一：MNIST数据集
MNIST是一个包含很多手写数字图片的数据集，一共4个二进制压缩文件
分别是test set images，test set labels，training set images，training set labels
training set包括60000个样本，test set包括10000个样本。
test set中前5000个样本来自原始的NISTtraining set，后5000个样本来自原始的NIST test set，因此，前5000个样本比后5000个样本更简单和干净。
每个样本是28*28像素的图片
二：tensorflow构建模型识别MNIST
导入数据：
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
import tensorflow as tf
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10]) #真实值
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, w) + b) #预测值
softmax的目的：将输出转化为是每个数字的概率
#计算交叉熵
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label *tf.log(y), reduction_indices=[1]))
train = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
交叉熵：衡量预测值与真实值之间的差别，当然是越小越好
公式为：
其中y'是真实值，y为预测值
最后用梯度下降法优化参数即可
在Session中运行graph：
total_steps = 5000
batch_size = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(total_steps+1):
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(train,feed_dict={x: batch_x, y_label: batch_y})
预测正确率：
correct_prediction = tf.equal(tf.argmax(y, axis=1), tf.argmax(y_label, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.argmax()函数返回axis轴上最大值的index
tf.equal()函数返回的是布尔值，需要用tf.cast()方法转为tf.float32类型
最后在test set上进行预测：
step_per_test = 100
if step % step_per_test == 0:
print(step, sess.run(accuracy, feed_dict={x: mnist.test.images, y_label: mnist.test.labels}))
完整代码如下：
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_label = tf.placeholder(tf.float32, [None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, w) + b) #计算交叉熵
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label *tf.log(y), reduction_indices=[1]))
train = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
#eval
correct_prediction = tf.equal(tf.argmax(y, axis=1), tf.argmax(y_label, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) total_steps = 5000
batch_size = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(total_steps+1):
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(train,feed_dict={x: batch_x, y_label: batch_y}) step_per_test = 100
if step % step_per_test == 0:
print(step, sess.run(accuracy, feed_dict={x: mnist.test.images, y_label: mnist.test.labels}))
运行结果：
准确率为0.92左右
后面我们会构建更好的模型达到更高的正确率。
相关链接：
基于tensorflow的MNIST手写字识别（一）--白话卷积神经网络模型