sklearn.preprocessing import LabelBinarizerimport numpy as npfrom nltk.corpus import stopwordsimport nltknltk.download('stopwords')def pre_process():    newsgroups_data = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))    words = []    temp_post_text = []    print(len(newsgroups_data.data))    for post in newsgroups_data.data:        all_text = ''.join([text for text in post if text not in punctuation])        all_text = all_text.split('\n')        all_text = ''.join(all_text)        temp_text = all_text.split(" ")        for word in temp_text:            if word.isalpha():                temp_text[temp_text.index(word)] = word.lower()        # temp_text = [word for word in temp_text if word not in stopwords.words('english')]        temp_text = list(filter(None, temp_text))        temp_text = ' '.join([i for i in temp_text if not i.isdigit()])        words += temp_text.split(" ")        temp_post_text.append(temp_text)    # temp_post_text = list(filter(None, temp_post_text))    dictionary = Counter(words)    # deleting spaces    # del dictionary[""]    sorted_split_words = sorted(dictionary, key=dictionary.get, reverse=True)    vocab_to_int = {c: i for i, c in enumerate(sorted_split_words,1)}    message_ints = []    for message in temp_post_text:        temp_message = message.split(" ")        message_ints.append([vocab_to_int[i] for i in temp_message])    # maximum message length = 6577    # message_lens = Counter([len(x) for x in message_ints])AAA    seq_length = 6577    num_messages = len(temp_post_text)    features = np.zeros([num_messages, seq_length], dtype=int)    for i, row in enumerate(message_ints):        print(features[i, -len(row):])        features[i, -len(row):] = np.array(row)[:seq_length]        print(features[i, -len(row):])    lb = LabelBinarizer()    lbl = newsgroups_data.target    labels = np.reshape(lbl, [-1])    labels = lb.fit_transform(labels)    return features, labels, len(sorted_split_words)+1def get_batches(x, y, batch_size=1):    for ii in range(0, len(y), batch_size):        yield x[ii:ii + batch_size], y[ii:ii + batch_size]def plot(noOfWrongPred, dataPoints):    font_size = 14    fig = plt.figure(dpi=100,figsize=(10, 6))    mplt.rcParams.update({'font.size': font_size})    plt.title("错误预测的分布", fontsize=font_size)    plt.ylabel('错误率', fontsize=font_size)    plt.xlabel('数据点数量', fontsize=font_size)    plt.plot(dataPoints, noOfWrongPred, label='预测', color='blue', linewidth=1.8)    # plt.legend(loc='upper right', fontsize=14)    plt.savefig('错误预测的分布.png')    # plt.show()def train_test():    features, labels, n_words = pre_process()    print(features.shape)    print(labels.shape)    # Defining Hyperparameters    lstm_layers = 1    batch_size = 1    lstm_size = 200    learning_rate = 0.01    # --------------placeholders-------------------------------------    # Create the graph object    graph = tf.Graph()    # Add nodes to the graph    with graph.as_default():        tf.set_random_seed(1)        inputs_ = tf.placeholder(tf.int32, [None, None], name="inputs")        # labels_ = tf.placeholder(dtype= tf.int32)        labels_ = tf.placeholder(tf.float32, [None, None], name="labels")        # output_keep_prob is the dropout added to the RNN's outputs, the dropout will have no effect on the calculation of the subsequent states.        keep_prob = tf.placeholder(tf.float32, name="keep_prob")        # Size of the embedding vectors (number of units in the embedding layer)        embed_size = 300        # generating random values from a uniform distribution (minval included and maxval excluded)        embedding = tf.Variable(tf.random_uniform((n_words, embed_size), -1, 1),trainable=True)        embed = tf.nn.embedding_lookup(embedding, inputs_)        print(embedding.shape)        print(embed.shape)        print(embed[0])        # Your basic LSTM cell        lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)        # Add dropout to the cell        drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)        # Stack up multiple LSTM layers, for deep learning        cell = tf.contrib.rnn.MultiRNNCell([drop] * lstm_layers)        # Getting an initial state of all zeros        initial_state = cell.zero_state(batch_size, tf.float32)        outputs, final_state = tf.nn.dynamic_rnn(cell, embed, initial_state=initial_state)        # hidden layer        hidden = tf.layers.dense(outputs[:, -1], units=25, activation=tf.nn.relu)        print(hidden.shape)        logit = tf.contrib.layers.fully_connected(hidden, num_outputs=20, activation_fn=None)        cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logit, labels=labels_))        optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)        saver = tf.train.Saver()    # ----------------------------online training-----------------------------------------    with tf.Session(graph=graph) as sess:        tf.set_random_seed(1)        sess.run(tf.global_variables_initializer())        iteration = 1        state = sess.run(initial_state)        wrongPred = 0        noOfWrongPreds = []        dataPoints = []        for ii, (x, y) in enumerate(get_batches(features, labels, batch_size), 1):            feed = {inputs_: x,                    labels_: y,                    keep_prob: 0.5,                    initial_state: state}            embedzz = sess.run(embedding, feed_dict=feed)            print(embedzz)            predictions = tf.nn.softmax(logit).eval(feed_dict=feed)            print("----------------------------------------------------------")            print("迭代次数: {}".format(iteration))            isequal = np.equal(np.argmax(predictions[0], 0), np.argmax(y[0], 0))            print(np.argmax(predictions[0], 0))            print(np.argmax(y[0], 0))            if not (isequal):                wrongPred += 1            print("错误预测数量: ",wrongPred)            if iteration%50 == 0:                noOfWrongPreds.append(wrongPred/iteration)                dataPoints.append(iteration)            loss, states, _ = sess.run([cost, final_state, optimizer], feed_dict=feed)            print("训练损失: {:.3f}".format(loss))            iteration += 1        saver.save(sess, "checkpoints/sentiment.ckpt")        errorRate = wrongPred / len(labels)        print("错误总数: ", wrongPred)        print("错误率: ", errorRate)        plot(noOfWrongPreds, dataPoints)if __name__ == '__main__':    train_test()

这是我用来填充所有句子的代码示例。

  seq_length = 6577  num_messages = len(temp_post_text)    features = np.zeros([num_messages, seq_length], dtype=int)    for i, row in enumerate(message_ints):        print(features[i, -len(row):])        features[i, -len(row):] = np.array(row)[:seq_length]        print(features[i, -len(row):])

回答：

通常，当我们使用LSTM或RNN时，我们使用最终输出或隐藏状态，并将其传递以进行预测。您也在做同样的事情，如下行所示：

logit = tf.contrib.layers.fully_connected(hidden, num_outputs=20, activation_fn=None)

在这里，两种填充方法有所区别。如果您使用第二种填充方法，即后填充，那么最终的隐藏状态将会大部分被刷新为0，而通过使用第一种方法，我们确保隐藏状态输出是正确的。