使用python中的tensorflow生成更加优美的莎士比亚风格诗句

下面是使用Python中的TensorFlow生成更加优美的莎士比亚风格诗句的示例代码：

import tensorflow as tf
import numpy as np

# 定义模型参数
num_epochs = 50
batch_size = 64
rnn_size = 256
num_layers = 2
learning_rate = 0.01
keep_prob = 0.5

# 读取数据
with open('shakespeare.txt', 'r') as f:
    text = f.read()

# 构建字符映射表
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = {i: c for i, c in enumerate(vocab)}
encoded = np.array([vocab_to_int[c] for c in text], dtype=np.int32)

# 构建输入数据和标签
seq_length = 100
num_seqs = len(encoded) // seq_length
inputs = np.zeros((num_seqs, seq_length), dtype=np.int32)
labels = np.zeros((num_seqs, seq_length), dtype=np.int32)
for i in range(num_seqs):
    inputs[i] = encoded[i * seq_length:(i + 1) * seq_length]
    labels[i] = encoded[i * seq_length + 1:(i + 1) * seq_length + 1]

# 构建模型
inputs_placeholder = tf.placeholder(tf.int32, [None, None], name='inputs')
labels_placeholder = tf.placeholder(tf.int32, [None, None], name='labels')
keep_prob_placeholder = tf.placeholder(tf.float32, name='keep_prob')

embedding_size = 128
rnn_inputs = tf.contrib.layers.embed_sequence(inputs_placeholder, len(vocab), embedding_size)

cell = tf.contrib.rnn.BasicLSTMCell(rnn_size)
drop = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=keep_prob_placeholder)
stacked_rnn = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)
initial_state = stacked_rnn.zero_state(batch_size, tf.float32)
outputs, final_state = tf.nn.dynamic_rnn(stacked_rnn, rnn_inputs, initial_state=initial_state)
logits = tf.contrib.layers.fully_connected(outputs, len(vocab), activation_fn=None)

# 定义损失函数和优化器
loss = tf.contrib.seq2seq.sequence_loss(
    logits,
    labels_placeholder,
    tf.ones([batch_size, seq_length], dtype=tf.float32),
    average_across_timesteps=False,
    average_across_batch=True
)
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss)

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for epoch in range(num_epochs):
        state = sess.run(initial_state)
        for i in range(num_batches):
            x = inputs[i * batch_size:(i + 1) * batch_size]
            y = labels[i * batch_size:(i + 1) * batch_size]
            feed = {inputs_placeholder: x, labels_placeholder: y, initial_state: state, keep_prob_placeholder: keep_prob}
            batch_loss, state, _ = sess.run([loss, final_state, train_op], feed_dict=feed)
        print('Epoch {}/{}...'.format(epoch + 1, num_epochs), 'Batch Loss: {:.4f}'.format(batch_loss))

    # 生成新的文本
    gen_length = 500
    prime_words = 'To be or not to be:'
    gen_sentences = prime_words
    prev_state = sess.run(initial_state, feed_dict={batch_size: 1})
    for word in prime_words.split():
        x = np.zeros((1, 1))
        x[0, 0] = vocab_to_int[word]
        feed = {inputs_placeholder: x, initial_state: prev_state, keep_prob_placeholder: 1.0}
        prev_state = sess.run(final_state, feed_dict=feed)

    for i in range(gen_length):
        feed = {inputs_placeholder: x, initial_state: prev_state, keep_prob_placeholder: 1.0}
        preds, prev_state = sess.run([probs, final_state], feed_dict=feed)

        pred = preds[0]
        next_index = np.random.choice(len(pred), p=pred)
        next_char = int_to_vocab[next_index]
        gen_sentences += next_char
        x = np.zeros((1, 1))
        x[0, 0] = next_index

    print(gen_sentences)

这段代码中，我们首先读取了莎士比亚的诗歌作为训练数据，并构建了字符映射表。然后，我们使用TensorFlow搭建了一个LSTM模型，并对模型进行了训练。最后，我们使用训练好的模型生成了新的莎士比亚风格的诗句。