周杰伦几乎陪伴了每个90后的青春，那如果AI写杰伦风格的歌词会写成怎样呢？
首先当然我们需要准备杰伦的歌词，这里一共收录了他的十几张专辑，近5000多行歌词。
原文档格式：
第一步数据预处理
def preprocess(data):
"""
对文本中的字符进行替换，空格转换成逗号；换行变为句号。
"""
data = data.replace(' ', '，')
data = data.replace('\n', '。')
words = jieba.lcut(data, cut_all=False) # 全模式切词
return words处理后结果：
前10个词: ['想要', '有', '直升机', '。', '想要', '和', '你', '飞到', '宇宙', '去']将处理完的数据写入内存并将文本转换完数字
# 构造词典及映射
vocab = set(text)
vocab_to_int = {w: idx for idx, w in enumerate(vocab)}
int_to_vocab = {idx: w for idx, w in enumerate(vocab)}
# 转换文本为整数
int_text = [vocab_to_int[w] for w in text]构建神经网络
a. 构建输入层
def get_inputs():
inputs = tf.placeholder(tf.int32, [None, None], name='inputs')
targets = tf.placeholder(tf.int32, [None, None], name='targets')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return inputs, targets, learning_rateb. 构建堆叠RNN单元
其中rnn_size指的是RNN隐层神经元个数
def get_init_cell(batch_size, rnn_size):
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
cell = tf.contrib.rnn.MultiRNNCell([lstm])
initial_state = cell.zero_state(batch_size, tf.float32)
initial_state = tf.identity(initial_state, 'initial_state')
return cell, initial_statec. Word Embedding
因为单词太多，所以需要进行embedding，模型中加入Embedding层来降低输入词的维度
def get_embed(input_data, vocab_size, embed_dim):
embedding = tf.Variable(tf.random_uniform([vocab_size, embed_dim], -1, 1))
embed = tf.nn.embedding_lookup(embedding, input_data)
return embedd. 构建神经网络，将RNN层与全连接层相连
其中cell为RNN单元； rnn_size: RNN隐层结点数量；input_data即input tensor；vocab_size：词汇表大小； embed_dim: 嵌入层大小
def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):
embed = get_embed(input_data, vocab_size, embed_dim)
outputs, final_state = build_rnn(cell, embed)
logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None)
return logits, final_statee. 构造batch
这里我们根据batch_size和seq_length分为len//(batch_size*seq_length)个batch，每个batch包含输入和对应的目标输出
def get_batches(int_text, batch_size, seq_length):
'''
构造batch
'''
batch = batch_size * seq_length
n_batch = len(int_text) // batch
int_text = np.array(int_text[:batch * n_batch]) # 保留能构成完整batch的数量
int_text_targets = np.zeros_like(int_text)
int_text_targets[:-1], int_text_targets[-1] = int_text[1:], int_text[0]
# 切分
x = np.split(int_text.reshape(batch_size, -1), n_batch, -1)
y = np.split(int_text_targets.reshape(batch_size, -1), n_batch, -1)
return np.stack((x, y), axis=1) # 组合模型训练
from tensorflow.contrib import seq2seq
train_graph = tf.Graph()
with train_graph.as_default():
vocab_size = len(int_to_vocab) # vocab_size
input_text, targets, lr = get_inputs() # 输入tensor
input_data_shape = tf.shape(input_text)
# 初始化RNN
cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)
logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size, embed_dim)
# 计算softmax层概率
probs = tf.nn.softmax(logits, name='probs')
# 损失函数
cost = seq2seq.sequence_loss(
logits,
targets,
tf.ones([input_data_shape[0], input_data_shape[1]]))
# 优化函数
optimizer = tf.train.AdamOptimizer(lr)
# Gradient Clipping
gradients = optimizer.compute_gradients(cost)
capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]
train_op = optimizer.apply_gradients(capped_gradients)训练结果
Epoch 72 Batch 24/33 train_loss = 0.108
Epoch 75 Batch 25/33 train_loss = 0.104
Epoch 78 Batch 26/33 train_loss = 0.096
Epoch 81 Batch 27/33 train_loss = 0.111
Epoch 84 Batch 28/33 train_loss = 0.119
Epoch 87 Batch 29/33 train_loss = 0.130
Epoch 90 Batch 30/33 train_loss = 0.141
Epoch 93 Batch 31/33 train_loss = 0.138
Epoch 96 Batch 32/33 train_loss = 0.153
Model Trained and Savedtrain_loss还不错，不过可能过拟合了。
最后让我们加载模型，看看生成情况
# 加载模型
loader = tf.train.import_meta_graph(save_dir + '.meta')
loader.restore(sess, save_dir)
# 获取训练的结果参数
input_text, initial_state, final_state, probs = get_tensors(loaded_graph)
# Sentences generation setup
gen_sentences = [prime_word]
prev_state = sess.run(initial_state, {input_text: np.array([[1]])})
# 生成句子
for n in range(gen_length):
dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]
dyn_seq_length = len(dyn_input[0])
# 预测
probabilities, prev_state = sess.run(
[probs, final_state],
{input_text: dyn_input, initial_state: prev_state})
# 选择单词进行文本生成，用来以一定的概率生成下一个词
pred_word = pick_word(probabilities[0][dyn_seq_length - 1], int_to_vocab)
gen_sentences.append(pred_word)哎哟不错哦！
最后的最后我还扩大了歌词库，这次引入了更多流行歌手，来看看效果吧。
好像更不错了！
如果你也喜欢杰伦，请点赞并分享生成的歌词。
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