MNIST 数据 分类

分类和回归的区别在于输出变量的类型上。 通俗理解定量输出是回归，或者说是连续变量预测； 定性输出是分类，

或者说是离散变量预测。如预测房价这是一个回归任务； 把东西分成几类, 比如猫狗猪牛，就是一个分类任务。

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data   #首先准备数据
mnist =input_data.read_data_sets('MNIST_data',one_hot=True)
   #调用add_layer函数搭建一个最简单的训练网络结构，只有输入层和输出层。  
def add_layer(inputs, in_size, out_size,activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs
def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs})
    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})
    return result
#其中输入数据是784个特征，输出数据是10个特征，激励采用softmax函数
xs=tf.placeholder(tf.float32,[None,784]) #28*28
ys=tf.placeholder(tf.float32,[None,10])
prediction=add_layer(xs,784,10,activation_function=tf.nn.softmax)
#loss
cross_entropy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))

train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

init = tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)
for i in range(1000):
    batch_xs,batch_ys=mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
    if i%50==0:
        print(compute_accuracy(
            mnist.test.images,mnist.test.labels))