1、RNN(Recurrent Neural Network)循环神经网络模型
详见RNN循环神经网络:https://www.cnblogs.com/pinard/p/6509630.html
2、LSTM(Long Short Term Memory)长短期记忆神经网络模型
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.contrib import rnn # 载入数据集 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # 输入图片是28*28 n_inputs = 28 # 输入一行,一行有28个数据(28个像素点),即输入序列长度为28 max_time = 28 # 一共28行 lstm_size = 100 # 隐层单元 n_classes = 10 # 10个分类 batch_size = 50 # 每批次50个样本 n_batch = mnist.train.num_examples // batch_size # 计算一共有多少个批次 # 这里的none表示第一个维度可以是任意的长度 x = tf.placeholder(tf.float32, [None, 784]) # 正确的标签 y = tf.placeholder(tf.float32, [None, 10]) # 初始化权值 weights = tf.Variable(tf.truncated_normal([lstm_size, n_classes], stddev=0.1)) # 初始化偏置值 biases = tf.Variable(tf.constant(0.1, shape=[n_classes])) # 定义RNN网络 def RNN(X, weights, biases): inputs = tf.reshape(X, [-1, max_time, n_inputs]) # 定义LSTM基本CELL lstm_cell = rnn.BasicLSTMCell(lstm_size) # final_state[0]是cell state # final_state[1]是hidden_state outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, inputs, dtype=tf.float32) results = tf.nn.softmax(tf.matmul(final_state[1], weights) + biases) return results # 计算RNN的返回结果 prediction = RNN(x, weights, biases) # 损失函数 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)) # 使用AdamOptimizer进行优化 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # 结果存放在一个布尔型列表中 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax返回一维张量中最大的值所在的位置 # 求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 把correct_prediction变为float32类型 # 初始化 init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for epoch in range(21): for batch in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys}) acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}) print("Iter " + str(epoch) + ", Testing Accuracy= " + str(acc))
结果为:
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