基于多层感知机的手写数字识别(Tensorflow实现)

标签：数据   optimizer   学习   range   red   NPU   数字识别   tensor   rect   

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os

mnist = input_data.read_data_sets(‘MNIST_data‘, one_hot=True)

class MNISTModel(object):
    def __init__(self, lr, batch_size, iter_num):
        self.lr = lr
        self.batch_size = batch_size
        self.iter_num = iter_num
        # 定义模型结构
        # 输入张量，这里还没有数据，先占个地方，所以叫“placeholder”
        self.x = tf.placeholder(tf.float32, [None, 784])   # 图像是28*28的大小
        self.y = tf.placeholder(tf.float32, [None, 10])    # 输出是0-9的one-hot向量
        self.h = tf.layers.dense(self.x, 100, activation=tf.nn.relu, use_bias=True, kernel_initializer=tf.truncated_normal_initializer) # 一个全连接层
        self.y_ = tf.layers.dense(self.h, 10, use_bias=True, kernel_initializer=tf.truncated_normal_initializer) # 全连接层
        
        # 使用交叉熵损失函数
        self.loss = tf.losses.softmax_cross_entropy(self.y, self.y_)
        self.optimizer = tf.train.AdamOptimizer()
        self.train_step = self.optimizer.minimize(self.loss)
        
        # 用于模型训练
        self.correct_prediction = tf.equal(tf.argmax(self.y, axis=1), tf.argmax(self.y_, axis=1))
        self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))
        
        # 用于保存训练好的模型
        self.saver = tf.train.Saver()
        
    def train(self):
        with tf.Session() as sess:            #  打开一个会话。可以想象成浏览器打开一个标签页一样，直观地理解一下
            sess.run(tf.global_variables_initializer())  # 先初始化所有变量。
            for i in range(self.iter_num):
                batch_x, batch_y = mnist.train.next_batch(self.batch_size)   # 读取一批数据
                loss, _ = sess.run([self.loss, self.train_step], feed_dict={self.x: batch_x, self.y: batch_y})   # 每调用一次sess.run，就像拧开水管一样，所有self.loss和self.train_step涉及到的运算都会被调用一次。
                if i%1000 == 0:    
                    train_accuracy = sess.run(self.accuracy, feed_dict={self.x: batch_x, self.y: batch_y})  # 把训练集数据装填进去
                    test_x, test_y = mnist.test.next_batch(self.batch_size)
                    test_accuracy = sess.run(self.accuracy, feed_dict={self.x: test_x, self.y: test_y})   # 把测试集数据装填进去
                    print( ‘iter\t%i\tloss\t%f\ttrain_accuracy\t%f\ttest_accuracy\t%f‘ % (i,loss,train_accuracy,test_accuracy))
            self.saver.save(sess, ‘model/mnistModel‘) # 保存模型

    def test(self):
        with tf.Session() as sess:
            self.saver.restore(sess, ‘model/mnistModel‘)
            Accuracy = []
            for i in range(150):
                test_x, test_y = mnist.test.next_batch(self.batch_size)
                test_accuracy = sess.run(self.accuracy, feed_dict={self.x: test_x, self.y: test_y})
                Accuracy.append(test_accuracy)
            print (‘==‘ * 15)
            print (‘Test Accuracy: ‘, np.mean(np.array(Accuracy)))

model = MNISTModel(0.001, 64, 40000)   # 学习率为0.001，每批传入64张图，训练40000次
model.train()      # 训练模型
model.test()       #测试模型

基于多层感知机的手写数字识别(Tensorflow实现)

标签：数据   optimizer   学习   range   red   NPU   数字识别   tensor   rect   

原文地址：https://www.cnblogs.com/shayue/p/10386107.html