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caffe CIFAR 10 database

（一）：CIFAR数据集:

Caffe 深度学习框架上手教程

image的个数：60000

image的大小：32*32*3

class的个数：10 （飞机；汽车；鸟；猫；鹿；狗；青蛙；马；船；卡车）

50000张用于训练，10000张用于测试。

（二）

train batch有5个每个里面有10000张图片

test batch有1个每一类1000张图片

batches.meta为class的名字

每个batch的有10000个data和相对应的label

label从0到9

有一个名为batches.meta.txt的文件。这是一个ASCII文件，将范围为0-9的数字标签映射到有意义的类名。它只是10个类名的列表，每行一个。行i上的类名称对应于数字标签i。

（三）

在caffe中自带的CIFAR数据集进行训练的prototxt和结构如下：

cifar10_quick_train_test.prototxt

name: "CIFAR10_quick"
	layer {
	name: "cifar"
	type: "Data"
	top: "data"
	top: "label"
	include {
	phase: TRAIN
	}
	transform_param {
	mean_file: "examples/cifar10/mean.binaryproto"
	}
	data_param {
	source: "examples/cifar10/cifar10_train_lmdb"
	batch_size: 100
	backend: LMDB
	}
	}
	layer {
	name: "cifar"
	type: "Data"
	top: "data"
	top: "label"
	include {
	phase: TEST
	}
	transform_param {
	mean_file: "examples/cifar10/mean.binaryproto"
	}
	data_param {
	source: "examples/cifar10/cifar10_test_lmdb"
	batch_size: 100
	backend: LMDB
	}
	}
	layer {
	name: "conv1"
	type: "Convolution"
	bottom: "data"
	top: "conv1"
	param {
	lr_mult: 1
	}
	param {
	lr_mult: 2
	}
	convolution_param {
	num_output: 32
	pad: 2
	kernel_size: 5
	stride: 1
	weight_filler {
	type: "gaussian"
	std: 0.0001
	}
	bias_filler {
	type: "constant"
	}
	}
	}
	layer {
	name: "pool1"
	type: "Pooling"
	bottom: "conv1"
	top: "pool1"
	pooling_param {
	pool: MAX
	kernel_size: 3
	stride: 2
	}
	}
	layer {
	name: "relu1"
	type: "ReLU"
	bottom: "pool1"
	top: "pool1"
	}
	layer {
	name: "conv2"
	type: "Convolution"
	bottom: "pool1"
	top: "conv2"
	param {
	lr_mult: 1
	}
	param {
	lr_mult: 2
	}
	convolution_param {
	num_output: 32
	pad: 2
	kernel_size: 5
	stride: 1
	weight_filler {
	type: "gaussian"
	std: 0.01
	}
	bias_filler {
	type: "constant"
	}
	}
	}
	layer {
	name: "relu2"
	type: "ReLU"
	bottom: "conv2"
	top: "conv2"
	}
	layer {
	name: "pool2"
	type: "Pooling"
	bottom: "conv2"
	top: "pool2"
	pooling_param {
	pool: AVE
	kernel_size: 3
	stride: 2
	}
	}
	layer {
	name: "conv3"
	type: "Convolution"
	bottom: "pool2"
	top: "conv3"
	param {
	lr_mult: 1
	}
	param {
	lr_mult: 2
	}
	convolution_param {
	num_output: 64
	pad: 2
	kernel_size: 5
	stride: 1
	weight_filler {
	type: "gaussian"
	std: 0.01
	}
	bias_filler {
	type: "constant"
	}
	}
	}
	layer {
	name: "relu3"
	type: "ReLU"
	bottom: "conv3"
	top: "conv3"
	}
	layer {
	name: "pool3"
	type: "Pooling"
	bottom: "conv3"
	top: "pool3"
	pooling_param {
	pool: AVE
	kernel_size: 3
	stride: 2
	}
	}
	layer {
	name: "ip1"
	type: "InnerProduct"
	bottom: "pool3"
	top: "ip1"
	param {
	lr_mult: 1
	}
	param {
	lr_mult: 2
	}
	inner_product_param {
	num_output: 64
	weight_filler {
	type: "gaussian"
	std: 0.1
	}
	bias_filler {
	type: "constant"
	}
	}
	}
	layer {
	name: "ip2"
	type: "InnerProduct"
	bottom: "ip1"
	top: "ip2"
	param {
	lr_mult: 1
	}
	param {
	lr_mult: 2
	}
	inner_product_param {
	num_output: 10
	weight_filler {
	type: "gaussian"
	std: 0.1
	}
	bias_filler {
	type: "constant"
	}
	}
	}
	layer {
	name: "accuracy"
	type: "Accuracy"
	bottom: "ip2"
	bottom: "label"
	top: "accuracy"
	include {
	phase: TEST
	}
	}
	layer {
	name: "loss"
	type: "SoftmaxWithLoss"
	bottom: "ip2"
	bottom: "label"
	top: "loss"
	}