tensorflow实现手写体数字识别

luxuantao

发布于 2021-02-24 11:24:31

1K0

发布于 2021-02-24 11:24:31

调用tensorflow实现手写体数字识别。

内容参考自北京大学曹健教授的tensorflow课程，在此感谢代码原址：https://github.com/cj0012/AI-Practice-Tensorflow-Notes

之前在人工智能课上自己手动搭建过一个BP神经网络实现MNIST数据集的手写体数字识别，使用的是c++，最终准确率的上限在95%至96%左右（毕竟水平有限）。这次不一样了，使用tensorflow进行实验，准确率确实提高了不少。可能有人会觉得tensorflow有点过时，现在的大企业不怎么用tensorflow了，但我觉得，对于初学者来说，tensorflow还是不错的选择。

实验环境

ubuntu 16.04
python 2.7.13
tensorflow 1.3.0
MNIST数据集

我不清楚windows下可不可以装tensorflow，反正很久以前是不行，现在好像可以了？？？

总体框架

主要分为三个部分如下：

前向传播过程(mnist_forward.py)
进行网络参数优化方法的反向传播过程 ( mnist_backward.py )
验证模型准确率的测试过程(mnist_test.py)

每个部分都单独写成了一个模块文件。

源代码

mnist_forward.py

import tensorflow as tf

INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500

def get_weight(shape, regularizer):
	w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
	if regularizer != None:
		tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
	return w

def get_bias(shape):
	b = tf.Variable(tf.zeros(shape))
	return b

def forward(x, regularizer):	
	w1 = get_weight([INPUT_NODE, LAYER1_NODE], regularizer)
	b1 = get_bias([LAYER1_NODE])
	y1 = tf.nn.relu(tf.matmul(x, w1) + b1)

	w2 = get_weight([LAYER1_NODE, OUTPUT_NODE], regularizer)
	b2 = get_bias([OUTPUT_NODE])
	y = tf.matmul(y1, w2) + b2	
	return y

mnist_backward.py

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

STEPS = 50000
BATCH_SIZE = 200
LEARNING_RATE_BASE = 0.1
LEARNING_RATE_DECAY = 0.99
REGULIZER = 0.0001
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = "./model/"
MODEL_NAME = "mnist_model"

def backward(mnist):
	x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
	y_ = tf.placeholder(tf.float32, [None, mnist_forward.OUTPUT_NODE])
	y = mnist_forward.forward(x, REGULIZER)
	global_step = tf.Variable(0, trainable = False)

	ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_,-1))
	cem = tf.reduce_mean(ce)
	loss = cem + tf.add_n(tf.get_collection('losses'))

	learning_rate = tf.train.exponential_decay(
		LEARNING_RATE_BASE,
		global_step,
		mnist.train.num_examples / BATCH_SIZE,
		LEARNING_RATE_DECAY,
		staircase = True
		)

	train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
	ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
	ema_op = ema.apply(tf.trainable_variables())

	with tf.control_dependencies([train_step, ema_op]):
		train_op = tf.no_op(name = 'train')

	saver = tf.train.Saver()

	with tf.Session() as sess:
		init_op = tf.global_variables_initializer()
		sess.run(init_op)

		ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
		if ckpt and ckpt.model_checkpoint_path:
			saver.restore(sess, ckpt.model_checkpoint_path)

		for i in range(STEPS):
			xs,ys = mnist.train.next_batch(BATCH_SIZE)
			_,loss_value,step = sess.run([train_op, loss, global_step], feed_dict={x:xs, y_:ys})
			if i % 1000 == 0:
				print "after %d training steps, loss on training batch is %g" % (step, loss_value)
				saver.save(sess, os.path.join(MODEL_SAVE_PATH,MODEL_NAME), global_step=global_step)	

def main():
	mnist = input_data.read_data_sets("./data/", one_hot=True)
	backward(mnist)

if __name__ == '__main__':
	main()

mnist_test.py

#coding utf-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_forward
import mnist_backward
TEST_INTERVAL_SECS = 5

def test(mnist):
	with tf.Graph().as_default() as g:
		x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
		y_ = tf.placeholder(tf.float32, [None, mnist_forward.OUTPUT_NODE])
		y = mnist_forward.forward(x, None)

		ema = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY)
		ema_restore = ema.variables_to_restore()
		saver = tf.train.Saver(ema_restore)

		correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
		accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

		while True:
			with tf.Session() as sess:
				ckpt = tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH)
				if ckpt and ckpt.model_checkpoint_path:
					saver.restore(sess, ckpt.model_checkpoint_path)
					global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
					accuracy_score = sess.run(accuracy, feed_dict={x:mnist.test.images, y_:mnist.test.labels})
					print "after %s training steps, test accuracy = %g" % (global_step, accuracy_score)
				else:
					print "no checkpoint file found"
					return
			time.sleep(TEST_INTERVAL_SECS)

def main():
	mnist = input_data.read_data_sets("./data/", one_hot=True)
	test(mnist)

if __name__ == '__main__':
	main()

代码解析

mnist_forward.py

INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500

在前向传播过程中，规定网络输入结点为 784 个(代表每张输入图片的像素个数)，隐藏层节点 500 个(一层)，输出节点 10 个(表示输出为数字 0-9的十分类)

w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))

参数满足截断正态分布

if regularizer != None:
	tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))

使用正则化,将每个参数的正则化损失加到总损失中

mnist_backward.py

ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_,-1))
	cem = tf.reduce_mean(ce)
	loss = cem + tf.add_n(tf.get_collection('losses'))

softmax()函数与交叉熵一起使用，再加上正则化损失以此计算得到总的偏差值

learning_rate = tf.train.exponential_decay(
		LEARNING_RATE_BASE,
		global_step,
		mnist.train.num_examples / BATCH_SIZE,
		LEARNING_RATE_DECAY,
		staircase = True
		)

使用指数衰减学习率使模型在训练的前期快速收敛接近较优解

train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

使用梯度下降算法

ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
	ema_op = ema.apply(tf.trainable_variables())

	with tf.control_dependencies([train_step, ema_op]):
		train_op = tf.no_op(name = 'train')

在模型训练时引入滑动平均使模型在测试数据上表现的更加健壮

saver = tf.train.Saver()

ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
		if ckpt and ckpt.model_checkpoint_path:
			saver.restore(sess, ckpt.model_checkpoint_path)

saver.save(sess, os.path.join(MODEL_SAVE_PATH,MODEL_NAME), global_step=global_step)

断点续训，因为训练时间比较长，万一断电了呢。。。为了使得之前训练好的参数（w,b）能够保留下来，调用了saver进行保存和恢复

mnist_test.py

TEST_INTERVAL_SECS = 5

time.sleep(TEST_INTERVAL_SECS)

每隔五秒钟测试一次

ema = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGES_DECAY)
		ema_restore = ema.variables_to_restore()
		saver = tf.train.Saver(ema_restore)

实例化具有滑动平均的 saver 对象，从而在会话被加载时，模型中的所有参数被赋值为各自的滑动平均值，增强模型的稳定性

操作步骤

可能你已经注意到了，mnist_forward.py中并没有main函数（说明这个文件只是一个模块用来被其他文件调用，本例中是mnist_backward.py调用了mnist_forward.py），而另两个文件都有main函数。

终端输入python mnist_backward.py，开始训练，会显示实时的损失值，如：after 15005 training steps, loss on training batch is 0.159981
再开一个终端，输入python mnist_test.py，进行同步的测试（每五秒一次），会显示实时的损失值，如：after 15005 training steps, test accuracy = 0.979

实验结果

代码里设置的总循环数是50000次，但是我只跑到15000次（毕竟时间比较久），但已经能有98%的准确率了，如果全跑完的话，预计能达到99%以上。

本文参与腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。

原始发表：2018-09-03，如有侵权请联系 cloudcommunity@tencent.com 删除

tensorflow

python

腾讯云测试服务

本文分享自作者个人站点/博客前往查看

如有侵权，请联系 cloudcommunity@tencent.com 删除。

本文参与腾讯云自媒体同步曝光计划，欢迎热爱写作的你一起参与！

tensorflow

python

腾讯云测试服务

登录后参与评论

0 条评论

热度