RNN和LSTM的原理可以看這篇文章
以下是RNN在手寫數字識別上的簡單應用

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.contrib import rnn#重置graph，不然會出錯 tf.reset_default_graph()# 導入數據 mnist = input_data.read_data_sets('MNIST_data', one_hot=True)# 輸入圖片28*28 n_inputs = 28 # 輸入一行，28個像素點 max_time = 28 # 共28行 lstm_size = 100 # 隱層單元 n_classes = 10 batch_size = 50 n_batch = mnist.train.num_examples // batch_size# None表示第一個維度可以是任意的長度 # 定義兩個placeholder x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10])# 初始化權值 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]))# 定義RNN網絡 def RNN(X, weights, biases):# inputs=[batbatch_size,max_time,n_input]inputs = tf.reshape(X, [-1, max_time, n_inputs])# 定義LSTM基本celllstm_cell = rnn.BasicLSTMCell(lstm_size)# 1.0版本改了很多# 原代碼是這樣的：# lstm_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)# 應該改為：# from tensorflow.contrib import rnn# lstm_cell = rnn.BasicLSTMCell(lstm_size)# final_state[0]是cell_state# final_state[1]是hidden_stateoutputs, 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# 計算RNN的返回結果 prediction = RNN(x, weights, biases) # 損失函數 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction)) # 使用Adam train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # 結果存放在一個布爾列表中 correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)) # 求準確率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))with tf.Session() as sess:sess.run(tf.global_variables_initializer())for epoch in range(6):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))

輸出結果是這樣：

iter0,testing accuracy=0.7495 iter1,testing accuracy=0.841 iter2,testing accuracy=0.89 iter3,testing accuracy=0.9112 iter4,testing accuracy=0.9081 iter5,testing accuracy=0.9211

總結

以上是生活随笔為你收集整理的RNN的手写数字识别的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。