深度学习之卷积神经网络(CNN)详解与代码实现（二）

用Tensorflow实现卷积神经网络(CNN)

本文系作者原创，转载请注明出处:https://www.cnblogs.com/further-further-further/p/10737065.html

目录

1.踩过的坑(tensorflow)

2.tensorboard

3.代码实现（python3.5）

4.运行结果以及分析

1.踩过的坑(tensorflow)

上一章CNN中各个算法都是纯手工实现的，可能存在一些难以发现的问题，这也是准确率不高的一个原因，这章主要利用tensorflow框架来实现卷积神经网络，数据源还是cifar（具体下载见上一章）

在利用tensorflow框架实现CNN时，需要注意以下几点：

1.输入数据定义时，x只是起到占位符的作用（看不到真实值，只是为了能够运行代码，获取相应的tensor节点，这一点跟我们之前代码流程完全相反， 真正数据流的执行在session会话里）

x：输入数据，y_： 标签数据，keep_prob: 概率因子，防止过拟合。

定义，且是全局变量。

x = tf.placeholder(tf.float32, [None, 3072], name='x') 

y_ = tf.placeholder(tf.float32, [None, 10], name='y_')

keep_prob = tf.placeholder(tf.float32)

后面在session里必须要初始化

sess.run(tf.global_variables_initializer())

在session run时必须要传得到该tensor节点含有参数值（x, y_, keep_prob）

train_accuracy = accuracy.eval(feed_dict={
                    x: batch[0], y_: batch[1], keep_prob: 1.0})

2.原始数据集标签要向量化；

例如cifar有10个类别，如果类别标签是 6 对应向量[0,0,0,0,0,1,0,0,0,0]

3.知道每一步操作的数据大小的变化，不然，报错的时候很难定位（个人认为这也是tensorflow的弊端，无法实时追踪定位）；

注意padding = 'SAME'和'VALID'的区别

padding = 'SAME' => Height_后 = Height_前/Strides 跟padding无关  向上取整

padding = 'VALID'=>  Height_后 = (Height_前 - Filter + 1)/Strides  向上取整

4.打印tensorboard流程图，可以直观看到每步操作数据大小的变化；

2. tensorboard

tensorboard就是一个数据结构流程图的可视化工具，通过tensorboard流程图，可以直观看到神经网络的每一步操作以及数据流的变化。

操作步骤：

1. 在session会话里加入如下代码，打印结果会在当前代码文件相同路径的tensorboard文件下，默认是

tf.summary.FileWriter("tensorboard/", sess.graph)

2. 在运行里输入cmd，然后输入（前提是安装好了tensorboard => pip install  tensorboard）

tensorboard --logdir=D:\Project\python\myProject\CNN\tensorflow\captchaIdentify\tensorboard --host=127.0.0.1

'D:\Project\python\myProject\CNN\tensorflow\captchaIdentify\tensorboard' 是我生成的tensorboard文件的绝对路径，你替换成你自己的就可以了。

正确运行后会显示 ‘Tensorboard at http://127.0.0.1:6006’，说明tensorboard服务已经起来了，在浏览器页面输入

http://127.0.0.1:6006即可显示流程图。

3.代码实现（python3.6）

代码逻辑实现相对比较简单，在一些重要逻辑实现上，我已做了注释，如果大家有什么疑义，可以留言给我，我们一起交流。

因为原始图片数据集太大，不好上传，大家可以直接在http://www.cs.toronto.edu/~kriz/cifar.html下载CIFAR-10 python version，

有163M，放在代码文件同路径下即可。

cifar放置路径

start.py

 # coding=utf-8
 # Disable linter warnings to maintain consistency with tutorial.
 # pylint: disable=invalid-name
 # pylint: disable=g-bad-import-order
 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
 '''
  卷积神经网络实现10类(airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)
  60000张图片的识别
  5000次，准确率有 58%；
  20000次，准确率有 68.89%；
  相比mnist数字图片识别准确度低，原因有：
  mnist训练图片是灰度图片，纹理简单，数字的可变性小，而cifar是彩色图片，纹理复杂，动物可变性大；
 '''
 try:
     from . import datesets
 except Exception:
     import datesets

 FLAGS = None

 def deepnn(x):
     with tf.name_scope('reshape'):
         x_image = tf.reshape(x, [-1, 32, 32, 3])
     ## 第一层卷积操作 ##
     with tf.name_scope('conv1'):
         W_conv1 = weight_variable([5, 5, 3, 32])
         b_conv1 = bias_variable([32])
         h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

     with tf.name_scope('pool1'):
         h_pool1 = max_pool_2x2(h_conv1)

     # Second convolutional layer -- maps 32 feature maps to 64.
     ## 第二层卷积操作 ##
     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)

     with tf.name_scope('pool2'):
         h_pool2 = max_pool_2x2(h_conv2)

     ## 第三层全连接操作 ##
     with tf.name_scope('fc1'):
         W_fc1 = weight_variable([8 * 8 * 64, 1024])
         b_fc1 = bias_variable([1024])
         h_pool2_flat = tf.reshape(h_pool2, [-1, 8 * 8 * 64])
         h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

     with tf.name_scope('dropout'):
         keep_prob = tf.placeholder(tf.float32)
         h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

     ## 第四层输出操作 ##
     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

 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')

 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 main(_):
     # Import data
     mnist = datesets.read_data_sets(train_dir = '.\\cifar-10-batches-py\\', one_hot=True)

     # Create the model
     # 声明一个占位符，None表示输入图片的数量不定，28*28图片分辨率
     x = tf.placeholder(tf.float32, [None, 3072], name='x')

     # 类别是0-9总共10个类别，对应输出分类结果
     y_ = tf.placeholder(tf.float32, [None, 10], name='y_')
     y_conv, keep_prob = deepnn(x)
     # 通过softmax-loss求交叉熵
     with tf.name_scope('loss'):
         cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)
     # 求均值
     cross_entropy = tf.reduce_mean(cross_entropy)
     # 计算梯度，更新参数值
     with tf.name_scope('adam_optimizer'):
         train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

     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)

    # graph_location = tempfile.mkdtemp()
    # print('Saving graph to: %s' % graph_location)
    # train_writer.add_graph(tf.get_default_graph())

     with tf.Session() as sess:
         # 打印流程图
         writer = tf.summary.FileWriter("tensorboard/", sess.graph)
         sess.run(tf.global_variables_initializer())
         for i in range(20000):
             batch = mnist.train.next_batch(50)
             if i % 1000 == 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})

         print('test accuracy %g' % accuracy.eval(feed_dict={
             x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

 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)

datasets.py

 import numpy
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import random_seed
 from six.moves import xrange
 from tensorflow.contrib.learn.python.learn.datasets import base
 import pickle
 import os

 class DataSet(object):
     """Container class for a dataset (deprecated).

     THIS CLASS IS DEPRECATED. See
     [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
     for general migration instructions.
     """
     def __init__(self,
                  images,
                  labels,
                  fake_data=False,
                  one_hot=False,
                  dtype=dtypes.float32,
                  reshape=True,
                  seed=None):
         """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]`.  Seed arg provides for convenient deterministic testing.
         """
         seed1, seed2 = random_seed.get_seed(seed)
         # If op level seed is not set, use whatever graph level seed is returned
         numpy.random.seed(seed1 if seed is None else seed2)
         dtype = dtypes.as_dtype(dtype).base_dtype
         if dtype not in (dtypes.uint8, dtypes.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)
             if reshape:
                 assert images.shape[3] == 3
                 images = images.reshape(images.shape[0],
                                         images.shape[1] * images.shape[2] * images.shape[3])
             if dtype == dtypes.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, shuffle=True):
         """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
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
             perm0 = numpy.arange(self._num_examples)
             numpy.random.shuffle(perm0)
             self._images = self.images[perm0]
             self._labels = self.labels[perm0]
         # Go to the next epoch
         if start + batch_size > self._num_examples:
             # Finished epoch
             self._epochs_completed += 1
             # Get the rest examples in this epoch
             rest_num_examples = self._num_examples - start
             images_rest_part = self._images[start:self._num_examples]
             labels_rest_part = self._labels[start:self._num_examples]
             # Shuffle the data
             if shuffle:
                 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 - rest_num_examples
             end = self._index_in_epoch
             images_new_part = self._images[start:end]
             labels_new_part = self._labels[start:end]
             return numpy.concatenate(
                 (images_rest_part, images_new_part), axis=0), numpy.concatenate(
                 (labels_rest_part, labels_new_part), axis=0)
         else:
             self._index_in_epoch += batch_size
             end = self._index_in_epoch
             return self._images[start:end], self._labels[start:end]

 def read_data_sets(train_dir,
                    one_hot=False,
                    dtype=dtypes.float32,
                    reshape=True,
                    validation_size=5000,
                    seed=None):

     train_images,train_labels,test_images,test_labels = load_CIFAR10(train_dir)
     if not 0 <= validation_size <= len(train_images):
         raise ValueError('Validation size should be between 0 and {}. Received: {}.'
                          .format(len(train_images), validation_size))

     validation_images = train_images[:validation_size]
     validation_labels = train_labels[:validation_size]
     validation_labels = dense_to_one_hot(validation_labels, 10)
     train_images = train_images[validation_size:]
     train_labels = train_labels[validation_size:]
     train_labels = dense_to_one_hot(train_labels, 10)

     test_labels = dense_to_one_hot(test_labels, 10)

     options = dict(dtype=dtype, reshape=reshape, seed=seed)
     train = DataSet(train_images, train_labels, **options)
     validation = DataSet(validation_images, validation_labels, **options)
     test = DataSet(test_images, test_labels, **options)

     return base.Datasets(train=train, validation=validation, test=test)

 def load_CIFAR_batch(filename):
     """ load single batch of cifar """
     with open(filename, 'rb') as f:
         datadict = pickle.load(f, encoding='bytes')
         X = datadict[b'data']
         Y = datadict[b'labels']
         X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")
         Y = numpy.array(Y)
         return X, Y

 def load_CIFAR10(ROOT):
     """ load all of cifar """
     xs = []
     ys = []
     for b in range(1,6):
         f = os.path.join(ROOT, 'data_batch_%d' % (b, ))
         X, Y = load_CIFAR_batch(f)
         xs.append(X)
         ys.append(Y)
     Xtr = numpy.concatenate(xs)
     Ytr = numpy.concatenate(ys)
     del X, Y
     Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
     return Xtr, Ytr, Xte, Yte

 def dense_to_one_hot(labels_dense, num_classes):
     """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

4.运行结果以及分析

这里选取55000张图片作为训练样本，测试样本选取5000张。

tensorboard可视流程图

运行5000次，测试准确率：58%

运行20000次，测试准确率：68.89%

运行40000次，测试准确率71.95%

分析：由最后一张图片可以看出，20000 - 30000次时测试准确率=> 70.27% ->71.44%，30000 - 40000次时=> 71.44% -> 71.95%

而训练准确率已经达到100%，说明测试准确率已经趋于一个稳定值，再增加训练次数，测试准确率提高的可能性不大。

如果想要继续提高测试准确率，就只能增加训练样本。

不要让懒惰占据你的大脑，不要让妥协拖垮了你的人生。青春就是一张票，能不能赶上时代的快车，你的步伐就掌握在你的脚下。