# mnist 逻辑回归

import numpy as np

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf

# from PIL import Image

import matplotlib.image as mpimg

import matplotlib.pyplot as plt # plt 用于显示图片

from PIL import Image, ImageFilter

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)

from mnist import MNIST

mndata = MNIST(path='MNIST_data/',gz=True)

x = tf.placeholder(tf.float32, [None, 784]) # x定义为占位符，待计算图运行的时候才去读取数据图片

W = tf.Variable(tf.zeros([784, 10])) # 权重w初始化为0

b = tf.Variable(tf.zeros([10])) # b也初始化为0

y = tf.nn.softmax(tf.matmul(x, W) + b) # 创建线性模型

y_ = tf.placeholder(tf.float32, [None, 10]) # 图片的实际标签，为0~9的数字

# 使用Tensorflow自带的交叉熵函数

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

sess = tf.Session()

# 变量初始化

init = tf.global_variables_initializer()

sess.run(init)

saver = tf.train.Saver()

for i in range(1000):

batch_xs, batch_ys = mnist.train.next_batch(500)

sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

# save_path = saver.save(sess, "./mnistsoftmax/my_model_final.ckpt")

import cv2

images, labels = mndata.load_testing()

num = 8000

image = images[num]

label = labels[num]

# 打印图片

print(mndata.display(image))

print('这张图片的实际数字是: ' + str(label))

def inverse_color(image):

return img2

# 测试新图片，并输出预测值

# mp = mpimg.imread('./1.png')

im = Image.open('./1.png').convert('L')

# _im = mnist.train.images[1,:]

# print(_im.shape)

# _im = _im.reshape(28,28)

# print(im.getdata())

tv = list(im.getdata()) #get pixel values

#normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.

tva = [ (255-x)*1.0/255.0 for x in tv]

# print([tva].shape)

# img_gray = cv2.imread('./1.png')

# img_gray = cv2.cvtColor(img_gray, cv2.COLOR_BGR2GRAY)

# mp = tf.cast(mp, tf.float32)

# a = mp.reshape(1, 784)

# cv2.imshow('1',img_gray)

# plt.imshow(img_gray) # 显示图片

# plt.axis('off') # 不显示坐标轴

# plt.show()

# plt.figure()

# plt.imshow(_im)

# plt.show()

# print(mp.reshape(-1).shape)

print(np.array(image).reshape(1, 784).shape)

# print(img_gray)

y = tf.nn.softmax(y) # 为了打印出预测值，我们这里增加一步通过softmax函数处理后来输出一个向量

result = sess.run(y, feed_dict={x: [tva]}) # result是一个向量，通过索引来判断图片数字

print('预测值为：')

print(result)