mnist 缩减版 练手 tensorflow python

标签：window   show   保存   normal   显示图片   amp   nis   保存图像   ast   

import os
os.environ[‘TF_CPP_MIN_LOG_LEVEL‘] = ‘3 ‘

import numpy as np
import cv2 as cv
import wx
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("C:\\Users\\HHQ\Desktop\\tangjun\\minist\\minist_data\\minist", one_hot=True)
import tensorflow as tf
x = tf.placeholder(dtype=tf.float32,shape=[None,784])
W = tf.Variable(tf.zeros([784,10]),dtype=tf.float32)
b = tf.Variable(tf.zeros([10]),tf.float32)
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder( dtype=tf.float32,shape=[None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))                                  #损失函数

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)  #优化器

#定义测试的准确率                                                          #ragmaax（）0表示按列，1表示按行，输出该列或行的最大值的下标值；equal（）表示相等返回值为True或False
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))             #执行测试样本的准确率（全部的样本），计算相等值，为bool值，则为1和0
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))         #将全部的bool型转换为float32类型，在求平均值


init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

for i in range(300):
    batch_xs, batch_ys = mnist.train.next_batch(50)
    if i % 10==0:
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
        retr =sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})
        print("step %d, training accuracy %g" % (i, retr))
    else:
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
retr1 = sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
# print(retr1)
#显示图片
check_img=cv.imread("C:\\Users\\HHQ\Desktop\\tangjun\\minist\\minist_data\\minist_jpg\\read\\read.jpg",0)  #测试数字保存图像
# cv.namedWindow(‘训练图片‘)
# cv.imshow(‘训练图片‘,a)
# cv.waitKey(0)
check_st=check_img.reshape(1,784)                                                         #将其转换所需要的数组
result=sess.run(y,feed_dict={x:check_st})
result_end=np.argmax(result)
#显示在界面上
app=wx.App()
win = wx.Frame(None,title = "图片预测分类显示", id=2,size=(410,335),pos=(0,0),name=‘nihao‘)
loadButton = wx.Button(win, label = str(result_end),pos = (0,0),size = (410,335))
font = wx.Font(150, wx.DEFAULT, wx.NORMAL, wx.NORMAL, False)
loadButton.SetFont(font)
win.Show()
app.MainLoop()

mnist 缩减版 练手 tensorflow python

标签：window   show   保存   normal   显示图片   amp   nis   保存图像   ast   

原文地址：https://www.cnblogs.com/tangjunjun/p/10914204.html