所有的ML模型或者DL 模型 都是下面这四个固定套路的步骤

1.获取到所需数据

2.开始搭建模型

3.计算采用何种loss函数

4.选择batch,epoch,feed数据 

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets('./tmp/tensorflow/mnist/input_data',one_hot=True) # 下载数据
x = tf.placeholder(tf.float32,[None,784]) # 输入占位符
yresult = tf.placeholder(tf.float32,[None,10]) #输入数据真实的label

w = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,w) + b) # 用不用激励函数 都可以的其实
cross_entropy = -tf.reduce_sum(yresult * tf.log(y)) # loss  值
train_setp = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) #梯度下降法
init  = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    for i in range(1000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        argv1,loss = sess.run([train_setp,cross_entropy],feed_dict={x:batch_xs,yresult:batch_ys}) #如果想知道corss_entropy试试变化值 加入就好。
        if i % 200 == 0:
            print (loss)

    current_prediction = tf.equal(tf.argmax(y,1),tf.argmax(yresult,1)) # compare real and calculate
    accuracy = tf.reduce_mean(tf.cast(current_prediction,tf.float32))  # 数据类型转换 然后求匹配上的概率
    result = sess.run(accuracy,feed_dict={x:mnist.test.images,yresult:mnist.test.labels}) # test数据入口
    print(str(result * 100) + '%')