TensorFlow-- Chapter06 MNIST手写数字识别

举报
北山啦 发表于 2021/04/22 23:23:22 2021/04/22
【摘要】 TensorFlow-- Chapter06 MNIST手写数字识别,tensorboard的使用。

TensorFlow-- Chapter06 MNIST手写数字识别

TensorFlow-- Chapter06 MNIST手写数字识别,tensorboard的使用。
作者:北山啦

在这里插入图片描述

理论部分

MNIST手写数字识别数据集

>由于课程教了1.8版本的操作,所以在这里我会总结TensorFlow1.8版本的编程基础知识

其中包含了训练集 55000,验证集 5000,测试集 10000
在这里插入图片描述

数据集的划分

在这里插入图片描述

拆分数据

在这里插入图片描述

工作流程

在这里插入图片描述
在这里插入图片描述
在这里插入图片描述

新的工作流程

在这里插入图片描述

逻辑回归

在这里插入图片描述

Sigmod函数

在这里插入图片描述

损失函数

在这里插入图片描述
在这里插入图片描述

在这里插入图片描述

多元分类

softmax思想
在这里插入图片描述
在这里插入图片描述

实战代码

import warnings
warnings.filterwarnings("ignore")
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets(r".\data\MNIST_data", one_hot=True)
mnist[0]
<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet at 0x23dfc542a20>
print(mnist.train.num_examples)
mnist.test.num_examples

55000





10000
mnist.train.labels
array([[0., 0., 0., ..., 1., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 1., 0.]])
print(mnist.train.images.shape)
mnist.test.images.shape
(55000, 784)





(10000, 784)
mnist.train.images[0]
array([0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.3803922 , 0.37647063, 0.3019608 ,
       0.46274513, 0.2392157 , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.3529412 , 0.5411765 , 0.9215687 ,
       0.9215687 , 0.9215687 , 0.9215687 , 0.9215687 , 0.9215687 ,
       0.9843138 , 0.9843138 , 0.9725491 , 0.9960785 , 0.9607844 ,
       0.9215687 , 0.74509805, 0.08235294, 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.54901963,
       0.9843138 , 0.9960785 , 0.9960785 , 0.9960785 , 0.9960785 ,
       0.9960785 , 0.9960785 , 0.9960785 , 0.9960785 , 0.9960785 ,
       0.9960785 , 0.9960785 , 0.9960785 , 0.9960785 , 0.9960785 ,
       0.7411765 , 0.09019608, 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.8862746 , 0.9960785 , 0.81568635,
       0.7803922 , 0.7803922 , 0.7803922 , 0.7803922 , 0.54509807,
       0.2392157 , 0.2392157 , 0.2392157 , 0.2392157 , 0.2392157 ,
       0.5019608 , 0.8705883 , 0.9960785 , 0.9960785 , 0.7411765 ,
       0.08235294, 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.14901961, 0.32156864, 0.0509804 , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.13333334,
       0.8352942 , 0.9960785 , 0.9960785 , 0.45098042, 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.32941177, 0.9960785 ,
       0.9960785 , 0.9176471 , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.32941177, 0.9960785 , 0.9960785 , 0.9176471 ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.4156863 , 0.6156863 ,
       0.9960785 , 0.9960785 , 0.95294124, 0.20000002, 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.09803922, 0.45882356, 0.8941177 , 0.8941177 ,
       0.8941177 , 0.9921569 , 0.9960785 , 0.9960785 , 0.9960785 ,
       0.9960785 , 0.94117653, 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.26666668, 0.4666667 , 0.86274517,
       0.9960785 , 0.9960785 , 0.9960785 , 0.9960785 , 0.9960785 ,
       0.9960785 , 0.9960785 , 0.9960785 , 0.9960785 , 0.5568628 ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.14509805, 0.73333335,
       0.9921569 , 0.9960785 , 0.9960785 , 0.9960785 , 0.8745099 ,
       0.8078432 , 0.8078432 , 0.29411766, 0.26666668, 0.8431373 ,
       0.9960785 , 0.9960785 , 0.45882356, 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.4431373 , 0.8588236 , 0.9960785 , 0.9490197 , 0.89019614,
       0.45098042, 0.34901962, 0.12156864, 0.        , 0.        ,
       0.        , 0.        , 0.7843138 , 0.9960785 , 0.9450981 ,
       0.16078432, 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.6627451 , 0.9960785 ,
       0.6901961 , 0.24313727, 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.18823531,
       0.9058824 , 0.9960785 , 0.9176471 , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.07058824, 0.48627454, 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.32941177, 0.9960785 , 0.9960785 ,
       0.6509804 , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.54509807, 0.9960785 , 0.9333334 , 0.22352943, 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.8235295 , 0.9803922 , 0.9960785 ,
       0.65882355, 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.9490197 , 0.9960785 , 0.93725497, 0.22352943, 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.34901962, 0.9843138 , 0.9450981 ,
       0.3372549 , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.01960784,
       0.8078432 , 0.96470594, 0.6156863 , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.01568628, 0.45882356, 0.27058825,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        ], dtype=float32)

可视化

import matplotlib.pyplot as plt

def plot_image(image):
    plt.imshow(image.reshape(28, 28), cmap = 'binary')
    plt.show()
plot_image(mnist.train.images[288])

在这里插入图片描述

x = tf.placeholder(tf.float32, [None, 784], name= "X")
y = tf.placeholder(tf.float32, [None, 10], name= "Y")
H1_NN = 256
W1 = tf.Variable(tf.random_normal([784, H1_NN]))
b1 = tf.Variable(tf.zeros([H1_NN]))

Y1 = tf.nn.relu(tf.matmul(x, W1) + b1)
W2 = tf.Variable(tf.random_normal([H1_NN, 10]))
b2 = tf.Variable(tf.zeros([10]))

forward = tf.matmul(Y1, W2) + b2
pred = tf.nn.softmax(forward)
loss_fuction = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=forward ,labels=y))

WARNING:tensorflow:From <ipython-input-13-1127016930ab>:1: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See @{tf.nn.softmax_cross_entropy_with_logits_v2}.
train_epochs = 40
batch_size = 50
total_batch = int(mnist.train.num_examples/batch_size)
display_step = 1
learning_rate = 0.01
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss_fuction)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(pred, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(pred, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
image_shaped_input = tf.reshape(x,[-1,28,28,1])
tf.summary.image('input',image_shaped_input,10)
tf.summary.histogram('forward',forward)
tf.summary.scalar('loss',loss_fuction)
tf.summary.scalar('accuracy',accuracy)
<tf.Tensor 'accuracy:0' shape=() dtype=string>
from time import time
startTime = time()

sess = tf.Session()
sess.run(tf.global_variables_initializer())
merged_summary_op = tf.summary.merge_all()
writer = tf.summary.FileWriter('log/',sess.graph)

for epoch in range(train_epochs):
    for batch in range(total_batch):
        xs, ys = mnist.train.next_batch(batch_size)
        sess.run(optimizer, feed_dict={x: xs, y: ys})
        summary_str = sess.run(merged_summary_op,feed_dict={x:xs,y:ys})
        writer.add_summary(summary_str,epoch)
    loss,acc = sess.run([loss_fuction,accuracy], feed_dict={x: mnist.validation.images,\
                                                            y: mnist.validation.labels})
    
    if (epoch+1) % display_step == 0:
        print("Train Epoch", '%02d' % (epoch+1), \
             "Loss=", "{:.9f}".format(loss), "Accuracy=", "{:.4f}".format(acc))
        
duration = time() - startTime
print("Train Finished takes:" "{:.2f}".format(duration))
Train Epoch 01 Loss= 1.259438753 Accuracy= 0.9368
Train Epoch 02 Loss= 0.717698812 Accuracy= 0.9446
Train Epoch 03 Loss= 0.575311124 Accuracy= 0.9472
Train Epoch 04 Loss= 0.448238075 Accuracy= 0.9552
Train Epoch 05 Loss= 0.413602978 Accuracy= 0.9506
Train Epoch 06 Loss= 0.428873390 Accuracy= 0.9518
Train Epoch 07 Loss= 0.398006409 Accuracy= 0.9592
Train Epoch 08 Loss= 0.290548950 Accuracy= 0.9694
Train Epoch 09 Loss= 0.370046228 Accuracy= 0.9640
Train Epoch 10 Loss= 0.360535949 Accuracy= 0.9634
Train Epoch 11 Loss= 0.458259851 Accuracy= 0.9576
Train Epoch 12 Loss= 0.346073866 Accuracy= 0.9626
Train Epoch 13 Loss= 0.486990929 Accuracy= 0.9626

TensorBoard可视化

#x = tf.placeholder(tf.float32,[None,784],name="X")
image_shaped_input = tf.reshape(x,[-1,28,28,1])
tf.summary.image("input",image_shaped_input,10)
tf.summary.histogram("forward",x)

将loss损失以标量显示

tf.summary.scalar("loss",loss)

将accruacy标准率以标量显示

tf.summary.scalar("accuracy",accuracy)

训练模型

sess = tf.Session()
sess.run(tf.global_variables_initializer())

合并所有的summary

merged_summary_op = tf.summary.merge_all()
writer = tf.summary.FileWriter("log/",tf.get_default_graph())

TensorBoard

tf.reset_default_graph()
for epoch in range(train_epochs):
    for batch in range(total_batch):
        xs,ys = mnist.train.next_batch(batch_size)
        sess.run(optimizer,feed_dict = {x:xs,y:ys})
        summay_str = sess.run(merged_summary_op,feed_dict={x:xs,y:ys})
        writer.add_summary(summary_str,eopch)
    loss,acc = sess.run([loss_function,accuracy],feed_dict={x:mnist.validation.images,
                                                           y:mnist.validation.lables})

利用TensorBoard可视化TensorFlow运行状态

  • TensorBoard是TensorFlow的可视化工具
  • 通过Tensor Flow程序运行过程中输出的日志文件可视化TensorFlow程序的运行状态
  • TensorBoard和TensorFlow程序跑在不同的进程中
    在这里插入图片描述

产生日志文件

  • ==tf.reset_default_graph()==:清除default graph和不断增加的节点

在这里插入图片描述

启动TensorBoard

  1. 在Anaconda Prompt中==进入日志存放的目录==
    image.png

  2. 运行TensorBoard
    将日志的地址只想程序日志输出的地址

tensorboard --logdir=D:\log

image.png

  1. 通过给定的网址,进入即可

TensorBoard常用API总结

在这里插入图片描述

到这里就结束了,如果对你有帮助,欢迎点赞关注评论,你的点赞对我很重要。作者:北山啦

【版权声明】本文为华为云社区用户原创内容,转载时必须标注文章的来源(华为云社区)、文章链接、文章作者等基本信息, 否则作者和本社区有权追究责任。如果您发现本社区中有涉嫌抄袭的内容,欢迎发送邮件进行举报,并提供相关证据,一经查实,本社区将立刻删除涉嫌侵权内容,举报邮箱: cloudbbs@huaweicloud.com
  • 点赞
  • 收藏
  • 关注作者

评论(0

0/1000
抱歉,系统识别当前为高风险访问,暂不支持该操作

全部回复

上滑加载中

设置昵称

在此一键设置昵称,即可参与社区互动!

*长度不超过10个汉字或20个英文字符,设置后3个月内不可修改。

*长度不超过10个汉字或20个英文字符,设置后3个月内不可修改。

举报
请填写举报理由
0/200