TensorFlow 实现基于 CNN 数字识别的代码

任务时间：时间未知

前期准备

TensorFlow 相关 API 可以到在实验 TensorFlow – 相关 API 中学习。

训练数据下载：

wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/t10k-images-idx3-ubyte.gz
wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/t10k-labels-idx1-ubyte.gz
wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/train-images-idx3-ubyte.gz
wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/train-labels-idx1-ubyte.gz

CNN 模型构建

示例代码：

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

示例代码：/home/ubuntu/mnist_model.py

#!/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)

训练 CNN 模型

示例代码：

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：

示例代码：/home/ubuntu/train_mnist_model.py

#!/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='./',
                      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

训练的时间会较长，可以喝杯茶耐心等待。

执行结果：

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

测试 CNN 模型

下载测试图片

下载 test_num.zip

cd /home/ubuntu
wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/test_num.zip

解压测试图片包

解压 test_num.zip，其中 1-9.png 为 1-9 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：

示例代码：/home/ubuntu/predict_mnist_model.py

#!/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

执行结果：

1

你可以修改 1.png 为 1-9.png 中任意一个

完成

任务时间：时间未知

恭喜，您已完成本实验内容

您可进行更多 TensorFlow 的系列教程：

• TensorFlow – 相关 API
• TensorFlow – 线性回归

关于 TensorFlow 的更多资料可参考 TensorFlow 官网 。

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