14、循环神经网络（RNN）及其应用详解

循环神经网络（RNN）及其应用详解

1. RNN图像分类器

RNN在图像分类任务中有着重要的应用。下面我们将详细介绍如何使用RNN构建一个图像分类器，并给出具体的代码实现和运行结果。

1.1 代码实现

import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10
n_input = 28
n_steps = 28
n_hidden = 128
n_classes = 10

x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])

weights = {
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
    'out': tf.Variable(tf.random_normal([n_classes]))
}

def RNN(x, weights, biases):
    x = tf.transpose(x