Rnn-demo_rnn demo

 x是28*28的手写数字图片，y是该图片对应的数字   

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
 
mnist=input_data.read_data_sets('D:/tensorflow/MNIST_data/mnist',one_hot=True)
 
batch_size=100
n_batch=mnist.train.num_examples//batch_size
max_time=28
n_inputs=28
lstm_size=100
n_class=10
 
def Weights_variables():
    initial=tf.truncated_normal([lstm_size,n_class],stddev=0.1)
    return tf.Variable(initial)
 
def biases_variables():
    initial=tf.constant(0.1,shape=[n_class])
    return tf.Variable(initial)
 
with tf.name_scope('RNN'):
    def RNN(X):    
        inputs=tf.reshape(X,[-1,max_time,n_inputs])
        lstm_cell=tf.nn.rnn_cell.BasicLSTMCell(lstm_size)
        output,final_output=tf.nn.dynamic_rnn(lstm_cell,inputs,dtype=tf.float32)
        return final_output[1]
 
with tf.name_scope('input'):
    x=tf.placeholder(tf.float32,[None,784])
    y=tf.placeholder(tf.float32,[None,10])
 
 
hiddlen=RNN(x)
with tf.name_scope('W'):
    w=Weights_variables()
with tf.name_scope('b'):
    b=biases_variables()
with tf.name_scope('prediction'):
    prediction=tf.nn.softmax(tf.matmul(hiddlen,w)+b)
    
with tf.name_scope('loss'):
    loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
    tf.summary.scalar('loss',loss)
with tf.name_scope('train_step'):
    train_step=tf.train.AdamOptimizer(0.0001).minimize(loss)
 
with tf.name_scope('Accuracy'): 
    prediction_value=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
    Accuracy=tf.reduce_mean(tf.cast(prediction_value,dtype=tf.float32))
    tf.summary.scalar('Accuracy',Accuracy)
 
 
init=tf.global_variables_initializer()
merged=tf.summary.merge_all()
 
with tf.Session() as sess:
    sess.run(init)
    writer_train=tf.summary.FileWriter('logs/train',sess.graph)
    writer_test=tf.summary.FileWriter('logs/test',sess.graph)
    for batch in range(n_batch-250):
        batch_xs,batch_ys=mnist.train.next_batch(batch_size)
        sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
        summary=sess.run(merged,feed_dict={x:batch_xs,y:batch_ys})
        writer_train.add_summary(summary,batch)
        
        batch_xs,batch_ys=mnist.test.next_batch(batch_size)
        summary=sess.run(merged,feed_dict={x:batch_xs,y:batch_ys})
        writer_test.add_summary(summary,batch)

其中在RNN输出又加了softmax来预测0~9这10个数字中各种的概率，如下:

可以看到准确率呈现上升趋势，因为demo中训练次数较少，而且为了快点出结果，这里还特意减少了250个batch即

range(n_batch-250):

所以结果不是很好