LetNet是Caffe入门级的网络模型，是一个用来识别手写数字的最经典的卷积神经网络（CNN）,是Yann LeCun在1998年设计并提出的。
1.下载数据并解压
$ cd ~/caffe/data/mnist
$ ./get_mnist.sh
下载
训练样本：train-images-idx3-ubyte.gz
训练标签：train-labels-idx1-ubyte.gz
测试数据：t10k-images-idx3-ubyte.gz
测试标签：t10k-labels-idx1-ubyte.gz
并解压。
get_mnist.sh文件有点问题，修改如下：
#!/usr/bin/env sh
# This scripts downloads the mnist data and unzips it.
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"
echo "Downloading..."
wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
echo "Unzipping..."
gunzip train-images-idx3-ubyte.gz
gunzip train-labels-idx1-ubyte.gz
gunzip t10k-images-idx3-ubyte.gz
gunzip t10k-labels-idx1-ubyte.gz
echo "Done.."
2.生成LMDB
LMDB是一个超级快、超级小的Key-Value数据存储服务，使用内存映射文件，其读取性能和内存数据读取数据库一样。
$ cd ~/caffe
$ ./examples/mnist/create_mnist.sh
create_mnist.sh文件利用caffe/build/examples/mnist/convert_mnist_data.bin工具，将mnist数据转化为caffe可用的lmdb格式文件，让后将mnist-train-lmdb和mnist-test-lmdb文件放在caffe/example/mnist目录下。
3.网络配置
LeNet的网络配置再caffe/examples/mnist/lenet_train_test_prototxt文件中。
基本不要修改什么
4. 训练网络
$ ./examples/mnist/train_lenet.sh
此脚本实际上是再执行./examples/mnist/lenet_solver.prototxt中的内容
训练了很长时间
fc@fc-pc:~/caffe$ ./examples/mnist/train_lenet.sh
I0112 13:56:57.294195 4605 caffe.cpp:218] Using GPUs 0
I0112 13:56:57.440893 4605 caffe.cpp:223] GPU 0: GeForce GT 635M
I0112 13:56:58.157379 4605 solver.cpp:44] Initializing solver from parameters:
test_iter: 100
test_interval: 500
base_lr: 0.01
display: 100
max_iter: 10000
lr_policy: "inv"
gamma: 0.0001
power: 0.75
momentum: 0.9
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
solver_mode: GPU
device_id: 0
net: "examples/mnist/lenet_train_test.prototxt"
train_state {
level: 0
stage: ""
}
I0112 13:56:58.157559 4605 solver.cpp:87] Creating training net from net file: examples/mnist/lenet_train_test.prototxt
I0112 13:56:58.164427 4605 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer mnist
I0112 13:56:58.164482 4605 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0112 13:56:58.164703 4605 net.cpp:51] Initializing net from parameters:
name: "LeNet"
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_train_lmdb"
batch_size: 64
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
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: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
I0112 13:56:58.164865 4605 layer_factory.hpp:77] Creating layer mnist
I0112 13:56:58.165019 4605 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_train_lmdb
I0112 13:56:58.165051 4605 net.cpp:84] Creating Layer mnist
I0112 13:56:58.165066 4605 net.cpp:380] mnist -> data
I0112 13:56:58.165098 4605 net.cpp:380] mnist -> label
I0112 13:56:58.165763 4605 data_layer.cpp:45] output data size: 64,1,28,28
I0112 13:56:58.166887 4605 net.cpp:122] Setting up mnist
I0112 13:56:58.166927 4605 net.cpp:129] Top shape: 64 1 28 28 (50176)
I0112 13:56:58.166939 4605 net.cpp:129] Top shape: 64 (64)
I0112 13:56:58.166949 4605 net.cpp:137] Memory required for data: 200960
I0112 13:56:58.166966 4605 layer_factory.hpp:77] Creating layer conv1
I0112 13:56:58.167002 4605 net.cpp:84] Creating Layer conv1
I0112 13:56:58.167016 4605 net.cpp:406] conv1 <- data
I0112 13:56:58.167037 4605 net.cpp:380] conv1 -> conv1
I0112 13:56:58.168071 4605 net.cpp:122] Setting up conv1
I0112 13:56:58.168107 4605 net.cpp:129] Top shape: 64 20 24 24 (737280)
I0112 13:56:58.168115 4605 net.cpp:137] Memory required for data: 3150080
I0112 13:56:58.168153 4605 layer_factory.hpp:77] Creating layer pool1
I0112 13:56:58.168172 4605 net.cpp:84] Creating Layer pool1
I0112 13:56:58.168211 4605 net.cpp:406] pool1 <- conv1
I0112 13:56:58.168227 4605 net.cpp:380] pool1 -> pool1
I0112 13:56:58.168300 4605 net.cpp:122] Setting up pool1
I0112 13:56:58.168318 4605 net.cpp:129] Top shape: 64 20 12 12 (184320)
I0112 13:56:58.168326 4605 net.cpp:137] Memory required for data: 3887360
I0112 13:56:58.168340 4605 layer_factory.hpp:77] Creating layer conv2
I0112 13:56:58.168362 4605 net.cpp:84] Creating Layer conv2
I0112 13:56:58.168375 4605 net.cpp:406] conv2 <- pool1
I0112 13:56:58.168390 4605 net.cpp:380] conv2 -> conv2
I0112 13:56:58.169091 4605 net.cpp:122] Setting up conv2
I0112 13:56:58.169117 4605 net.cpp:129] Top shape: 64 50 8 8 (204800)
I0112 13:56:58.169127 4605 net.cpp:137] Memory required for data: 4706560
I0112 13:56:58.169147 4605 layer_factory.hpp:77] Creating layer pool2
I0112 13:56:58.169167 4605 net.cpp:84] Creating Layer pool2
I0112 13:56:58.169178 4605 net.cpp:406] pool2 <- conv2
I0112 13:56:58.169191 4605 net.cpp:380] pool2 -> pool2
I0112 13:56:58.169292 4605 net.cpp:122] Setting up pool2
I0112 13:56:58.169311 4605 net.cpp:129] Top shape: 64 50 4 4 (51200)
I0112 13:56:58.169318 4605 net.cpp:137] Memory required for data: 4911360
I0112 13:56:58.169327 4605 layer_factory.hpp:77] Creating layer ip1
I0112 13:56:58.169340 4605 net.cpp:84] Creating Layer ip1
I0112 13:56:58.169349 4605 net.cpp:406] ip1 <- pool2
I0112 13:56:58.169364 4605 net.cpp:380] ip1 -> ip1
I0112 13:56:58.174051 4605 net.cpp:122] Setting up ip1
I0112 13:56:58.174082 4605 net.cpp:129] Top shape: 64 500 (32000)
I0112 13:56:58.174096 4605 net.cpp:137] Memory required for data: 5039360
I0112 13:56:58.174114 4605 layer_factory.hpp:77] Creating layer relu1
I0112 13:56:58.174127 4605 net.cpp:84] Creating Layer relu1
I0112 13:56:58.174134 4605 net.cpp:406] relu1 <- ip1
I0112 13:56:58.174146 4605 net.cpp:367] relu1 -> ip1 (in-place)
I0112 13:56:58.174160 4605 net.cpp:122] Setting up relu1
I0112 13:56:58.174166 4605 net.cpp:129] Top shape: 64 500 (32000)
I0112 13:56:58.174171 4605 net.cpp:137] Memory required for data: 5167360
I0112 13:56:58.174177 4605 layer_factory.hpp:77] Creating layer ip2
I0112 13:56:58.174187 4605 net.cpp:84] Creating Layer ip2
I0112 13:56:58.174192 4605 net.cpp:406] ip2 <- ip1
I0112 13:56:58.174202 4605 net.cpp:380] ip2 -> ip2
I0112 13:56:58.174839 4605 net.cpp:122] Setting up ip2
I0112 13:56:58.174861 4605 net.cpp:129] Top shape: 64 10 (640)
I0112 13:56:58.174867 4605 net.cpp:137] Memory required for data: 5169920
I0112 13:56:58.174880 4605 layer_factory.hpp:77] Creating layer loss
I0112 13:56:58.174892 4605 net.cpp:84] Creating Layer loss
I0112 13:56:58.174898 4605 net.cpp:406] loss <- ip2
I0112 13:56:58.174906 4605 net.cpp:406] loss <- label
I0112 13:56:58.174917 4605 net.cpp:380] loss -> loss
I0112 13:56:58.174937 4605 layer_factory.hpp:77] Creating layer loss
I0112 13:56:58.175021 4605 net.cpp:122] Setting up loss
I0112 13:56:58.175031 4605 net.cpp:129] Top shape: (1)
I0112 13:56:58.175037 4605 net.cpp:132] with loss weight 1
I0112 13:56:58.175063 4605 net.cpp:137] Memory required for data: 5169924
I0112 13:56:58.175070 4605 net.cpp:198] loss needs backward computation.
I0112 13:56:58.175079 4605 net.cpp:198] ip2 needs backward computation.
I0112 13:56:58.175086 4605 net.cpp:198] relu1 needs backward computation.
I0112 13:56:58.175092 4605 net.cpp:198] ip1 needs backward computation.
I0112 13:56:58.175098 4605 net.cpp:198] pool2 needs backward computation.
I0112 13:56:58.175104 4605 net.cpp:198] conv2 needs backward computation.
I0112 13:56:58.175110 4605 net.cpp:198] pool1 needs backward computation.
I0112 13:56:58.175117 4605 net.cpp:198] conv1 needs backward computation.
I0112 13:56:58.175123 4605 net.cpp:200] mnist does not need backward computation.
I0112 13:56:58.175128 4605 net.cpp:242] This network produces output loss
I0112 13:56:58.175140 4605 net.cpp:255] Network initialization done.
I0112 13:56:58.175314 4605 solver.cpp:172] Creating test net (#0) specified by net file: examples/mnist/lenet_train_test.prototxt
I0112 13:56:58.175365 4605 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist
I0112 13:56:58.175464 4605 net.cpp:51] Initializing net from parameters:
name: "LeNet"
state {
phase: TEST
}
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_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: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
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: "xavier"
}
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"
}
I0112 13:56:58.175591 4605 layer_factory.hpp:77] Creating layer mnist
I0112 13:56:58.175654 4605 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_test_lmdb
I0112 13:56:58.175673 4605 net.cpp:84] Creating Layer mnist
I0112 13:56:58.175683 4605 net.cpp:380] mnist -> data
I0112 13:56:58.175694 4605 net.cpp:380] mnist -> label
I0112 13:56:58.175781 4605 data_layer.cpp:45] output data size: 100,1,28,28
I0112 13:56:58.177809 4605 net.cpp:122] Setting up mnist
I0112 13:56:58.177858 4605 net.cpp:129] Top shape: 100 1 28 28 (78400)
I0112 13:56:58.177866 4605 net.cpp:129] Top shape: 100 (100)
I0112 13:56:58.177871 4605 net.cpp:137] Memory required for data: 314000
I0112 13:56:58.177881 4605 layer_factory.hpp:77] Creating layer label_mnist_1_split
I0112 13:56:58.177896 4605 net.cpp:84] Creating Layer label_mnist_1_split
I0112 13:56:58.177904 4605 net.cpp:406] label_mnist_1_split <- label
I0112 13:56:58.177917 4605 net.cpp:380] label_mnist_1_split -> label_mnist_1_split_0
I0112 13:56:58.177932 4605 net.cpp:380] label_mnist_1_split -> label_mnist_1_split_1
I0112 13:56:58.178033 4605 net.cpp:122] Setting up label_mnist_1_split
I0112 13:56:58.178045 4605 net.cpp:129] Top shape: 100 (100)
I0112 13:56:58.178051 4605 net.cpp:129] Top shape: 100 (100)
I0112 13:56:58.178057 4605 net.cpp:137] Memory required for data: 314800
I0112 13:56:58.178062 4605 layer_factory.hpp:77] Creating layer conv1
I0112 13:56:58.178081 4605 net.cpp:84] Creating Layer conv1
I0112 13:56:58.178087 4605 net.cpp:406] conv1 <- data
I0112 13:56:58.178097 4605 net.cpp:380] conv1 -> conv1
I0112 13:56:58.178328 4605 net.cpp:122] Setting up conv1
I0112 13:56:58.178339 4605 net.cpp:129] Top shape: 100 20 24 24 (1152000)
I0112 13:56:58.178345 4605 net.cpp:137] Memory required for data: 4922800
I0112 13:56:58.178359 4605 layer_factory.hpp:77] Creating layer pool1
I0112 13:56:58.178395 4605 net.cpp:84] Creating Layer pool1
I0112 13:56:58.178401 4605 net.cpp:406] pool1 <- conv1
I0112 13:56:58.178411 4605 net.cpp:380] pool1 -> pool1
I0112 13:56:58.178453 4605 net.cpp:122] Setting up pool1
I0112 13:56:58.178463 4605 net.cpp:129] Top shape: 100 20 12 12 (288000)
I0112 13:56:58.178469 4605 net.cpp:137] Memory required for data: 6074800
I0112 13:56:58.178474 4605 layer_factory.hpp:77] Creating layer conv2
I0112 13:56:58.178488 4605 net.cpp:84] Creating Layer conv2
I0112 13:56:58.178495 4605 net.cpp:406] conv2 <- pool1
I0112 13:56:58.178505 4605 net.cpp:380] conv2 -> conv2
I0112 13:56:58.178928 4605 net.cpp:122] Setting up conv2
I0112 13:56:58.178944 4605 net.cpp:129] Top shape: 100 50 8 8 (320000)
I0112 13:56:58.178951 4605 net.cpp:137] Memory required for data: 7354800
I0112 13:56:58.178967 4605 layer_factory.hpp:77] Creating layer pool2
I0112 13:56:58.178977 4605 net.cpp:84] Creating Layer pool2
I0112 13:56:58.178983 4605 net.cpp:406] pool2 <- conv2
I0112 13:56:58.178998 4605 net.cpp:380] pool2 -> pool2
I0112 13:56:58.179042 4605 net.cpp:122] Setting up pool2
I0112 13:56:58.179051 4605 net.cpp:129] Top shape: 100 50 4 4 (80000)
I0112 13:56:58.179060 4605 net.cpp:137] Memory required for data: 7674800
I0112 13:56:58.179067 4605 layer_factory.hpp:77] Creating layer ip1
I0112 13:56:58.179078 4605 net.cpp:84] Creating Layer ip1
I0112 13:56:58.179085 4605 net.cpp:406] ip1 <- pool2
I0112 13:56:58.179095 4605 net.cpp:380] ip1 -> ip1
I0112 13:56:58.182580 4605 net.cpp:122] Setting up ip1
I0112 13:56:58.182610 4605 net.cpp:129] Top shape: 100 500 (50000)
I0112 13:56:58.182616 4605 net.cpp:137] Memory required for data: 7874800
I0112 13:56:58.182636 4605 layer_factory.hpp:77] Creating layer relu1
I0112 13:56:58.182648 4605 net.cpp:84] Creating Layer relu1
I0112 13:56:58.182656 4605 net.cpp:406] relu1 <- ip1
I0112 13:56:58.182665 4605 net.cpp:367] relu1 -> ip1 (in-place)
I0112 13:56:58.182677 4605 net.cpp:122] Setting up relu1
I0112 13:56:58.182685 4605 net.cpp:129] Top shape: 100 500 (50000)
I0112 13:56:58.182690 4605 net.cpp:137] Memory required for data: 8074800
I0112 13:56:58.182695 4605 layer_factory.hpp:77] Creating layer ip2
I0112 13:56:58.182708 4605 net.cpp:84] Creating Layer ip2
I0112 13:56:58.182714 4605 net.cpp:406] ip2 <- ip1
I0112 13:56:58.182724 4605 net.cpp:380] ip2 -> ip2
I0112 13:56:58.182857 4605 net.cpp:122] Setting up ip2
I0112 13:56:58.182868 4605 net.cpp:129] Top shape: 100 10 (1000)
I0112 13:56:58.182873 4605 net.cpp:137] Memory required for data: 8078800
I0112 13:56:58.182883 4605 layer_factory.hpp:77] Creating layer ip2_ip2_0_split
I0112 13:56:58.182890 4605 net.cpp:84] Creating Layer ip2_ip2_0_split
I0112 13:56:58.182896 4605 net.cpp:406] ip2_ip2_0_split <- ip2
I0112 13:56:58.182905 4605 net.cpp:380] ip2_ip2_0_split -> ip2_ip2_0_split_0
I0112 13:56:58.182915 4605 net.cpp:380] ip2_ip2_0_split -> ip2_ip2_0_split_1
I0112 13:56:58.182950 4605 net.cpp:122] Setting up ip2_ip2_0_split
I0112 13:56:58.182961 4605 net.cpp:129] Top shape: 100 10 (1000)
I0112 13:56:58.182968 4605 net.cpp:129] Top shape: 100 10 (1000)
I0112 13:56:58.182973 4605 net.cpp:137] Memory required for data: 8086800
I0112 13:56:58.182979 4605 layer_factory.hpp:77] Creating layer accuracy
I0112 13:56:58.182988 4605 net.cpp:84] Creating Layer accuracy
I0112 13:56:58.182994 4605 net.cpp:406] accuracy <- ip2_ip2_0_split_0
I0112 13:56:58.183001 4605 net.cpp:406] accuracy <- label_mnist_1_split_0
I0112 13:56:58.183009 4605 net.cpp:380] accuracy -> accuracy
I0112 13:56:58.183022 4605 net.cpp:122] Setting up accuracy
I0112 13:56:58.183029 4605 net.cpp:129] Top shape: (1)
I0112 13:56:58.183035 4605 net.cpp:137] Memory required for data: 8086804
I0112 13:56:58.183040 4605 layer_factory.hpp:77] Creating layer loss
I0112 13:56:58.183048 4605 net.cpp:84] Creating Layer loss
I0112 13:56:58.183054 4605 net.cpp:406] loss <- ip2_ip2_0_split_1
I0112 13:56:58.183061 4605 net.cpp:406] loss <- label_mnist_1_split_1
I0112 13:56:58.183095 4605 net.cpp:380] loss -> loss
I0112 13:56:58.183106 4605 layer_factory.hpp:77] Creating layer loss
I0112 13:56:58.183189 4605 net.cpp:122] Setting up loss
I0112 13:56:58.183199 4605 net.cpp:129] Top shape: (1)
I0112 13:56:58.183204 4605 net.cpp:132] with loss weight 1
I0112 13:56:58.183219 4605 net.cpp:137] Memory required for data: 8086808
I0112 13:56:58.183226 4605 net.cpp:198] loss needs backward computation.
I0112 13:56:58.183233 4605 net.cpp:200] accuracy does not need backward computation.
I0112 13:56:58.183239 4605 net.cpp:198] ip2_ip2_0_split needs backward computation.
I0112 13:56:58.183245 4605 net.cpp:198] ip2 needs backward computation.
I0112 13:56:58.183251 4605 net.cpp:198] relu1 needs backward computation.
I0112 13:56:58.183257 4605 net.cpp:198] ip1 needs backward computation.
I0112 13:56:58.183262 4605 net.cpp:198] pool2 needs backward computation.
I0112 13:56:58.183269 4605 net.cpp:198] conv2 needs backward computation.
I0112 13:56:58.183274 4605 net.cpp:198] pool1 needs backward computation.
I0112 13:56:58.183281 4605 net.cpp:198] conv1 needs backward computation.
I0112 13:56:58.183290 4605 net.cpp:200] label_mnist_1_split does not need backward computation.
I0112 13:56:58.183296 4605 net.cpp:200] mnist does not need backward computation.
I0112 13:56:58.183301 4605 net.cpp:242] This network produces output accuracy
I0112 13:56:58.183307 4605 net.cpp:242] This network produces output loss
I0112 13:56:58.183326 4605 net.cpp:255] Network initialization done.
I0112 13:56:58.183372 4605 solver.cpp:56] Solver scaffolding done.
I0112 13:56:58.183619 4605 caffe.cpp:248] Starting Optimization
I0112 13:56:58.183629 4605 solver.cpp:272] Solving LeNet
I0112 13:56:58.183634 4605 solver.cpp:273] Learning Rate Policy: inv
I0112 13:56:58.184096 4605 solver.cpp:330] Iteration 0, Testing net (#0)
I0112 13:57:00.461726 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:57:00.555826 4605 solver.cpp:397] Test net output #0: accuracy = 0.1078
I0112 13:57:00.555896 4605 solver.cpp:397] Test net output #1: loss = 2.30511 (* 1 = 2.30511 loss)
I0112 13:57:00.607538 4605 solver.cpp:218] Iteration 0 (-4.39738e+28 iter/s, 2.42383s/100 iters), loss = 2.28738
I0112 13:57:00.607615 4605 solver.cpp:237] Train net output #0: loss = 2.28738 (* 1 = 2.28738 loss)
I0112 13:57:00.607636 4605 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0112 13:57:05.810878 4605 solver.cpp:218] Iteration 100 (19.219 iter/s, 5.2032s/100 iters), loss = 0.243285
I0112 13:57:05.810941 4605 solver.cpp:237] Train net output #0: loss = 0.243285 (* 1 = 0.243285 loss)
I0112 13:57:05.810958 4605 sgd_solver.cpp:105] Iteration 100, lr = 0.00992565
I0112 13:57:11.014245 4605 solver.cpp:218] Iteration 200 (19.2188 iter/s, 5.20325s/100 iters), loss = 0.141302
I0112 13:57:11.014315 4605 solver.cpp:237] Train net output #0: loss = 0.141302 (* 1 = 0.141302 loss)
I0112 13:57:11.014328 4605 sgd_solver.cpp:105] Iteration 200, lr = 0.00985258
I0112 13:57:16.216323 4605 solver.cpp:218] Iteration 300 (19.2236 iter/s, 5.20195s/100 iters), loss = 0.177265
I0112 13:57:16.216384 4605 solver.cpp:237] Train net output #0: loss = 0.177265 (* 1 = 0.177265 loss)
I0112 13:57:16.216398 4605 sgd_solver.cpp:105] Iteration 300, lr = 0.00978075
I0112 13:57:21.422519 4605 solver.cpp:218] Iteration 400 (19.2083 iter/s, 5.20607s/100 iters), loss = 0.0997172
I0112 13:57:21.422600 4605 solver.cpp:237] Train net output #0: loss = 0.0997172 (* 1 = 0.0997172 loss)
I0112 13:57:21.422614 4605 sgd_solver.cpp:105] Iteration 400, lr = 0.00971013
I0112 13:57:26.540989 4605 solver.cpp:330] Iteration 500, Testing net (#0)
I0112 13:57:28.847373 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:57:28.941399 4605 solver.cpp:397] Test net output #0: accuracy = 0.9714
I0112 13:57:28.941442 4605 solver.cpp:397] Test net output #1: loss = 0.0865218 (* 1 = 0.0865218 loss)
I0112 13:57:28.991828 4605 solver.cpp:218] Iteration 500 (13.2115 iter/s, 7.56914s/100 iters), loss = 0.112185
I0112 13:57:28.991883 4605 solver.cpp:237] Train net output #0: loss = 0.112185 (* 1 = 0.112185 loss)
I0112 13:57:28.991895 4605 sgd_solver.cpp:105] Iteration 500, lr = 0.00964069
I0112 13:57:34.193409 4605 solver.cpp:218] Iteration 600 (19.2253 iter/s, 5.20147s/100 iters), loss = 0.12664
I0112 13:57:34.193473 4605 solver.cpp:237] Train net output #0: loss = 0.12664 (* 1 = 0.12664 loss)
I0112 13:57:34.193487 4605 sgd_solver.cpp:105] Iteration 600, lr = 0.0095724
I0112 13:57:39.398228 4605 solver.cpp:218] Iteration 700 (19.2134 iter/s, 5.2047s/100 iters), loss = 0.0915156
I0112 13:57:39.398288 4605 solver.cpp:237] Train net output #0: loss = 0.0915156 (* 1 = 0.0915156 loss)
I0112 13:57:39.398303 4605 sgd_solver.cpp:105] Iteration 700, lr = 0.00950522
I0112 13:57:44.600630 4605 solver.cpp:218] Iteration 800 (19.2223 iter/s, 5.20229s/100 iters), loss = 0.177842
I0112 13:57:44.600690 4605 solver.cpp:237] Train net output #0: loss = 0.177842 (* 1 = 0.177842 loss)
I0112 13:57:44.600700 4605 sgd_solver.cpp:105] Iteration 800, lr = 0.00943913
I0112 13:57:49.804349 4605 solver.cpp:218] Iteration 900 (19.2175 iter/s, 5.2036s/100 iters), loss = 0.163748
I0112 13:57:49.804414 4605 solver.cpp:237] Train net output #0: loss = 0.163748 (* 1 = 0.163748 loss)
I0112 13:57:49.804431 4605 sgd_solver.cpp:105] Iteration 900, lr = 0.00937411
I0112 13:57:51.522743 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:57:54.921766 4605 solver.cpp:330] Iteration 1000, Testing net (#0)
I0112 13:57:57.231189 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:57:57.325402 4605 solver.cpp:397] Test net output #0: accuracy = 0.9824
I0112 13:57:57.325449 4605 solver.cpp:397] Test net output #1: loss = 0.0566883 (* 1 = 0.0566883 loss)
I0112 13:57:57.375991 4605 solver.cpp:218] Iteration 1000 (13.2075 iter/s, 7.57145s/100 iters), loss = 0.0991981
I0112 13:57:57.376060 4605 solver.cpp:237] Train net output #0: loss = 0.099198 (* 1 = 0.099198 loss)
I0112 13:57:57.376073 4605 sgd_solver.cpp:105] Iteration 1000, lr = 0.00931012
I0112 13:58:02.578084 4605 solver.cpp:218] Iteration 1100 (19.2235 iter/s, 5.20196s/100 iters), loss = 0.0074663
I0112 13:58:02.578302 4605 solver.cpp:237] Train net output #0: loss = 0.00746628 (* 1 = 0.00746628 loss)
I0112 13:58:02.578320 4605 sgd_solver.cpp:105] Iteration 1100, lr = 0.00924715
I0112 13:58:07.777390 4605 solver.cpp:218] Iteration 1200 (19.2344 iter/s, 5.19903s/100 iters), loss = 0.0371387
I0112 13:58:07.777452 4605 solver.cpp:237] Train net output #0: loss = 0.0371387 (* 1 = 0.0371387 loss)
I0112 13:58:07.777467 4605 sgd_solver.cpp:105] Iteration 1200, lr = 0.00918515
I0112 13:58:12.977054 4605 solver.cpp:218] Iteration 1300 (19.2325 iter/s, 5.19954s/100 iters), loss = 0.0171492
I0112 13:58:12.977105 4605 solver.cpp:237] Train net output #0: loss = 0.0171492 (* 1 = 0.0171492 loss)
I0112 13:58:12.977118 4605 sgd_solver.cpp:105] Iteration 1300, lr = 0.00912412
I0112 13:58:18.175503 4605 solver.cpp:218] Iteration 1400 (19.237 iter/s, 5.19833s/100 iters), loss = 0.00672234
I0112 13:58:18.175563 4605 solver.cpp:237] Train net output #0: loss = 0.0067223 (* 1 = 0.0067223 loss)
I0112 13:58:18.175576 4605 sgd_solver.cpp:105] Iteration 1400, lr = 0.00906403
I0112 13:58:23.290197 4605 solver.cpp:330] Iteration 1500, Testing net (#0)
I0112 13:58:25.597092 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:58:25.691124 4605 solver.cpp:397] Test net output #0: accuracy = 0.984
I0112 13:58:25.691167 4605 solver.cpp:397] Test net output #1: loss = 0.0488746 (* 1 = 0.0488746 loss)
I0112 13:58:25.741626 4605 solver.cpp:218] Iteration 1500 (13.2171 iter/s, 7.56596s/100 iters), loss = 0.101924
I0112 13:58:25.741715 4605 solver.cpp:237] Train net output #0: loss = 0.101924 (* 1 = 0.101924 loss)
I0112 13:58:25.741730 4605 sgd_solver.cpp:105] Iteration 1500, lr = 0.00900485
I0112 13:58:30.940945 4605 solver.cpp:218] Iteration 1600 (19.2339 iter/s, 5.19916s/100 iters), loss = 0.119907
I0112 13:58:30.940995 4605 solver.cpp:237] Train net output #0: loss = 0.119907 (* 1 = 0.119907 loss)
I0112 13:58:30.941007 4605 sgd_solver.cpp:105] Iteration 1600, lr = 0.00894657
I0112 13:58:36.140640 4605 solver.cpp:218] Iteration 1700 (19.2323 iter/s, 5.19957s/100 iters), loss = 0.0359353
I0112 13:58:36.140813 4605 solver.cpp:237] Train net output #0: loss = 0.0359352 (* 1 = 0.0359352 loss)
I0112 13:58:36.140833 4605 sgd_solver.cpp:105] Iteration 1700, lr = 0.00888916
I0112 13:58:41.339848 4605 solver.cpp:218] Iteration 1800 (19.2346 iter/s, 5.19897s/100 iters), loss = 0.016428
I0112 13:58:41.339906 4605 solver.cpp:237] Train net output #0: loss = 0.0164279 (* 1 = 0.0164279 loss)
I0112 13:58:41.339920 4605 sgd_solver.cpp:105] Iteration 1800, lr = 0.0088326
I0112 13:58:44.980896 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:58:46.538303 4605 solver.cpp:218] Iteration 1900 (19.2369 iter/s, 5.19834s/100 iters), loss = 0.127803
I0112 13:58:46.538360 4605 solver.cpp:237] Train net output #0: loss = 0.127803 (* 1 = 0.127803 loss)
I0112 13:58:46.538373 4605 sgd_solver.cpp:105] Iteration 1900, lr = 0.00877687
I0112 13:58:51.650697 4605 solver.cpp:330] Iteration 2000, Testing net (#0)
I0112 13:58:53.958813 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:58:54.052958 4605 solver.cpp:397] Test net output #0: accuracy = 0.9861
I0112 13:58:54.052999 4605 solver.cpp:397] Test net output #1: loss = 0.0423396 (* 1 = 0.0423396 loss)
I0112 13:58:54.103466 4605 solver.cpp:218] Iteration 2000 (13.2188 iter/s, 7.56501s/100 iters), loss = 0.0270834
I0112 13:58:54.103543 4605 solver.cpp:237] Train net output #0: loss = 0.0270833 (* 1 = 0.0270833 loss)
I0112 13:58:54.103557 4605 sgd_solver.cpp:105] Iteration 2000, lr = 0.00872196
I0112 13:58:59.303009 4605 solver.cpp:218] Iteration 2100 (19.233 iter/s, 5.1994s/100 iters), loss = 0.013996
I0112 13:58:59.303068 4605 solver.cpp:237] Train net output #0: loss = 0.0139959 (* 1 = 0.0139959 loss)
I0112 13:58:59.303081 4605 sgd_solver.cpp:105] Iteration 2100, lr = 0.00866784
I0112 13:59:04.504382 4605 solver.cpp:218] Iteration 2200 (19.2261 iter/s, 5.20126s/100 iters), loss = 0.0149617
I0112 13:59:04.504441 4605 solver.cpp:237] Train net output #0: loss = 0.0149617 (* 1 = 0.0149617 loss)
I0112 13:59:04.504456 4605 sgd_solver.cpp:105] Iteration 2200, lr = 0.0086145
I0112 13:59:09.704370 4605 solver.cpp:218] Iteration 2300 (19.2312 iter/s, 5.19987s/100 iters), loss = 0.107121
I0112 13:59:09.704478 4605 solver.cpp:237] Train net output #0: loss = 0.107121 (* 1 = 0.107121 loss)
I0112 13:59:09.704491 4605 sgd_solver.cpp:105] Iteration 2300, lr = 0.00856192
I0112 13:59:14.906342 4605 solver.cpp:218] Iteration 2400 (19.2241 iter/s, 5.20181s/100 iters), loss = 0.0108024
I0112 13:59:14.906400 4605 solver.cpp:237] Train net output #0: loss = 0.0108024 (* 1 = 0.0108024 loss)
I0112 13:59:14.906414 4605 sgd_solver.cpp:105] Iteration 2400, lr = 0.00851008
I0112 13:59:20.023326 4605 solver.cpp:330] Iteration 2500, Testing net (#0)
I0112 13:59:22.333452 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:59:22.427494 4605 solver.cpp:397] Test net output #0: accuracy = 0.9836
I0112 13:59:22.427541 4605 solver.cpp:397] Test net output #1: loss = 0.050619 (* 1 = 0.050619 loss)
I0112 13:59:22.478123 4605 solver.cpp:218] Iteration 2500 (13.2072 iter/s, 7.57164s/100 iters), loss = 0.0319451
I0112 13:59:22.478199 4605 solver.cpp:237] Train net output #0: loss = 0.0319451 (* 1 = 0.0319451 loss)
I0112 13:59:22.478215 4605 sgd_solver.cpp:105] Iteration 2500, lr = 0.00845897
I0112 13:59:27.693603 4605 solver.cpp:218] Iteration 2600 (19.1742 iter/s, 5.21535s/100 iters), loss = 0.0723398
I0112 13:59:27.693655 4605 solver.cpp:237] Train net output #0: loss = 0.0723398 (* 1 = 0.0723398 loss)
I0112 13:59:27.693667 4605 sgd_solver.cpp:105] Iteration 2600, lr = 0.00840857
I0112 13:59:32.893954 4605 solver.cpp:218] Iteration 2700 (19.2299 iter/s, 5.20024s/100 iters), loss = 0.0818715
I0112 13:59:32.894017 4605 solver.cpp:237] Train net output #0: loss = 0.0818715 (* 1 = 0.0818715 loss)
I0112 13:59:32.894032 4605 sgd_solver.cpp:105] Iteration 2700, lr = 0.00835886
I0112 13:59:38.094995 4605 solver.cpp:218] Iteration 2800 (19.2273 iter/s, 5.20093s/100 iters), loss = 0.00166001
I0112 13:59:38.095055 4605 solver.cpp:237] Train net output #0: loss = 0.00165998 (* 1 = 0.00165998 loss)
I0112 13:59:38.095067 4605 sgd_solver.cpp:105] Iteration 2800, lr = 0.00830984
I0112 13:59:38.512676 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:59:43.295424 4605 solver.cpp:218] Iteration 2900 (19.2296 iter/s, 5.20033s/100 iters), loss = 0.0198968
I0112 13:59:43.295598 4605 solver.cpp:237] Train net output #0: loss = 0.0198968 (* 1 = 0.0198968 loss)
I0112 13:59:43.295616 4605 sgd_solver.cpp:105] Iteration 2900, lr = 0.00826148
I0112 13:59:48.410835 4605 solver.cpp:330] Iteration 3000, Testing net (#0)
I0112 13:59:50.717015 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 13:59:50.810612 4605 solver.cpp:397] Test net output #0: accuracy = 0.9867
I0112 13:59:50.810650 4605 solver.cpp:397] Test net output #1: loss = 0.0373926 (* 1 = 0.0373926 loss)
I0112 13:59:50.861148 4605 solver.cpp:218] Iteration 3000 (13.218 iter/s, 7.56547s/100 iters), loss = 0.00747836
I0112 13:59:50.861227 4605 solver.cpp:237] Train net output #0: loss = 0.00747831 (* 1 = 0.00747831 loss)
I0112 13:59:50.861239 4605 sgd_solver.cpp:105] Iteration 3000, lr = 0.00821377
I0112 13:59:56.060351 4605 solver.cpp:218] Iteration 3100 (19.2342 iter/s, 5.19908s/100 iters), loss = 0.00772881
I0112 13:59:56.060405 4605 solver.cpp:237] Train net output #0: loss = 0.00772875 (* 1 = 0.00772875 loss)
I0112 13:59:56.060427 4605 sgd_solver.cpp:105] Iteration 3100, lr = 0.0081667
I0112 14:00:01.275904 4605 solver.cpp:218] Iteration 3200 (19.1739 iter/s, 5.21542s/100 iters), loss = 0.00692212
I0112 14:00:01.276000 4605 solver.cpp:237] Train net output #0: loss = 0.00692206 (* 1 = 0.00692206 loss)
I0112 14:00:01.276022 4605 sgd_solver.cpp:105] Iteration 3200, lr = 0.00812025
I0112 14:00:06.483846 4605 solver.cpp:218] Iteration 3300 (19.202 iter/s, 5.20779s/100 iters), loss = 0.0332235
I0112 14:00:06.483925 4605 solver.cpp:237] Train net output #0: loss = 0.0332235 (* 1 = 0.0332235 loss)
I0112 14:00:06.483939 4605 sgd_solver.cpp:105] Iteration 3300, lr = 0.00807442
I0112 14:00:11.688371 4605 solver.cpp:218] Iteration 3400 (19.2145 iter/s, 5.20439s/100 iters), loss = 0.0107628
I0112 14:00:11.688437 4605 solver.cpp:237] Train net output #0: loss = 0.0107627 (* 1 = 0.0107627 loss)
I0112 14:00:11.688452 4605 sgd_solver.cpp:105] Iteration 3400, lr = 0.00802918
I0112 14:00:16.806285 4605 solver.cpp:330] Iteration 3500, Testing net (#0)
I0112 14:00:19.114704 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:00:19.208638 4605 solver.cpp:397] Test net output #0: accuracy = 0.9853
I0112 14:00:19.208679 4605 solver.cpp:397] Test net output #1: loss = 0.0436789 (* 1 = 0.0436789 loss)
I0112 14:00:19.259240 4605 solver.cpp:218] Iteration 3500 (13.2088 iter/s, 7.57073s/100 iters), loss = 0.00571759
I0112 14:00:19.259318 4605 solver.cpp:237] Train net output #0: loss = 0.00571754 (* 1 = 0.00571754 loss)
I0112 14:00:19.259337 4605 sgd_solver.cpp:105] Iteration 3500, lr = 0.00798454
I0112 14:00:24.462276 4605 solver.cpp:218] Iteration 3600 (19.2201 iter/s, 5.2029s/100 iters), loss = 0.0379283
I0112 14:00:24.462355 4605 solver.cpp:237] Train net output #0: loss = 0.0379283 (* 1 = 0.0379283 loss)
I0112 14:00:24.462369 4605 sgd_solver.cpp:105] Iteration 3600, lr = 0.00794046
I0112 14:00:29.667263 4605 solver.cpp:218] Iteration 3700 (19.2128 iter/s, 5.20486s/100 iters), loss = 0.0133145
I0112 14:00:29.667327 4605 solver.cpp:237] Train net output #0: loss = 0.0133144 (* 1 = 0.0133144 loss)
I0112 14:00:29.667342 4605 sgd_solver.cpp:105] Iteration 3700, lr = 0.00789695
I0112 14:00:32.008819 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:00:34.870044 4605 solver.cpp:218] Iteration 3800 (19.2209 iter/s, 5.20267s/100 iters), loss = 0.00914176
I0112 14:00:34.870115 4605 solver.cpp:237] Train net output #0: loss = 0.00914171 (* 1 = 0.00914171 loss)
I0112 14:00:34.870128 4605 sgd_solver.cpp:105] Iteration 3800, lr = 0.007854
I0112 14:00:40.072796 4605 solver.cpp:218] Iteration 3900 (19.221 iter/s, 5.20264s/100 iters), loss = 0.015734
I0112 14:00:40.072865 4605 solver.cpp:237] Train net output #0: loss = 0.0157339 (* 1 = 0.0157339 loss)
I0112 14:00:40.072880 4605 sgd_solver.cpp:105] Iteration 3900, lr = 0.00781158
I0112 14:00:45.190907 4605 solver.cpp:330] Iteration 4000, Testing net (#0)
I0112 14:00:47.501956 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:00:47.595811 4605 solver.cpp:397] Test net output #0: accuracy = 0.9899
I0112 14:00:47.595860 4605 solver.cpp:397] Test net output #1: loss = 0.029029 (* 1 = 0.029029 loss)
I0112 14:00:47.646351 4605 solver.cpp:218] Iteration 4000 (13.2041 iter/s, 7.57341s/100 iters), loss = 0.0225254
I0112 14:00:47.646433 4605 solver.cpp:237] Train net output #0: loss = 0.0225254 (* 1 = 0.0225254 loss)
I0112 14:00:47.646450 4605 sgd_solver.cpp:105] Iteration 4000, lr = 0.0077697
I0112 14:00:52.850426 4605 solver.cpp:218] Iteration 4100 (19.2162 iter/s, 5.20394s/100 iters), loss = 0.017278
I0112 14:00:52.850503 4605 solver.cpp:237] Train net output #0: loss = 0.017278 (* 1 = 0.017278 loss)
I0112 14:00:52.850517 4605 sgd_solver.cpp:105] Iteration 4100, lr = 0.00772833
I0112 14:00:58.057740 4605 solver.cpp:218] Iteration 4200 (19.2042 iter/s, 5.20719s/100 iters), loss = 0.0154973
I0112 14:00:58.057804 4605 solver.cpp:237] Train net output #0: loss = 0.0154973 (* 1 = 0.0154973 loss)
I0112 14:00:58.057817 4605 sgd_solver.cpp:105] Iteration 4200, lr = 0.00768748
I0112 14:01:03.262459 4605 solver.cpp:218] Iteration 4300 (19.2137 iter/s, 5.20461s/100 iters), loss = 0.0672259
I0112 14:01:03.262518 4605 solver.cpp:237] Train net output #0: loss = 0.0672259 (* 1 = 0.0672259 loss)
I0112 14:01:03.262534 4605 sgd_solver.cpp:105] Iteration 4300, lr = 0.00764712
I0112 14:01:08.466617 4605 solver.cpp:218] Iteration 4400 (19.2158 iter/s, 5.20404s/100 iters), loss = 0.0157733
I0112 14:01:08.466697 4605 solver.cpp:237] Train net output #0: loss = 0.0157734 (* 1 = 0.0157734 loss)
I0112 14:01:08.466714 4605 sgd_solver.cpp:105] Iteration 4400, lr = 0.00760726
I0112 14:01:13.588131 4605 solver.cpp:330] Iteration 4500, Testing net (#0)
I0112 14:01:15.898298 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:01:15.991850 4605 solver.cpp:397] Test net output #0: accuracy = 0.9867
I0112 14:01:15.991889 4605 solver.cpp:397] Test net output #1: loss = 0.0401761 (* 1 = 0.0401761 loss)
I0112 14:01:16.042362 4605 solver.cpp:218] Iteration 4500 (13.2003 iter/s, 7.57556s/100 iters), loss = 0.00598018
I0112 14:01:16.042477 4605 solver.cpp:237] Train net output #0: loss = 0.00598019 (* 1 = 0.00598019 loss)
I0112 14:01:16.042500 4605 sgd_solver.cpp:105] Iteration 4500, lr = 0.00756788
I0112 14:01:21.251485 4605 solver.cpp:218] Iteration 4600 (19.1977 iter/s, 5.20895s/100 iters), loss = 0.014779
I0112 14:01:21.251601 4605 solver.cpp:237] Train net output #0: loss = 0.014779 (* 1 = 0.014779 loss)
I0112 14:01:21.251623 4605 sgd_solver.cpp:105] Iteration 4600, lr = 0.00752897
I0112 14:01:25.575083 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:01:26.457192 4605 solver.cpp:218] Iteration 4700 (19.2103 iter/s, 5.20553s/100 iters), loss = 0.00893127
I0112 14:01:26.457307 4605 solver.cpp:237] Train net output #0: loss = 0.00893128 (* 1 = 0.00893128 loss)
I0112 14:01:26.457329 4605 sgd_solver.cpp:105] Iteration 4700, lr = 0.00749052
I0112 14:01:31.663359 4605 solver.cpp:218] Iteration 4800 (19.2086 iter/s, 5.206s/100 iters), loss = 0.0135461
I0112 14:01:31.663432 4605 solver.cpp:237] Train net output #0: loss = 0.0135461 (* 1 = 0.0135461 loss)
I0112 14:01:31.663452 4605 sgd_solver.cpp:105] Iteration 4800, lr = 0.00745253
I0112 14:01:36.866758 4605 solver.cpp:218] Iteration 4900 (19.2187 iter/s, 5.20327s/100 iters), loss = 0.00427028
I0112 14:01:36.866837 4605 solver.cpp:237] Train net output #0: loss = 0.00427027 (* 1 = 0.00427027 loss)
I0112 14:01:36.866849 4605 sgd_solver.cpp:105] Iteration 4900, lr = 0.00741498
I0112 14:01:41.992313 4605 solver.cpp:447] Snapshotting to binary proto file examples/mnist/lenet_iter_5000.caffemodel
I0112 14:01:42.035707 4605 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_5000.solverstate
I0112 14:01:42.039849 4605 solver.cpp:330] Iteration 5000, Testing net (#0)
I0112 14:01:44.312611 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:01:44.406785 4605 solver.cpp:397] Test net output #0: accuracy = 0.9902
I0112 14:01:44.406824 4605 solver.cpp:397] Test net output #1: loss = 0.0296651 (* 1 = 0.0296651 loss)
I0112 14:01:44.457312 4605 solver.cpp:218] Iteration 5000 (13.1745 iter/s, 7.5904s/100 iters), loss = 0.0190112
I0112 14:01:44.457389 4605 solver.cpp:237] Train net output #0: loss = 0.0190112 (* 1 = 0.0190112 loss)
I0112 14:01:44.457402 4605 sgd_solver.cpp:105] Iteration 5000, lr = 0.00737788
I0112 14:01:49.656071 4605 solver.cpp:218] Iteration 5100 (19.2358 iter/s, 5.19864s/100 iters), loss = 0.0170202
I0112 14:01:49.656131 4605 solver.cpp:237] Train net output #0: loss = 0.0170202 (* 1 = 0.0170202 loss)
I0112 14:01:49.656143 4605 sgd_solver.cpp:105] Iteration 5100, lr = 0.0073412
I0112 14:01:54.855412 4605 solver.cpp:218] Iteration 5200 (19.2336 iter/s, 5.19924s/100 iters), loss = 0.00894152
I0112 14:01:54.855540 4605 solver.cpp:237] Train net output #0: loss = 0.00894146 (* 1 = 0.00894146 loss)
I0112 14:01:54.855554 4605 sgd_solver.cpp:105] Iteration 5200, lr = 0.00730495
I0112 14:02:00.054531 4605 solver.cpp:218] Iteration 5300 (19.2346 iter/s, 5.19895s/100 iters), loss = 0.000942099
I0112 14:02:00.054582 4605 solver.cpp:237] Train net output #0: loss = 0.000942038 (* 1 = 0.000942038 loss)
I0112 14:02:00.054595 4605 sgd_solver.cpp:105] Iteration 5300, lr = 0.00726911
I0112 14:02:05.254628 4605 solver.cpp:218] Iteration 5400 (19.2308 iter/s, 5.2s/100 iters), loss = 0.0121031
I0112 14:02:05.254685 4605 solver.cpp:237] Train net output #0: loss = 0.0121031 (* 1 = 0.0121031 loss)
I0112 14:02:05.254699 4605 sgd_solver.cpp:105] Iteration 5400, lr = 0.00723368
I0112 14:02:10.368352 4605 solver.cpp:330] Iteration 5500, Testing net (#0)
I0112 14:02:12.675007 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:02:12.768986 4605 solver.cpp:397] Test net output #0: accuracy = 0.9892
I0112 14:02:12.769026 4605 solver.cpp:397] Test net output #1: loss = 0.031965 (* 1 = 0.031965 loss)
I0112 14:02:12.819501 4605 solver.cpp:218] Iteration 5500 (13.2192 iter/s, 7.56475s/100 iters), loss = 0.01058
I0112 14:02:12.819579 4605 solver.cpp:237] Train net output #0: loss = 0.01058 (* 1 = 0.01058 loss)
I0112 14:02:12.819593 4605 sgd_solver.cpp:105] Iteration 5500, lr = 0.00719865
I0112 14:02:18.017997 4605 solver.cpp:218] Iteration 5600 (19.2368 iter/s, 5.19837s/100 iters), loss = 0.000863581
I0112 14:02:18.018057 4605 solver.cpp:237] Train net output #0: loss = 0.000863529 (* 1 = 0.000863529 loss)
I0112 14:02:18.018071 4605 sgd_solver.cpp:105] Iteration 5600, lr = 0.00716402
I0112 14:02:19.059535 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:02:23.215595 4605 solver.cpp:218] Iteration 5700 (19.24 iter/s, 5.1975s/100 iters), loss = 0.00275054
I0112 14:02:23.215651 4605 solver.cpp:237] Train net output #0: loss = 0.0027505 (* 1 = 0.0027505 loss)
I0112 14:02:23.215662 4605 sgd_solver.cpp:105] Iteration 5700, lr = 0.00712977
I0112 14:02:28.416524 4605 solver.cpp:218] Iteration 5800 (19.2277 iter/s, 5.20083s/100 iters), loss = 0.0391812
I0112 14:02:28.416708 4605 solver.cpp:237] Train net output #0: loss = 0.0391811 (* 1 = 0.0391811 loss)
I0112 14:02:28.416728 4605 sgd_solver.cpp:105] Iteration 5800, lr = 0.0070959
I0112 14:02:33.617794 4605 solver.cpp:218] Iteration 5900 (19.2269 iter/s, 5.20105s/100 iters), loss = 0.00609078
I0112 14:02:33.617852 4605 solver.cpp:237] Train net output #0: loss = 0.00609072 (* 1 = 0.00609072 loss)
I0112 14:02:33.617866 4605 sgd_solver.cpp:105] Iteration 5900, lr = 0.0070624
I0112 14:02:38.732347 4605 solver.cpp:330] Iteration 6000, Testing net (#0)
I0112 14:02:41.039494 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:02:41.133369 4605 solver.cpp:397] Test net output #0: accuracy = 0.9904
I0112 14:02:41.133410 4605 solver.cpp:397] Test net output #1: loss = 0.0272586 (* 1 = 0.0272586 loss)
I0112 14:02:41.183835 4605 solver.cpp:218] Iteration 6000 (13.2172 iter/s, 7.56592s/100 iters), loss = 0.00282902
I0112 14:02:41.183910 4605 solver.cpp:237] Train net output #0: loss = 0.00282896 (* 1 = 0.00282896 loss)
I0112 14:02:41.183924 4605 sgd_solver.cpp:105] Iteration 6000, lr = 0.00702927
I0112 14:02:46.383450 4605 solver.cpp:218] Iteration 6100 (19.2327 iter/s, 5.19949s/100 iters), loss = 0.00361349
I0112 14:02:46.383522 4605 solver.cpp:237] Train net output #0: loss = 0.00361343 (* 1 = 0.00361343 loss)
I0112 14:02:46.383538 4605 sgd_solver.cpp:105] Iteration 6100, lr = 0.0069965
I0112 14:02:51.583528 4605 solver.cpp:218] Iteration 6200 (19.2309 iter/s, 5.19997s/100 iters), loss = 0.00890321
I0112 14:02:51.583590 4605 solver.cpp:237] Train net output #0: loss = 0.00890316 (* 1 = 0.00890316 loss)
I0112 14:02:51.583602 4605 sgd_solver.cpp:105] Iteration 6200, lr = 0.00696408
I0112 14:02:56.782480 4605 solver.cpp:218] Iteration 6300 (19.235 iter/s, 5.19885s/100 iters), loss = 0.00851892
I0112 14:02:56.782539 4605 solver.cpp:237] Train net output #0: loss = 0.00851887 (* 1 = 0.00851887 loss)
I0112 14:02:56.782553 4605 sgd_solver.cpp:105] Iteration 6300, lr = 0.00693201
I0112 14:03:01.982347 4605 solver.cpp:218] Iteration 6400 (19.2316 iter/s, 5.19977s/100 iters), loss = 0.00671306
I0112 14:03:01.982455 4605 solver.cpp:237] Train net output #0: loss = 0.00671302 (* 1 = 0.00671302 loss)
I0112 14:03:01.982470 4605 sgd_solver.cpp:105] Iteration 6400, lr = 0.00690029
I0112 14:03:07.094683 4605 solver.cpp:330] Iteration 6500, Testing net (#0)
I0112 14:03:09.401098 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:03:09.494998 4605 solver.cpp:397] Test net output #0: accuracy = 0.9898
I0112 14:03:09.495038 4605 solver.cpp:397] Test net output #1: loss = 0.0315893 (* 1 = 0.0315893 loss)
I0112 14:03:09.545534 4605 solver.cpp:218] Iteration 6500 (13.2222 iter/s, 7.56302s/100 iters), loss = 0.0187188
I0112 14:03:09.545611 4605 solver.cpp:237] Train net output #0: loss = 0.0187188 (* 1 = 0.0187188 loss)
I0112 14:03:09.545625 4605 sgd_solver.cpp:105] Iteration 6500, lr = 0.0068689
I0112 14:03:12.562772 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:03:14.745219 4605 solver.cpp:218] Iteration 6600 (19.2324 iter/s, 5.19956s/100 iters), loss = 0.0217157
I0112 14:03:14.745277 4605 solver.cpp:237] Train net output #0: loss = 0.0217157 (* 1 = 0.0217157 loss)
I0112 14:03:14.745290 4605 sgd_solver.cpp:105] Iteration 6600, lr = 0.00683784
I0112 14:03:19.943637 4605 solver.cpp:218] Iteration 6700 (19.237 iter/s, 5.19833s/100 iters), loss = 0.00719072
I0112 14:03:19.943696 4605 solver.cpp:237] Train net output #0: loss = 0.0071907 (* 1 = 0.0071907 loss)
I0112 14:03:19.943711 4605 sgd_solver.cpp:105] Iteration 6700, lr = 0.00680711
I0112 14:03:25.152591 4605 solver.cpp:218] Iteration 6800 (19.1981 iter/s, 5.20884s/100 iters), loss = 0.00298378
I0112 14:03:25.152668 4605 solver.cpp:237] Train net output #0: loss = 0.00298375 (* 1 = 0.00298375 loss)
I0112 14:03:25.152689 4605 sgd_solver.cpp:105] Iteration 6800, lr = 0.0067767
I0112 14:03:30.365628 4605 solver.cpp:218] Iteration 6900 (19.1831 iter/s, 5.21291s/100 iters), loss = 0.00810112
I0112 14:03:30.365710 4605 solver.cpp:237] Train net output #0: loss = 0.00810109 (* 1 = 0.00810109 loss)
I0112 14:03:30.365727 4605 sgd_solver.cpp:105] Iteration 6900, lr = 0.0067466
I0112 14:03:35.493791 4605 solver.cpp:330] Iteration 7000, Testing net (#0)
I0112 14:03:37.809173 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:03:37.903611 4605 solver.cpp:397] Test net output #0: accuracy = 0.9908
I0112 14:03:37.903656 4605 solver.cpp:397] Test net output #1: loss = 0.0282443 (* 1 = 0.0282443 loss)
I0112 14:03:37.954181 4605 solver.cpp:218] Iteration 7000 (13.178 iter/s, 7.58842s/100 iters), loss = 0.0118699
I0112 14:03:37.954258 4605 solver.cpp:237] Train net output #0: loss = 0.0118699 (* 1 = 0.0118699 loss)
I0112 14:03:37.954277 4605 sgd_solver.cpp:105] Iteration 7000, lr = 0.00671681
I0112 14:03:43.163439 4605 solver.cpp:218] Iteration 7100 (19.197 iter/s, 5.20914s/100 iters), loss = 0.00973831
I0112 14:03:43.163498 4605 solver.cpp:237] Train net output #0: loss = 0.00973828 (* 1 = 0.00973828 loss)
I0112 14:03:43.163511 4605 sgd_solver.cpp:105] Iteration 7100, lr = 0.00668733
I0112 14:03:48.370353 4605 solver.cpp:218] Iteration 7200 (19.2056 iter/s, 5.20682s/100 iters), loss = 0.00335247
I0112 14:03:48.370426 4605 solver.cpp:237] Train net output #0: loss = 0.00335244 (* 1 = 0.00335244 loss)
I0112 14:03:48.370446 4605 sgd_solver.cpp:105] Iteration 7200, lr = 0.00665815
I0112 14:03:53.576642 4605 solver.cpp:218] Iteration 7300 (19.2079 iter/s, 5.20618s/100 iters), loss = 0.0219852
I0112 14:03:53.576716 4605 solver.cpp:237] Train net output #0: loss = 0.0219852 (* 1 = 0.0219852 loss)
I0112 14:03:53.576733 4605 sgd_solver.cpp:105] Iteration 7300, lr = 0.00662927
I0112 14:03:58.785342 4605 solver.cpp:218] Iteration 7400 (19.199 iter/s, 5.2086s/100 iters), loss = 0.00455252
I0112 14:03:58.785408 4605 solver.cpp:237] Train net output #0: loss = 0.00455249 (* 1 = 0.00455249 loss)
I0112 14:03:58.785421 4605 sgd_solver.cpp:105] Iteration 7400, lr = 0.00660067
I0112 14:04:03.736567 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:04:03.910609 4605 solver.cpp:330] Iteration 7500, Testing net (#0)
I0112 14:04:06.219141 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:04:06.312794 4605 solver.cpp:397] Test net output #0: accuracy = 0.9891
I0112 14:04:06.312847 4605 solver.cpp:397] Test net output #1: loss = 0.0323302 (* 1 = 0.0323302 loss)
I0112 14:04:06.363332 4605 solver.cpp:218] Iteration 7500 (13.1963 iter/s, 7.57787s/100 iters), loss = 0.00170278
I0112 14:04:06.363407 4605 solver.cpp:237] Train net output #0: loss = 0.00170275 (* 1 = 0.00170275 loss)
I0112 14:04:06.363432 4605 sgd_solver.cpp:105] Iteration 7500, lr = 0.00657236
I0112 14:04:11.567973 4605 solver.cpp:218] Iteration 7600 (19.2141 iter/s, 5.20452s/100 iters), loss = 0.00527642
I0112 14:04:11.568038 4605 solver.cpp:237] Train net output #0: loss = 0.00527638 (* 1 = 0.00527638 loss)
I0112 14:04:11.568053 4605 sgd_solver.cpp:105] Iteration 7600, lr = 0.00654433
I0112 14:04:16.778379 4605 solver.cpp:218] Iteration 7700 (19.1928 iter/s, 5.21029s/100 iters), loss = 0.0344356
I0112 14:04:16.778457 4605 solver.cpp:237] Train net output #0: loss = 0.0344355 (* 1 = 0.0344355 loss)
I0112 14:04:16.778471 4605 sgd_solver.cpp:105] Iteration 7700, lr = 0.00651658
I0112 14:04:21.987366 4605 solver.cpp:218] Iteration 7800 (19.1981 iter/s, 5.20886s/100 iters), loss = 0.00238596
I0112 14:04:21.987444 4605 solver.cpp:237] Train net output #0: loss = 0.0023859 (* 1 = 0.0023859 loss)
I0112 14:04:21.987462 4605 sgd_solver.cpp:105] Iteration 7800, lr = 0.00648911
I0112 14:04:27.199033 4605 solver.cpp:218] Iteration 7900 (19.1882 iter/s, 5.21154s/100 iters), loss = 0.00611803
I0112 14:04:27.199168 4605 solver.cpp:237] Train net output #0: loss = 0.00611798 (* 1 = 0.00611798 loss)
I0112 14:04:27.199187 4605 sgd_solver.cpp:105] Iteration 7900, lr = 0.0064619
I0112 14:04:32.325093 4605 solver.cpp:330] Iteration 8000, Testing net (#0)
I0112 14:04:34.645023 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:04:34.738813 4605 solver.cpp:397] Test net output #0: accuracy = 0.9903
I0112 14:04:34.738867 4605 solver.cpp:397] Test net output #1: loss = 0.0289319 (* 1 = 0.0289319 loss)
I0112 14:04:34.789491 4605 solver.cpp:218] Iteration 8000 (13.1748 iter/s, 7.59026s/100 iters), loss = 0.00690512
I0112 14:04:34.789590 4605 solver.cpp:237] Train net output #0: loss = 0.00690506 (* 1 = 0.00690506 loss)
I0112 14:04:34.789618 4605 sgd_solver.cpp:105] Iteration 8000, lr = 0.00643496
I0112 14:04:39.997692 4605 solver.cpp:218] Iteration 8100 (19.201 iter/s, 5.20807s/100 iters), loss = 0.0260982
I0112 14:04:39.997867 4605 solver.cpp:237] Train net output #0: loss = 0.0260982 (* 1 = 0.0260982 loss)
I0112 14:04:39.997889 4605 sgd_solver.cpp:105] Iteration 8100, lr = 0.00640827
I0112 14:04:45.208073 4605 solver.cpp:218] Iteration 8200 (19.1932 iter/s, 5.21017s/100 iters), loss = 0.00465579
I0112 14:04:45.208140 4605 solver.cpp:237] Train net output #0: loss = 0.00465573 (* 1 = 0.00465573 loss)
I0112 14:04:45.208156 4605 sgd_solver.cpp:105] Iteration 8200, lr = 0.00638185
I0112 14:04:50.419154 4605 solver.cpp:218] Iteration 8300 (19.1903 iter/s, 5.21097s/100 iters), loss = 0.0238528
I0112 14:04:50.419225 4605 solver.cpp:237] Train net output #0: loss = 0.0238527 (* 1 = 0.0238527 loss)
I0112 14:04:50.419245 4605 sgd_solver.cpp:105] Iteration 8300, lr = 0.00635567
I0112 14:04:55.623467 4605 solver.cpp:218] Iteration 8400 (19.2152 iter/s, 5.20421s/100 iters), loss = 0.011262
I0112 14:04:55.623533 4605 solver.cpp:237] Train net output #0: loss = 0.011262 (* 1 = 0.011262 loss)
I0112 14:04:55.623548 4605 sgd_solver.cpp:105] Iteration 8400, lr = 0.00632975
I0112 14:04:57.341358 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:05:00.740424 4605 solver.cpp:330] Iteration 8500, Testing net (#0)
I0112 14:05:03.046005 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:05:03.140305 4605 solver.cpp:397] Test net output #0: accuracy = 0.9902
I0112 14:05:03.140347 4605 solver.cpp:397] Test net output #1: loss = 0.0298106 (* 1 = 0.0298106 loss)
I0112 14:05:03.190874 4605 solver.cpp:218] Iteration 8500 (13.2148 iter/s, 7.56729s/100 iters), loss = 0.00609464
I0112 14:05:03.190951 4605 solver.cpp:237] Train net output #0: loss = 0.00609459 (* 1 = 0.00609459 loss)
I0112 14:05:03.190965 4605 sgd_solver.cpp:105] Iteration 8500, lr = 0.00630407
I0112 14:05:08.389690 4605 solver.cpp:218] Iteration 8600 (19.2356 iter/s, 5.1987s/100 iters), loss = 0.000793173
I0112 14:05:08.389744 4605 solver.cpp:237] Train net output #0: loss = 0.000793116 (* 1 = 0.000793116 loss)
I0112 14:05:08.389755 4605 sgd_solver.cpp:105] Iteration 8600, lr = 0.00627864
I0112 14:05:13.589684 4605 solver.cpp:218] Iteration 8700 (19.2311 iter/s, 5.1999s/100 iters), loss = 0.0026464
I0112 14:05:13.589817 4605 solver.cpp:237] Train net output #0: loss = 0.00264634 (* 1 = 0.00264634 loss)
I0112 14:05:13.589830 4605 sgd_solver.cpp:105] Iteration 8700, lr = 0.00625344
I0112 14:05:18.790073 4605 solver.cpp:218] Iteration 8800 (19.23 iter/s, 5.20021s/100 iters), loss = 0.00182293
I0112 14:05:18.790135 4605 solver.cpp:237] Train net output #0: loss = 0.00182288 (* 1 = 0.00182288 loss)
I0112 14:05:18.790148 4605 sgd_solver.cpp:105] Iteration 8800, lr = 0.00622847
I0112 14:05:23.994278 4605 solver.cpp:218] Iteration 8900 (19.2156 iter/s, 5.2041s/100 iters), loss = 0.000394624
I0112 14:05:23.994349 4605 solver.cpp:237] Train net output #0: loss = 0.00039457 (* 1 = 0.00039457 loss)
I0112 14:05:23.994364 4605 sgd_solver.cpp:105] Iteration 8900, lr = 0.00620374
I0112 14:05:29.116060 4605 solver.cpp:330] Iteration 9000, Testing net (#0)
I0112 14:05:31.432765 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:05:31.526996 4605 solver.cpp:397] Test net output #0: accuracy = 0.9903
I0112 14:05:31.527047 4605 solver.cpp:397] Test net output #1: loss = 0.0287546 (* 1 = 0.0287546 loss)
I0112 14:05:31.577587 4605 solver.cpp:218] Iteration 9000 (13.1871 iter/s, 7.58318s/100 iters), loss = 0.018936
I0112 14:05:31.577656 4605 solver.cpp:237] Train net output #0: loss = 0.0189359 (* 1 = 0.0189359 loss)
I0112 14:05:31.577672 4605 sgd_solver.cpp:105] Iteration 9000, lr = 0.00617924
I0112 14:05:36.784974 4605 solver.cpp:218] Iteration 9100 (19.2039 iter/s, 5.20727s/100 iters), loss = 0.00699512
I0112 14:05:36.785063 4605 solver.cpp:237] Train net output #0: loss = 0.00699507 (* 1 = 0.00699507 loss)
I0112 14:05:36.785086 4605 sgd_solver.cpp:105] Iteration 9100, lr = 0.00615496
I0112 14:05:41.987784 4605 solver.cpp:218] Iteration 9200 (19.2209 iter/s, 5.20268s/100 iters), loss = 0.00425531
I0112 14:05:41.987859 4605 solver.cpp:237] Train net output #0: loss = 0.00425526 (* 1 = 0.00425526 loss)
I0112 14:05:41.987876 4605 sgd_solver.cpp:105] Iteration 9200, lr = 0.0061309
I0112 14:05:47.193492 4605 solver.cpp:218] Iteration 9300 (19.2101 iter/s, 5.2056s/100 iters), loss = 0.00811363
I0112 14:05:47.193666 4605 solver.cpp:237] Train net output #0: loss = 0.00811357 (* 1 = 0.00811357 loss)
I0112 14:05:47.193682 4605 sgd_solver.cpp:105] Iteration 9300, lr = 0.00610706
I0112 14:05:50.841812 4616 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:05:52.401444 4605 solver.cpp:218] Iteration 9400 (19.2022 iter/s, 5.20774s/100 iters), loss = 0.0287375
I0112 14:05:52.401510 4605 solver.cpp:237] Train net output #0: loss = 0.0287374 (* 1 = 0.0287374 loss)
I0112 14:05:52.401528 4605 sgd_solver.cpp:105] Iteration 9400, lr = 0.00608343
I0112 14:05:57.522619 4605 solver.cpp:330] Iteration 9500, Testing net (#0)
I0112 14:05:59.834517 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:05:59.928700 4605 solver.cpp:397] Test net output #0: accuracy = 0.9882
I0112 14:05:59.928741 4605 solver.cpp:397] Test net output #1: loss = 0.0368194 (* 1 = 0.0368194 loss)
I0112 14:05:59.979233 4605 solver.cpp:218] Iteration 9500 (13.1967 iter/s, 7.57766s/100 iters), loss = 0.0047314
I0112 14:05:59.979310 4605 solver.cpp:237] Train net output #0: loss = 0.00473134 (* 1 = 0.00473134 loss)
I0112 14:05:59.979342 4605 sgd_solver.cpp:105] Iteration 9500, lr = 0.00606002
I0112 14:06:05.184339 4605 solver.cpp:218] Iteration 9600 (19.2123 iter/s, 5.20499s/100 iters), loss = 0.00195558
I0112 14:06:05.184397 4605 solver.cpp:237] Train net output #0: loss = 0.00195552 (* 1 = 0.00195552 loss)
I0112 14:06:05.184412 4605 sgd_solver.cpp:105] Iteration 9600, lr = 0.00603682
I0112 14:06:10.397572 4605 solver.cpp:218] Iteration 9700 (19.1823 iter/s, 5.21314s/100 iters), loss = 0.00291929
I0112 14:06:10.397629 4605 solver.cpp:237] Train net output #0: loss = 0.00291923 (* 1 = 0.00291923 loss)
I0112 14:06:10.397644 4605 sgd_solver.cpp:105] Iteration 9700, lr = 0.00601382
I0112 14:06:15.601755 4605 solver.cpp:218] Iteration 9800 (19.2157 iter/s, 5.20408s/100 iters), loss = 0.0147848
I0112 14:06:15.601830 4605 solver.cpp:237] Train net output #0: loss = 0.0147848 (* 1 = 0.0147848 loss)
I0112 14:06:15.601847 4605 sgd_solver.cpp:105] Iteration 9800, lr = 0.00599102
I0112 14:06:20.808406 4605 solver.cpp:218] Iteration 9900 (19.2066 iter/s, 5.20655s/100 iters), loss = 0.00615287
I0112 14:06:20.808533 4605 solver.cpp:237] Train net output #0: loss = 0.00615281 (* 1 = 0.00615281 loss)
I0112 14:06:20.808552 4605 sgd_solver.cpp:105] Iteration 9900, lr = 0.00596843
I0112 14:06:25.933642 4605 solver.cpp:447] Snapshotting to binary proto file examples/mnist/lenet_iter_10000.caffemodel
I0112 14:06:25.976433 4605 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_10000.solverstate
I0112 14:06:25.995990 4605 solver.cpp:310] Iteration 10000, loss = 0.00368586
I0112 14:06:25.996039 4605 solver.cpp:330] Iteration 10000, Testing net (#0)
I0112 14:06:28.273962 4617 data_layer.cpp:73] Restarting data prefetching from start.
I0112 14:06:28.368412 4605 solver.cpp:397] Test net output #0: accuracy = 0.991
I0112 14:06:28.368463 4605 solver.cpp:397] Test net output #1: loss = 0.0278615 (* 1 = 0.0278615 loss)
I0112 14:06:28.368472 4605 solver.cpp:315] Optimization Done.
I0112 14:06:28.368477 4605 caffe.cpp:259] Optimization Done.
训练完成
训练完成
可以看到accruacy平均成功率0.991
5. 获取mnist样本子库中的图片
get_images.py
#coding=utf-8
import struct
import numpy as np
import matplotlib.pyplot as plt
import Image
#二进制的形式读入/home/fc/software/mnist/data/train-images-idx3-ubyte
filename='/home/fc/software/mnist/data/t10k-images-idx3-ubyte'
binfile=open(filename,'rb')
buf=binfile.read()
index=0
magic,numImages,numRows,numColumns=struct.unpack_from('>IIII',buf,index)
index+=struct.calcsize('>IIII')
#将每张图片按照格式存储到对应位置
for image in range(0,numImages):
im=struct.unpack_from('>784B',buf,index)
index+=struct.calcsize('>784B')
#这里注意 Image对象的dtype是uint8，需要转换
im=np.array(im,dtype='uint8')
im=im.reshape(28,28)
# fig=plt.figure()
# plotwindow=fig.add_subplot(111)
# plt.imshow(im,cmap='gray')
# plt.show()
im=Image.fromarray(im)
im.save('./test-images/test-image_%s.bmp'%image,'bmp')
6.mnist手写测试
将之前取出的图片放到caffe/examples/images/下，编写如下代码进行测试：
#!/usr/bin/env python
#coding=utf-8
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import caffe
#设置caffe_root目录
caffe_root = '/home/fc/caffe/'
#添加系统环境变量/home/fc/caffe/python
sys.path.insert(0,caffe_root + 'python')
#指定LetNet网络文件
MODEL_FILE = '/home/fc/caffe/examples/mnist/lenet.prototxt'
#训练好的model文件
PRETRAINED = '/home/fc/caffe/examples/mnist/lenet_iter_10000.caffemodel'
#测试图片路径
IMAGE_FILE = '/home/fc/caffe/examples/images/test-image_0.bmp'
#caffe接口载入文件
input_image = caffe.io.load_image(IMAGE_FILE,color = False)
#载入LetNet分类器
net = caffe.Classifier(MODEL_FILE,PRETRAINED)
#预测图片，进行分类
prediction = net.predict([input_image],oversample = False)
caffe.set_mode_cpu()
print 'predicted class:' ,prediction[0].argmax()
运行后
$ ./mnist_test.py
WARNING: Logging before InitGoogleLogging() is written to STDERR
W0112 19:51:41.881881 10487 _caffe.cpp:139] DEPRECATION WARNING - deprecated use of Python interface
W0112 19:51:41.881916 10487 _caffe.cpp:140] Use this instead (with the named "weights" parameter):
W0112 19:51:41.881930 10487 _caffe.cpp:142] Net('/home/fc/caffe/examples/mnist/lenet.prototxt', 1, weights='/home/fc/caffe/examples/mnist/lenet_iter_10000.caffemodel')
I0112 19:51:42.112020 10487 net.cpp:51] Initializing net from parameters:
name: "LeNet"
state {
phase: TEST
level: 0
}
layer {
name: "data"
type: "Input"
top: "data"
input_param {
shape {
dim: 64
dim: 1
dim: 28
dim: 28
}
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
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: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob"
}
I0112 19:51:42.112149 10487 layer_factory.hpp:77] Creating layer data
I0112 19:51:42.112165 10487 net.cpp:84] Creating Layer data
I0112 19:51:42.112174 10487 net.cpp:380] data -> data
I0112 19:51:42.112195 10487 net.cpp:122] Setting up data
I0112 19:51:42.112207 10487 net.cpp:129] Top shape: 64 1 28 28 (50176)
I0112 19:51:42.112212 10487 net.cpp:137] Memory required for data: 200704
I0112 19:51:42.112220 10487 layer_factory.hpp:77] Creating layer conv1
I0112 19:51:42.112233 10487 net.cpp:84] Creating Layer conv1
I0112 19:51:42.112239 10487 net.cpp:406] conv1 <- data
I0112 19:51:42.112247 10487 net.cpp:380] conv1 -> conv1
I0112 19:51:42.112313 10487 net.cpp:122] Setting up conv1
I0112 19:51:42.112325 10487 net.cpp:129] Top shape: 64 20 24 24 (737280)
I0112 19:51:42.112331 10487 net.cpp:137] Memory required for data: 3149824
I0112 19:51:42.112347 10487 layer_factory.hpp:77] Creating layer pool1
I0112 19:51:42.112356 10487 net.cpp:84] Creating Layer pool1
I0112 19:51:42.112362 10487 net.cpp:406] pool1 <- conv1
I0112 19:51:42.112370 10487 net.cpp:380] pool1 -> pool1
I0112 19:51:42.112385 10487 net.cpp:122] Setting up pool1
I0112 19:51:42.112392 10487 net.cpp:129] Top shape: 64 20 12 12 (184320)
I0112 19:51:42.112398 10487 net.cpp:137] Memory required for data: 3887104
I0112 19:51:42.112403 10487 layer_factory.hpp:77] Creating layer conv2
I0112 19:51:42.112412 10487 net.cpp:84] Creating Layer conv2
I0112 19:51:42.112418 10487 net.cpp:406] conv2 <- pool1
I0112 19:51:42.112426 10487 net.cpp:380] conv2 -> conv2
I0112 19:51:42.112623 10487 net.cpp:122] Setting up conv2
I0112 19:51:42.112634 10487 net.cpp:129] Top shape: 64 50 8 8 (204800)
I0112 19:51:42.112640 10487 net.cpp:137] Memory required for data: 4706304
I0112 19:51:42.112651 10487 layer_factory.hpp:77] Creating layer pool2
I0112 19:51:42.112661 10487 net.cpp:84] Creating Layer pool2
I0112 19:51:42.112668 10487 net.cpp:406] pool2 <- conv2
I0112 19:51:42.112675 10487 net.cpp:380] pool2 -> pool2
I0112 19:51:42.112686 10487 net.cpp:122] Setting up pool2
I0112 19:51:42.112694 10487 net.cpp:129] Top shape: 64 50 4 4 (51200)
I0112 19:51:42.112699 10487 net.cpp:137] Memory required for data: 4911104
I0112 19:51:42.112705 10487 layer_factory.hpp:77] Creating layer ip1
I0112 19:51:42.112715 10487 net.cpp:84] Creating Layer ip1
I0112 19:51:42.112721 10487 net.cpp:406] ip1 <- pool2
I0112 19:51:42.112730 10487 net.cpp:380] ip1 -> ip1
I0112 19:51:42.115571 10487 net.cpp:122] Setting up ip1
I0112 19:51:42.115591 10487 net.cpp:129] Top shape: 64 500 (32000)
I0112 19:51:42.115597 10487 net.cpp:137] Memory required for data: 5039104
I0112 19:51:42.115612 10487 layer_factory.hpp:77] Creating layer relu1
I0112 19:51:42.115622 10487 net.cpp:84] Creating Layer relu1
I0112 19:51:42.115628 10487 net.cpp:406] relu1 <- ip1
I0112 19:51:42.115635 10487 net.cpp:367] relu1 -> ip1 (in-place)
I0112 19:51:42.115645 10487 net.cpp:122] Setting up relu1
I0112 19:51:42.115653 10487 net.cpp:129] Top shape: 64 500 (32000)
I0112 19:51:42.115658 10487 net.cpp:137] Memory required for data: 5167104
I0112 19:51:42.115664 10487 layer_factory.hpp:77] Creating layer ip2
I0112 19:51:42.115671 10487 net.cpp:84] Creating Layer ip2
I0112 19:51:42.115676 10487 net.cpp:406] ip2 <- ip1
I0112 19:51:42.115685 10487 net.cpp:380] ip2 -> ip2
I0112 19:51:42.115737 10487 net.cpp:122] Setting up ip2
I0112 19:51:42.115746 10487 net.cpp:129] Top shape: 64 10 (640)
I0112 19:51:42.115751 10487 net.cpp:137] Memory required for data: 5169664
I0112 19:51:42.115759 10487 layer_factory.hpp:77] Creating layer prob
I0112 19:51:42.115768 10487 net.cpp:84] Creating Layer prob
I0112 19:51:42.115774 10487 net.cpp:406] prob <- ip2
I0112 19:51:42.115782 10487 net.cpp:380] prob -> prob
I0112 19:51:42.115794 10487 net.cpp:122] Setting up prob
I0112 19:51:42.115802 10487 net.cpp:129] Top shape: 64 10 (640)
I0112 19:51:42.115808 10487 net.cpp:137] Memory required for data: 5172224
I0112 19:51:42.115813 10487 net.cpp:200] prob does not need backward computation.
I0112 19:51:42.115819 10487 net.cpp:200] ip2 does not need backward computation.
I0112 19:51:42.115825 10487 net.cpp:200] relu1 does not need backward computation.
I0112 19:51:42.115830 10487 net.cpp:200] ip1 does not need backward computation.
I0112 19:51:42.115836 10487 net.cpp:200] pool2 does not need backward computation.
I0112 19:51:42.115842 10487 net.cpp:200] conv2 does not need backward computation.
I0112 19:51:42.115847 10487 net.cpp:200] pool1 does not need backward computation.
I0112 19:51:42.115854 10487 net.cpp:200] conv1 does not need backward computation.
I0112 19:51:42.115859 10487 net.cpp:200] data does not need backward computation.
I0112 19:51:42.115865 10487 net.cpp:242] This network produces output prob
I0112 19:51:42.115875 10487 net.cpp:255] Network initialization done.
I0112 19:51:42.117382 10487 net.cpp:744] Ignoring source layer mnist
I0112 19:51:42.117758 10487 net.cpp:744] Ignoring source layer loss
predicted class: 7
识别出的数字是7，成功的识别出数字。
