import tensorflow as tf
from tensorflow.contrib import rnn

import numpy as np
import input_data

input_vec_size = lstm_size = 28
time_step_size = 28

batch_size = 128
test_size = 256

def init_weights(shape):
    return tf.Varibale(tf.random_normal(shape,stddev = 0.01))

def model(X,W,B,lstm_size):
    XT = tf.transpose(X,[1,0,2])
    XR = tf.reshape(XT,[-1,lstm_size])
    X_split = tf.spilt(XR,time_step_size,0)
    lstm = rnn.BasiclLSTMCell(lstm_size,forget_bias=1.0,state_is_tuple=True)
    outputs,_states = rnn.static_rnn(lstm,X_split,dtype=tf.float32)
    return tf.matmul(outputs[-1],W + B,lstm.state_size)

mnist = input_data.read_data_sets("MINIST_data/",one_hot=True)
trX,trY,teX,teY = mnist.train.images,mnist.train.labels,mnist.test.images,mnist.test.labels
trX = trX.reshape(-1,28,28)
teX = teX.reshape(-1,28,28)
X = tf.placeholder("float",[None,28,28])
Y = tf.placeholder("float",[None,10])
W = init_weights([lstm_size,10])
B = init_weights([10])

py_x,state_size = model(X,W,B,lstm_size)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x,labels=Y))
train_op = tf.train.RMSPropOptimizer(0.001,0.9.minimize(cost))
predict_op = tf.argmax(py_x,1)

session_conf = tf.ConfigProto()
session_conf.gpu_options.allow_growth = True

with tf.Session(config=session_conf) as sess:
    tf.global_variables_initializer().run()
    for i in range(100):
        for start,end in zip(range(0,len(trX),batch_size),range(batch_size,len(trX)+1,batch_size)):
            sess.run(train_op,feed_dict={X:trX[start:end],Y:trY[start:end]})
        s = len(teX)
        test_indices = np.arange(len(teX))
        np.random.shuffle(test_indices)
        test_indices = test_indices[0:test_size]
        
        print(i,np.mean(np.argmax(teY[test_indices,axis=1] == 
                                  sess.run(predict_op,feed_dict={X:teX[test_indices]}))))

以上是莫煩教程中的程式，小哥哥聲音很好聽，人也長得很帥，滿分推薦。