Skip to content

Instantly share code, notes, and snippets.

@fxrcode

fxrcode/nvidia-docker_bash.log

Last active May 11, 2017 18:45
Show Gist options
  • Save fxrcode/37d25b9e04007011d0644c1f09df571c to your computer and use it in GitHub Desktop.
Save fxrcode/37d25b9e04007011d0644c1f09df571c to your computer and use it in GitHub Desktop.
Download ZIP
May11_Caffe_Nvidia-Docker_1st_MNIST
Raw
nvidia-docker_bash.log
139 p1@p1-xos:~⟫ sudo nvidia-docker run -ti bvlc/caffe:gpu /bin/bash
root@0a1c1b0432ff:/workspace# pwd
/workspace
root@0a1c1b0432ff:/workspace# lsb_release
bash: lsb_release: command not found
root@0a1c1b0432ff:/workspace# uname -a
Linux 0a1c1b0432ff 4.8.0-51-generic #54~16.04.1-Ubuntu SMP Wed Apr 26 16:00:28 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux
root@0a1c1b0432ff:/workspace# nvidia-
nvidia-cuda-mps-control nvidia-debugdump nvidia-smi
nvidia-cuda-mps-server nvidia-persistenced
root@0a1c1b0432ff:/workspace# nvidia-smi
Raw
test_mnist.log
root@0a1c1b0432ff:/opt/caffe# ./build/tools/caffe test -model=examples/mnist/lenet_train_test.prototxt -weights=examples/mnist/lenet_iter_10000.caffemodel
I0511 18:43:46.670570 534 caffe.cpp:284] Use CPU.
I0511 18:43:46.936956 534 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist
I0511 18:43:46.937157 534 net.cpp:51] Initializing net from parameters:
name: "LeNet"
state {
phase: TEST
level: 0
stage: ""
}
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"
}
I0511 18:43:46.937371 534 layer_factory.hpp:77] Creating layer mnist
I0511 18:43:46.937526 534 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_test_lmdb
I0511 18:43:46.937548 534 net.cpp:84] Creating Layer mnist
I0511 18:43:46.937556 534 net.cpp:380] mnist -> data
I0511 18:43:46.937592 534 net.cpp:380] mnist -> label
I0511 18:43:46.937618 534 data_layer.cpp:45] output data size: 100,1,28,28
I0511 18:43:46.938709 534 net.cpp:122] Setting up mnist
I0511 18:43:46.938736 534 net.cpp:129] Top shape: 100 1 28 28 (78400)
I0511 18:43:46.938741 534 net.cpp:129] Top shape: 100 (100)
I0511 18:43:46.938745 534 net.cpp:137] Memory required for data: 314000
I0511 18:43:46.938751 534 layer_factory.hpp:77] Creating layer label_mnist_1_split
I0511 18:43:46.938778 534 net.cpp:84] Creating Layer label_mnist_1_split
I0511 18:43:46.938784 534 net.cpp:406] label_mnist_1_split <- label
I0511 18:43:46.938794 534 net.cpp:380] label_mnist_1_split -> label_mnist_1_split_0
I0511 18:43:46.938802 534 net.cpp:380] label_mnist_1_split -> label_mnist_1_split_1
I0511 18:43:46.938809 534 net.cpp:122] Setting up label_mnist_1_split
I0511 18:43:46.938813 534 net.cpp:129] Top shape: 100 (100)
I0511 18:43:46.938817 534 net.cpp:129] Top shape: 100 (100)
I0511 18:43:46.938822 534 net.cpp:137] Memory required for data: 314800
I0511 18:43:46.938824 534 layer_factory.hpp:77] Creating layer conv1
I0511 18:43:46.938837 534 net.cpp:84] Creating Layer conv1
I0511 18:43:46.938840 534 net.cpp:406] conv1 <- data
I0511 18:43:46.938846 534 net.cpp:380] conv1 -> conv1
I0511 18:43:47.354722 534 net.cpp:122] Setting up conv1
I0511 18:43:47.354750 534 net.cpp:129] Top shape: 100 20 24 24 (1152000)
I0511 18:43:47.354753 534 net.cpp:137] Memory required for data: 4922800
I0511 18:43:47.354769 534 layer_factory.hpp:77] Creating layer pool1
I0511 18:43:47.354854 534 net.cpp:84] Creating Layer pool1
I0511 18:43:47.354873 534 net.cpp:406] pool1 <- conv1
I0511 18:43:47.354894 534 net.cpp:380] pool1 -> pool1
I0511 18:43:47.354955 534 net.cpp:122] Setting up pool1
I0511 18:43:47.354961 534 net.cpp:129] Top shape: 100 20 12 12 (288000)
I0511 18:43:47.354965 534 net.cpp:137] Memory required for data: 6074800
I0511 18:43:47.354971 534 layer_factory.hpp:77] Creating layer conv2
I0511 18:43:47.354986 534 net.cpp:84] Creating Layer conv2
I0511 18:43:47.354990 534 net.cpp:406] conv2 <- pool1
I0511 18:43:47.354995 534 net.cpp:380] conv2 -> conv2
I0511 18:43:47.356437 534 net.cpp:122] Setting up conv2
I0511 18:43:47.356446 534 net.cpp:129] Top shape: 100 50 8 8 (320000)
I0511 18:43:47.356451 534 net.cpp:137] Memory required for data: 7354800
I0511 18:43:47.356472 534 layer_factory.hpp:77] Creating layer pool2
I0511 18:43:47.356479 534 net.cpp:84] Creating Layer pool2
I0511 18:43:47.356483 534 net.cpp:406] pool2 <- conv2
I0511 18:43:47.356489 534 net.cpp:380] pool2 -> pool2
I0511 18:43:47.356498 534 net.cpp:122] Setting up pool2
I0511 18:43:47.356503 534 net.cpp:129] Top shape: 100 50 4 4 (80000)
I0511 18:43:47.356519 534 net.cpp:137] Memory required for data: 7674800
I0511 18:43:47.356523 534 layer_factory.hpp:77] Creating layer ip1
I0511 18:43:47.356529 534 net.cpp:84] Creating Layer ip1
I0511 18:43:47.356533 534 net.cpp:406] ip1 <- pool2
I0511 18:43:47.356536 534 net.cpp:380] ip1 -> ip1
I0511 18:43:47.358913 534 net.cpp:122] Setting up ip1
I0511 18:43:47.358921 534 net.cpp:129] Top shape: 100 500 (50000)
I0511 18:43:47.358923 534 net.cpp:137] Memory required for data: 7874800
I0511 18:43:47.358932 534 layer_factory.hpp:77] Creating layer relu1
I0511 18:43:47.358950 534 net.cpp:84] Creating Layer relu1
I0511 18:43:47.358953 534 net.cpp:406] relu1 <- ip1
I0511 18:43:47.358959 534 net.cpp:367] relu1 -> ip1 (in-place)
I0511 18:43:47.359544 534 net.cpp:122] Setting up relu1
I0511 18:43:47.359552 534 net.cpp:129] Top shape: 100 500 (50000)
I0511 18:43:47.359556 534 net.cpp:137] Memory required for data: 8074800
I0511 18:43:47.359560 534 layer_factory.hpp:77] Creating layer ip2
I0511 18:43:47.359568 534 net.cpp:84] Creating Layer ip2
I0511 18:43:47.359572 534 net.cpp:406] ip2 <- ip1
I0511 18:43:47.359578 534 net.cpp:380] ip2 -> ip2
I0511 18:43:47.359614 534 net.cpp:122] Setting up ip2
I0511 18:43:47.359618 534 net.cpp:129] Top shape: 100 10 (1000)
I0511 18:43:47.359622 534 net.cpp:137] Memory required for data: 8078800
I0511 18:43:47.359627 534 layer_factory.hpp:77] Creating layer ip2_ip2_0_split
I0511 18:43:47.359632 534 net.cpp:84] Creating Layer ip2_ip2_0_split
I0511 18:43:47.359637 534 net.cpp:406] ip2_ip2_0_split <- ip2
I0511 18:43:47.359640 534 net.cpp:380] ip2_ip2_0_split -> ip2_ip2_0_split_0
I0511 18:43:47.359647 534 net.cpp:380] ip2_ip2_0_split -> ip2_ip2_0_split_1
I0511 18:43:47.359652 534 net.cpp:122] Setting up ip2_ip2_0_split
I0511 18:43:47.359657 534 net.cpp:129] Top shape: 100 10 (1000)
I0511 18:43:47.359660 534 net.cpp:129] Top shape: 100 10 (1000)
I0511 18:43:47.359663 534 net.cpp:137] Memory required for data: 8086800
I0511 18:43:47.359666 534 layer_factory.hpp:77] Creating layer accuracy
I0511 18:43:47.359671 534 net.cpp:84] Creating Layer accuracy
I0511 18:43:47.359675 534 net.cpp:406] accuracy <- ip2_ip2_0_split_0
I0511 18:43:47.359678 534 net.cpp:406] accuracy <- label_mnist_1_split_0
I0511 18:43:47.359683 534 net.cpp:380] accuracy -> accuracy
I0511 18:43:47.359690 534 net.cpp:122] Setting up accuracy
I0511 18:43:47.359694 534 net.cpp:129] Top shape: (1)
I0511 18:43:47.359697 534 net.cpp:137] Memory required for data: 8086804
I0511 18:43:47.359700 534 layer_factory.hpp:77] Creating layer loss
I0511 18:43:47.359704 534 net.cpp:84] Creating Layer loss
I0511 18:43:47.359707 534 net.cpp:406] loss <- ip2_ip2_0_split_1
I0511 18:43:47.359710 534 net.cpp:406] loss <- label_mnist_1_split_1
I0511 18:43:47.359714 534 net.cpp:380] loss -> loss
I0511 18:43:47.359737 534 layer_factory.hpp:77] Creating layer loss
I0511 18:43:47.359885 534 net.cpp:122] Setting up loss
I0511 18:43:47.359891 534 net.cpp:129] Top shape: (1)
I0511 18:43:47.359895 534 net.cpp:132] with loss weight 1
I0511 18:43:47.359917 534 net.cpp:137] Memory required for data: 8086808
I0511 18:43:47.359921 534 net.cpp:198] loss needs backward computation.
I0511 18:43:47.359927 534 net.cpp:200] accuracy does not need backward computation.
I0511 18:43:47.359932 534 net.cpp:198] ip2_ip2_0_split needs backward computation.
I0511 18:43:47.359936 534 net.cpp:198] ip2 needs backward computation.
I0511 18:43:47.359939 534 net.cpp:198] relu1 needs backward computation.
I0511 18:43:47.359943 534 net.cpp:198] ip1 needs backward computation.
I0511 18:43:47.359946 534 net.cpp:198] pool2 needs backward computation.
I0511 18:43:47.359951 534 net.cpp:198] conv2 needs backward computation.
I0511 18:43:47.359953 534 net.cpp:198] pool1 needs backward computation.
I0511 18:43:47.359957 534 net.cpp:198] conv1 needs backward computation.
I0511 18:43:47.359962 534 net.cpp:200] label_mnist_1_split does not need backward computation.
I0511 18:43:47.359966 534 net.cpp:200] mnist does not need backward computation.
I0511 18:43:47.359971 534 net.cpp:242] This network produces output accuracy
I0511 18:43:47.359973 534 net.cpp:242] This network produces output loss
I0511 18:43:47.359982 534 net.cpp:255] Network initialization done.
I0511 18:43:47.361605 534 caffe.cpp:290] Running for 50 iterations.
I0511 18:43:47.457063 534 caffe.cpp:313] Batch 0, accuracy = 0.98
I0511 18:43:47.457088 534 caffe.cpp:313] Batch 0, loss = 0.0183153
I0511 18:43:47.503298 534 caffe.cpp:313] Batch 1, accuracy = 1
I0511 18:43:47.503320 534 caffe.cpp:313] Batch 1, loss = 0.0100282
I0511 18:43:47.549146 534 caffe.cpp:313] Batch 2, accuracy = 0.99
I0511 18:43:47.549167 534 caffe.cpp:313] Batch 2, loss = 0.0349494
I0511 18:43:47.594298 534 caffe.cpp:313] Batch 3, accuracy = 0.99
I0511 18:43:47.594318 534 caffe.cpp:313] Batch 3, loss = 0.0195375
I0511 18:43:47.639641 534 caffe.cpp:313] Batch 4, accuracy = 0.98
I0511 18:43:47.639662 534 caffe.cpp:313] Batch 4, loss = 0.0445807
I0511 18:43:47.685220 534 caffe.cpp:313] Batch 5, accuracy = 0.99
I0511 18:43:47.685240 534 caffe.cpp:313] Batch 5, loss = 0.0397243
I0511 18:43:47.730939 534 caffe.cpp:313] Batch 6, accuracy = 0.98
I0511 18:43:47.730958 534 caffe.cpp:313] Batch 6, loss = 0.0736036
I0511 18:43:47.776216 534 caffe.cpp:313] Batch 7, accuracy = 0.99
I0511 18:43:47.776237 534 caffe.cpp:313] Batch 7, loss = 0.0487895
I0511 18:43:47.821794 534 caffe.cpp:313] Batch 8, accuracy = 1
I0511 18:43:47.821813 534 caffe.cpp:313] Batch 8, loss = 0.00737563
I0511 18:43:47.867640 534 caffe.cpp:313] Batch 9, accuracy = 0.98
I0511 18:43:47.867660 534 caffe.cpp:313] Batch 9, loss = 0.0331768
I0511 18:43:47.913029 534 caffe.cpp:313] Batch 10, accuracy = 0.98
I0511 18:43:47.913048 534 caffe.cpp:313] Batch 10, loss = 0.0608895
I0511 18:43:47.958816 534 caffe.cpp:313] Batch 11, accuracy = 0.98
I0511 18:43:47.958837 534 caffe.cpp:313] Batch 11, loss = 0.0378952
I0511 18:43:48.004338 534 caffe.cpp:313] Batch 12, accuracy = 0.95
I0511 18:43:48.004355 534 caffe.cpp:313] Batch 12, loss = 0.168317
I0511 18:43:48.050546 534 caffe.cpp:313] Batch 13, accuracy = 0.98
I0511 18:43:48.050567 534 caffe.cpp:313] Batch 13, loss = 0.0382882
I0511 18:43:48.096148 534 caffe.cpp:313] Batch 14, accuracy = 1
I0511 18:43:48.096168 534 caffe.cpp:313] Batch 14, loss = 0.0089751
I0511 18:43:48.142673 534 caffe.cpp:313] Batch 15, accuracy = 0.99
I0511 18:43:48.142694 534 caffe.cpp:313] Batch 15, loss = 0.0316318
I0511 18:43:48.188145 534 caffe.cpp:313] Batch 16, accuracy = 1
I0511 18:43:48.188166 534 caffe.cpp:313] Batch 16, loss = 0.0158275
I0511 18:43:48.233964 534 caffe.cpp:313] Batch 17, accuracy = 0.99
I0511 18:43:48.233986 534 caffe.cpp:313] Batch 17, loss = 0.0329577
I0511 18:43:48.279325 534 caffe.cpp:313] Batch 18, accuracy = 1
I0511 18:43:48.279369 534 caffe.cpp:313] Batch 18, loss = 0.00947101
I0511 18:43:48.325031 534 caffe.cpp:313] Batch 19, accuracy = 0.99
I0511 18:43:48.325052 534 caffe.cpp:313] Batch 19, loss = 0.0584296
I0511 18:43:48.370820 534 caffe.cpp:313] Batch 20, accuracy = 0.98
I0511 18:43:48.370842 534 caffe.cpp:313] Batch 20, loss = 0.0893826
I0511 18:43:48.416714 534 caffe.cpp:313] Batch 21, accuracy = 0.97
I0511 18:43:48.416733 534 caffe.cpp:313] Batch 21, loss = 0.0563625
I0511 18:43:48.463677 534 caffe.cpp:313] Batch 22, accuracy = 0.99
I0511 18:43:48.463696 534 caffe.cpp:313] Batch 22, loss = 0.0275421
I0511 18:43:48.509598 534 caffe.cpp:313] Batch 23, accuracy = 1
I0511 18:43:48.509618 534 caffe.cpp:313] Batch 23, loss = 0.0178095
I0511 18:43:48.555642 534 caffe.cpp:313] Batch 24, accuracy = 0.99
I0511 18:43:48.555661 534 caffe.cpp:313] Batch 24, loss = 0.0440083
I0511 18:43:48.601516 534 caffe.cpp:313] Batch 25, accuracy = 0.99
I0511 18:43:48.601536 534 caffe.cpp:313] Batch 25, loss = 0.08853
I0511 18:43:48.647208 534 caffe.cpp:313] Batch 26, accuracy = 0.98
I0511 18:43:48.647228 534 caffe.cpp:313] Batch 26, loss = 0.104539
I0511 18:43:48.692878 534 caffe.cpp:313] Batch 27, accuracy = 0.99
I0511 18:43:48.692899 534 caffe.cpp:313] Batch 27, loss = 0.0166222
I0511 18:43:48.738636 534 caffe.cpp:313] Batch 28, accuracy = 0.98
I0511 18:43:48.738657 534 caffe.cpp:313] Batch 28, loss = 0.0579117
I0511 18:43:48.783928 534 caffe.cpp:313] Batch 29, accuracy = 0.96
I0511 18:43:48.783947 534 caffe.cpp:313] Batch 29, loss = 0.117745
I0511 18:43:48.829802 534 caffe.cpp:313] Batch 30, accuracy = 1
I0511 18:43:48.829823 534 caffe.cpp:313] Batch 30, loss = 0.0201589
I0511 18:43:48.875820 534 caffe.cpp:313] Batch 31, accuracy = 1
I0511 18:43:48.875840 534 caffe.cpp:313] Batch 31, loss = 0.00375302
I0511 18:43:48.921403 534 caffe.cpp:313] Batch 32, accuracy = 1
I0511 18:43:48.921422 534 caffe.cpp:313] Batch 32, loss = 0.00625853
I0511 18:43:48.967042 534 caffe.cpp:313] Batch 33, accuracy = 1
I0511 18:43:48.967061 534 caffe.cpp:313] Batch 33, loss = 0.00372678
I0511 18:43:49.015280 534 caffe.cpp:313] Batch 34, accuracy = 0.99
I0511 18:43:49.015301 534 caffe.cpp:313] Batch 34, loss = 0.0505239
I0511 18:43:49.065428 534 caffe.cpp:313] Batch 35, accuracy = 0.97
I0511 18:43:49.065460 534 caffe.cpp:313] Batch 35, loss = 0.106434
I0511 18:43:49.115597 534 caffe.cpp:313] Batch 36, accuracy = 1
I0511 18:43:49.115624 534 caffe.cpp:313] Batch 36, loss = 0.00438205
I0511 18:43:49.165475 534 caffe.cpp:313] Batch 37, accuracy = 0.98
I0511 18:43:49.165503 534 caffe.cpp:313] Batch 37, loss = 0.0538784
I0511 18:43:49.214176 534 caffe.cpp:313] Batch 38, accuracy = 1
I0511 18:43:49.214228 534 caffe.cpp:313] Batch 38, loss = 0.0154643
I0511 18:43:49.261406 534 caffe.cpp:313] Batch 39, accuracy = 0.98
I0511 18:43:49.261427 534 caffe.cpp:313] Batch 39, loss = 0.0390331
I0511 18:43:49.307471 534 caffe.cpp:313] Batch 40, accuracy = 0.98
I0511 18:43:49.307490 534 caffe.cpp:313] Batch 40, loss = 0.0450034
I0511 18:43:49.353212 534 caffe.cpp:313] Batch 41, accuracy = 0.98
I0511 18:43:49.353232 534 caffe.cpp:313] Batch 41, loss = 0.0778003
I0511 18:43:49.399161 534 caffe.cpp:313] Batch 42, accuracy = 0.99
I0511 18:43:49.399180 534 caffe.cpp:313] Batch 42, loss = 0.0391202
I0511 18:43:49.445185 534 caffe.cpp:313] Batch 43, accuracy = 1
I0511 18:43:49.445204 534 caffe.cpp:313] Batch 43, loss = 0.0153738
I0511 18:43:49.492451 534 caffe.cpp:313] Batch 44, accuracy = 0.98
I0511 18:43:49.492471 534 caffe.cpp:313] Batch 44, loss = 0.0297577
I0511 18:43:49.538363 534 caffe.cpp:313] Batch 45, accuracy = 0.98
I0511 18:43:49.538383 534 caffe.cpp:313] Batch 45, loss = 0.0279536
I0511 18:43:49.583789 534 caffe.cpp:313] Batch 46, accuracy = 1
I0511 18:43:49.583808 534 caffe.cpp:313] Batch 46, loss = 0.00990225
I0511 18:43:49.629189 534 caffe.cpp:313] Batch 47, accuracy = 0.99
I0511 18:43:49.629209 534 caffe.cpp:313] Batch 47, loss = 0.0110674
I0511 18:43:49.675367 534 caffe.cpp:313] Batch 48, accuracy = 0.98
I0511 18:43:49.675389 534 caffe.cpp:313] Batch 48, loss = 0.0835327
I0511 18:43:49.720705 534 caffe.cpp:313] Batch 49, accuracy = 1
I0511 18:43:49.720723 534 caffe.cpp:313] Batch 49, loss = 0.00869715
I0511 18:43:49.720726 534 caffe.cpp:318] Loss: 0.0413007
I0511 18:43:49.720738 534 caffe.cpp:330] accuracy = 0.9874
I0511 18:43:49.720748 534 caffe.cpp:330] loss = 0.0413007 (* 1 = 0.0413007 loss)
root@0a1c1b0432ff:/opt/caffe#
Raw
train_mnist.txt
root@0a1c1b0432ff:/opt/caffe/data/mnist# ./get_mnist.sh
Downloading...
--2017-05-11 18:11:40-- http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6
Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 9912422 (9.5M) [application/x-gzip]
Saving to: 'train-images-idx3-ubyte.gz'
train-images-idx3-ubyte.gz 100%[=================================================>] 9.45M 667KB/s in 14s
2017-05-11 18:11:54 (700 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]
--2017-05-11 18:11:54-- http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6
Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.
HTTP request sent, awaiting response... 200 OK
lsof strace
The following NEW packages will be installed:
htop
0 upgraded, 1 newly installed, 0 to remove and 24 not upgraded.
Need to get 76.4 kB of archives.
After this operation, 215 kB of additional disk space will be used.
Get:1 http://archive.ubuntu.com/ubuntu xenial-updates/universe amd64 htop amd64 2.0.1-1ubuntu1 [76.4 kB]
Fetched 76.4 kB in 0s (128 kB/s)
debconf: delaying package configuration, since apt-utils is not installed
Selecting previously unselected package htop.
(Reading database ... 39335 files and directories currently installed.)
Preparing to unpack .../htop_2.0.1-1ubuntu1_amd64.deb ...
Unpacking htop (2.0.1-1ubuntu1) ...
Processing triggers for mime-support (3.59ubuntu1) ...
Setting up htop (2.0.1-1ubuntu1) ...
root@0a1c1b0432ff:/opt/caffe# htop
root@0a1c1b0432ff:/opt/caffe# vim
root@0a1c1b0432ff:/opt/caffe# vi Makefile.config
root@0a1c1b0432ff:/opt/caffe# cd data/mnist/
root@0a1c1b0432ff:/opt/caffe/data/mnist# ./get_mnist.sh
Downloading...
--2017-05-11 18:11:40-- http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6
Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 9912422 (9.5M) [application/x-gzip]
Saving to: 'train-images-idx3-ubyte.gz'
train-images-idx3-ubyte.gz 100%[=================================================>] 9.45M 667KB/s in 14s
2017-05-11 18:11:54 (700 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]
--2017-05-11 18:11:54-- http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6
Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 28881 (28K) [application/x-gzip]
Saving to: 'train-labels-idx1-ubyte.gz'
train-labels-idx1-ubyte.gz 100%[=================================================>] 28.20K --.-KB/s in 0.07s
2017-05-11 18:11:54 (377 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]
--2017-05-11 18:11:54-- http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6
Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1648877 (1.6M) [application/x-gzip]
Saving to: 't10k-images-idx3-ubyte.gz'
t10k-images-idx3-ubyte.gz 100%[=================================================>] 1.57M 767KB/s in 2.1s
2017-05-11 18:11:57 (767 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]
--2017-05-11 18:11:57-- http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6
Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4542 (4.4K) [application/x-gzip]
Saving to: 't10k-labels-idx1-ubyte.gz'
t10k-labels-idx1-ubyte.gz 100%[=================================================>] 4.44K --.-KB/s in 0s
2017-05-11 18:11:57 (339 MB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]
root@0a1c1b0432ff:/opt/caffe/data/mnist# cd $CAFFE_ROOT
root@0a1c1b0432ff:/opt/caffe# ./examples/mnist/create_mnist.sh
Creating lmdb...
I0511 18:12:50.087297 511 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_train_lmdb
I0511 18:12:50.087496 511 convert_mnist_data.cpp:88] A total of 60000 items.
I0511 18:12:50.087502 511 convert_mnist_data.cpp:89] Rows: 28 Cols: 28
I0511 18:12:54.769269 511 convert_mnist_data.cpp:108] Processed 60000 files.
I0511 18:12:55.185133 516 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_test_lmdb
I0511 18:12:55.185369 516 convert_mnist_data.cpp:88] A total of 10000 items.
I0511 18:12:55.185376 516 convert_mnist_data.cpp:89] Rows: 28 Cols: 28
I0511 18:12:55.915678 516 convert_mnist_data.cpp:108] Processed 10000 files.
Done.
root@0a1c1b0432ff:/opt/caffe# time ./examples/mnist/train_lenet.sh
I0511 18:13:14.300827 522 caffe.cpp:218] Using GPUs 0
I0511 18:13:14.330199 522 caffe.cpp:223] GPU 0: GeForce GTX 1080
I0511 18:13:14.614657 522 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: ""
}
I0511 18:13:14.614894 522 solver.cpp:87] Creating training net from net file: examples/mnist/lenet_train_test.prototxt
I0511 18:13:14.615150 522 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer mnist
I0511 18:13:14.615164 522 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0511 18:13:14.615250 522 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"
}
I0511 18:13:14.615370 522 layer_factory.hpp:77] Creating layer mnist
I0511 18:13:14.615486 522 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_train_lmdb
I0511 18:13:14.615514 522 net.cpp:84] Creating Layer mnist
I0511 18:13:14.615521 522 net.cpp:380] mnist -> data
I0511 18:13:14.615545 522 net.cpp:380] mnist -> label
I0511 18:13:14.616421 522 data_layer.cpp:45] output data size: 64,1,28,28
I0511 18:13:14.618146 522 net.cpp:122] Setting up mnist
I0511 18:13:14.618163 522 net.cpp:129] Top shape: 64 1 28 28 (50176)
I0511 18:13:14.618168 522 net.cpp:129] Top shape: 64 (64)
I0511 18:13:14.618172 522 net.cpp:137] Memory required for data: 200960
I0511 18:13:14.618182 522 layer_factory.hpp:77] Creating layer conv1
I0511 18:13:14.618207 522 net.cpp:84] Creating Layer conv1
I0511 18:13:14.618216 522 net.cpp:406] conv1 <- data
I0511 18:13:14.618229 522 net.cpp:380] conv1 -> conv1
I0511 18:13:15.536608 522 net.cpp:122] Setting up conv1
I0511 18:13:15.536633 522 net.cpp:129] Top shape: 64 20 24 24 (737280)
I0511 18:13:15.536650 522 net.cpp:137] Memory required for data: 3150080
I0511 18:13:15.536698 522 layer_factory.hpp:77] Creating layer pool1
I0511 18:13:15.536727 522 net.cpp:84] Creating Layer pool1
I0511 18:13:15.536798 522 net.cpp:406] pool1 <- conv1
I0511 18:13:15.536820 522 net.cpp:380] pool1 -> pool1
I0511 18:13:15.536887 522 net.cpp:122] Setting up pool1
I0511 18:13:15.536896 522 net.cpp:129] Top shape: 64 20 12 12 (184320)
I0511 18:13:15.536905 522 net.cpp:137] Memory required for data: 3887360
I0511 18:13:15.536908 522 layer_factory.hpp:77] Creating layer conv2
I0511 18:13:15.536921 522 net.cpp:84] Creating Layer conv2
I0511 18:13:15.536923 522 net.cpp:406] conv2 <- pool1
I0511 18:13:15.536929 522 net.cpp:380] conv2 -> conv2
I0511 18:13:15.538609 522 net.cpp:122] Setting up conv2
I0511 18:13:15.538635 522 net.cpp:129] Top shape: 64 50 8 8 (204800)
I0511 18:13:15.538640 522 net.cpp:137] Memory required for data: 4706560
I0511 18:13:15.538650 522 layer_factory.hpp:77] Creating layer pool2
I0511 18:13:15.538671 522 net.cpp:84] Creating Layer pool2
I0511 18:13:15.538676 522 net.cpp:406] pool2 <- conv2
I0511 18:13:15.538681 522 net.cpp:380] pool2 -> pool2
I0511 18:13:15.538718 522 net.cpp:122] Setting up pool2
I0511 18:13:15.538724 522 net.cpp:129] Top shape: 64 50 4 4 (51200)
I0511 18:13:15.538743 522 net.cpp:137] Memory required for data: 4911360
I0511 18:13:15.538745 522 layer_factory.hpp:77] Creating layer ip1
I0511 18:13:15.538753 522 net.cpp:84] Creating Layer ip1
I0511 18:13:15.538758 522 net.cpp:406] ip1 <- pool2
I0511 18:13:15.538763 522 net.cpp:380] ip1 -> ip1
I0511 18:13:15.541519 522 net.cpp:122] Setting up ip1
I0511 18:13:15.541533 522 net.cpp:129] Top shape: 64 500 (32000)
I0511 18:13:15.541537 522 net.cpp:137] Memory required for data: 5039360
I0511 18:13:15.541560 522 layer_factory.hpp:77] Creating layer relu1
I0511 18:13:15.541581 522 net.cpp:84] Creating Layer relu1
I0511 18:13:15.541585 522 net.cpp:406] relu1 <- ip1
I0511 18:13:15.541602 522 net.cpp:367] relu1 -> ip1 (in-place)
I0511 18:13:15.542383 522 net.cpp:122] Setting up relu1
I0511 18:13:15.542393 522 net.cpp:129] Top shape: 64 500 (32000)
I0511 18:13:15.542397 522 net.cpp:137] Memory required for data: 5167360
I0511 18:13:15.542399 522 layer_factory.hpp:77] Creating layer ip2
I0511 18:13:15.542421 522 net.cpp:84] Creating Layer ip2
I0511 18:13:15.542439 522 net.cpp:406] ip2 <- ip1
I0511 18:13:15.542445 522 net.cpp:380] ip2 -> ip2
I0511 18:13:15.543305 522 net.cpp:122] Setting up ip2
I0511 18:13:15.543316 522 net.cpp:129] Top shape: 64 10 (640)
I0511 18:13:15.543318 522 net.cpp:137] Memory required for data: 5169920
I0511 18:13:15.543339 522 layer_factory.hpp:77] Creating layer loss
I0511 18:13:15.543360 522 net.cpp:84] Creating Layer loss
I0511 18:13:15.543365 522 net.cpp:406] loss <- ip2
I0511 18:13:15.543382 522 net.cpp:406] loss <- label
I0511 18:13:15.543391 522 net.cpp:380] loss -> loss
I0511 18:13:15.543411 522 layer_factory.hpp:77] Creating layer loss
I0511 18:13:15.543712 522 net.cpp:122] Setting up loss
I0511 18:13:15.543722 522 net.cpp:129] Top shape: (1)
I0511 18:13:15.543725 522 net.cpp:132] with loss weight 1
I0511 18:13:15.543748 522 net.cpp:137] Memory required for data: 5169924
I0511 18:13:15.543751 522 net.cpp:198] loss needs backward computation.
I0511 18:13:15.543761 522 net.cpp:198] ip2 needs backward computation.
I0511 18:13:15.543766 522 net.cpp:198] relu1 needs backward computation.
I0511 18:13:15.543768 522 net.cpp:198] ip1 needs backward computation.
I0511 18:13:15.543771 522 net.cpp:198] pool2 needs backward computation.
I0511 18:13:15.543774 522 net.cpp:198] conv2 needs backward computation.
I0511 18:13:15.543777 522 net.cpp:198] pool1 needs backward computation.
I0511 18:13:15.543781 522 net.cpp:198] conv1 needs backward computation.
I0511 18:13:15.543783 522 net.cpp:200] mnist does not need backward computation.
I0511 18:13:15.543787 522 net.cpp:242] This network produces output loss
I0511 18:13:15.543794 522 net.cpp:255] Network initialization done.
I0511 18:13:15.543949 522 solver.cpp:172] Creating test net (#0) specified by net file: examples/mnist/lenet_train_test.prototxt
I0511 18:13:15.543982 522 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist
I0511 18:13:15.544051 522 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"
}
I0511 18:13:15.544185 522 layer_factory.hpp:77] Creating layer mnist
I0511 18:13:15.544245 522 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_test_lmdb
I0511 18:13:15.544258 522 net.cpp:84] Creating Layer mnist
I0511 18:13:15.544262 522 net.cpp:380] mnist -> data
I0511 18:13:15.544268 522 net.cpp:380] mnist -> label
I0511 18:13:15.544350 522 data_layer.cpp:45] output data size: 100,1,28,28
I0511 18:13:15.546480 522 net.cpp:122] Setting up mnist
I0511 18:13:15.546519 522 net.cpp:129] Top shape: 100 1 28 28 (78400)
I0511 18:13:15.546525 522 net.cpp:129] Top shape: 100 (100)
I0511 18:13:15.546527 522 net.cpp:137] Memory required for data: 314000
I0511 18:13:15.546533 522 layer_factory.hpp:77] Creating layer label_mnist_1_split
I0511 18:13:15.546561 522 net.cpp:84] Creating Layer label_mnist_1_split
I0511 18:13:15.546566 522 net.cpp:406] label_mnist_1_split <- label
I0511 18:13:15.546571 522 net.cpp:380] label_mnist_1_split -> label_mnist_1_split_0
I0511 18:13:15.546581 522 net.cpp:380] label_mnist_1_split -> label_mnist_1_split_1
I0511 18:13:15.546643 522 net.cpp:122] Setting up label_mnist_1_split
I0511 18:13:15.546649 522 net.cpp:129] Top shape: 100 (100)
I0511 18:13:15.546653 522 net.cpp:129] Top shape: 100 (100)
I0511 18:13:15.546654 522 net.cpp:137] Memory required for data: 314800
I0511 18:13:15.546658 522 layer_factory.hpp:77] Creating layer conv1
I0511 18:13:15.546670 522 net.cpp:84] Creating Layer conv1
I0511 18:13:15.546674 522 net.cpp:406] conv1 <- data
I0511 18:13:15.546679 522 net.cpp:380] conv1 -> conv1
I0511 18:13:15.548213 522 net.cpp:122] Setting up conv1
I0511 18:13:15.548245 522 net.cpp:129] Top shape: 100 20 24 24 (1152000)
I0511 18:13:15.548250 522 net.cpp:137] Memory required for data: 4922800
I0511 18:13:15.548276 522 layer_factory.hpp:77] Creating layer pool1
I0511 18:13:15.548315 522 net.cpp:84] Creating Layer pool1
I0511 18:13:15.548318 522 net.cpp:406] pool1 <- conv1
I0511 18:13:15.548324 522 net.cpp:380] pool1 -> pool1
I0511 18:13:15.548362 522 net.cpp:122] Setting up pool1
I0511 18:13:15.548367 522 net.cpp:129] Top shape: 100 20 12 12 (288000)
I0511 18:13:15.548372 522 net.cpp:137] Memory required for data: 6074800
I0511 18:13:15.548375 522 layer_factory.hpp:77] Creating layer conv2
I0511 18:13:15.548384 522 net.cpp:84] Creating Layer conv2
I0511 18:13:15.548389 522 net.cpp:406] conv2 <- pool1
I0511 18:13:15.548394 522 net.cpp:380] conv2 -> conv2
I0511 18:13:15.549770 522 net.cpp:122] Setting up conv2
I0511 18:13:15.549818 522 net.cpp:129] Top shape: 100 50 8 8 (320000)
I0511 18:13:15.549821 522 net.cpp:137] Memory required for data: 7354800
I0511 18:13:15.549832 522 layer_factory.hpp:77] Creating layer pool2
I0511 18:13:15.549844 522 net.cpp:84] Creating Layer pool2
I0511 18:13:15.549847 522 net.cpp:406] pool2 <- conv2
I0511 18:13:15.549851 522 net.cpp:380] pool2 -> pool2
I0511 18:13:15.549888 522 net.cpp:122] Setting up pool2
I0511 18:13:15.549895 522 net.cpp:129] Top shape: 100 50 4 4 (80000)
I0511 18:13:15.549897 522 net.cpp:137] Memory required for data: 7674800
I0511 18:13:15.549901 522 layer_factory.hpp:77] Creating layer ip1
I0511 18:13:15.549908 522 net.cpp:84] Creating Layer ip1
I0511 18:13:15.549911 522 net.cpp:406] ip1 <- pool2
I0511 18:13:15.549916 522 net.cpp:380] ip1 -> ip1
I0511 18:13:15.552865 522 net.cpp:122] Setting up ip1
I0511 18:13:15.552901 522 net.cpp:129] Top shape: 100 500 (50000)
I0511 18:13:15.552906 522 net.cpp:137] Memory required for data: 7874800
I0511 18:13:15.552937 522 layer_factory.hpp:77] Creating layer relu1
I0511 18:13:15.552961 522 net.cpp:84] Creating Layer relu1
I0511 18:13:15.552965 522 net.cpp:406] relu1 <- ip1
I0511 18:13:15.552989 522 net.cpp:367] relu1 -> ip1 (in-place)
I0511 18:13:15.553544 522 net.cpp:122] Setting up relu1
I0511 18:13:15.553557 522 net.cpp:129] Top shape: 100 500 (50000)
I0511 18:13:15.553561 522 net.cpp:137] Memory required for data: 8074800
I0511 18:13:15.553565 522 layer_factory.hpp:77] Creating layer ip2
I0511 18:13:15.554859 522 net.cpp:84] Creating Layer ip2
I0511 18:13:15.554870 522 net.cpp:406] ip2 <- ip1
I0511 18:13:15.554883 522 net.cpp:380] ip2 -> ip2
I0511 18:13:15.555135 522 net.cpp:122] Setting up ip2
I0511 18:13:15.555141 522 net.cpp:129] Top shape: 100 10 (1000)
I0511 18:13:15.555145 522 net.cpp:137] Memory required for data: 8078800
I0511 18:13:15.555151 522 layer_factory.hpp:77] Creating layer ip2_ip2_0_split
I0511 18:13:15.555157 522 net.cpp:84] Creating Layer ip2_ip2_0_split
I0511 18:13:15.555160 522 net.cpp:406] ip2_ip2_0_split <- ip2
I0511 18:13:15.555166 522 net.cpp:380] ip2_ip2_0_split -> ip2_ip2_0_split_0
I0511 18:13:15.555171 522 net.cpp:380] ip2_ip2_0_split -> ip2_ip2_0_split_1
I0511 18:13:15.555202 522 net.cpp:122] Setting up ip2_ip2_0_split
I0511 18:13:15.555207 522 net.cpp:129] Top shape: 100 10 (1000)
I0511 18:13:15.555210 522 net.cpp:129] Top shape: 100 10 (1000)
I0511 18:13:15.555213 522 net.cpp:137] Memory required for data: 8086800
I0511 18:13:15.555217 522 layer_factory.hpp:77] Creating layer accuracy
I0511 18:13:15.555222 522 net.cpp:84] Creating Layer accuracy
I0511 18:13:15.555227 522 net.cpp:406] accuracy <- ip2_ip2_0_split_0
I0511 18:13:15.555230 522 net.cpp:406] accuracy <- label_mnist_1_split_0
I0511 18:13:15.555235 522 net.cpp:380] accuracy -> accuracy
I0511 18:13:15.555243 522 net.cpp:122] Setting up accuracy
I0511 18:13:15.555248 522 net.cpp:129] Top shape: (1)
I0511 18:13:15.555250 522 net.cpp:137] Memory required for data: 8086804
I0511 18:13:15.555253 522 layer_factory.hpp:77] Creating layer loss
I0511 18:13:15.555258 522 net.cpp:84] Creating Layer loss
I0511 18:13:15.555260 522 net.cpp:406] loss <- ip2_ip2_0_split_1
I0511 18:13:15.555263 522 net.cpp:406] loss <- label_mnist_1_split_1
I0511 18:13:15.555294 522 net.cpp:380] loss -> loss
I0511 18:13:15.555302 522 layer_factory.hpp:77] Creating layer loss
I0511 18:13:15.556092 522 net.cpp:122] Setting up loss
I0511 18:13:15.556102 522 net.cpp:129] Top shape: (1)
I0511 18:13:15.556105 522 net.cpp:132] with loss weight 1
I0511 18:13:15.556116 522 net.cpp:137] Memory required for data: 8086808
I0511 18:13:15.556120 522 net.cpp:198] loss needs backward computation.
I0511 18:13:15.556125 522 net.cpp:200] accuracy does not need backward computation.
I0511 18:13:15.556128 522 net.cpp:198] ip2_ip2_0_split needs backward computation.
I0511 18:13:15.556133 522 net.cpp:198] ip2 needs backward computation.
I0511 18:13:15.556138 522 net.cpp:198] relu1 needs backward computation.
I0511 18:13:15.556140 522 net.cpp:198] ip1 needs backward computation.
I0511 18:13:15.556144 522 net.cpp:198] pool2 needs backward computation.
I0511 18:13:15.556149 522 net.cpp:198] conv2 needs backward computation.
I0511 18:13:15.556152 522 net.cpp:198] pool1 needs backward computation.
I0511 18:13:15.556155 522 net.cpp:198] conv1 needs backward computation.
I0511 18:13:15.556159 522 net.cpp:200] label_mnist_1_split does not need backward computation.
I0511 18:13:15.556164 522 net.cpp:200] mnist does not need backward computation.
I0511 18:13:15.556166 522 net.cpp:242] This network produces output accuracy
I0511 18:13:15.556170 522 net.cpp:242] This network produces output loss
I0511 18:13:15.556180 522 net.cpp:255] Network initialization done.
I0511 18:13:15.556228 522 solver.cpp:56] Solver scaffolding done.
I0511 18:13:15.556490 522 caffe.cpp:248] Starting Optimization
I0511 18:13:15.556499 522 solver.cpp:272] Solving LeNet
I0511 18:13:15.556504 522 solver.cpp:273] Learning Rate Policy: inv
I0511 18:13:15.557133 522 solver.cpp:330] Iteration 0, Testing net (#0)
I0511 18:13:15.564887 522 blocking_queue.cpp:49] Waiting for data
I0511 18:13:15.632856 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:15.633618 522 solver.cpp:397] Test net output #0: accuracy = 0.1305
I0511 18:13:15.633646 522 solver.cpp:397] Test net output #1: loss = 2.33209 (* 1 = 2.33209 loss)
I0511 18:13:15.636749 522 solver.cpp:218] Iteration 0 (0 iter/s, 0.0802253s/100 iters), loss = 2.31964
I0511 18:13:15.636775 522 solver.cpp:237] Train net output #0: loss = 2.31964 (* 1 = 2.31964 loss)
I0511 18:13:15.636787 522 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0511 18:13:15.797138 522 solver.cpp:218] Iteration 100 (623.635 iter/s, 0.16035s/100 iters), loss = 0.23069
I0511 18:13:15.797174 522 solver.cpp:237] Train net output #0: loss = 0.23069 (* 1 = 0.23069 loss)
I0511 18:13:15.797183 522 sgd_solver.cpp:105] Iteration 100, lr = 0.00992565
I0511 18:13:15.946821 522 solver.cpp:218] Iteration 200 (668.805 iter/s, 0.14952s/100 iters), loss = 0.136102
I0511 18:13:15.946861 522 solver.cpp:237] Train net output #0: loss = 0.136102 (* 1 = 0.136102 loss)
I0511 18:13:15.946868 522 sgd_solver.cpp:105] Iteration 200, lr = 0.00985258
I0511 18:13:16.091234 522 solver.cpp:218] Iteration 300 (692.686 iter/s, 0.144366s/100 iters), loss = 0.164669
I0511 18:13:16.091267 522 solver.cpp:237] Train net output #0: loss = 0.164668 (* 1 = 0.164668 loss)
I0511 18:13:16.091272 522 sgd_solver.cpp:105] Iteration 300, lr = 0.00978075
I0511 18:13:16.236135 522 solver.cpp:218] Iteration 400 (690.323 iter/s, 0.14486s/100 iters), loss = 0.0880876
I0511 18:13:16.236169 522 solver.cpp:237] Train net output #0: loss = 0.0880875 (* 1 = 0.0880875 loss)
I0511 18:13:16.236176 522 sgd_solver.cpp:105] Iteration 400, lr = 0.00971013
I0511 18:13:16.378613 522 solver.cpp:330] Iteration 500, Testing net (#0)
I0511 18:13:16.437780 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:16.440119 522 solver.cpp:397] Test net output #0: accuracy = 0.9742
I0511 18:13:16.440145 522 solver.cpp:397] Test net output #1: loss = 0.0827839 (* 1 = 0.0827839 loss)
I0511 18:13:16.441550 522 solver.cpp:218] Iteration 500 (486.907 iter/s, 0.205378s/100 iters), loss = 0.10879
I0511 18:13:16.441591 522 solver.cpp:237] Train net output #0: loss = 0.10879 (* 1 = 0.10879 loss)
I0511 18:13:16.441601 522 sgd_solver.cpp:105] Iteration 500, lr = 0.00964069
I0511 18:13:16.591470 522 solver.cpp:218] Iteration 600 (667.249 iter/s, 0.149869s/100 iters), loss = 0.081639
I0511 18:13:16.591503 522 solver.cpp:237] Train net output #0: loss = 0.0816389 (* 1 = 0.0816389 loss)
I0511 18:13:16.591509 522 sgd_solver.cpp:105] Iteration 600, lr = 0.0095724
I0511 18:13:16.737135 522 solver.cpp:218] Iteration 700 (686.705 iter/s, 0.145623s/100 iters), loss = 0.127346
I0511 18:13:16.737169 522 solver.cpp:237] Train net output #0: loss = 0.127346 (* 1 = 0.127346 loss)
I0511 18:13:16.737174 522 sgd_solver.cpp:105] Iteration 700, lr = 0.00950522
I0511 18:13:16.883033 522 solver.cpp:218] Iteration 800 (685.598 iter/s, 0.145858s/100 iters), loss = 0.226645
I0511 18:13:16.883067 522 solver.cpp:237] Train net output #0: loss = 0.226645 (* 1 = 0.226645 loss)
I0511 18:13:16.883074 522 sgd_solver.cpp:105] Iteration 800, lr = 0.00943913
I0511 18:13:17.028226 522 solver.cpp:218] Iteration 900 (688.948 iter/s, 0.145149s/100 iters), loss = 0.215599
I0511 18:13:17.028270 522 solver.cpp:237] Train net output #0: loss = 0.215599 (* 1 = 0.215599 loss)
I0511 18:13:17.028276 522 sgd_solver.cpp:105] Iteration 900, lr = 0.00937411
I0511 18:13:17.078196 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:17.174376 522 solver.cpp:330] Iteration 1000, Testing net (#0)
I0511 18:13:17.233332 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:17.235406 522 solver.cpp:397] Test net output #0: accuracy = 0.9819
I0511 18:13:17.235430 522 solver.cpp:397] Test net output #1: loss = 0.0564125 (* 1 = 0.0564125 loss)
I0511 18:13:17.236907 522 solver.cpp:218] Iteration 1000 (479.575 iter/s, 0.208518s/100 iters), loss = 0.083173
I0511 18:13:17.236925 522 solver.cpp:237] Train net output #0: loss = 0.0831728 (* 1 = 0.0831728 loss)
I0511 18:13:17.236934 522 sgd_solver.cpp:105] Iteration 1000, lr = 0.00931012
I0511 18:13:17.383625 522 solver.cpp:218] Iteration 1100 (681.702 iter/s, 0.146692s/100 iters), loss = 0.00793706
I0511 18:13:17.383659 522 solver.cpp:237] Train net output #0: loss = 0.00793688 (* 1 = 0.00793688 loss)
I0511 18:13:17.383666 522 sgd_solver.cpp:105] Iteration 1100, lr = 0.00924715
I0511 18:13:17.529206 522 solver.cpp:218] Iteration 1200 (687.11 iter/s, 0.145537s/100 iters), loss = 0.0231605
I0511 18:13:17.529242 522 solver.cpp:237] Train net output #0: loss = 0.0231603 (* 1 = 0.0231603 loss)
I0511 18:13:17.529249 522 sgd_solver.cpp:105] Iteration 1200, lr = 0.00918515
I0511 18:13:17.674496 522 solver.cpp:218] Iteration 1300 (688.637 iter/s, 0.145214s/100 iters), loss = 0.0206333
I0511 18:13:17.674530 522 solver.cpp:237] Train net output #0: loss = 0.0206331 (* 1 = 0.0206331 loss)
I0511 18:13:17.674535 522 sgd_solver.cpp:105] Iteration 1300, lr = 0.00912412
I0511 18:13:17.820415 522 solver.cpp:218] Iteration 1400 (685.509 iter/s, 0.145877s/100 iters), loss = 0.00657667
I0511 18:13:17.820447 522 solver.cpp:237] Train net output #0: loss = 0.00657648 (* 1 = 0.00657648 loss)
I0511 18:13:17.820453 522 sgd_solver.cpp:105] Iteration 1400, lr = 0.00906403
I0511 18:13:17.964541 522 solver.cpp:330] Iteration 1500, Testing net (#0)
I0511 18:13:18.023663 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:18.025812 522 solver.cpp:397] Test net output #0: accuracy = 0.9861
I0511 18:13:18.025835 522 solver.cpp:397] Test net output #1: loss = 0.046159 (* 1 = 0.046159 loss)
I0511 18:13:18.027185 522 solver.cpp:218] Iteration 1500 (483.71 iter/s, 0.206735s/100 iters), loss = 0.0619466
I0511 18:13:18.027220 522 solver.cpp:237] Train net output #0: loss = 0.0619464 (* 1 = 0.0619464 loss)
I0511 18:13:18.027228 522 sgd_solver.cpp:105] Iteration 1500, lr = 0.00900485
I0511 18:13:18.176481 522 solver.cpp:218] Iteration 1600 (669.952 iter/s, 0.149264s/100 iters), loss = 0.112313
I0511 18:13:18.176542 522 solver.cpp:237] Train net output #0: loss = 0.112313 (* 1 = 0.112313 loss)
I0511 18:13:18.176548 522 sgd_solver.cpp:105] Iteration 1600, lr = 0.00894657
I0511 18:13:18.322377 522 solver.cpp:218] Iteration 1700 (685.731 iter/s, 0.14583s/100 iters), loss = 0.017051
I0511 18:13:18.322412 522 solver.cpp:237] Train net output #0: loss = 0.0170508 (* 1 = 0.0170508 loss)
I0511 18:13:18.322417 522 sgd_solver.cpp:105] Iteration 1700, lr = 0.00888916
I0511 18:13:18.467898 522 solver.cpp:218] Iteration 1800 (687.381 iter/s, 0.14548s/100 iters), loss = 0.0144987
I0511 18:13:18.467931 522 solver.cpp:237] Train net output #0: loss = 0.0144985 (* 1 = 0.0144985 loss)
I0511 18:13:18.467938 522 sgd_solver.cpp:105] Iteration 1800, lr = 0.0088326
I0511 18:13:18.570827 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:18.616331 522 solver.cpp:218] Iteration 1900 (673.896 iter/s, 0.148391s/100 iters), loss = 0.104943
I0511 18:13:18.616365 522 solver.cpp:237] Train net output #0: loss = 0.104943 (* 1 = 0.104943 loss)
I0511 18:13:18.616372 522 sgd_solver.cpp:105] Iteration 1900, lr = 0.00877687
I0511 18:13:18.759907 522 solver.cpp:330] Iteration 2000, Testing net (#0)
I0511 18:13:18.820415 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:18.822129 522 solver.cpp:397] Test net output #0: accuracy = 0.9871
I0511 18:13:18.822154 522 solver.cpp:397] Test net output #1: loss = 0.041054 (* 1 = 0.041054 loss)
I0511 18:13:18.823673 522 solver.cpp:218] Iteration 2000 (482.386 iter/s, 0.207303s/100 iters), loss = 0.0163311
I0511 18:13:18.823691 522 solver.cpp:237] Train net output #0: loss = 0.0163309 (* 1 = 0.0163309 loss)
I0511 18:13:18.823700 522 sgd_solver.cpp:105] Iteration 2000, lr = 0.00872196
I0511 18:13:18.969692 522 solver.cpp:218] Iteration 2100 (684.966 iter/s, 0.145993s/100 iters), loss = 0.0194948
I0511 18:13:18.969727 522 solver.cpp:237] Train net output #0: loss = 0.0194946 (* 1 = 0.0194946 loss)
I0511 18:13:18.969732 522 sgd_solver.cpp:105] Iteration 2100, lr = 0.00866784
I0511 18:13:19.115437 522 solver.cpp:218] Iteration 2200 (686.328 iter/s, 0.145703s/100 iters), loss = 0.0156107
I0511 18:13:19.115471 522 solver.cpp:237] Train net output #0: loss = 0.0156106 (* 1 = 0.0156106 loss)
I0511 18:13:19.115478 522 sgd_solver.cpp:105] Iteration 2200, lr = 0.0086145
I0511 18:13:19.261158 522 solver.cpp:218] Iteration 2300 (686.437 iter/s, 0.14568s/100 iters), loss = 0.0664188
I0511 18:13:19.261194 522 solver.cpp:237] Train net output #0: loss = 0.0664187 (* 1 = 0.0664187 loss)
I0511 18:13:19.261200 522 sgd_solver.cpp:105] Iteration 2300, lr = 0.00856192
I0511 18:13:19.407133 522 solver.cpp:218] Iteration 2400 (685.783 iter/s, 0.145819s/100 iters), loss = 0.00531314
I0511 18:13:19.407166 522 solver.cpp:237] Train net output #0: loss = 0.00531299 (* 1 = 0.00531299 loss)
I0511 18:13:19.407172 522 sgd_solver.cpp:105] Iteration 2400, lr = 0.00851008
I0511 18:13:19.551811 522 solver.cpp:330] Iteration 2500, Testing net (#0)
I0511 18:13:19.609726 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:19.611837 522 solver.cpp:397] Test net output #0: accuracy = 0.9859
I0511 18:13:19.611865 522 solver.cpp:397] Test net output #1: loss = 0.0455339 (* 1 = 0.0455339 loss)
I0511 18:13:19.613373 522 solver.cpp:218] Iteration 2500 (484.971 iter/s, 0.206198s/100 iters), loss = 0.0333312
I0511 18:13:19.613409 522 solver.cpp:237] Train net output #0: loss = 0.0333311 (* 1 = 0.0333311 loss)
I0511 18:13:19.613435 522 sgd_solver.cpp:105] Iteration 2500, lr = 0.00845897
I0511 18:13:19.760716 522 solver.cpp:218] Iteration 2600 (679.419 iter/s, 0.147185s/100 iters), loss = 0.0638409
I0511 18:13:19.760751 522 solver.cpp:237] Train net output #0: loss = 0.0638407 (* 1 = 0.0638407 loss)
I0511 18:13:19.760756 522 sgd_solver.cpp:105] Iteration 2600, lr = 0.00840857
I0511 18:13:19.907687 522 solver.cpp:218] Iteration 2700 (680.599 iter/s, 0.14693s/100 iters), loss = 0.0564011
I0511 18:13:19.907721 522 solver.cpp:237] Train net output #0: loss = 0.0564009 (* 1 = 0.0564009 loss)
I0511 18:13:19.907727 522 sgd_solver.cpp:105] Iteration 2700, lr = 0.00835886
I0511 18:13:20.053236 522 solver.cpp:218] Iteration 2800 (687.257 iter/s, 0.145506s/100 iters), loss = 0.00203006
I0511 18:13:20.053268 522 solver.cpp:237] Train net output #0: loss = 0.00202993 (* 1 = 0.00202993 loss)
I0511 18:13:20.053275 522 sgd_solver.cpp:105] Iteration 2800, lr = 0.00830984
I0511 18:13:20.066148 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:20.201350 522 solver.cpp:218] Iteration 2900 (675.345 iter/s, 0.148072s/100 iters), loss = 0.0234769
I0511 18:13:20.201383 522 solver.cpp:237] Train net output #0: loss = 0.0234769 (* 1 = 0.0234769 loss)
I0511 18:13:20.201388 522 sgd_solver.cpp:105] Iteration 2900, lr = 0.00826148
I0511 18:13:20.343715 522 solver.cpp:330] Iteration 3000, Testing net (#0)
I0511 18:13:20.402021 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:20.404017 522 solver.cpp:397] Test net output #0: accuracy = 0.9873
I0511 18:13:20.404072 522 solver.cpp:397] Test net output #1: loss = 0.0373697 (* 1 = 0.0373697 loss)
I0511 18:13:20.405506 522 solver.cpp:218] Iteration 3000 (489.913 iter/s, 0.204118s/100 iters), loss = 0.00960388
I0511 18:13:20.405526 522 solver.cpp:237] Train net output #0: loss = 0.00960378 (* 1 = 0.00960378 loss)
I0511 18:13:20.405534 522 sgd_solver.cpp:105] Iteration 3000, lr = 0.00821377
I0511 18:13:20.553649 522 solver.cpp:218] Iteration 3100 (680.166 iter/s, 0.147023s/100 iters), loss = 0.0212637
I0511 18:13:20.553680 522 solver.cpp:237] Train net output #0: loss = 0.0212636 (* 1 = 0.0212636 loss)
I0511 18:13:20.553686 522 sgd_solver.cpp:105] Iteration 3100, lr = 0.0081667
I0511 18:13:20.700845 522 solver.cpp:218] Iteration 3200 (679.552 iter/s, 0.147156s/100 iters), loss = 0.00852448
I0511 18:13:20.700877 522 solver.cpp:237] Train net output #0: loss = 0.00852438 (* 1 = 0.00852438 loss)
I0511 18:13:20.700883 522 sgd_solver.cpp:105] Iteration 3200, lr = 0.00812025
I0511 18:13:20.847440 522 solver.cpp:218] Iteration 3300 (682.348 iter/s, 0.146553s/100 iters), loss = 0.021878
I0511 18:13:20.847476 522 solver.cpp:237] Train net output #0: loss = 0.0218779 (* 1 = 0.0218779 loss)
I0511 18:13:20.847484 522 sgd_solver.cpp:105] Iteration 3300, lr = 0.00807442
I0511 18:13:20.993345 522 solver.cpp:218] Iteration 3400 (686.487 iter/s, 0.145669s/100 iters), loss = 0.00832714
I0511 18:13:20.993378 522 solver.cpp:237] Train net output #0: loss = 0.00832703 (* 1 = 0.00832703 loss)
I0511 18:13:20.993384 522 sgd_solver.cpp:105] Iteration 3400, lr = 0.00802918
I0511 18:13:21.136865 522 solver.cpp:330] Iteration 3500, Testing net (#0)
I0511 18:13:21.196266 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:21.198382 522 solver.cpp:397] Test net output #0: accuracy = 0.9867
I0511 18:13:21.198410 522 solver.cpp:397] Test net output #1: loss = 0.0415424 (* 1 = 0.0415424 loss)
I0511 18:13:21.199802 522 solver.cpp:218] Iteration 3500 (484.448 iter/s, 0.206421s/100 iters), loss = 0.00665751
I0511 18:13:21.199821 522 solver.cpp:237] Train net output #0: loss = 0.00665739 (* 1 = 0.00665739 loss)
I0511 18:13:21.199831 522 sgd_solver.cpp:105] Iteration 3500, lr = 0.00798454
I0511 18:13:21.344651 522 solver.cpp:218] Iteration 3600 (690.5 iter/s, 0.144823s/100 iters), loss = 0.0309221
I0511 18:13:21.344687 522 solver.cpp:237] Train net output #0: loss = 0.030922 (* 1 = 0.030922 loss)
I0511 18:13:21.344693 522 sgd_solver.cpp:105] Iteration 3600, lr = 0.00794046
I0511 18:13:21.491075 522 solver.cpp:218] Iteration 3700 (683.786 iter/s, 0.146245s/100 iters), loss = 0.0109689
I0511 18:13:21.491107 522 solver.cpp:237] Train net output #0: loss = 0.0109688 (* 1 = 0.0109688 loss)
I0511 18:13:21.491142 522 sgd_solver.cpp:105] Iteration 3700, lr = 0.00789695
I0511 18:13:21.558027 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:21.638844 522 solver.cpp:218] Iteration 3800 (676.923 iter/s, 0.147727s/100 iters), loss = 0.00859293
I0511 18:13:21.638876 522 solver.cpp:237] Train net output #0: loss = 0.00859279 (* 1 = 0.00859279 loss)
I0511 18:13:21.638881 522 sgd_solver.cpp:105] Iteration 3800, lr = 0.007854
I0511 18:13:21.785058 522 solver.cpp:218] Iteration 3900 (684.119 iter/s, 0.146173s/100 iters), loss = 0.0278121
I0511 18:13:21.785091 522 solver.cpp:237] Train net output #0: loss = 0.0278119 (* 1 = 0.0278119 loss)
I0511 18:13:21.785096 522 sgd_solver.cpp:105] Iteration 3900, lr = 0.00781158
I0511 18:13:21.929765 522 solver.cpp:330] Iteration 4000, Testing net (#0)
I0511 18:13:21.989279 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:21.990739 522 solver.cpp:397] Test net output #0: accuracy = 0.9889
I0511 18:13:21.990762 522 solver.cpp:397] Test net output #1: loss = 0.0308643 (* 1 = 0.0308643 loss)
I0511 18:13:21.992018 522 solver.cpp:218] Iteration 4000 (483.27 iter/s, 0.206924s/100 iters), loss = 0.015232
I0511 18:13:21.992035 522 solver.cpp:237] Train net output #0: loss = 0.0152319 (* 1 = 0.0152319 loss)
I0511 18:13:21.992043 522 sgd_solver.cpp:105] Iteration 4000, lr = 0.0077697
I0511 18:13:22.138000 522 solver.cpp:218] Iteration 4100 (685.148 iter/s, 0.145954s/100 iters), loss = 0.0402459
I0511 18:13:22.138032 522 solver.cpp:237] Train net output #0: loss = 0.0402458 (* 1 = 0.0402458 loss)
I0511 18:13:22.138038 522 sgd_solver.cpp:105] Iteration 4100, lr = 0.00772833
I0511 18:13:22.283874 522 solver.cpp:218] Iteration 4200 (685.716 iter/s, 0.145833s/100 iters), loss = 0.0181314
I0511 18:13:22.283906 522 solver.cpp:237] Train net output #0: loss = 0.0181313 (* 1 = 0.0181313 loss)
I0511 18:13:22.283912 522 sgd_solver.cpp:105] Iteration 4200, lr = 0.00768748
I0511 18:13:22.429419 522 solver.cpp:218] Iteration 4300 (687.28 iter/s, 0.145501s/100 iters), loss = 0.0389803
I0511 18:13:22.429458 522 solver.cpp:237] Train net output #0: loss = 0.0389802 (* 1 = 0.0389802 loss)
I0511 18:13:22.429466 522 sgd_solver.cpp:105] Iteration 4300, lr = 0.00764712
I0511 18:13:22.575494 522 solver.cpp:218] Iteration 4400 (685.182 iter/s, 0.145947s/100 iters), loss = 0.0210854
I0511 18:13:22.575526 522 solver.cpp:237] Train net output #0: loss = 0.0210852 (* 1 = 0.0210852 loss)
I0511 18:13:22.575531 522 sgd_solver.cpp:105] Iteration 4400, lr = 0.00760726
I0511 18:13:22.720794 522 solver.cpp:330] Iteration 4500, Testing net (#0)
I0511 18:13:22.783684 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:22.785125 522 solver.cpp:397] Test net output #0: accuracy = 0.9887
I0511 18:13:22.785150 522 solver.cpp:397] Test net output #1: loss = 0.0357179 (* 1 = 0.0357179 loss)
I0511 18:13:22.786545 522 solver.cpp:218] Iteration 4500 (473.898 iter/s, 0.211016s/100 iters), loss = 0.00440531
I0511 18:13:22.786563 522 solver.cpp:237] Train net output #0: loss = 0.00440517 (* 1 = 0.00440517 loss)
I0511 18:13:22.786571 522 sgd_solver.cpp:105] Iteration 4500, lr = 0.00756788
I0511 18:13:22.931895 522 solver.cpp:218] Iteration 4600 (688.122 iter/s, 0.145323s/100 iters), loss = 0.0116534
I0511 18:13:22.931931 522 solver.cpp:237] Train net output #0: loss = 0.0116532 (* 1 = 0.0116532 loss)
I0511 18:13:22.931938 522 sgd_solver.cpp:105] Iteration 4600, lr = 0.00752897
I0511 18:13:23.055052 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:23.079150 522 solver.cpp:218] Iteration 4700 (679.305 iter/s, 0.147209s/100 iters), loss = 0.00333786
I0511 18:13:23.079187 522 solver.cpp:237] Train net output #0: loss = 0.0033377 (* 1 = 0.0033377 loss)
I0511 18:13:23.079195 522 sgd_solver.cpp:105] Iteration 4700, lr = 0.00749052
I0511 18:13:23.225785 522 solver.cpp:218] Iteration 4800 (682.314 iter/s, 0.14656s/100 iters), loss = 0.014052
I0511 18:13:23.225845 522 solver.cpp:237] Train net output #0: loss = 0.0140519 (* 1 = 0.0140519 loss)
I0511 18:13:23.225852 522 sgd_solver.cpp:105] Iteration 4800, lr = 0.00745253
I0511 18:13:23.371039 522 solver.cpp:218] Iteration 4900 (688.768 iter/s, 0.145187s/100 iters), loss = 0.00534247
I0511 18:13:23.371071 522 solver.cpp:237] Train net output #0: loss = 0.00534232 (* 1 = 0.00534232 loss)
I0511 18:13:23.371078 522 sgd_solver.cpp:105] Iteration 4900, lr = 0.00741498
I0511 18:13:23.516530 522 solver.cpp:447] Snapshotting to binary proto file examples/mnist/lenet_iter_5000.caffemodel
I0511 18:13:23.523075 522 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_5000.solverstate
I0511 18:13:23.525369 522 solver.cpp:330] Iteration 5000, Testing net (#0)
I0511 18:13:23.583453 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:23.585058 522 solver.cpp:397] Test net output #0: accuracy = 0.9893
I0511 18:13:23.585083 522 solver.cpp:397] Test net output #1: loss = 0.0301518 (* 1 = 0.0301518 loss)
I0511 18:13:23.586496 522 solver.cpp:218] Iteration 5000 (464.208 iter/s, 0.215421s/100 iters), loss = 0.0314256
I0511 18:13:23.586529 522 solver.cpp:237] Train net output #0: loss = 0.0314255 (* 1 = 0.0314255 loss)
I0511 18:13:23.586537 522 sgd_solver.cpp:105] Iteration 5000, lr = 0.00737788
I0511 18:13:23.733778 522 solver.cpp:218] Iteration 5100 (679.14 iter/s, 0.147245s/100 iters), loss = 0.0165372
I0511 18:13:23.733811 522 solver.cpp:237] Train net output #0: loss = 0.016537 (* 1 = 0.016537 loss)
I0511 18:13:23.733819 522 sgd_solver.cpp:105] Iteration 5100, lr = 0.0073412
I0511 18:13:23.881382 522 solver.cpp:218] Iteration 5200 (677.69 iter/s, 0.14756s/100 iters), loss = 0.00644915
I0511 18:13:23.881418 522 solver.cpp:237] Train net output #0: loss = 0.00644896 (* 1 = 0.00644896 loss)
I0511 18:13:23.881423 522 sgd_solver.cpp:105] Iteration 5200, lr = 0.00730495
I0511 18:13:24.027318 522 solver.cpp:218] Iteration 5300 (685.966 iter/s, 0.14578s/100 iters), loss = 0.000801772
I0511 18:13:24.027353 522 solver.cpp:237] Train net output #0: loss = 0.000801578 (* 1 = 0.000801578 loss)
I0511 18:13:24.027357 522 sgd_solver.cpp:105] Iteration 5300, lr = 0.00726911
I0511 18:13:24.174664 522 solver.cpp:218] Iteration 5400 (678.876 iter/s, 0.147302s/100 iters), loss = 0.00711174
I0511 18:13:24.174695 522 solver.cpp:237] Train net output #0: loss = 0.00711156 (* 1 = 0.00711156 loss)
I0511 18:13:24.174701 522 sgd_solver.cpp:105] Iteration 5400, lr = 0.00723368
I0511 18:13:24.319320 522 solver.cpp:330] Iteration 5500, Testing net (#0)
I0511 18:13:24.377249 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:24.379405 522 solver.cpp:397] Test net output #0: accuracy = 0.99
I0511 18:13:24.379559 522 solver.cpp:397] Test net output #1: loss = 0.0318279 (* 1 = 0.0318279 loss)
I0511 18:13:24.381283 522 solver.cpp:218] Iteration 5500 (484.077 iter/s, 0.206579s/100 iters), loss = 0.0179214
I0511 18:13:24.381309 522 solver.cpp:237] Train net output #0: loss = 0.0179212 (* 1 = 0.0179212 loss)
I0511 18:13:24.381319 522 sgd_solver.cpp:105] Iteration 5500, lr = 0.00719865
I0511 18:13:24.531106 522 solver.cpp:218] Iteration 5600 (672.471 iter/s, 0.148705s/100 iters), loss = 0.00110432
I0511 18:13:24.531141 522 solver.cpp:237] Train net output #0: loss = 0.00110413 (* 1 = 0.00110413 loss)
I0511 18:13:24.531147 522 sgd_solver.cpp:105] Iteration 5600, lr = 0.00716402
I0511 18:13:24.562065 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:24.677906 522 solver.cpp:218] Iteration 5700 (681.929 iter/s, 0.146643s/100 iters), loss = 0.00285689
I0511 18:13:24.677940 522 solver.cpp:237] Train net output #0: loss = 0.00285671 (* 1 = 0.00285671 loss)
I0511 18:13:24.677945 522 sgd_solver.cpp:105] Iteration 5700, lr = 0.00712977
I0511 18:13:24.826856 522 solver.cpp:218] Iteration 5800 (672.027 iter/s, 0.148804s/100 iters), loss = 0.0375963
I0511 18:13:24.826916 522 solver.cpp:237] Train net output #0: loss = 0.0375961 (* 1 = 0.0375961 loss)
I0511 18:13:24.826922 522 sgd_solver.cpp:105] Iteration 5800, lr = 0.0070959
I0511 18:13:24.972806 522 solver.cpp:218] Iteration 5900 (685.476 iter/s, 0.145884s/100 iters), loss = 0.00753866
I0511 18:13:24.972841 522 solver.cpp:237] Train net output #0: loss = 0.00753848 (* 1 = 0.00753848 loss)
I0511 18:13:24.972847 522 sgd_solver.cpp:105] Iteration 5900, lr = 0.0070624
I0511 18:13:25.117579 522 solver.cpp:330] Iteration 6000, Testing net (#0)
I0511 18:13:25.177829 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:25.180385 522 solver.cpp:397] Test net output #0: accuracy = 0.9907
I0511 18:13:25.180413 522 solver.cpp:397] Test net output #1: loss = 0.0283849 (* 1 = 0.0283849 loss)
I0511 18:13:25.182206 522 solver.cpp:218] Iteration 6000 (477.677 iter/s, 0.209346s/100 iters), loss = 0.00364922
I0511 18:13:25.182230 522 solver.cpp:237] Train net output #0: loss = 0.00364905 (* 1 = 0.00364905 loss)
I0511 18:13:25.182241 522 sgd_solver.cpp:105] Iteration 6000, lr = 0.00702927
I0511 18:13:25.329059 522 solver.cpp:218] Iteration 6100 (681.11 iter/s, 0.146819s/100 iters), loss = 0.00189618
I0511 18:13:25.329097 522 solver.cpp:237] Train net output #0: loss = 0.001896 (* 1 = 0.001896 loss)
I0511 18:13:25.329102 522 sgd_solver.cpp:105] Iteration 6100, lr = 0.0069965
I0511 18:13:25.475404 522 solver.cpp:218] Iteration 6200 (683.676 iter/s, 0.146268s/100 iters), loss = 0.0114807
I0511 18:13:25.475436 522 solver.cpp:237] Train net output #0: loss = 0.0114806 (* 1 = 0.0114806 loss)
I0511 18:13:25.475442 522 sgd_solver.cpp:105] Iteration 6200, lr = 0.00696408
I0511 18:13:25.621726 522 solver.cpp:218] Iteration 6300 (683.611 iter/s, 0.146282s/100 iters), loss = 0.00935609
I0511 18:13:25.621759 522 solver.cpp:237] Train net output #0: loss = 0.00935592 (* 1 = 0.00935592 loss)
I0511 18:13:25.621765 522 sgd_solver.cpp:105] Iteration 6300, lr = 0.00693201
I0511 18:13:25.769366 522 solver.cpp:218] Iteration 6400 (677.529 iter/s, 0.147595s/100 iters), loss = 0.0066119
I0511 18:13:25.769410 522 solver.cpp:237] Train net output #0: loss = 0.00661173 (* 1 = 0.00661173 loss)
I0511 18:13:25.769418 522 sgd_solver.cpp:105] Iteration 6400, lr = 0.00690029
I0511 18:13:25.916312 522 solver.cpp:330] Iteration 6500, Testing net (#0)
I0511 18:13:25.975299 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:25.976896 522 solver.cpp:397] Test net output #0: accuracy = 0.9905
I0511 18:13:25.976920 522 solver.cpp:397] Test net output #1: loss = 0.0312985 (* 1 = 0.0312985 loss)
I0511 18:13:25.978351 522 solver.cpp:218] Iteration 6500 (478.611 iter/s, 0.208938s/100 iters), loss = 0.0117257
I0511 18:13:25.978369 522 solver.cpp:237] Train net output #0: loss = 0.0117255 (* 1 = 0.0117255 loss)
I0511 18:13:25.978377 522 sgd_solver.cpp:105] Iteration 6500, lr = 0.0068689
I0511 18:13:26.064824 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:26.127346 522 solver.cpp:218] Iteration 6600 (672.244 iter/s, 0.148755s/100 iters), loss = 0.0246324
I0511 18:13:26.127377 522 solver.cpp:237] Train net output #0: loss = 0.0246322 (* 1 = 0.0246322 loss)
I0511 18:13:26.127383 522 sgd_solver.cpp:105] Iteration 6600, lr = 0.00683784
I0511 18:13:26.273828 522 solver.cpp:218] Iteration 6700 (682.855 iter/s, 0.146444s/100 iters), loss = 0.0110486
I0511 18:13:26.273862 522 solver.cpp:237] Train net output #0: loss = 0.0110484 (* 1 = 0.0110484 loss)
I0511 18:13:26.273867 522 sgd_solver.cpp:105] Iteration 6700, lr = 0.00680711
I0511 18:13:26.422065 522 solver.cpp:218] Iteration 6800 (674.787 iter/s, 0.148195s/100 iters), loss = 0.0046916
I0511 18:13:26.422096 522 solver.cpp:237] Train net output #0: loss = 0.00469141 (* 1 = 0.00469141 loss)
I0511 18:13:26.422116 522 sgd_solver.cpp:105] Iteration 6800, lr = 0.0067767
I0511 18:13:26.569128 522 solver.cpp:218] Iteration 6900 (680.168 iter/s, 0.147022s/100 iters), loss = 0.00297426
I0511 18:13:26.569187 522 solver.cpp:237] Train net output #0: loss = 0.00297408 (* 1 = 0.00297408 loss)
I0511 18:13:26.569195 522 sgd_solver.cpp:105] Iteration 6900, lr = 0.0067466
I0511 18:13:26.712939 522 solver.cpp:330] Iteration 7000, Testing net (#0)
I0511 18:13:26.771999 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:26.774022 522 solver.cpp:397] Test net output #0: accuracy = 0.9896
I0511 18:13:26.774050 522 solver.cpp:397] Test net output #1: loss = 0.0316401 (* 1 = 0.0316401 loss)
I0511 18:13:26.776863 522 solver.cpp:218] Iteration 7000 (481.526 iter/s, 0.207673s/100 iters), loss = 0.00752981
I0511 18:13:26.776882 522 solver.cpp:237] Train net output #0: loss = 0.00752964 (* 1 = 0.00752964 loss)
I0511 18:13:26.776890 522 sgd_solver.cpp:105] Iteration 7000, lr = 0.00671681
I0511 18:13:26.923746 522 solver.cpp:218] Iteration 7100 (680.944 iter/s, 0.146855s/100 iters), loss = 0.0114956
I0511 18:13:26.923779 522 solver.cpp:237] Train net output #0: loss = 0.0114955 (* 1 = 0.0114955 loss)
I0511 18:13:26.923784 522 sgd_solver.cpp:105] Iteration 7100, lr = 0.00668733
I0511 18:13:27.071755 522 solver.cpp:218] Iteration 7200 (675.826 iter/s, 0.147967s/100 iters), loss = 0.00500658
I0511 18:13:27.071789 522 solver.cpp:237] Train net output #0: loss = 0.00500643 (* 1 = 0.00500643 loss)
I0511 18:13:27.071794 522 sgd_solver.cpp:105] Iteration 7200, lr = 0.00665815
I0511 18:13:27.219657 522 solver.cpp:218] Iteration 7300 (676.313 iter/s, 0.14786s/100 iters), loss = 0.0206898
I0511 18:13:27.219691 522 solver.cpp:237] Train net output #0: loss = 0.0206897 (* 1 = 0.0206897 loss)
I0511 18:13:27.219696 522 sgd_solver.cpp:105] Iteration 7300, lr = 0.00662927
I0511 18:13:27.365396 522 solver.cpp:218] Iteration 7400 (686.352 iter/s, 0.145698s/100 iters), loss = 0.00565326
I0511 18:13:27.365506 522 solver.cpp:237] Train net output #0: loss = 0.0056531 (* 1 = 0.0056531 loss)
I0511 18:13:27.365514 522 sgd_solver.cpp:105] Iteration 7400, lr = 0.00660067
I0511 18:13:27.505698 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:27.510330 522 solver.cpp:330] Iteration 7500, Testing net (#0)
I0511 18:13:27.570782 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:27.572875 522 solver.cpp:397] Test net output #0: accuracy = 0.9899
I0511 18:13:27.572896 522 solver.cpp:397] Test net output #1: loss = 0.0319173 (* 1 = 0.0319173 loss)
I0511 18:13:27.574172 522 solver.cpp:218] Iteration 7500 (479.24 iter/s, 0.208664s/100 iters), loss = 0.00329135
I0511 18:13:27.574193 522 solver.cpp:237] Train net output #0: loss = 0.0032912 (* 1 = 0.0032912 loss)
I0511 18:13:27.574201 522 sgd_solver.cpp:105] Iteration 7500, lr = 0.00657236
I0511 18:13:27.721329 522 solver.cpp:218] Iteration 7600 (679.684 iter/s, 0.147127s/100 iters), loss = 0.00848904
I0511 18:13:27.721362 522 solver.cpp:237] Train net output #0: loss = 0.00848889 (* 1 = 0.00848889 loss)
I0511 18:13:27.721369 522 sgd_solver.cpp:105] Iteration 7600, lr = 0.00654433
I0511 18:13:27.867367 522 solver.cpp:218] Iteration 7700 (684.951 iter/s, 0.145996s/100 iters), loss = 0.0191951
I0511 18:13:27.867403 522 solver.cpp:237] Train net output #0: loss = 0.0191949 (* 1 = 0.0191949 loss)
I0511 18:13:27.867408 522 sgd_solver.cpp:105] Iteration 7700, lr = 0.00651658
I0511 18:13:28.014005 522 solver.cpp:218] Iteration 7800 (682.167 iter/s, 0.146592s/100 iters), loss = 0.00262706
I0511 18:13:28.014055 522 solver.cpp:237] Train net output #0: loss = 0.00262691 (* 1 = 0.00262691 loss)
I0511 18:13:28.014065 522 sgd_solver.cpp:105] Iteration 7800, lr = 0.00648911
I0511 18:13:28.159793 522 solver.cpp:218] Iteration 7900 (686.352 iter/s, 0.145698s/100 iters), loss = 0.00477146
I0511 18:13:28.159826 522 solver.cpp:237] Train net output #0: loss = 0.00477131 (* 1 = 0.00477131 loss)
I0511 18:13:28.159832 522 sgd_solver.cpp:105] Iteration 7900, lr = 0.0064619
I0511 18:13:28.304602 522 solver.cpp:330] Iteration 8000, Testing net (#0)
I0511 18:13:28.363370 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:28.366027 522 solver.cpp:397] Test net output #0: accuracy = 0.9901
I0511 18:13:28.366067 522 solver.cpp:397] Test net output #1: loss = 0.0294478 (* 1 = 0.0294478 loss)
I0511 18:13:28.367393 522 solver.cpp:218] Iteration 8000 (481.78 iter/s, 0.207564s/100 iters), loss = 0.00637796
I0511 18:13:28.367413 522 solver.cpp:237] Train net output #0: loss = 0.00637782 (* 1 = 0.00637782 loss)
I0511 18:13:28.367420 522 sgd_solver.cpp:105] Iteration 8000, lr = 0.00643496
I0511 18:13:28.515964 522 solver.cpp:218] Iteration 8100 (673.203 iter/s, 0.148543s/100 iters), loss = 0.0107222
I0511 18:13:28.515998 522 solver.cpp:237] Train net output #0: loss = 0.010722 (* 1 = 0.010722 loss)
I0511 18:13:28.516005 522 sgd_solver.cpp:105] Iteration 8100, lr = 0.00640827
I0511 18:13:28.661173 522 solver.cpp:218] Iteration 8200 (688.875 iter/s, 0.145164s/100 iters), loss = 0.00806826
I0511 18:13:28.661219 522 solver.cpp:237] Train net output #0: loss = 0.00806812 (* 1 = 0.00806812 loss)
I0511 18:13:28.661226 522 sgd_solver.cpp:105] Iteration 8200, lr = 0.00638185
I0511 18:13:28.808029 522 solver.cpp:218] Iteration 8300 (681.681 iter/s, 0.146696s/100 iters), loss = 0.0342842
I0511 18:13:28.808063 522 solver.cpp:237] Train net output #0: loss = 0.0342841 (* 1 = 0.0342841 loss)
I0511 18:13:28.808068 522 sgd_solver.cpp:105] Iteration 8300, lr = 0.00635567
I0511 18:13:28.954053 522 solver.cpp:218] Iteration 8400 (685.01 iter/s, 0.145983s/100 iters), loss = 0.00681243
I0511 18:13:28.954087 522 solver.cpp:237] Train net output #0: loss = 0.00681229 (* 1 = 0.00681229 loss)
I0511 18:13:28.954094 522 sgd_solver.cpp:105] Iteration 8400, lr = 0.00632975
I0511 18:13:29.004021 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:29.100819 522 solver.cpp:330] Iteration 8500, Testing net (#0)
I0511 18:13:29.159569 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:29.162251 522 solver.cpp:397] Test net output #0: accuracy = 0.9909
I0511 18:13:29.162277 522 solver.cpp:397] Test net output #1: loss = 0.029258 (* 1 = 0.029258 loss)
I0511 18:13:29.163722 522 solver.cpp:218] Iteration 8500 (477.04 iter/s, 0.209626s/100 iters), loss = 0.00784863
I0511 18:13:29.163755 522 solver.cpp:237] Train net output #0: loss = 0.00784848 (* 1 = 0.00784848 loss)
I0511 18:13:29.163779 522 sgd_solver.cpp:105] Iteration 8500, lr = 0.00630407
I0511 18:13:29.312664 522 solver.cpp:218] Iteration 8600 (671.585 iter/s, 0.148902s/100 iters), loss = 0.00079122
I0511 18:13:29.312713 522 solver.cpp:237] Train net output #0: loss = 0.00079108 (* 1 = 0.00079108 loss)
I0511 18:13:29.312736 522 sgd_solver.cpp:105] Iteration 8600, lr = 0.00627864
I0511 18:13:29.458751 522 solver.cpp:218] Iteration 8700 (684.788 iter/s, 0.146031s/100 iters), loss = 0.00329326
I0511 18:13:29.458784 522 solver.cpp:237] Train net output #0: loss = 0.00329312 (* 1 = 0.00329312 loss)
I0511 18:13:29.458789 522 sgd_solver.cpp:105] Iteration 8700, lr = 0.00625344
I0511 18:13:29.606690 522 solver.cpp:218] Iteration 8800 (676.14 iter/s, 0.147898s/100 iters), loss = 0.00163464
I0511 18:13:29.606722 522 solver.cpp:237] Train net output #0: loss = 0.0016345 (* 1 = 0.0016345 loss)
I0511 18:13:29.606729 522 sgd_solver.cpp:105] Iteration 8800, lr = 0.00622847
I0511 18:13:29.753203 522 solver.cpp:218] Iteration 8900 (682.725 iter/s, 0.146472s/100 iters), loss = 0.000360605
I0511 18:13:29.753237 522 solver.cpp:237] Train net output #0: loss = 0.000360461 (* 1 = 0.000360461 loss)
I0511 18:13:29.753242 522 sgd_solver.cpp:105] Iteration 8900, lr = 0.00620374
I0511 18:13:29.897269 522 solver.cpp:330] Iteration 9000, Testing net (#0)
I0511 18:13:29.956251 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:29.958307 522 solver.cpp:397] Test net output #0: accuracy = 0.99
I0511 18:13:29.958350 522 solver.cpp:397] Test net output #1: loss = 0.0294408 (* 1 = 0.0294408 loss)
I0511 18:13:29.959731 522 solver.cpp:218] Iteration 9000 (484.282 iter/s, 0.206491s/100 iters), loss = 0.0125023
I0511 18:13:29.959749 522 solver.cpp:237] Train net output #0: loss = 0.0125022 (* 1 = 0.0125022 loss)
I0511 18:13:29.959758 522 sgd_solver.cpp:105] Iteration 9000, lr = 0.00617924
I0511 18:13:30.106549 522 solver.cpp:218] Iteration 9100 (681.243 iter/s, 0.14679s/100 iters), loss = 0.00944676
I0511 18:13:30.106582 522 solver.cpp:237] Train net output #0: loss = 0.00944661 (* 1 = 0.00944661 loss)
I0511 18:13:30.106588 522 sgd_solver.cpp:105] Iteration 9100, lr = 0.00615496
I0511 18:13:30.252221 522 solver.cpp:218] Iteration 9200 (686.666 iter/s, 0.145631s/100 iters), loss = 0.00169064
I0511 18:13:30.252255 522 solver.cpp:237] Train net output #0: loss = 0.00169048 (* 1 = 0.00169048 loss)
I0511 18:13:30.252261 522 sgd_solver.cpp:105] Iteration 9200, lr = 0.0061309
I0511 18:13:30.397877 522 solver.cpp:218] Iteration 9300 (686.744 iter/s, 0.145615s/100 iters), loss = 0.00590399
I0511 18:13:30.397912 522 solver.cpp:237] Train net output #0: loss = 0.00590384 (* 1 = 0.00590384 loss)
I0511 18:13:30.397918 522 sgd_solver.cpp:105] Iteration 9300, lr = 0.00610706
I0511 18:13:30.501073 527 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:30.546329 522 solver.cpp:218] Iteration 9400 (673.817 iter/s, 0.148408s/100 iters), loss = 0.0222062
I0511 18:13:30.546365 522 solver.cpp:237] Train net output #0: loss = 0.022206 (* 1 = 0.022206 loss)
I0511 18:13:30.546372 522 sgd_solver.cpp:105] Iteration 9400, lr = 0.00608343
I0511 18:13:30.689489 522 solver.cpp:330] Iteration 9500, Testing net (#0)
I0511 18:13:30.749977 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:30.750983 522 solver.cpp:397] Test net output #0: accuracy = 0.9886
I0511 18:13:30.751006 522 solver.cpp:397] Test net output #1: loss = 0.0348373 (* 1 = 0.0348373 loss)
I0511 18:13:30.752398 522 solver.cpp:218] Iteration 9500 (485.365 iter/s, 0.20603s/100 iters), loss = 0.00510515
I0511 18:13:30.752418 522 solver.cpp:237] Train net output #0: loss = 0.00510499 (* 1 = 0.00510499 loss)
I0511 18:13:30.752425 522 sgd_solver.cpp:105] Iteration 9500, lr = 0.00606002
I0511 18:13:30.901768 522 solver.cpp:218] Iteration 9600 (669.672 iter/s, 0.149327s/100 iters), loss = 0.00206747
I0511 18:13:30.901801 522 solver.cpp:237] Train net output #0: loss = 0.00206731 (* 1 = 0.00206731 loss)
I0511 18:13:30.901808 522 sgd_solver.cpp:105] Iteration 9600, lr = 0.00603682
I0511 18:13:31.048707 522 solver.cpp:218] Iteration 9700 (680.75 iter/s, 0.146897s/100 iters), loss = 0.0022947
I0511 18:13:31.048831 522 solver.cpp:237] Train net output #0: loss = 0.00229454 (* 1 = 0.00229454 loss)
I0511 18:13:31.048842 522 sgd_solver.cpp:105] Iteration 9700, lr = 0.00601382
I0511 18:13:31.197224 522 solver.cpp:218] Iteration 9800 (674.045 iter/s, 0.148358s/100 iters), loss = 0.0104893
I0511 18:13:31.197260 522 solver.cpp:237] Train net output #0: loss = 0.0104892 (* 1 = 0.0104892 loss)
I0511 18:13:31.197268 522 sgd_solver.cpp:105] Iteration 9800, lr = 0.00599102
I0511 18:13:31.343189 522 solver.cpp:218] Iteration 9900 (685.87 iter/s, 0.1458s/100 iters), loss = 0.00333754
I0511 18:13:31.343222 522 solver.cpp:237] Train net output #0: loss = 0.00333739 (* 1 = 0.00333739 loss)
I0511 18:13:31.343227 522 sgd_solver.cpp:105] Iteration 9900, lr = 0.00596843
I0511 18:13:31.488759 522 solver.cpp:447] Snapshotting to binary proto file examples/mnist/lenet_iter_10000.caffemodel
I0511 18:13:31.493667 522 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_10000.solverstate
I0511 18:13:31.496690 522 solver.cpp:310] Iteration 10000, loss = 0.00347438
I0511 18:13:31.496726 522 solver.cpp:330] Iteration 10000, Testing net (#0)
I0511 18:13:31.556267 528 data_layer.cpp:73] Restarting data prefetching from start.
I0511 18:13:31.558285 522 solver.cpp:397] Test net output #0: accuracy = 0.9906
I0511 18:13:31.558308 522 solver.cpp:397] Test net output #1: loss = 0.0289746 (* 1 = 0.0289746 loss)
I0511 18:13:31.558313 522 solver.cpp:315] Optimization Done.
I0511 18:13:31.558317 522 caffe.cpp:259] Optimization Done.
real 0m17.463s
user 0m13.620s
sys 0m6.268s
root@0a1c1b0432ff:/opt/caffe# ls
CMakeLists.txt INSTALL.md Makefile.config build data examples models src
CONTRIBUTING.md LICENSE Makefile.config.example caffe.cloc docker include python tools
CONTRIBUTORS.md Makefile README.md cmake docs matlab scripts
root@0a1c1b0432ff:/opt/caffe#
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment