使用Python和Tensorflow写一个简单的RNN模型的代码，用于递归地训练和预测数据集

好的，下面是一个简单的RNN模型的代码，用于递归地训练和预测数据集，使用Python和TensorFlow实现：

import tensorflow as tf
import numpy as np

# 设置随机种子
tf.set_random_seed(111)

# 加载数据集
data = open('input.txt', 'r').read()
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
print('数据集大小: ', data_size)
print('词汇表大小: ', vocab_size)

# 创建词汇表映射
char_to_ix = { ch:i for i,ch in enumerate(chars) }
ix_to_char = { i:ch for i,ch in enumerate(chars) }

# 定义超参数
hidden_size = 100
seq_length = 25
learning_rate = 1e-1

# 定义输入和输出
inputs = tf.placeholder(shape=[None, vocab_size], dtype=tf.float32, name='inputs')
targets = tf.placeholder(shape=[None, vocab_size], dtype=tf.float32, name='targets')

# 定义RNN模型
cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
init_state = cell.zero_state(batch_size=1, dtype=tf.float32)

# 创建RNN模型的权重和偏置
W = tf.Variable(tf.random_normal([hidden_size, vocab_size]), name='W')
b = tf.Variable(tf.zeros([vocab_size]), name='b')

# 创建RNN模型
outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, initial_state=init_state)
logits = tf.matmul(outputs[:, -1], W) + b
prediction = tf.nn.softmax(logits)

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=targets))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)

# 定义训练函数
def train(data, num_epochs):
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        training_loss = 0
        for epoch in range(num_epochs):
            state = sess.run(init_state)
            for i in range(0, data_size - seq_length, seq_length):
                inputs_batch = np.zeros((1, vocab_size))
                targets_batch = np.zeros((1, vocab_size))
                for j in range(seq_length):
                    inputs_batch[0, char_to_ix[data[i+j]]] = 1
                    targets_batch[0, char_to_ix[data[i+j+1]]] = 1
                feed_dict = {inputs: inputs_batch, targets: targets_batch, init_state: state}
                training_loss_, state, _ = sess.run([loss, final_state, optimizer], feed_dict=feed_dict)
                training_loss += training_loss_
            if epoch % 10 == 0:
                print('Epoch: {}/{}'.format(epoch, num_epochs), 'Training Loss: {:.3f}'.format(training_loss))
                training_loss = 0

        # 生成新数据
        state = sess.run(cell.zero_state(1, tf.float32))
        new_data = ''
        input_ = np.zeros((1, vocab_size))
        input_[0, char_to_ix[data[0]]] = 1
        for i in range(data_size):
            feed_dict = {inputs: input_, init_state: state}
            prediction_, state = sess.run([prediction, final_state], feed_dict=feed_dict)
            index = np.random.choice(range(vocab_size), p=prediction_.ravel())
            new_char = ix_to_char[index]
            new_data += new_char
            input_ = np.zeros((1, vocab_size))
            input_[0, char_to_ix[new_char]] = 1

    return new_data

# 训练模型并生成新数据
new_data = train(data, 1000)
print(new_data)

在这个代码中，我们定义了一个基本的RNN模型，并使用TensorFlow的`dynamic_rnn`函数来创建模型。我们使用交叉熵损失函数和Adam优化器来训练模型，并使用训练好的模型来生成新的数据。