经典手写数字mnist数据集识别

今天是我的第一篇博客，就从最基本的用神经网络识别手写数字mnist数据集开始。。。本博客资源来源于网络，为了提供给自己和刚开始接触机器学习和深度学习的同学参考一下，如有雷同请自行忽略。。。
以下三块程序是初学者可以学习用的，不包含图片预处理和可视化部分，采用CPU运算。
mnist_inference.py代码部分，主要定义了神经网络的结构参数和前向传播的过程。（先上传代码，后期会加上注释）

# -*- coding: utf-8 -*-
"""
Created on Mon Jul 10 11:36:35 2017

@author: cxl
"""

import tensorflow as tf
INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500

def get_weight_variable(shape,regularizer):
    weights = tf.get_variable("weights",shape,
        initializer = tf.truncated_normal_initializer(stddev=0.1))

    if regularizer != None:
        tf.add_to_collection('losses',regularizer(weights))
    return weights


def inference(input_tensor,regularizer):
    with tf.variable_scope('layer1'):
        weights =get_weight_variable([INPUT_NODE,LAYER1_NODE],regularizer)
        biases = tf.get_variable("biases",[LAYER1_NODE],
            initializer = tf.constant_initializer(0.0))
        layer1 = tf.nn.relu(tf.matmul(input_tensor,weights)+biases)

    with tf.variable_scope('layer2'):
        weights = get_weight_variable(
            [LAYER1_NODE,OUTPUT_NODE],regularizer)
        biases = tf.get_variable(
            "biases",[OUTPUT_NODE],initializer=tf.constant_initializer(0.0))
        layer2 = tf.matmul(layer1,weights)+biases
    return layer2

mnist_train.py代码部分，主要定义了神经网络的训练过程。

# -*- coding: utf-8 -*-
"""
Created on Mon Jul 10 15:45:22 2017

@author: cxl
"""

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

BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY=0.99
REGULARAZTION_RATE=0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY=0.99

MODEL_SAVE_PATH = "./path/to/model/"
MODEL_NAME = "model.ckpt"

def train(mnist):
    x=tf.placeholder(tf.float32,[None,mnist_inference.INPUT_NODE],
                     name='x-input')
    y_=tf.placeholder(tf.float32,[None,mnist_inference.OUTPUT_NODE],
                     name='y-input')

    regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
    y = mnist_inference.inference(x,regularizer)

    global_step = tf.Variable(0,trainable=False)
    variable_averages = tf.train.ExponentialMovingAverage(
        MOVING_AVERAGE_DECAY,global_step)
    variables_averages_op=variable_averages.apply(tf.trainable_variables())

    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=y,labels=tf.argmax(y_,1))
    cross_entropy_mean=tf.reduce_mean(cross_entropy)
    loss = cross_entropy_mean + 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)
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
    with tf.control_dependencies([train_step,variables_averages_op]):
        train_op = tf.no_op(name = 'train')


    saver = tf.train.Saver()
    with tf.Session() as sess:
        tf.initialize_all_variables().run()
        for i in range(TRAINING_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 step(s),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(argv = None):
    mnist = input_data.read_data_sets("/tmp/data",one_hot=True)    
    train(mnist)


if __name__=='__main__':
    tf.app.run()

mnist_eval.py代码部分，主要定义了神经网络的测试过程。

# -*- coding: utf-8 -*-
"""
Created on Mon Jul 10 23:29:34 2017

@author: cxl
"""

import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

import mnist_inference
import mnist_train

EVAL_INTERVAL_SECS = 10

def evaluate(mnist):
    #with tf.Graph().as_default() as g:
    x=tf.placeholder(tf.float32,[None,mnist_inference.INPUT_NODE],name = 'x-input')
    y_= tf.placeholder(tf.float32,[None,mnist_inference.OUTPUT_NODE],name = 'y-input')
    validate_feed = {x:mnist.validation.images,y_:mnist.validation.labels}

    y = mnist_inference.inference(x,None)
    correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

    variable_averages = tf.train.ExponentialMovingAverage(
        mnist_train.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)

    while True:
        with tf.Session() as sess:
            ckpt = tf.train.get_checkpoint_state(mnist_train.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 = validate_feed)
                print("After %s training step(s),validation"
                      "accuracy = %g" % (global_step,accuracy_score))
            else:
                print("No checkpoint file found")
                return
        time.sleep(EVAL_INTERVAL_SECS)


def main(argv = None):
    mnist = input_data.read_data_sets("/tmp/data",one_hot = True)
    evaluate(mnist)


if __name__ == '__main__':
    tf.app.run()

第一次写博客，以后会把我自己学习机器学习/深度学习的过程都写下来，供自己和有兴趣的没有基础的小伙伴们一起学习，一起进步，我以后也会不断提高自己的博客质量和代码水平的。。。