emmm……这周最后一天了

0x00.前言

仍然是腾讯云开发者实验室(beta),今天换个实验做:


0x01.引用

1.0 TensorFlow 实现基于 CNN 数字识别的代码

1.1 前期准备

TensorFlow相关API可以到在实验TensorFlow - 相关 API中学习。
唔,这就尴尬了,这节课我还没看呢……

1.2 CNN模型构建

现在您可以在/home/ubuntu目录下创建源文件mnist_model.py,内容可参考:

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#!/usr/bin/python
# -*- coding: utf-8 -*

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys
import tempfile

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None


def deepnn(x):

with tf.name_scope('reshape'):
x_image = tf.reshape(x, [-1, 28, 28, 1])

#第一层卷积层,卷积核为5*5,生成32个feature maps.
with tf.name_scope('conv1'):
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) #激活函数采用relu

# 第一层池化层,下采样2.
with tf.name_scope('pool1'):
h_pool1 = max_pool_2x2(h_conv1)

# 第二层卷积层,卷积核为5*5,生成64个feature maps
with tf.name_scope('conv2'):
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)#激活函数采用relu

# 第二层池化层,下采样2.
with tf.name_scope('pool2'):
h_pool2 = max_pool_2x2(h_conv2)

#第一层全连接层,将7x7x64个feature maps与1024个features全连接
with tf.name_scope('fc1'):
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)

#dropout层,训练时候随机让某些隐含层节点权重不工作
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 第二层全连接层,1024个features和10个features全连接
with tf.name_scope('fc2'):
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
return y_conv, keep_prob

#卷积
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

#池化
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
#权重
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)

#偏置
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)

1.3 训练 CNN 模型

现在您可以在/home/ubuntu目录下创建源文件train_mnist_model.py,内容可参考:

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#!/usr/bin/python
# -*- coding: utf-8 -*

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys
import tempfile

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

import mnist_model

FLAGS = None


def main(_):
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

#输入变量,mnist图片大小为28*28
x = tf.placeholder(tf.float32, [None, 784])

#输出变量,数字是1-10
y_ = tf.placeholder(tf.float32, [None, 10])

# 构建网络,输入—>第一层卷积—>第一层池化—>第二层卷积—>第二层池化—>第一层全连接—>第二层全连接
y_conv, keep_prob = mnist_model.deepnn(x)

#第一步对网络最后一层的输出做一个softmax,第二步将softmax输出和实际样本做一个交叉熵
#cross_entropy返回的是向量
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
logits=y_conv)

#求cross_entropy向量的平均值得到交叉熵
cross_entropy = tf.reduce_mean(cross_entropy)

#AdamOptimizer是Adam优化算法:一个寻找全局最优点的优化算法,引入二次方梯度校验
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

#在测试集上的精确度
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)

#将神经网络图模型保存本地,可以通过浏览器查看可视化网络结构
graph_location = tempfile.mkdtemp()
print('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())

#将训练的网络保存下来
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(5000):
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})#输入是字典,表示tensorflow被feed的值
print('step %d, training accuracy %g' % (i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

test_accuracy = 0
for i in range(200):
batch = mnist.test.next_batch(50)
test_accuracy += accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0}) / 200;

print('test accuracy %g' % test_accuracy)

save_path = saver.save(sess,"mnist_cnn_model.ckpt")

if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str,
default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

然后执行:
cd /home/ubuntu
python train_mnist_model.py
P.S.请无视;,原文给的……
训练的时间会较长,可以喝杯茶耐心等待。
喝茶,哈哈哈……看了下速度实在是太慢了,直接下一步吧……
执行结果:

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step 3600, training accuracy 0.98
step 3700, training accuracy 0.98
step 3800, training accuracy 0.96
step 3900, training accuracy 1
step 4000, training accuracy 0.98
step 4100, training accuracy 0.96
step 4200, training accuracy 1
step 4300, training accuracy 1
step 4400, training accuracy 0.98
step 4500, training accuracy 0.98
step 4600, training accuracy 0.98
step 4700, training accuracy 1
step 4800, training accuracy 0.98
step 4900, training accuracy 1
test accuracy 0.9862

1.4 测试 CNN 模型

下载测试图片
下载test_num.zip
cd /home/ubuntu
wget http://tensorflow-1253902462.cosgz.myqcloud.com/test_num.zip
解压测试图片包
解压test_num.zip,其中1-9.png!webp1-9数字图片。
unzip test_num.zip
实现predict代码
现在您可以在/home/ubuntu目录下创建源文件predict_mnist_model.py,内容可参考:

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#!/usr/bin/python
# -*- coding: utf-8 -*

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys
import tempfile

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

import mnist_model
from PIL import Image, ImageFilter

def load_data(argv):

grayimage = Image.open(argv).convert('L')
width = float(grayimage.size[0])
height = float(grayimage.size[1])
newImage = Image.new('L', (28, 28), (255))

if width > height:
nheight = int(round((20.0/width*height),0))
if (nheigth == 0):
nheigth = 1
img = grayimage.resize((20,nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
wtop = int(round(((28 - nheight)/2),0))
newImage.paste(img, (4, wtop))
else:
nwidth = int(round((20.0/height*width),0))
if (nwidth == 0):
nwidth = 1
img = grayimage.resize((nwidth,20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
wleft = int(round(((28 - nwidth)/2),0))
newImage.paste(img, (wleft, 4))

tv = list(newImage.getdata())
tva = [ (255-x)*1.0/255.0 for x in tv]
return tva

def main(argv):

imvalue = load_data(argv)

x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
y_conv, keep_prob = mnist_model.deepnn(x)

y_predict = tf.nn.softmax(y_conv)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, "mnist_cnn_model.ckpt")
prediction=tf.argmax(y_predict,1)
predint = prediction.eval(feed_dict={x: [imvalue],keep_prob: 1.0}, session=sess)
print (predint[0])

if __name__ == "__main__":
main(sys.argv[1])

然后执行:
cd /home/ubuntu
python predict_mnist_model.py 1.png!webp
执行结果:
1


你可以修改1.png!webp1-9.png!webp中任意一个
既然都这么说了,那我就全部试一下……

emmm……我要看下原图

0x02.后记

文件浏览器的刷新按钮好像坏掉了,实验做到最后文件也没改变……手动点下上层文件夹就好了
我感觉我又水了一篇文章……
未完待续……