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   源码介绍

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利用mnist数据集做深度学习小实例

# -*- coding: utf-8 -*-
"""
Created on Fri Apr 20 09:03:09 2018

@author: Administrator
"""

import tensorflow as tf 
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import os 
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

#对一个张量进行全面的汇总(均值，标准差，最大最小值，直方图)
# (用于 TensorBoard 可视化).
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
            tf.summary.scalar('stddev', stddev)
            tf.summary.scalar('max', tf.reduce_max(var))
            tf.summary.scalar('min', tf.reduce_min(var))
            tf.summary.histogram('histogram', var)

print('~~~~~~~~~~开始设计计算图~~~~~~~~')
# 告诉TensorFlow模型将会被构建在默认的Graph上.
with tf.Graph().as_default():
    
    #定义输入节点
    with tf.name_scope('DataInput'):
        X = tf.placeholder(tf.float32, shape=[None, 784], name='X')
        Y_true = tf.placeholder(tf.float32, shape=[None, 10], name='Y_true')
    
    #将数据转换为图片    
    with tf.name_scope('input_reshape'):
        image_shaped_input = tf.reshape(X, [-1, 28, 28, 1])
        tf.summary.image('input', image_shaped_input, 10)
        
    #定义前项输入过程
    with tf.name_scope('Interface'):
        W = tf.Variable(tf.zeros([784,10]), name='Wights')
        variable_summaries(W)
        b = tf.Variable(tf.zeros([10]), name='Bias')
        variable_summaries(b)
        Y_pred = tf.add(tf.matmul(X, W), b)
        tf.summary.histogram('pred', Y_pred)
        
    #定义损失节点
    with tf.name_scope('Loss'):
        #使用Softmax将前项预测结果转换为概率
        with tf.name_scope('Softmax'):
            Y_pred_prob = tf.nn.softmax(logits=Y_pred)
            tf.summary.histogram('pred_prob', Y_pred_prob)
        #使用交叉熵损失
        with tf.name_scope('cross_entropy'):
            TrainLoss = tf.reduce_mean(-tf.reduce_sum(Y_true*tf.log(Y_pred_prob),axis=1))
        
    tf.summary.scalar('cross_entropy', TrainLoss)

    #定义训练节点
    with tf.name_scope('Train'):
        # Optimizer: 创建一个梯度下降优化器
        Optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
        # Train: 定义训练节点将梯度下降法应用于Loss
        TrainStep = Optimizer.minimize(TrainLoss)

 #Evaluate: 定义评估节点
    with tf.name_scope('Evaluate'):
        correct_prediction = tf.equal(tf.argmax(Y_pred_prob, 1), tf.argmax(Y_true, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.summary.scalar('accuracy', accuracy)
    
    #整合所有节点，写入事件文件
    merged = tf.summary.merge_all()

    # Initial:添加所有Variable类型的变量的初始化节点
    InitOp = tf.global_variables_initializer()

    print('把计算图写入事件文件，在TensorBoard里面查看')
    train_writer = tf.summary.FileWriter(logdir='logs/mnist_softmax', graph=tf.get_default_graph())
    #writer.close()

    #训练过程
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)#获取数据
    #启动会化
    sess = tf.Session()
    sess.run(InitOp)#初始化所有变量W,b
    # 开始按批次训练，总共训练1000个批次，每个批次100个样本
    for i in range(1000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        summary, _, train_loss= sess.run([merged, TrainStep, TrainLoss],
                                feed_dict={X:batch_xs, Y_true: batch_ys})
        #print("train step: ", i, ", train_loss: ", train_loss)
        train_writer.add_summary(summary, i)
        
        #对模型进行评估
        accuracy_score = sess.run(accuracy,feed_dict={X: mnist.test.images,
                                                        Y_true: mnist.test.labels})
        #print("模型准确率：", accuracy_score)
    
    train_writer.close()