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。

 

 

 

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