tensorflow识别自己手写数字
作者:juezhanangle 发布时间:2022-10-12 20:25:04
tensorflow作为google开源的项目,现在赶超了caffe,好像成为最受欢迎的深度学习框架。确实在编写的时候更能感受到代码的真实存在,这点和caffe不同,caffe通过编写配置文件进行网络的生成。环境tensorflow是0.10的版本,注意其他版本有的语句会有错误,这是tensorflow版本之间的兼容问题。
还需要安装PIL:pip install Pillow
图片的格式:
– 图像标准化,可安装在20×20像素的框内,同时保留其长宽比。
– 图片都集中在一个28×28的图像中。
– 像素以列为主进行排序。像素值0到255,0表示背景(白色),255表示前景(黑色)。
创建一个.png的文件,背景是白色的,手写的字体是黑色的,
下面是数据测试的代码,一个两层的卷积神经网,然后用save进行模型的保存。
# coding: UTF-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import input_data
'''''
得到数据
'''
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
training = mnist.train.images
trainlable = mnist.train.labels
testing = mnist.test.images
testlabel = mnist.test.labels
print ("MNIST loaded")
# 获取交互式的方式
sess = tf.InteractiveSession()
# 初始化变量
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
'''''
生成权重函数,其中shape是数据的形状
'''
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
'''''
生成偏执项 其中shape是数据形状
'''
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# 保存网络训练的参数
saver = tf.train.Saver()
sess.run(tf.initialize_all_variables())
for i in range(8000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print "step %d, training accuracy %g"%(i, train_accuracy)
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
save_path = saver.save(sess, "model_mnist.ckpt")
print("Model saved in life:", save_path)
print "test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
其中input_data.py如下代码,是进行mnist数据集的下载的:代码是由mnist数据集提供的官方下载的版本。
# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import tensorflow.python.platform
import numpy
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
def maybe_download(filename, work_directory):
"""Download the data from Yann's website, unless it's already here."""
if not os.path.exists(work_directory):
os.mkdir(work_directory)
filepath = os.path.join(work_directory, filename)
if not os.path.exists(filepath):
filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
statinfo = os.stat(filepath)
print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
return filepath
def _read32(bytestream):
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
def extract_images(filename):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
raise ValueError(
'Invalid magic number %d in MNIST image file: %s' %
(magic, filename))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data
def dense_to_one_hot(labels_dense, num_classes=10):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = numpy.arange(num_labels) * num_classes
labels_one_hot = numpy.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
def extract_labels(filename, one_hot=False):
"""Extract the labels into a 1D uint8 numpy array [index]."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
magic = _read32(bytestream)
if magic != 2049:
raise ValueError(
'Invalid magic number %d in MNIST label file: %s' %
(magic, filename))
num_items = _read32(bytestream)
buf = bytestream.read(num_items)
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
if one_hot:
return dense_to_one_hot(labels)
return labels
class DataSet(object):
def __init__(self, images, labels, fake_data=False, one_hot=False,
dtype=tf.float32):
"""Construct a DataSet.
one_hot arg is used only if fake_data is true. `dtype` can be either
`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
`[0, 1]`.
"""
dtype = tf.as_dtype(dtype).base_dtype
if dtype not in (tf.uint8, tf.float32):
raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
dtype)
if fake_data:
self._num_examples = 10000
self.one_hot = one_hot
else:
assert images.shape[0] == labels.shape[0], (
'images.shape: %s labels.shape: %s' % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
if dtype == tf.float32:
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0
@property
def images(self):
return self._images
@property
def labels(self):
return self._labels
@property
def num_examples(self):
return self._num_examples
@property
def epochs_completed(self):
return self._epochs_completed
def next_batch(self, batch_size, fake_data=False):
"""Return the next `batch_size` examples from this data set."""
if fake_data:
fake_image = [1] * 784
if self.one_hot:
fake_label = [1] + [0] * 9
else:
fake_label = 0
return [fake_image for _ in xrange(batch_size)], [
fake_label for _ in xrange(batch_size)]
start = self._index_in_epoch
self._index_in_epoch += batch_size
if self._index_in_epoch > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Shuffle the data
perm = numpy.arange(self._num_examples)
numpy.random.shuffle(perm)
self._images = self._images[perm]
self._labels = self._labels[perm]
# Start next epoch
start = 0
self._index_in_epoch = batch_size
assert batch_size <= self._num_examples
end = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
def read_data_sets(train_dir, fake_data=False, one_hot=False, dtype=tf.float32):
class DataSets(object):
pass
data_sets = DataSets()
if fake_data:
def fake():
return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
data_sets.train = fake()
data_sets.validation = fake()
data_sets.test = fake()
return data_sets
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
VALIDATION_SIZE = 5000
local_file = maybe_download(TRAIN_IMAGES, train_dir)
train_images = extract_images(local_file)
local_file = maybe_download(TRAIN_LABELS, train_dir)
train_labels = extract_labels(local_file, one_hot=one_hot)
local_file = maybe_download(TEST_IMAGES, train_dir)
test_images = extract_images(local_file)
local_file = maybe_download(TEST_LABELS, train_dir)
test_labels = extract_labels(local_file, one_hot=one_hot)
validation_images = train_images[:VALIDATION_SIZE]
validation_labels = train_labels[:VALIDATION_SIZE]
train_images = train_images[VALIDATION_SIZE:]
train_labels = train_labels[VALIDATION_SIZE:]
data_sets.train = DataSet(train_images, train_labels, dtype=dtype)
data_sets.validation = DataSet(validation_images, validation_labels,
dtype=dtype)
data_sets.test = DataSet(test_images, test_labels, dtype=dtype)
return data_sets
然后进行代码的测试:
# import modules
import sys
import tensorflow as tf
from PIL import Image, ImageFilter
def predictint(imvalue):
"""
This function returns the predicted integer.
The imput is the pixel values from the imageprepare() function.
"""
# Define the model (same as when creating the model file)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
init_op = tf.initialize_all_variables()
saver = tf.train.Saver()
"""
Load the model_mnist.ckpt file
file is stored in the same directory as this python script is started
Use the model to predict the integer. Integer is returend as list.
Based on the documentatoin at
https://www.tensorflow.org/versions/master/how_tos/variables/index.html
"""
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, "model_mnist.ckpt")
# print ("Model restored.")
prediction = tf.argmax(y_conv, 1)
return prediction.eval(feed_dict={x: [imvalue], keep_prob: 1.0}, session=sess)
def imageprepare(argv):
"""
This function returns the pixel values.
The imput is a png file location.
"""
im = Image.open(argv).convert('L')
width = float(im.size[0])
height = float(im.size[1])
newImage = Image.new('L', (28, 28), (255)) # creates white canvas of 28x28 pixels
if width > height: # check which dimension is bigger
# Width is bigger. Width becomes 20 pixels.
nheight = int(round((20.0 / width * height), 0)) # resize height according to ratio width
if (nheight == 0): # rare case but minimum is 1 pixel
nheigth = 1
# resize and sharpen
img = im.resize((20, nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
wtop = int(round(((28 - nheight) / 2), 0)) # caculate horizontal pozition
newImage.paste(img, (4, wtop)) # paste resized image on white canvas
else:
# Height is bigger. Heigth becomes 20 pixels.
nwidth = int(round((20.0 / height * width), 0)) # resize width according to ratio height
if (nwidth == 0): # rare case but minimum is 1 pixel
nwidth = 1
# resize and sharpen
img = im.resize((nwidth, 20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
wleft = int(round(((28 - nwidth) / 2), 0)) # caculate vertical pozition
newImage.paste(img, (wleft, 4)) # paste resized image on white canvas
# newImage.save("sample.png")
tv = list(newImage.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]
return tva
# print(tva)
def main(argv):
"""
Main function.
"""
imvalue = imageprepare(argv)
predint = predictint(imvalue)
print (predint[0]) # first value in list
if __name__ == "__main__":
main('2.png')
其中我用于测试的代码如下:
可以将图片另存到路径下面,然后进行测试。
(1)载入我的手写数字的图像。
(2)将图像转换为黑白(模式“L”)
(3)确定原始图像的尺寸是最大的
(4)调整图像的大小,使得最大尺寸(醚的高度及宽度)为20像素,并且以相同的比例最小化尺寸刻度。
(5)锐化图像。这会极大地强化结果。
(6)把图像粘贴在28×28像素的白色画布上。在最大的尺寸上从顶部或侧面居中图像4个像素。最大尺寸始终是20个像素和4 + 20 + 4 = 28,最小尺寸被定位在28和缩放的图像的新的大小之间差的一半。
(7)获取新的图像(画布+居中的图像)的像素值。
(8)归一化像素值到0和1之间的一个值(这也在TensorFlow MNIST教程中完成)。其中0是白色的,1是纯黑色。从步骤7得到的像素值是与之相反的,其中255是白色的,0黑色,所以数值必须反转。下述公式包括反转和规格化(255-X)* 1.0 / 255.0
来源:http://blog.csdn.net/juezhanangle/article/details/73018584
猜你喜欢
- 见以下两个文件:showimage.asp Change the HTTP header
- Python个人博客程序开发实例框架设计中,我们已经完成了 数据库设计、数据准备、模板架构、表单设计、视图函数设计、电子邮件支持 等总体设计
- FSO,正如UFO般令人激动、令人神往,当然更多的亦是让人欢喜让人忧。君不见某空间服务商广告:100MB空间只要60RMB/年,支持数据库,
- 1.普通的输出:print(str)#str是任意一个字符串,数字···2.格式化输出: print('1,2,%s,%d'
- 学习目的: 掌握ADO.NET打开SQL SERVER数据库的方法。 今天做个非常普通的例子,做一个用户登录框。主要是通过这个练习认识一下S
- 前言:macOS自带的Apache可以提供通过http://localhost:8081访问本地文件服务,那么python有没有类似功能的库
- 一、图像二值化1.效果2.源码import cv2import numpy as npimport matplotlib.pyplot as
- 最近用Python写了个 * ,需要部署到Linux环境的服务器上,由于之前本地开发时使用virtualenv,使用这个虚拟环境有个好处是项目
- 1.图片来源该图片来源于百度图片,如果侵权,请联系我删除!图片仅用于知识交流。2.读取图片并显示imread():读取图片;imshow()
- 1、一些准备工作 安装djangopip install django创建django项目进入项目代码存放目录执行命令:djang
- 建立一个数据库表维护规范在一个定期基础而非等到问题出现才实施数据库表的检查是一个好主意。应该考虑到建立一个预防性维护的时间表,以协助自动问题
- 在tensorflow中,默认指定占用所有的GPU,如需指定占用的GPU,可以在命令行中:export CUDA_VISIBLE_DEVIC
- 前言:Python smtplib 教程:展示了如何使用 smtplib 模块在 Python 中发送电子邮件。 要发送电子邮件,我们使用
- 一、前言如果你自己打印过东西,应该有过这种经历。如果用自己拍的图片,在手机上看感觉还是清晰可见,但是一打印出来就是漆黑一片。比如下面这两张图
- 本文实例讲述了Python简单获取网卡名称及其IP地址的方法。分享给大家供大家参考,具体如下:windows上想知道网卡和IP地址,可以使用
- 前言:以往看到我博客的小伙伴可能都知道,我的前言一般都是吐槽和讲废话环节,哈哈哈哈。今天难得休息,最近可真是太忙了,博主已经连续一年都在99
- 经过总结,Python创建多线程主要有如下两种方法:函数类接下来,我们就来揭开多线程的神秘面纱。1. 用函数创建多线程在Python3中,P
- 1.前期准备1.打开Terminal终端,执行以下命令,将项目所需要的依赖包,都记录到一个文件内备用。pip freeze >requ
- 最近在实习,boss给布置了一个python的小任务,学习过程中发现copy()和deepcopy()这对好 * 实在是有点过分,搞的博主就有
- 本文实例讲述了Python基于pillow判断图片完整性的方法。分享给大家供大家参考,具体如下:1、安装第三方库。pip install p