学习十一

手写数字识别

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

def full_connected():
    #获取真实数据
    mnist=input_data.read_data_sets("./data/input_data/",one_hot=True)
    #1.建立数据的占位符 x[None,784] y_true [None,10]
    with tf.variable_scope("data"):
        x=tf.placeholder(tf.float32,[None,784])  #提供数据
        y_true=tf.placeholder(tf.int32,[None,10])
        #2建立一个全连接层的神经网络 w[784,10] b[10]
    with tf.variable_scope("data"):
        weight = tf.Variable(tf.random_normal([784, 10], mean=0.0, stddev=1.0), name="w")
        bias = tf.Variable(tf.constant(0.0, shape=[10]))
        # 预测None个样本的输出结matrix=[None,784]*[784,10]+[10]=[None,10]
        y_predict = tf.matmul(x, weight) + bias



    with tf.variable_scope("soft_cross"):
        #求平均交叉熵损失
        loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_predict))

    #梯度下降求出损失
    with tf.variable_scope("optimizer"):
        train_op=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
    #计算准确率
    with tf.variable_scope("acc"):
        equal_list=tf.equal(tf.argmax(y_true,1),tf.argmax(y_predict,1)) #找出预测值里面最大的和真实值进行比较，如果两个位置相等就为一，不相等就是0
        #equal_list None个样本 [1,1,0,1,0,0.....]
        accuracy=tf.reduce_mean(tf.cast(equal_list,tf.float32)) #将求出的一相加除以所有值，就是准确率
    #定义一个初始化变量op
    init_op=tf.global_variables_initializer()
    #创建一个saver
    saver=tf.train.Saver()
    #开启会话训练
    with tf.Session() as sess:
        sess.run(init_op)
        

        #迭代训练，更新参数预测
        for i in range(2000):
            #取出真实存在的特征值和目标值
            mnist_x,mnist_y=mnist.train.next_batch(50)
            #运行train_op训练
            sess.run(train_op,feed_dict={x:mnist_x,y_true:mnist_y })
            print("训练第%d步，准确率为:%f"%(i,sess.run(accuracy,feed_dict={x:mnist_x,y_true:mnist_y })))
        saver.save(sess,"./ckpt/fc_model")






    return None
if __name__=="__main__":
    full_connected()