TensorFlow - 基于 CNN 数字识别

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实验内容

本教程采用简单的卷积神经网络模型,模型为: 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。

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实验资源

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软件环境

Ubuntu 14.04 64 位

目录

# TensorFlow - 基于 CNN 数字识别 ## TensorFlow 实现基于 CNN 数字识别的代码 ### 前期准备 TensorFlow 相关 API 可以到在实验 [TensorFlow - 相关 API][https://cloud.tencent.com/developer/labs/lab/10187] 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][create-example] 目录下[创建源文件 mnist_model.py][create-example],内容可参考: > <locate for="create-example" path="/home/ubuntu" verb="create" hint="右击创建 mnist_model.py" /> ``` /// <example verb="create" file="/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) ``` > <checker type="output-contains" command="ls /home/ubuntu/mnist_model.py" hint="请创建并执行 /home/ubuntu/mnist_model.py"> > <keyword regex="/home/ubuntu/mnist_model.py" /> > </checker> ### 训练 CNN 模型 #### 示例代码: 现在您可以在 [/home/ubuntu][create-example] 目录下[创建源文件 train_mnist_model.py][create-example],内容可参考: > <locate for="create-example" path="/home/ubuntu" verb="create" hint="右击创建 train_mnist_model.py" /> ```python /// <example verb="create" file="/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 ``` > <checker type="output-contains" command="ls /home/ubuntu/train_mnist_model.py" hint="请创建并执行 /home/ubuntu/train_mnist_model.py"> > <keyword regex="/home/ubuntu/train_mnist_model.py" /> > </checker> > <checker type="output-contains" command="ls /home/ubuntu/mnist_cnn_model.ckpt.meta" hint="请执行 /home/ubuntu/train_mnist_model.py"> > <keyword regex="/home/ubuntu/mnist_cnn_model.ckpt.meta" /> > </checker> ### 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 ``` > <checker type="output-contains" command="ls /home/ubuntu/test_num.zip" hint="请下载 test_num.zip"> > <keyword regex="/home/ubuntu/test_num.zip" /> > </checker> #### 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 ``` > <checker type="output-contains" command="ls /home/ubuntu/1.png" hint="请解压 test_num.zip"> > <keyword regex="/home/ubuntu/1.png" /> > </checker> #### 实现 predict 代码 现在您可以在 [/home/ubuntu][create-example] 目录下[创建源文件 predict_mnist_model.py][create-example],内容可参考: > <locate for="create-example" path="/home/ubuntu" verb="create" hint="右击创建 predict_mnist_model.py" /> ```python /// <example verb="create" file="/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]) ``` > <checker type="output-contains" command="ls /home/ubuntu/predict_mnist_model.py" hint="请创建并执行 /home/ubuntu/predict_mnist_model.py"> > <keyword regex="/home/ubuntu/predict_mnist_model.py" /> > </checker> 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 ### 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][https://cloud.tencent.com/developer/labs/lab/10187] * [TensorFlow - 线性回归][https://cloud.tencent.com/developer/labs/lab/10191] 关于 TensorFlow 的更多资料可参考 [TensorFlow 官网 ][https://www.tensorflow.org/]。