4用于cifar10的卷积神经网络-4.4/4.5cifar10数据集读取和数据增强扩充(上/下)

4用于cifar10的卷积神经网络-4.4/4.5cifar10数据集读取和数据增强扩充(上/下)
参考：
https://github.com/tensorflow
http://www.cs.toronto.edu/~kriz/cifar.html
http://tensorflow.org/tutorials/deep_cnn/
https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10

#-*- coding:utf-8 -*-
#实现简单卷积神经网络对MNIST数据集进行分类：conv2d + activation + pool + fc
import csv
import tensorflow as tf
import os
from tensorflow.examples.tutorials.mnist import input_data
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import sys
from six.moves import urllib
import tarfile
import cifar10_input

# 设置算法超参数
learning_rate_init = 0.001
training_epochs = 1
batch_size = 100
display_step = 10
# Network Parameters
n_input = 784 # MNIST data input (img shape: 28*28)
# n_classes = 10 # MNIST total classes (0-9 digits)

#数据集中输入图像的参数
dataset_dir='../CIFAR10_dataset'
image_size = 24
image_channel = 3
n_classes = 10 #CiFar10中类的数量

#从网址下载数据集存放到data_dir指定的目录中
def maybe_download_and_extract(data_dir):
    """下载并解压缩数据集 from Alex's website."""
    dest_directory = data_dir
    DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'
    if not os.path.exists(dest_directory):
        os.makedirs(dest_directory)
    filename = DATA_URL.split('/')[-1]
    filepath = os.path.join(dest_directory, filename)
    if not os.path.exists(filepath):
        def _progress(count, block_size, total_size):
            sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename,
                float(count * block_size) / float(total_size) * 100.0))
            sys.stdout.flush()
        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
        print()
        statinfo = os.stat(filepath)
        print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')

    extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin')
    if not os.path.exists(extracted_dir_path):
        tarfile.open(filepath, 'r:gz').extractall(dest_directory)

def get_distorted_train_batch(data_dir,batch_size):
    """Construct distorted input for CIFAR training using the Reader ops.

      Returns:
        images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
        labels: Labels. 1D tensor of [batch_size] size.

      Raises:
        ValueError: If no data_dir
      """
    if not data_dir:
        raise ValueError('Please supply a data_dir')
    data_dir = os.path.join(data_dir, 'cifar-10-batches-bin')
    images, labels = cifar10_input.distorted_inputs(data_dir=data_dir,batch_size=batch_size)
    return images,labels

def get_undistorted_eval_batch(data_dir,eval_data, batch_size):
    """Construct input for CIFAR evaluation using the Reader ops.
    Args:
        eval_data: bool, indicating if one should use the train or eval data set.
    Returns:
        images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
        labels: Labels. 1D tensor of [batch_size] size.
    Raises:
        ValueError: If no data_dir
    """
    if not data_dir:
        raise ValueError('Please supply a data_dir')
    data_dir = os.path.join(data_dir, 'cifar-10-batches-bin')
    images, labels = cifar10_input.inputs(eval_data=eval_data,data_dir=data_dir,batch_size=batch_size)
    return images,labels

#根据指定的维数返回初始化好的指定名称的权重 Variable
def WeightsVariable(shape, name_str, stddev=0.1):
    # initial = tf.random_normal(shape=shape, stddev=stddev, dtype=tf.float32)
    initial = tf.truncated_normal(shape=shape, stddev=stddev, dtype=tf.float32)
    return tf.Variable(initial, dtype=tf.float32, name=name_str)

#根据指定的维数返回初始化好的指定名称的偏置 Variable
def BiasesVariable(shape, name_str, init_value=0.00001):
    initial = tf.constant(init_value, shape=shape)
    return tf.Variable(initial, dtype=tf.float32, name=name_str)

# 二维卷积层activation(conv2d+bias)的封装
def Conv2d(x, W, b, stride=1, padding='SAME',activation=tf.nn.relu,act_name='relu'):
    with tf.name_scope('conv2d_bias'):
        y = tf.nn.conv2d(x, W, strides=[1, stride, stride, 1], padding=padding)
        y = tf.nn.bias_add(y, b)
    with tf.name_scope(act_name):
        y = activation(y)
    return y

# 二维池化层pool的封装
def Pool2d(x, pool= tf.nn.max_pool, k=2, stride=2,padding='SAME'):
    return pool(x, ksize=[1, k, k, 1], strides=[1, stride, stride, 1], padding=padding)

# 全连接层activate(wx+b)的封装
def FullyConnected(x, W, b, activate=tf.nn.relu, act_name='relu'):
    with tf.name_scope('Wx_b'):
        y = tf.matmul(x, W)
        y = tf.add(y, b)
    with tf.name_scope(act_name):
        y = activate(y)
    return y

def Inference(image_holder):
    # 第一个卷积层activate(conv2d + biase)
    with tf.name_scope('Conv2d_1'):
        conv1_kernels_num = 64
        weights = WeightsVariable(shape=[5, 5, image_channel, conv1_kernels_num],
                                  name_str='weights',stddev=5e-2)
        biases = BiasesVariable(shape=[conv1_kernels_num], name_str='biases',init_value=0.0)
        conv1_out = Conv2d(image_holder, weights, biases, stride=1, padding='SAME')

    # 第一个池化层(pool 2d)
    with tf.name_scope('Pool2d_1'):
        pool1_out = Pool2d(conv1_out, pool=tf.nn.max_pool, k=3, stride=2,padding='SAME')

    # 第二个卷积层activate(conv2d + biase)
    with tf.name_scope('Conv2d_2'):
        conv2_kernels_num = 64
        weights = WeightsVariable(shape=[5, 5, conv1_kernels_num, conv2_kernels_num],
                                  name_str='weights', stddev=5e-2)
        biases = BiasesVariable(shape=[conv2_kernels_num], name_str='biases', init_value=0.0)
        conv2_out = Conv2d(pool1_out, weights, biases, stride=1, padding='SAME')

    # 第二个池化层(pool 2d)
    with tf.name_scope('Pool2d_2'):
        pool2_out = Pool2d(conv2_out, pool=tf.nn.max_pool, k=3, stride=2, padding='SAME')

    #将二维特征图变换为一维特征向量
    with tf.name_scope('FeatsReshape'):
        features = tf.reshape(pool2_out, [batch_size,-1])
        feats_dim = features.get_shape()[1].value

    # 第一个全连接层(fully connected layer)
    with tf.name_scope('FC1_nonlinear'):
        fc1_units_num = 384
        weights = WeightsVariable(shape=[feats_dim, fc1_units_num],
                                  name_str='weights',stddev=4e-2)
        biases = BiasesVariable(shape=[fc1_units_num], name_str='biases',init_value=0.1)
        fc1_out = FullyConnected(features, weights, biases, activate=tf.nn.relu, act_name='relu')

    # 第二个全连接层(fully connected layer)
    with tf.name_scope('FC2_nonlinear'):
        fc2_units_num = 192
        weights = WeightsVariable(shape=[fc1_units_num, fc2_units_num],
                                  name_str='weights',stddev=4e-2)
        biases = BiasesVariable(shape=[fc2_units_num], name_str='biases',init_value=0.1)
        fc2_out = FullyConnected(fc1_out, weights, biases,activate=tf.nn.relu, act_name='relu')

    # 第三个全连接层(fully connected layer)
    with tf.name_scope('FC3_linear'):
        fc3_units_num = n_classes
        weights = WeightsVariable(shape=[fc2_units_num, fc3_units_num],
                                  name_str='weights',stddev=1.0/fc2_units_num)
        biases = BiasesVariable(shape=[fc3_units_num], name_str='biases',init_value=0.0)
        logits = FullyConnected(fc2_out, weights, biases,activate=tf.identity, act_name='linear')
    return logits

def TrainModel():
    #调用上面写的函数构造计算图
    with tf.Graph().as_default():

        # 计算图输入
        with tf.name_scope('Inputs'):
            image_holder = tf.placeholder(tf.float32, [batch_size, image_size,image_size,image_channel], name='images')
            labels_holder = tf.placeholder(tf.int32, [batch_size], name='labels')

        # 计算图前向推断过程
        with tf.name_scope('Inference'):
             logits = Inference(image_holder)

        # 定义损失层(loss layer)
        with tf.name_scope('Loss'):
            cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels_holder,logits=logits)
            cross_entropy_mean = tf.reduce_mean(cross_entropy)
            total_loss = cross_entropy_mean

        # 定义优化训练层(train layer)
        with tf.name_scope('Train'):
            learning_rate = tf.placeholder(tf.float32)
            global_step = tf.Variable(0, name='global_step', trainable=False, dtype=tf.int64)
            optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)
            trainer_op = optimizer.minimize(total_loss,global_step=global_step)

        # 定义模型评估层(evaluate layer)
        with tf.name_scope('Evaluate'):
            top_K_op = tf.nn.in_top_k(predictions=logits,targets=labels_holder,k=1)

        #定义获取训练样本批次的计算节点
        with tf.name_scope('GetTrainBatch'):
            image_train,labels_train = get_distorted_train_batch(data_dir=dataset_dir,batch_size=batch_size)

        # 定义获取测试样本批次的计算节点
        with tf.name_scope('GetTestBatch'):
            image_test, labels_test = get_undistorted_eval_batch(eval_data=True,data_dir=dataset_dir, batch_size=batch_size)

        # 添加所有变量的初始化节点
        init_op = tf.global_variables_initializer()

        print('把计算图写入事件文件，在TensorBoard里面查看')
        graph_writer = tf.summary.FileWriter(logdir='logs', graph=tf.get_default_graph())
        graph_writer.close()




def main(argv=None):
    maybe_download_and_extract(data_dir=dataset_dir)
    train_dir='train/'
    if tf.gfile.Exists(train_dir):
        tf.gfile.DeleteRecursively(train_dir)
    tf.gfile.MakeDirs(train_dir)
    TrainModel()

if __name__ =='__main__':
    tf.app.run()

计算图：