这一个textrnn文本分类的例子

好的，我可以为您提供一个基于TensorFlow的TextRNN文本分类的例子。

首先，您需要安装TensorFlow和numpy库。然后，您可以使用以下代码：

import tensorflow as tf
import numpy as np

# 定义超参数
num_epochs = 10
batch_size = 32
learning_rate = 0.001
hidden_size = 128
num_classes = 2

# 定义输入和标签占位符
inputs = tf.placeholder(tf.float32, [None, None, 1])
labels = tf.placeholder(tf.int32, [None])

# 定义TextRNN模型
def TextRNN(inputs, hidden_size, num_classes):
    cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
    outputs, _ = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
    last_output = outputs[:, -1, :]
    logits = tf.layers.dense(last_output, num_classes)
    return logits

# 构建模型
logits = TextRNN(inputs, hidden_size, num_classes)

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

# 定义准确率
correct_preds = tf.equal(tf.argmax(logits, 1), tf.cast(labels, tf.int64))
accuracy = tf.reduce_mean(tf.cast(correct_preds, tf.float32))

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(num_epochs):
        epoch_loss = 0
        epoch_accuracy = 0
        for i in range(0, len(train_data), batch_size):
            batch_inputs = train_data[i:i+batch_size]
            batch_labels = train_labels[i:i+batch_size]
            _, batch_loss, batch_accuracy = sess.run([optimizer, loss, accuracy], feed_dict={inputs: batch_inputs, labels: batch_labels})
            epoch_loss += batch_loss
            epoch_accuracy += batch_accuracy
        epoch_loss /= len(train_data) / batch_size
        epoch_accuracy /= len(train_data) / batch_size
        print('Epoch:', epoch+1, ' Loss:', epoch_loss, ' Accuracy:', epoch_accuracy)

    # 测试模型
    test_loss, test_accuracy = sess.run([loss, accuracy], feed_dict={inputs: test_data, labels: test_labels})
    print('Test Loss:', test_loss, ' Test Accuracy:', test_accuracy)

这个例子中，我们使用了一个基本的RNN单元，将其用于对文本进行分类。我们使用Adam优化器进行优化，并使用交叉熵作为损失函数。最终，我们在测试集上评估了模型的性能。

请注意，这只是一个基本的例子，您可能需要对代码进行更改以适应您的具体需求。