TensorFlow使用RNN实现手写数字识别

时间：2023-03-09 04:48:07
TensorFlow使用RNN实现手写数字识别

学习，笔记，有时间会加注释以及函数之间的逻辑关系。

# https://www.cnblogs.com/felixwang2/p/9190664.html
 # https://www.cnblogs.com/felixwang2/p/9190664.html

 # TensorFlow(十二)：使用RNN实现手写数字识别

 import tensorflow as tf

 from tensorflow.examples.tutorials.mnist import input_data

 # 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

 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=[batch_size, max_time, n_inputs]

     inputs = tf.reshape(X, [-1, max_time, n_inputs])

     # 定义LSTM基本CELL

     lstm_cell = tf.contrib.rnn.BasicLSTMCell(lstm_size)

     # final_state[state,batch_size,cell.state_size]

     # final_state[0]是cell state

     # final_state[1]是hidden_state

     # outputs: The RNN output 'Tensor'.

     #  If time_major == False (default), this will be a `Tensor` shaped:

     #       `[batch_size, max_time, cell.output_size]`.

     #  If time_major == True, this will be a `Tensor` shaped:

     #       `[max_time, batch_size, cell.output_size]`.

     # final_state 记录的是最后一次的输出结果

     # outputs 记录的是每一次的输出结果

     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_v2(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()

 gpu_options = tf.GPUOptions(allow_growth=True)

 with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:

     sess.run(init)

     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))

输出

Iter , Testing Accuracy= 0.6694

Iter , Testing Accuracy= 0.714

Iter , Testing Accuracy= 0.7984

Iter , Testing Accuracy= 0.8568

Iter , Testing Accuracy= 0.8863

Iter , Testing Accuracy= 0.9088

Process finished with exit code

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