使用Python和Tensorflow实现的基本RNN模型的代码，用于递归地训练和预测数据集

抱歉，上一段代码未完成，以下是完整的基本 RNN 模型的 Python 代码，包含训练和预测过程：

import tensorflow as tf
import numpy as np

# 定义超参数
num_epochs = 100
total_series_length = 50000
truncated_backprop_length = 15
state_size = 4
num_classes = 2
echo_step = 3
batch_size = 5
num_batches = total_series_length//batch_size//truncated_backprop_length

# 生成数据集
def generate_data():
    x = np.array(np.random.choice(2, total_series_length, p=[0.5, 0.5]))
    y = np.roll(x, echo_step)
    y[:echo_step] = 0
    x = x.reshape((batch_size, -1))
    y = y.reshape((batch_size, -1))
    return (x, y)

# 定义输入和输出占位符
batchX_placeholder = tf.placeholder(tf.float32, [batch_size, truncated_backprop_length])
batchY_placeholder = tf.placeholder(tf.int32, [batch_size, truncated_backprop_length])

# 定义 RNN 中的权重和偏置
W = tf.Variable(np.random.rand(state_size+1, state_size), dtype=tf.float32)
b = tf.Variable(np.zeros((1,state_size)), dtype=tf.float32)

W2 = tf.Variable(np.random.rand(state_size, num_classes),dtype=tf.float32)
b2 = tf.Variable(np.zeros((1,num_classes)), dtype=tf.float32)

# 定义 RNN 的状态向量 s0
init_state = tf.placeholder(tf.float32, [batch_size, state_size])
state_per_layer_list = tf.unstack(init_state, axis=0)
rnn_tuple_state = tuple(
    [tf.contrib.rnn.LSTMStateTuple(state_per_layer_list[idx][0], state_per_layer_list[idx][1])
     for idx in range(state_size)]
)

# 在时间序列上展开 RNN，并计算输出
current_state = rnn_tuple_state
states_series = []
for current_input in tf.unstack(batchX_placeholder, axis=1):
    current_input = tf.reshape(current_input, [batch_size, 1])
    input_and_state_concatenated = tf.concat([current_input, current_state[-1].h], 1)  
    next_state = tf.tanh(tf.matmul(input_and_state_concatenated, W) + b)  
    states_series.append(next_state)
    current_state = tuple([tf.contrib.rnn.LSTMStateTuple(next_state, current_state[idx].c) 
                           for idx in range(state_size)])

# 计算 logits 和预测值
logits_series = [tf.matmul(state, W2) + b2 for state in states_series] 
predictions_series = [tf.nn.softmax(logits) for logits in logits_series]

# 定义损失函数和优化器
losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels) 
          for logits, labels in zip(logits_series, tf.unstack(batchY_placeholder, axis=1))]
total_loss = tf.reduce_mean(losses)
train_step = tf.train.AdagradOptimizer(0.3).minimize(total_loss)

# 开始训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch_idx in range(num_epochs):
        x,y = generate_data()
        _current_state = np.zeros((batch_size, state_size))
        print("New data, epoch", epoch_idx)

        for batch_idx in range(num_batches):
            start_idx = batch_idx * truncated_backprop_length
            end_idx = start_idx + truncated_backprop_length

            batchX = x[:,start_idx:end_idx]
            batchY = y[:,start_idx:end_idx]

            _total_loss, _train_step, _current_state, _predictions_series = sess.run(
                [total_loss, train_step, current_state, predictions_series],
                feed_dict={
                    batchX_placeholder:batchX,
                    batchY_placeholder:batchY,
                    init_state:_current_state
                })

            if batch_idx%100 == 0:
                print("Step",batch_idx, "Batch loss", _total_loss)

# 使用模型进行预测
test_data = np.array(np.random.choice(2, 10, p=[0.5, 0.5]))
test_data = test_data.reshape((1, -1))
current_state = np.zeros((batch_size, state_size))
predicted_output = []
for i in range(test_data.shape[1]):
    output_probs, current_state = sess.run(
        [predictions_series[-1], current_state],
        feed_dict={
            batchX_placeholder: test_data[:,i].reshape((batch_size, 1)),
            init_state: current_state
        })
    predicted_output.append(output_probs[0,1])
    
print(predicted_output)

这段代码用于生成一个包含 50000 个数据点的二进制序列数据集，并使用 RNN 模型对其进行训练和预测。训练过程使用 Adagrad 优化器进行优化。在每个 epoch 中，使用生成数据集函数生成一个新的数据集，并使用 RNN 模型对其进行训练。在训练过程中，每 100 个 batch 输出一次损失值。预测过程中，使用训练好的模型对一个新的 10 个元素的数据序列进行预测，输出预测值序列中所有为 1 的概率。