Tensorflow代码学习-3-2MNIST数据集分类简单版本

MNIST数据集分类简单版本（课程：炼数成金）

GITHUB
CSDN
知乎

欢迎点亮star

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data   #手写数字相关的数据包

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)    #载入数据，{数据集包路径，把标签转化为只有0和1的形式}

#定义变量，即每个批次的大小
batch_size = 100    #一次放100章图片进去
n_batch = mnist.train.num_examples // batch_size   #计算一共有多少个批次；训练集数量（整除）一个批次大小

#定义两个placeholder
x = tf.placeholder(tf.float32,[None,784])    #[行不确定，列为784]
y = tf.placeholder(tf.float32,[None,10])    #数字为0-9，则为10

#创建简单的神经网络
W = tf.Variable(tf.zeros([784,10]))   #权重
b = tf.Variable(tf.zeros([10]))     #偏置
prediction = tf.nn.softmax(tf.matmul(x,W)+b)    #预测

#定义二次代价函数
loss = tf.reduce_mean(tf.square(y-prediction))
#使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量
init = tf.global_variables_initializer()

#准确数，结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))   #比较两个参数大小是否相同，同则返回为true，不同则返回为false；argmax()：返回张量中最大的值所在的位置

#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))   #cast()：将布尔型转换为32位的浮点型；（比方说9个T和1个F，则为9个1，1个0，即准确率为90%）

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(101):
        for batch in range(n_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
            
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter" + str(epoch) + ",Testing Accuracy" + str(acc))

Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
Iter0,Testing Accuracy0.8318
Iter1,Testing Accuracy0.8705
Iter2,Testing Accuracy0.8817
Iter3,Testing Accuracy0.8884
Iter4,Testing Accuracy0.894
Iter5,Testing Accuracy0.8966
Iter6,Testing Accuracy0.9004
Iter7,Testing Accuracy0.9012
Iter8,Testing Accuracy0.9037
Iter9,Testing Accuracy0.9055
Iter10,Testing Accuracy0.9067
Iter11,Testing Accuracy0.9072
Iter12,Testing Accuracy0.9081
Iter13,Testing Accuracy0.9095
Iter14,Testing Accuracy0.9096
Iter15,Testing Accuracy0.9109
Iter16,Testing Accuracy0.9122
Iter17,Testing Accuracy0.9121
Iter18,Testing Accuracy0.9126
Iter19,Testing Accuracy0.9128
Iter20,Testing Accuracy0.9144
Iter21,Testing Accuracy0.9151
Iter22,Testing Accuracy0.9153
Iter23,Testing Accuracy0.9162
Iter24,Testing Accuracy0.9163
Iter25,Testing Accuracy0.9161
Iter26,Testing Accuracy0.9166
Iter27,Testing Accuracy0.9169
Iter28,Testing Accuracy0.9185
Iter29,Testing Accuracy0.9176
Iter30,Testing Accuracy0.9176
Iter31,Testing Accuracy0.9184
Iter32,Testing Accuracy0.9174
Iter33,Testing Accuracy0.9186
Iter34,Testing Accuracy0.9183
Iter35,Testing Accuracy0.9187
Iter36,Testing Accuracy0.919
Iter37,Testing Accuracy0.9189
Iter38,Testing Accuracy0.9189
Iter39,Testing Accuracy0.9199
Iter40,Testing Accuracy0.9196
Iter41,Testing Accuracy0.9196
Iter42,Testing Accuracy0.9198
Iter43,Testing Accuracy0.9209
Iter44,Testing Accuracy0.9201
Iter45,Testing Accuracy0.9205
Iter46,Testing Accuracy0.921
Iter47,Testing Accuracy0.9213
Iter48,Testing Accuracy0.9212
Iter49,Testing Accuracy0.9209
Iter50,Testing Accuracy0.9216
Iter51,Testing Accuracy0.9215
Iter52,Testing Accuracy0.9216
Iter53,Testing Accuracy0.9219
Iter54,Testing Accuracy0.9219
Iter55,Testing Accuracy0.9222
Iter56,Testing Accuracy0.9222
Iter57,Testing Accuracy0.9228
Iter58,Testing Accuracy0.9232
Iter59,Testing Accuracy0.923
Iter60,Testing Accuracy0.9232
Iter61,Testing Accuracy0.9232
Iter62,Testing Accuracy0.9231
Iter63,Testing Accuracy0.9235
Iter64,Testing Accuracy0.9237
Iter65,Testing Accuracy0.9238
Iter66,Testing Accuracy0.9239
Iter67,Testing Accuracy0.9239
Iter68,Testing Accuracy0.9236
Iter69,Testing Accuracy0.924
Iter70,Testing Accuracy0.9241
Iter71,Testing Accuracy0.9242
Iter72,Testing Accuracy0.9243
Iter73,Testing Accuracy0.9245
Iter74,Testing Accuracy0.9245
Iter75,Testing Accuracy0.9245
Iter76,Testing Accuracy0.9252
Iter77,Testing Accuracy0.9242
Iter78,Testing Accuracy0.924
Iter79,Testing Accuracy0.9247
Iter80,Testing Accuracy0.9243
Iter81,Testing Accuracy0.9247
Iter82,Testing Accuracy0.9254
Iter83,Testing Accuracy0.9253
Iter84,Testing Accuracy0.9253
Iter85,Testing Accuracy0.9251
Iter86,Testing Accuracy0.9245
Iter87,Testing Accuracy0.9252
Iter88,Testing Accuracy0.9254
Iter89,Testing Accuracy0.9262
Iter90,Testing Accuracy0.9259
Iter91,Testing Accuracy0.9259
Iter92,Testing Accuracy0.9263
Iter93,Testing Accuracy0.926
Iter94,Testing Accuracy0.9261
Iter95,Testing Accuracy0.9261
Iter96,Testing Accuracy0.9264
Iter97,Testing Accuracy0.9262
Iter98,Testing Accuracy0.9254
Iter99,Testing Accuracy0.9258
Iter100,Testing Accuracy0.9258

点赞，关注，收藏👍，➕，👀点赞，关注，收藏👍，➕，👀点赞，关注，收藏👍，➕，👀
😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘😘
💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪💪