- 以MNIST数据集作为要处理的数据集
- 实现递归神经网络RNN
- 开发环境：jupyter notebook
- 运行：CPU
- 代码实现
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_size是神经元的个数
lstm_cell = tf.contrib.rnn.BasicLSTMCell(lstm_size)
#dynamic_rnn用于创建由RNNCell细胞制定的循环神经网络，对inputs进行动态展示
# final_state[0]是cell state
# final_state[1]是hidden_state
#outputs：RNN输出张量，final_state：最终状态，
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(20):
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))- 结果展示
See tf.nn.softmax_cross_entropy_with_logits_v2. Iter 0, Testing Accuracy= 0.7323 Iter 1, Testing Accuracy= 0.8588 Iter 2, Testing Accuracy= 0.9 Iter 3, Testing Accuracy= 0.9102 Iter 4, Testing Accuracy= 0.9219 Iter 5, Testing Accuracy= 0.9322 Iter 6, Testing Accuracy= 0.9392 Iter 7, Testing Accuracy= 0.9382 Iter 8, Testing Accuracy= 0.9429 Iter 9, Testing Accuracy= 0.9446 Iter 10, Testing Accuracy= 0.9485 Iter 11, Testing Accuracy= 0.9472 Iter 12, Testing Accuracy= 0.9491 Iter 13, Testing Accuracy= 0.956 Iter 14, Testing Accuracy= 0.9563 Iter 15, Testing Accuracy= 0.9585 Iter 16, Testing Accuracy= 0.9616 Iter 17, Testing Accuracy= 0.9623 Iter 18, Testing Accuracy= 0.9633 Iter 19, Testing Accuracy= 0.9622
精度可以达到96%以上