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在自然语言处理领域，语言生成任务一直是研究热点之一。为了更好地模拟莎士比亚的语言特点，我们采用多层LSTM结构进行语言建模。这种方法利用了LSTM的强大能力，能够捕捉长距离依赖关系，同时避免梯度消失问题。通过堆叠多个LSTM层，我们能够更准确地表示莎士比亚语言的复杂模式。
此外，我们将词汇空间从传统的词袋模型转换为字符级建模。这种方法能够更细致地捕捉语言的微观特征，从而生成更自然的文本。
本文将详细介绍我们的LSTM模型设计、训练过程以及实现效果。
首先，我们从互联网下载了大量莎士比亚作品的文本数据，并进行预处理。具体步骤如下：
# 下载并清洗数据print('Loading Shakespeare Data')if not os.path.isfile(os.path.join(data_dir, data_file)): print('Not found, downloading Shakespeare texts from www.gutenberg.org') shakespeare_url = 'http://www.gutenberg.org/cache/epub/100/pg100.txt' response = requests.get(shakespeare_url) shakespeare_file = response.content s_text = shakespeare_file.decode('utf-8') s_text = s_text[7675:] s_text = re.sub(r'[{}]', ' ', s_text).strip().lower()char_list = list(s_text) 接下来，我们从字符级别构建词表。通过统计字符频率，我们得到一个词到索引的映射表，以及索引到词的映射表。
def build_vocab(characters): character_counts = collections.Counter(characters) chars = character_counts.keys() vocab_to_ix_dict = {key: (inx + 1) for inx, key in enumerate(chars)} vocab_to_ix_dict['unknown'] = 0 ix_to_vocab_dict = {val: key for key, val in vocab_to_ix_dict.items()} return ix_to_vocab_dict, vocab_to_ix_dictprint('Building Shakespeare Vocab by Characters')ix2vocab, vocab2ix = build_vocab(char_list)vocab_size = len(ix2vocab)print('Vocabulary Length = {}'.format(vocab_size)) 在LSTM模型中，我们采用多层结构来捕捉复杂的语言模式。模型主要包括以下几个部分：
class LSTM_Model(): def __init__(self, rnn_size, num_layers, batch_size, learning_rate, training_seq_len, vocab_size, infer_sample=False): self.rnn_size = rnn_size self.num_layers = num_layers self.vocab_size = vocab_size self.infer_sample = infer_sample self.learning_rate = learning_rate if infer_sample: self.batch_size = 1 self.training_seq_len = 1 else: self.batch_size = batch_size self.training_seq_len = training_seq_len self.lstm_cell = tf.contrib.rnn.BasicLSTMCell(rnn_size) self.lstm_cell = tf.contrib.rnn.MultiRNNCell([self.lstm_cell for _ in range(self.num_layers)]) self.initial_state = self.lstm_cell.zero_state(self.batch_size, tf.float32) self.x_data = tf.placeholder(tf.int32, [self.batch_size, self.training_seq_len]) self.y_output = tf.placeholder(tf.int32, [self.batch_size, self.training_seq_len]) with tf.variable_scope('lstm_vars'): W = tf.get_variable('W', [self.rnn_size, self.vocab_size], tf.float32, tf.random_normal_initializer()) b = tf.get_variable('b', [self.vocab_size], tf.float32, tf.constant_initializer(0.0)) embedding_mat = tf.get_variable('embedding_mat', [self.vocab_size, self.rnn_size], tf.float32, tf.random_normal_initializer()) embedding_output = tf.nn.embedding_lookup(embedding_mat, self.x_data) rnn_inputs = tf.split(axis=1, num_or_size_splits=self.training_seq_len, value=embedding_output) rnn_inputs_trimmed = [tf.squeeze(x, [1]) for x in rnn_inputs] decoder = tf.contrib.legacy_seq2seq.rnn_decoder outputs, last_state = decoder(rnn_inputs_trimmed, self.initial_state, self.lstm_cell) output = tf.reshape(tf.concat(axis=1, values=outputs), [-1, self.rnn_size]) self.logit_output = tf.matmul(output, W) + b self.model_output = tf.nn.softmax(self.logit_output) loss_fun = tf.contrib.legacy_seq2seq.sequence_loss_by_example loss = loss_fun([self.logit_output], [tf.reshape(self.y_output, [-1])], [tf.ones([self.batch_size * self.training_seq_len])], self.vocab_size) self.cost = tf.reduce_sum(loss) / (self.batch_size * self.training_seq_len) self.final_state = last_state gradients, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tf.trainable_variables()), 4.5) optimizer = tf.train.AdamOptimizer(self.learning_rate) self.train_op = optimizer.apply_gradients(zip(gradients, tf.trainable_variables())) def sample(self, sess, words=ix2vocab, vocab=vocab2ix, num=20, prime_text='thou art'): state = sess.run(self.lstm_cell.zero_state(1, tf.float32)) char_list = list(prime_text) for char in char_list[:-1]: x = np.zeros((1, 1)) x[0, 0] = vocab[char] feed_dict = {self.x_data: x, self.initial_state: state} [state] = sess.run([self.final_state], feed_dict=feed_dict) out_sentence = prime_text char = char_list[-1] for n in range(num): x = np.zeros((1, 1)) x[0, 0] = vocab[char] feed_dict = {self.x_data: x, self.initial_state: state} [model_output, state] = sess.run([self.model_output, self.final_state], feed_dict=feed_dict) sample = np.argmax(model_output[0]) if sample == 0: break char = words[sample] out_sentence = out_sentence + char return out_sentence 在训练过程中，我们采用批量训练的方式来提高效率。以下是训练的主要步骤：
num_batches = int(len(s_text_ix) / (batch_size * training_seq_len)) + 1batches = np.array_split(s_text_ix, num_batches)batches = [np.resize(x, [batch_size, training_seq_len]) for x in batches]init = tf.global_variables_initializer()sess.run(init)train_loss = []iteration_count = 1for epoch in range(epochs): random.shuffle(batches) targets = [np.roll(x, -1, axis=1) for x in batches] print('Starting Epoch #{} of {}.'.format(epoch + 1, epochs)) state = sess.run(lstm_model.initial_state) for ix, batch in enumerate(batches): training_dict = {lstm_model.x_data: batch, lstm_model.y_output: targets[ix]} for i, (c, h) in enumerate(lstm_model.initial_state): training_dict[c] = state[i].c training_dict[h] = state[i].h temp_loss, state, _ = sess.run([lstm_model.cost, lstm_model.final_state, lstm_model.train_op], feed_dict=training_dict) train_loss.append(temp_loss) if iteration_count % 10 == 0: summary_nums = (iteration_count, epoch + 1, ix + 1, num_batches + 1, temp_loss) print('Iteration: {}, Epoch: {}, Batch: {} out of {}, Loss: {:.2f}'.format(*summary_nums)) if iteration_count % save_every == 0: model_file_name = os.path.join(full_model_dir, 'model') saver.save(sess, model_file_name, global_step=iteration_count) print('Model Saved To: {}'.format(model_file_name)) dictionary_file = os.path.join(full_model_dir, 'vocab.pkl') with open(dictionary_file, 'wb') as dict_file_conn: pickle.dump([vocab2ix, ix2vocab], dict_file_conn) if iteration_count % eval_every == 0: for sample in prime_texts: print(test_lstm_model.sample(sess, ix2vocab, vocab2ix, num=10, prime_text=sample)) iteration_count += 1plt.plot(train_loss, 'k-')plt.title('Sequence to Sequence Loss')plt.xlabel('Generation')plt.ylabel('Loss')plt.show() 通过实验，我们发现堆叠LSTM层的模型在莎士比亚语言生成任务中表现出色。生成的文本能够较好地保持原作的风格和语法特点。
通过以上方法，我们成功实现了基于字符级词汇的LSTM模型，并通过堆叠多层LSTM结构，显著提升了莎士比亚语言生成的准确性。这种方法不仅适用于莎士比亚作品，还可以扩展到其他语言的文本生成任务。
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