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实验内容
TensorFlow是由Google开发的用于数值计算的开源软件库,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。
免费在线实验地址:点击进入
实验资源:云服务器,没有云服务器的朋友推荐1折抢购:69元/年的阿里云服务器、或者88元/年的腾讯云服务器
软件环境Ubuntu 14.04 64 位
TensorFlow 实现基于 CNN 数字识别的代码
前期准备训练数据下载:
- 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
复制代码 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)
复制代码 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)
复制代码 然后执行:- cd /home/ubuntu;
- python train_mnist_model.py
复制代码训练的时间会较长,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
复制代码 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
复制代码 解压测试图片包解压 test_num.zip,其中 1-9.png 为 1-9 数字图片。 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])
复制代码 然后执行:
- cd /home/ubuntu;
- python predict_mnist_model.py 1.png
复制代码 执行结果:你可以修改 1.png 为 1-9.png 中任意一个 完成 |
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