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AMD Radeon GPU上でCaffeをセットアップする方法
Introduction
現在、PyTorch/Caffe2ベースが最先端ですが、github上にあるソースだと未だに旧Caffeをベースにしているものもありますので、hipCaffe(ROCm-Caffe)をUbuntu16.04+ROCmベースでセットアップする方法を記述します。
Installation
Requirements
- Ubuntu16.04
- ビルドに必要な基礎パッケージ一式
- ROCmドライバ
- MIOpenライブラリ（CUDAシミュレーションレイヤー）
- OpenCV2 or OpenCV3
- hipCaffe
以下にコマンド一行でコンパイルするスクリプトです。
curl -sL http://install.aieater.com/setup_rocm_caffe | bash -
以下のディレクトリでコンパイルが行われます。 ~/src/hipCaffe/
スクリプト内訳
sudo apt-get install -y \
g++-multilib \
libunwind-dev \
git \
cmake cmake-curses-gui \
vim \
emacs-nox \
curl \
wget \
rpm \
unzip \
bc
sudo apt-get install -y rocm
sudo apt-get install -y rocm-libs
sudo apt-get install -y miopen-hip miopengemm
sudo apt-get install -y \
pkg-config \
protobuf-compiler \
libprotobuf-dev \
libleveldb-dev \
libsnappy-dev \
libhdf5-serial-dev \
libatlas-base-dev \
libboost-all-dev \
libgflags-dev \
libgoogle-glog-dev \
liblmdb-dev \
libfftw3-dev \
libelf-dev
sudo pip3 install scikit-image scipy pyyaml protobuf
curl -sL http://install.aieater.com/setup_opencv | bash -
mkdir -p ~/src
cd ~/src
git clone https://github.com/ROCmSoftwarePlatform/hipCaffe.git
cd hipCaffe
cp ./Makefile.config.example ./Makefile.config
export USE_PKG_CONFIG=1
make -j$(nproc)
動作確認
MNISTで動作確認
cd ~/src/hipCaffe/
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
./examples/mnist/train_lenet.sh
CIFAR10で動作確認
cd ~/src/hipCaffe/
./data/cifar10/get_cifar10.sh
./examples/cifar10/create_cifar10.sh
./build/tools/caffe train --solver=examples/cifar10/cifar10_quick_solver.prototxt
johndoe@thiguhag:~/src/hipCaffe$ ./build/tools/caffe train --solver=examples/cifar10/cifar10_quick_solver.prototxt
I0331 11:06:32.843717 24302 caffe.cpp:217] Using GPUs 0
I0331 11:06:32.843881 24302 caffe.cpp:222] GPU 0: Vega 20
I0331 11:06:32.847487 24302 solver.cpp:48] Initializing solver from parameters:
test_iter: 100
test_interval: 500
base_lr: 0.001
display: 100
max_iter: 4000
lr_policy: "fixed"
momentum: 0.9
weight_decay: 0.004
snapshot: 4000
snapshot_prefix: "examples/cifar10/cifar10_quick"
solver_mode: GPU
device_id: 0
net: "examples/cifar10/cifar10_quick_train_test.prototxt"
train_state {
level: 0
stage: ""
}
snapshot_format: HDF5
I0331 11:06:32.847564 24302 solver.cpp:91] Creating training net from net file: examples/cifar10/cifar10_quick_train_test.prototxt
I0331 11:06:32.847661 24302 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer cifar
I0331 11:06:32.847671 24302 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0331 11:06:32.847679 24302 net.cpp:58] Initializing net from parameters:
name: "CIFAR10_quick"
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "cifar"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mean_file: "examples/cifar10/mean.binaryproto"
}
data_param {
source: "examples/cifar10/cifar10_train_lmdb"
batch_size: 100
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.0001
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "pool1"
top: "pool1"
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3"
top: "pool3"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool3"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 64
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
I0331 11:06:32.847806 24302 layer_factory.hpp:77] Creating layer cifar
I0331 11:06:32.847857 24302 internal_thread.cpp:23] Starting internal thread on device 0
I0331 11:06:32.847921 24302 net.cpp:100] Creating Layer cifar
I0331 11:06:32.847929 24302 net.cpp:408] cifar -> data
I0331 11:06:32.847935 24302 net.cpp:408] cifar -> label
I0331 11:06:32.847940 24306 internal_thread.cpp:40] Started internal thread on device 0
I0331 11:06:32.847949 24302 data_transformer.cpp:25] Loading mean file from: examples/cifar10/mean.binaryproto
I0331 11:06:32.853466 24306 db_lmdb.cpp:35] Opened lmdb examples/cifar10/cifar10_train_lmdb
I0331 11:06:32.853821 24302 data_layer.cpp:41] output data size: 100,3,32,32
I0331 11:06:32.856909 24302 internal_thread.cpp:23] Starting internal thread on device 0
I0331 11:06:32.857230 24302 net.cpp:150] Setting up cifar
I0331 11:06:32.857236 24302 net.cpp:157] Top shape: 100 3 32 32 (307200)
I0331 11:06:32.857249 24302 net.cpp:157] Top shape: 100 (100)
I0331 11:06:32.857254 24302 net.cpp:165] Memory required for data: 1229200
I0331 11:06:32.857259 24302 layer_factory.hpp:77] Creating layer conv1
I0331 11:06:32.857255 24307 internal_thread.cpp:40] Started internal thread on device 0
I0331 11:06:32.857275 24302 net.cpp:100] Creating Layer conv1
I0331 11:06:32.857280 24302 net.cpp:434] conv1 <- data
I0331 11:06:32.857285 24302 net.cpp:408] conv1 -> conv1
I0331 11:06:33.909878 24302 net.cpp:150] Setting up conv1
I0331 11:06:33.909896 24302 net.cpp:157] Top shape: 100 32 32 32 (3276800)
I0331 11:06:33.909901 24302 net.cpp:165] Memory required for data: 14336400
I0331 11:06:33.909910 24302 layer_factory.hpp:77] Creating layer pool1
I0331 11:06:33.909919 24302 net.cpp:100] Creating Layer pool1
I0331 11:06:33.909924 24302 net.cpp:434] pool1 <- conv1
I0331 11:06:33.909932 24302 net.cpp:408] pool1 -> pool1
I0331 11:06:33.913565 24302 net.cpp:150] Setting up pool1
I0331 11:06:33.913594 24302 net.cpp:157] Top shape: 100 32 16 16 (819200)
I0331 11:06:33.913609 24302 net.cpp:165] Memory required for data: 17613200
I0331 11:06:33.913619 24302 layer_factory.hpp:77] Creating layer relu1
I0331 11:06:33.913635 24302 net.cpp:100] Creating Layer relu1
I0331 11:06:33.913653 24302 net.cpp:434] relu1 <- pool1
I0331 11:06:33.913669 24302 net.cpp:395] relu1 -> pool1 (in-place)
I0331 11:06:33.916409 24302 net.cpp:150] Setting up relu1
I0331 11:06:33.916424 24302 net.cpp:157] Top shape: 100 32 16 16 (819200)
I0331 11:06:33.916435 24302 net.cpp:165] Memory required for data: 20890000
I0331 11:06:33.916441 24302 layer_factory.hpp:77] Creating layer conv2
I0331 11:06:33.916460 24302 net.cpp:100] Creating Layer conv2
I0331 11:06:33.916467 24302 net.cpp:434] conv2 <- pool1
I0331 11:06:33.916482 24302 net.cpp:408] conv2 -> conv2
I0331 11:06:34.069100 24302 net.cpp:150] Setting up conv2
I0331 11:06:34.069113 24302 net.cpp:157] Top shape: 100 32 16 16 (819200)
I0331 11:06:34.069116 24302 net.cpp:165] Memory required for data: 24166800
I0331 11:06:34.069123 24302 layer_factory.hpp:77] Creating layer relu2
I0331 11:06:34.069130 24302 net.cpp:100] Creating Layer relu2
I0331 11:06:34.069133 24302 net.cpp:434] relu2 <- conv2
I0331 11:06:34.069137 24302 net.cpp:395] relu2 -> conv2 (in-place)
I0331 11:06:34.071858 24302 net.cpp:150] Setting up relu2
I0331 11:06:34.071878 24302 net.cpp:157] Top shape: 100 32 16 16 (819200)
I0331 11:06:34.071892 24302 net.cpp:165] Memory required for data: 27443600
I0331 11:06:34.071904 24302 layer_factory.hpp:77] Creating layer pool2
I0331 11:06:34.071923 24302 net.cpp:100] Creating Layer pool2
I0331 11:06:34.071934 24302 net.cpp:434] pool2 <- conv2
I0331 11:06:34.071949 24302 net.cpp:408] pool2 -> pool2
I0331 11:06:34.074851 24302 net.cpp:150] Setting up pool2
I0331 11:06:34.074873 24302 net.cpp:157] Top shape: 100 32 8 8 (204800)
I0331 11:06:34.074887 24302 net.cpp:165] Memory required for data: 28262800
I0331 11:06:34.074895 24302 layer_factory.hpp:77] Creating layer conv3
I0331 11:06:34.074916 24302 net.cpp:100] Creating Layer conv3
I0331 11:06:34.074928 24302 net.cpp:434] conv3 <- pool2
I0331 11:06:34.074944 24302 net.cpp:408] conv3 -> conv3
I0331 11:06:34.229825 24302 net.cpp:150] Setting up conv3
I0331 11:06:34.229837 24302 net.cpp:157] Top shape: 100 64 8 8 (409600)
I0331 11:06:34.229842 24302 net.cpp:165] Memory required for data: 29901200
I0331 11:06:34.229849 24302 layer_factory.hpp:77] Creating layer relu3
I0331 11:06:34.229856 24302 net.cpp:100] Creating Layer relu3
I0331 11:06:34.229859 24302 net.cpp:434] relu3 <- conv3
I0331 11:06:34.229863 24302 net.cpp:395] relu3 -> conv3 (in-place)
I0331 11:06:34.233310 24302 net.cpp:150] Setting up relu3
I0331 11:06:34.233340 24302 net.cpp:157] Top shape: 100 64 8 8 (409600)
I0331 11:06:34.233355 24302 net.cpp:165] Memory required for data: 31539600
I0331 11:06:34.233366 24302 layer_factory.hpp:77] Creating layer pool3
I0331 11:06:34.233382 24302 net.cpp:100] Creating Layer pool3
I0331 11:06:34.233397 24302 net.cpp:434] pool3 <- conv3
I0331 11:06:34.233412 24302 net.cpp:408] pool3 -> pool3
I0331 11:06:34.236271 24302 net.cpp:150] Setting up pool3
I0331 11:06:34.236287 24302 net.cpp:157] Top shape: 100 64 4 4 (102400)
I0331 11:06:34.236297 24302 net.cpp:165] Memory required for data: 31949200
I0331 11:06:34.236304 24302 layer_factory.hpp:77] Creating layer ip1
I0331 11:06:34.236325 24302 net.cpp:100] Creating Layer ip1
I0331 11:06:34.236336 24302 net.cpp:434] ip1 <- pool3
I0331 11:06:34.236348 24302 net.cpp:408] ip1 -> ip1
I0331 11:06:34.238878 24302 net.cpp:150] Setting up ip1
I0331 11:06:34.238896 24302 net.cpp:157] Top shape: 100 64 (6400)
I0331 11:06:34.238907 24302 net.cpp:165] Memory required for data: 31974800
I0331 11:06:34.238921 24302 layer_factory.hpp:77] Creating layer ip2
I0331 11:06:34.238935 24302 net.cpp:100] Creating Layer ip2
I0331 11:06:34.238945 24302 net.cpp:434] ip2 <- ip1
I0331 11:06:34.238955 24302 net.cpp:408] ip2 -> ip2
I0331 11:06:34.239763 24302 net.cpp:150] Setting up ip2
I0331 11:06:34.239779 24302 net.cpp:157] Top shape: 100 10 (1000)
I0331 11:06:34.239790 24302 net.cpp:165] Memory required for data: 31978800
I0331 11:06:34.239805 24302 layer_factory.hpp:77] Creating layer loss
I0331 11:06:34.239823 24302 net.cpp:100] Creating Layer loss
I0331 11:06:34.239831 24302 net.cpp:434] loss <- ip2
I0331 11:06:34.239841 24302 net.cpp:434] loss <- label
I0331 11:06:34.239851 24302 net.cpp:408] loss -> loss
I0331 11:06:34.239881 24302 layer_factory.hpp:77] Creating layer loss
I0331 11:06:34.242909 24302 net.cpp:150] Setting up loss
I0331 11:06:34.242923 24302 net.cpp:157] Top shape: (1)
I0331 11:06:34.242931 24302 net.cpp:160] with loss weight 1
I0331 11:06:34.242945 24302 net.cpp:165] Memory required for data: 31978804
I0331 11:06:34.242951 24302 net.cpp:226] loss needs backward computation.
I0331 11:06:34.242960 24302 net.cpp:226] ip2 needs backward computation.
I0331 11:06:34.242966 24302 net.cpp:226] ip1 needs backward computation.
I0331 11:06:34.242972 24302 net.cpp:226] pool3 needs backward computation.
I0331 11:06:34.242978 24302 net.cpp:226] relu3 needs backward computation.
I0331 11:06:34.242985 24302 net.cpp:226] conv3 needs backward computation.
I0331 11:06:34.242990 24302 net.cpp:226] pool2 needs backward computation.
I0331 11:06:34.242997 24302 net.cpp:226] relu2 needs backward computation.
I0331 11:06:34.243002 24302 net.cpp:226] conv2 needs backward computation.
I0331 11:06:34.243010 24302 net.cpp:226] relu1 needs backward computation.
I0331 11:06:34.243016 24302 net.cpp:226] pool1 needs backward computation.
I0331 11:06:34.243022 24302 net.cpp:226] conv1 needs backward computation.
I0331 11:06:34.243029 24302 net.cpp:228] cifar does not need backward computation.
I0331 11:06:34.243036 24302 net.cpp:270] This network produces output loss
I0331 11:06:34.243049 24302 net.cpp:283] Network initialization done.
I0331 11:06:34.243284 24302 solver.cpp:181] Creating test net (#0) specified by net file: examples/cifar10/cifar10_quick_train_test.prototxt
I0331 11:06:34.243317 24302 net.cpp:322] The NetState phase (1) differed from the phase (0) specified by a rule in layer cifar
I0331 11:06:34.243336 24302 net.cpp:58] Initializing net from parameters:
name: "CIFAR10_quick"
state {
phase: TEST
}
layer {
name: "cifar"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mean_file: "examples/cifar10/mean.binaryproto"
}
data_param {
source: "examples/cifar10/cifar10_test_lmdb"
batch_size: 100
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.0001
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "pool1"
top: "pool1"
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3"
top: "pool3"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool3"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 64
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
I0331 11:06:34.243697 24302 layer_factory.hpp:77] Creating layer cifar
I0331 11:06:34.243782 24302 internal_thread.cpp:23] Starting internal thread on device 0
I0331 11:06:34.243839 24302 net.cpp:100] Creating Layer cifar
I0331 11:06:34.243852 24302 net.cpp:408] cifar -> data
I0331 11:06:34.243862 24302 net.cpp:408] cifar -> label
I0331 11:06:34.243872 24302 data_transformer.cpp:25] Loading mean file from: examples/cifar10/mean.binaryproto
I0331 11:06:34.243873 24322 internal_thread.cpp:40] Started internal thread on device 0
I0331 11:06:34.251590 24322 db_lmdb.cpp:35] Opened lmdb examples/cifar10/cifar10_test_lmdb
I0331 11:06:34.252020 24302 data_layer.cpp:41] output data size: 100,3,32,32
I0331 11:06:34.255237 24302 internal_thread.cpp:23] Starting internal thread on device 0
I0331 11:06:34.255578 24302 net.cpp:150] Setting up cifar
I0331 11:06:34.255586 24302 net.cpp:157] Top shape: 100 3 32 32 (307200)
I0331 11:06:34.255594 24302 net.cpp:157] Top shape: 100 (100)
I0331 11:06:34.255599 24302 net.cpp:165] Memory required for data: 1229200
I0331 11:06:34.255604 24302 layer_factory.hpp:77] Creating layer label_cifar_1_split
I0331 11:06:34.255617 24302 net.cpp:100] Creating Layer label_cifar_1_split
I0331 11:06:34.255622 24302 net.cpp:434] label_cifar_1_split <- label
I0331 11:06:34.255630 24302 net.cpp:408] label_cifar_1_split -> label_cifar_1_split_0
I0331 11:06:34.255632 24323 internal_thread.cpp:40] Started internal thread on device 0
I0331 11:06:34.255650 24302 net.cpp:408] label_cifar_1_split -> label_cifar_1_split_1
I0331 11:06:34.261567 24302 net.cpp:150] Setting up label_cifar_1_split
I0331 11:06:34.261586 24302 net.cpp:157] Top shape: 100 (100)
I0331 11:06:34.261595 24302 net.cpp:157] Top shape: 100 (100)
I0331 11:06:34.261620 24302 net.cpp:165] Memory required for data: 1230000
I0331 11:06:34.261626 24302 layer_factory.hpp:77] Creating layer conv1
I0331 11:06:34.261641 24302 net.cpp:100] Creating Layer conv1
I0331 11:06:34.261646 24302 net.cpp:434] conv1 <- data
I0331 11:06:34.261651 24302 net.cpp:408] conv1 -> conv1
I0331 11:06:34.405349 24302 net.cpp:150] Setting up conv1
I0331 11:06:34.405360 24302 net.cpp:157] Top shape: 100 32 32 32 (3276800)
I0331 11:06:34.405364 24302 net.cpp:165] Memory required for data: 14337200
I0331 11:06:34.405372 24302 layer_factory.hpp:77] Creating layer pool1
I0331 11:06:34.405380 24302 net.cpp:100] Creating Layer pool1
I0331 11:06:34.405382 24302 net.cpp:434] pool1 <- conv1
I0331 11:06:34.405386 24302 net.cpp:408] pool1 -> pool1
I0331 11:06:34.408463 24302 net.cpp:150] Setting up pool1
I0331 11:06:34.408470 24302 net.cpp:157] Top shape: 100 32 16 16 (819200)
I0331 11:06:34.408478 24302 net.cpp:165] Memory required for data: 17614000
I0331 11:06:34.408483 24302 layer_factory.hpp:77] Creating layer relu1
I0331 11:06:34.408491 24302 net.cpp:100] Creating Layer relu1
I0331 11:06:34.408496 24302 net.cpp:434] relu1 <- pool1
I0331 11:06:34.408502 24302 net.cpp:395] relu1 -> pool1 (in-place)
I0331 11:06:34.411069 24302 net.cpp:150] Setting up relu1
I0331 11:06:34.411075 24302 net.cpp:157] Top shape: 100 32 16 16 (819200)
I0331 11:06:34.411082 24302 net.cpp:165] Memory required for data: 20890800
I0331 11:06:34.411087 24302 layer_factory.hpp:77] Creating layer conv2
I0331 11:06:34.411098 24302 net.cpp:100] Creating Layer conv2
I0331 11:06:34.411103 24302 net.cpp:434] conv2 <- pool1
I0331 11:06:34.411110 24302 net.cpp:408] conv2 -> conv2
I0331 11:06:34.555790 24302 net.cpp:150] Setting up conv2
I0331 11:06:34.555802 24302 net.cpp:157] Top shape: 100 32 16 16 (819200)
I0331 11:06:34.555809 24302 net.cpp:165] Memory required for data: 24167600
I0331 11:06:34.555826 24302 layer_factory.hpp:77] Creating layer relu2
I0331 11:06:34.555835 24302 net.cpp:100] Creating Layer relu2
I0331 11:06:34.555841 24302 net.cpp:434] relu2 <- conv2
I0331 11:06:34.555847 24302 net.cpp:395] relu2 -> conv2 (in-place)
I0331 11:06:34.558418 24302 net.cpp:150] Setting up relu2
I0331 11:06:34.558424 24302 net.cpp:157] Top shape: 100 32 16 16 (819200)
I0331 11:06:34.558430 24302 net.cpp:165] Memory required for data: 27444400
I0331 11:06:34.558435 24302 layer_factory.hpp:77] Creating layer pool2
I0331 11:06:34.558442 24302 net.cpp:100] Creating Layer pool2
I0331 11:06:34.558447 24302 net.cpp:434] pool2 <- conv2
I0331 11:06:34.558454 24302 net.cpp:408] pool2 -> pool2
I0331 11:06:34.561081 24302 net.cpp:150] Setting up pool2
I0331 11:06:34.561089 24302 net.cpp:157] Top shape: 100 32 8 8 (204800)
I0331 11:06:34.561094 24302 net.cpp:165] Memory required for data: 28263600
I0331 11:06:34.561098 24302 layer_factory.hpp:77] Creating layer conv3
I0331 11:06:34.561111 24302 net.cpp:100] Creating Layer conv3
I0331 11:06:34.561116 24302 net.cpp:434] conv3 <- pool2
I0331 11:06:34.561122 24302 net.cpp:408] conv3 -> conv3
I0331 11:06:34.707540 24302 net.cpp:150] Setting up conv3
I0331 11:06:34.707552 24302 net.cpp:157] Top shape: 100 64 8 8 (409600)
I0331 11:06:34.707562 24302 net.cpp:165] Memory required for data: 29902000
I0331 11:06:34.707576 24302 layer_factory.hpp:77] Creating layer relu3
I0331 11:06:34.707585 24302 net.cpp:100] Creating Layer relu3
I0331 11:06:34.707590 24302 net.cpp:434] relu3 <- conv3
I0331 11:06:34.707597 24302 net.cpp:395] relu3 -> conv3 (in-place)
I0331 11:06:34.710366 24302 net.cpp:150] Setting up relu3
I0331 11:06:34.710372 24302 net.cpp:157] Top shape: 100 64 8 8 (409600)
I0331 11:06:34.710378 24302 net.cpp:165] Memory required for data: 31540400
I0331 11:06:34.710382 24302 layer_factory.hpp:77] Creating layer pool3
I0331 11:06:34.710391 24302 net.cpp:100] Creating Layer pool3
I0331 11:06:34.710395 24302 net.cpp:434] pool3 <- conv3
I0331 11:06:34.710402 24302 net.cpp:408] pool3 -> pool3
I0331 11:06:34.713034 24302 net.cpp:150] Setting up pool3
I0331 11:06:34.713042 24302 net.cpp:157] Top shape: 100 64 4 4 (102400)
I0331 11:06:34.713068 24302 net.cpp:165] Memory required for data: 31950000
I0331 11:06:34.713074 24302 layer_factory.hpp:77] Creating layer ip1
I0331 11:06:34.713083 24302 net.cpp:100] Creating Layer ip1
I0331 11:06:34.713088 24302 net.cpp:434] ip1 <- pool3
I0331 11:06:34.713095 24302 net.cpp:408] ip1 -> ip1
I0331 11:06:34.714015 24302 net.cpp:150] Setting up ip1
I0331 11:06:34.714020 24302 net.cpp:157] Top shape: 100 64 (6400)
I0331 11:06:34.714026 24302 net.cpp:165] Memory required for data: 31975600
I0331 11:06:34.714033 24302 layer_factory.hpp:77] Creating layer ip2
I0331 11:06:34.714041 24302 net.cpp:100] Creating Layer ip2
I0331 11:06:34.714046 24302 net.cpp:434] ip2 <- ip1
I0331 11:06:34.714053 24302 net.cpp:408] ip2 -> ip2
I0331 11:06:34.714442 24302 net.cpp:150] Setting up ip2
I0331 11:06:34.714448 24302 net.cpp:157] Top shape: 100 10 (1000)
I0331 11:06:34.714453 24302 net.cpp:165] Memory required for data: 31979600
I0331 11:06:34.714462 24302 layer_factory.hpp:77] Creating layer ip2_ip2_0_split
I0331 11:06:34.714468 24302 net.cpp:100] Creating Layer ip2_ip2_0_split
I0331 11:06:34.714474 24302 net.cpp:434] ip2_ip2_0_split <- ip2
I0331 11:06:34.714480 24302 net.cpp:408] ip2_ip2_0_split -> ip2_ip2_0_split_0
I0331 11:06:34.714489 24302 net.cpp:408] ip2_ip2_0_split -> ip2_ip2_0_split_1
I0331 11:06:34.714628 24302 net.cpp:150] Setting up ip2_ip2_0_split
I0331 11:06:34.714634 24302 net.cpp:157] Top shape: 100 10 (1000)
I0331 11:06:34.714639 24302 net.cpp:157] Top shape: 100 10 (1000)
I0331 11:06:34.714644 24302 net.cpp:165] Memory required for data: 31987600
I0331 11:06:34.714650 24302 layer_factory.hpp:77] Creating layer accuracy
I0331 11:06:34.714658 24302 net.cpp:100] Creating Layer accuracy
I0331 11:06:34.714664 24302 net.cpp:434] accuracy <- ip2_ip2_0_split_0
I0331 11:06:34.714669 24302 net.cpp:434] accuracy <- label_cifar_1_split_0
I0331 11:06:34.714676 24302 net.cpp:408] accuracy -> accuracy
I0331 11:06:34.714685 24302 net.cpp:150] Setting up accuracy
I0331 11:06:34.714690 24302 net.cpp:157] Top shape: (1)
I0331 11:06:34.714695 24302 net.cpp:165] Memory required for data: 31987604
I0331 11:06:34.714699 24302 layer_factory.hpp:77] Creating layer loss
I0331 11:06:34.714705 24302 net.cpp:100] Creating Layer loss
I0331 11:06:34.714710 24302 net.cpp:434] loss <- ip2_ip2_0_split_1
I0331 11:06:34.714715 24302 net.cpp:434] loss <- label_cifar_1_split_1
I0331 11:06:34.714721 24302 net.cpp:408] loss -> loss
I0331 11:06:34.714730 24302 layer_factory.hpp:77] Creating layer loss
I0331 11:06:34.717491 24302 net.cpp:150] Setting up loss
I0331 11:06:34.717497 24302 net.cpp:157] Top shape: (1)
I0331 11:06:34.717502 24302 net.cpp:160] with loss weight 1
I0331 11:06:34.717511 24302 net.cpp:165] Memory required for data: 31987608
I0331 11:06:34.717516 24302 net.cpp:226] loss needs backward computation.
I0331 11:06:34.717522 24302 net.cpp:228] accuracy does not need backward computation.
I0331 11:06:34.717527 24302 net.cpp:226] ip2_ip2_0_split needs backward computation.
I0331 11:06:34.717532 24302 net.cpp:226] ip2 needs backward computation.
I0331 11:06:34.717537 24302 net.cpp:226] ip1 needs backward computation.
I0331 11:06:34.717542 24302 net.cpp:226] pool3 needs backward computation.
I0331 11:06:34.717547 24302 net.cpp:226] relu3 needs backward computation.
I0331 11:06:34.717551 24302 net.cpp:226] conv3 needs backward computation.
I0331 11:06:34.717556 24302 net.cpp:226] pool2 needs backward computation.
I0331 11:06:34.717561 24302 net.cpp:226] relu2 needs backward computation.
I0331 11:06:34.717566 24302 net.cpp:226] conv2 needs backward computation.
I0331 11:06:34.717571 24302 net.cpp:226] relu1 needs backward computation.
I0331 11:06:34.717576 24302 net.cpp:226] pool1 needs backward computation.
I0331 11:06:34.717579 24302 net.cpp:226] conv1 needs backward computation.
I0331 11:06:34.717584 24302 net.cpp:228] label_cifar_1_split does not need backward computation.
I0331 11:06:34.717591 24302 net.cpp:228] cifar does not need backward computation.
I0331 11:06:34.717595 24302 net.cpp:270] This network produces output accuracy
I0331 11:06:34.717609 24302 net.cpp:270] This network produces output loss
I0331 11:06:34.717622 24302 net.cpp:283] Network initialization done.
I0331 11:06:34.717659 24302 solver.cpp:60] Solver scaffolding done.
I0331 11:06:34.719099 24302 caffe.cpp:251] Starting Optimization
I0331 11:06:34.719105 24302 solver.cpp:279] Solving CIFAR10_quick
I0331 11:06:34.719110 24302 solver.cpp:280] Learning Rate Policy: fixed
I0331 11:06:34.719523 24302 solver.cpp:337] Iteration 0, Testing net (#0)
I0331 11:06:34.916335 24302 solver.cpp:404] Test net output #0: accuracy = 0.0963
I0331 11:06:34.916357 24302 solver.cpp:404] Test net output #1: loss = 2.3027 (* 1 = 2.3027 loss)
I0331 11:06:34.923990 24302 solver.cpp:228] Iteration 0, loss = 230.337
I0331 11:06:34.924002 24302 solver.cpp:244] Train net output #0: loss = 2.30337 (* 1 = 2.30337 loss)
I0331 11:06:34.924007 24302 sgd_solver.cpp:106] Iteration 0, lr = 0.001
I0331 11:06:35.254720 24302 solver.cpp:228] Iteration 100, loss = 1.69312
I0331 11:06:35.254740 24302 solver.cpp:244] Train net output #0: loss = 1.69312 (* 1 = 1.69312 loss)
I0331 11:06:35.254745 24302 sgd_solver.cpp:106] Iteration 100, lr = 0.001
I0331 11:06:35.590265 24302 solver.cpp:228] Iteration 200, loss = 1.70744
I0331 11:06:35.590287 24302 solver.cpp:244] Train net output #0: loss = 1.70743 (* 1 = 1.70743 loss)
I0331 11:06:35.590291 24302 sgd_solver.cpp:106] Iteration 200, lr = 0.001
I0331 11:06:35.923384 24302 solver.cpp:228] Iteration 300, loss = 1.26461
I0331 11:06:35.923406 24302 solver.cpp:244] Train net output #0: loss = 1.26461 (* 1 = 1.26461 loss)
I0331 11:06:35.923410 24302 sgd_solver.cpp:106] Iteration 300, lr = 0.001
I0331 11:06:36.248895 24302 solver.cpp:228] Iteration 400, loss = 1.36565
I0331 11:06:36.248917 24302 solver.cpp:244] Train net output #0: loss = 1.36565 (* 1 = 1.36565 loss)
I0331 11:06:36.248922 24302 sgd_solver.cpp:106] Iteration 400, lr = 0.001
I0331 11:06:36.579169 24302 solver.cpp:337] Iteration 500, Testing net (#0)
I0331 11:06:36.751092 24302 solver.cpp:404] Test net output #0: accuracy = 0.5509
I0331 11:06:36.751114 24302 solver.cpp:404] Test net output #1: loss = 1.27784 (* 1 = 1.27784 loss)
I0331 11:06:36.754051 24302 solver.cpp:228] Iteration 500, loss = 1.25145
I0331 11:06:36.754062 24302 solver.cpp:244] Train net output #0: loss = 1.25144 (* 1 = 1.25144 loss)
I0331 11:06:36.754066 24302 sgd_solver.cpp:106] Iteration 500, lr = 0.001
I0331 11:06:37.088784 24302 solver.cpp:228] Iteration 600, loss = 1.25302
I0331 11:06:37.088806 24302 solver.cpp:244] Train net output #0: loss = 1.25302 (* 1 = 1.25302 loss)
I0331 11:06:37.088811 24302 sgd_solver.cpp:106] Iteration 600, lr = 0.001
I0331 11:06:37.420756 24302 solver.cpp:228] Iteration 700, loss = 1.2073
I0331 11:06:37.420778 24302 solver.cpp:244] Train net output #0: loss = 1.20729 (* 1 = 1.20729 loss)
I0331 11:06:37.420784 24302 sgd_solver.cpp:106] Iteration 700, lr = 0.001
I0331 11:06:37.749595 24302 solver.cpp:228] Iteration 800, loss = 1.08577
I0331 11:06:37.749617 24302 solver.cpp:244] Train net output #0: loss = 1.08576 (* 1 = 1.08576 loss)
I0331 11:06:37.749622 24302 sgd_solver.cpp:106] Iteration 800, lr = 0.001
I0331 11:06:38.087330 24302 solver.cpp:228] Iteration 900, loss = 0.979316
I0331 11:06:38.087352 24302 solver.cpp:244] Train net output #0: loss = 0.979311 (* 1 = 0.979311 loss)
I0331 11:06:38.087357 24302 sgd_solver.cpp:106] Iteration 900, lr = 0.001
I0331 11:06:38.420168 24302 solver.cpp:337] Iteration 1000, Testing net (#0)
I0331 11:06:38.593856 24302 solver.cpp:404] Test net output #0: accuracy = 0.6054
I0331 11:06:38.593878 24302 solver.cpp:404] Test net output #1: loss = 1.1508 (* 1 = 1.1508 loss)
I0331 11:06:38.596915 24302 solver.cpp:228] Iteration 1000, loss = 1.03848
I0331 11:06:38.596928 24302 solver.cpp:244] Train net output #0: loss = 1.03847 (* 1 = 1.03847 loss)
I0331 11:06:38.596935 24302 sgd_solver.cpp:106] Iteration 1000, lr = 0.001
I0331 11:06:38.926901 24302 solver.cpp:228] Iteration 1100, loss = 1.06011
I0331 11:06:38.926940 24302 solver.cpp:244] Train net output #0: loss = 1.06011 (* 1 = 1.06011 loss)
I0331 11:06:38.926945 24302 sgd_solver.cpp:106] Iteration 1100, lr = 0.001
I0331 11:06:39.255908 24302 solver.cpp:228] Iteration 1200, loss = 0.967729
I0331 11:06:39.255928 24302 solver.cpp:244] Train net output #0: loss = 0.967723 (* 1 = 0.967723 loss)
I0331 11:06:39.255934 24302 sgd_solver.cpp:106] Iteration 1200, lr = 0.001
I0331 11:06:39.587491 24302 solver.cpp:228] Iteration 1300, loss = 0.873639
I0331 11:06:39.587512 24302 solver.cpp:244] Train net output #0: loss = 0.873634 (* 1 = 0.873634 loss)
I0331 11:06:39.587517 24302 sgd_solver.cpp:106] Iteration 1300, lr = 0.001
I0331 11:06:39.916858 24302 solver.cpp:228] Iteration 1400, loss = 0.822912
I0331 11:06:39.916877 24302 solver.cpp:244] Train net output #0: loss = 0.822906 (* 1 = 0.822906 loss)
I0331 11:06:39.916882 24302 sgd_solver.cpp:106] Iteration 1400, lr = 0.001
I0331 11:06:40.243862 24302 solver.cpp:337] Iteration 1500, Testing net (#0)
I0331 11:06:40.418777 24302 solver.cpp:404] Test net output #0: accuracy = 0.6428
I0331 11:06:40.418798 24302 solver.cpp:404] Test net output #1: loss = 1.03695 (* 1 = 1.03695 loss)
I0331 11:06:40.422040 24302 solver.cpp:228] Iteration 1500, loss = 0.917664
I0331 11:06:40.422051 24302 solver.cpp:244] Train net output #0: loss = 0.917658 (* 1 = 0.917658 loss)
I0331 11:06:40.422056 24302 sgd_solver.cpp:106] Iteration 1500, lr = 0.001
I0331 11:06:40.751153 24302 solver.cpp:228] Iteration 1600, loss = 0.951443
I0331 11:06:40.751173 24302 solver.cpp:244] Train net output #0: loss = 0.951437 (* 1 = 0.951437 loss)
I0331 11:06:40.751178 24302 sgd_solver.cpp:106] Iteration 1600, lr = 0.001
I0331 11:06:41.082576 24302 solver.cpp:228] Iteration 1700, loss = 0.824344
I0331 11:06:41.082597 24302 solver.cpp:244] Train net output #0: loss = 0.824338 (* 1 = 0.824338 loss)
I0331 11:06:41.082602 24302 sgd_solver.cpp:106] Iteration 1700, lr = 0.001
I0331 11:06:41.412235 24302 solver.cpp:228] Iteration 1800, loss = 0.814171
I0331 11:06:41.412256 24302 solver.cpp:244] Train net output #0: loss = 0.814166 (* 1 = 0.814166 loss)
I0331 11:06:41.412261 24302 sgd_solver.cpp:106] Iteration 1800, lr = 0.001
I0331 11:06:41.743070 24302 solver.cpp:228] Iteration 1900, loss = 0.746516
I0331 11:06:41.743091 24302 solver.cpp:244] Train net output #0: loss = 0.74651 (* 1 = 0.74651 loss)
I0331 11:06:41.743096 24302 sgd_solver.cpp:106] Iteration 1900, lr = 0.001
I0331 11:06:42.072156 24302 solver.cpp:337] Iteration 2000, Testing net (#0)
I0331 11:06:42.247408 24302 solver.cpp:404] Test net output #0: accuracy = 0.6774
I0331 11:06:42.247429 24302 solver.cpp:404] Test net output #1: loss = 0.943321 (* 1 = 0.943321 loss)
I0331 11:06:42.250512 24302 solver.cpp:228] Iteration 2000, loss = 0.796382
I0331 11:06:42.250524 24302 solver.cpp:244] Train net output #0: loss = 0.796376 (* 1 = 0.796376 loss)
I0331 11:06:42.250528 24302 sgd_solver.cpp:106] Iteration 2000, lr = 0.001
I0331 11:06:42.582597 24302 solver.cpp:228] Iteration 2100, loss = 0.88666
I0331 11:06:42.582618 24302 solver.cpp:244] Train net output #0: loss = 0.886655 (* 1 = 0.886655 loss)
I0331 11:06:42.582623 24302 sgd_solver.cpp:106] Iteration 2100, lr = 0.001
I0331 11:06:42.911408 24302 solver.cpp:228] Iteration 2200, loss = 0.76949
I0331 11:06:42.911428 24302 solver.cpp:244] Train net output #0: loss = 0.769484 (* 1 = 0.769484 loss)
I0331 11:06:42.911433 24302 sgd_solver.cpp:106] Iteration 2200, lr = 0.001
I0331 11:06:43.243353 24302 solver.cpp:228] Iteration 2300, loss = 0.739908
I0331 11:06:43.243374 24302 solver.cpp:244] Train net output #0: loss = 0.739902 (* 1 = 0.739902 loss)
I0331 11:06:43.243379 24302 sgd_solver.cpp:106] Iteration 2300, lr = 0.001
I0331 11:06:43.573737 24302 solver.cpp:228] Iteration 2400, loss = 0.757853
I0331 11:06:43.573757 24302 solver.cpp:244] Train net output #0: loss = 0.757848 (* 1 = 0.757848 loss)
I0331 11:06:43.573762 24302 sgd_solver.cpp:106] Iteration 2400, lr = 0.001
I0331 11:06:43.900631 24302 solver.cpp:337] Iteration 2500, Testing net (#0)
I0331 11:06:44.083359 24302 solver.cpp:404] Test net output #0: accuracy = 0.6871
I0331 11:06:44.083381 24302 solver.cpp:404] Test net output #1: loss = 0.925407 (* 1 = 0.925407 loss)
I0331 11:06:44.086453 24302 solver.cpp:228] Iteration 2500, loss = 0.748057
I0331 11:06:44.086467 24302 solver.cpp:244] Train net output #0: loss = 0.748051 (* 1 = 0.748051 loss)
I0331 11:06:44.086472 24302 sgd_solver.cpp:106] Iteration 2500, lr = 0.001
I0331 11:06:44.417397 24302 solver.cpp:228] Iteration 2600, loss = 0.827012
I0331 11:06:44.417420 24302 solver.cpp:244] Train net output #0: loss = 0.827007 (* 1 = 0.827007 loss)
I0331 11:06:44.417425 24302 sgd_solver.cpp:106] Iteration 2600, lr = 0.001
I0331 11:06:44.752656 24302 solver.cpp:228] Iteration 2700, loss = 0.765873
I0331 11:06:44.752678 24302 solver.cpp:244] Train net output #0: loss = 0.765867 (* 1 = 0.765867 loss)
I0331 11:06:44.752684 24302 sgd_solver.cpp:106] Iteration 2700, lr = 0.001
I0331 11:06:45.087687 24302 solver.cpp:228] Iteration 2800, loss = 0.689853
I0331 11:06:45.087709 24302 solver.cpp:244] Train net output #0: loss = 0.689848 (* 1 = 0.689848 loss)
I0331 11:06:45.087714 24302 sgd_solver.cpp:106] Iteration 2800, lr = 0.001
I0331 11:06:45.419508 24302 solver.cpp:228] Iteration 2900, loss = 0.726292
I0331 11:06:45.419528 24302 solver.cpp:244] Train net output #0: loss = 0.726286 (* 1 = 0.726286 loss)
I0331 11:06:45.419533 24302 sgd_solver.cpp:106] Iteration 2900, lr = 0.001
I0331 11:06:45.754101 24302 solver.cpp:337] Iteration 3000, Testing net (#0)
I0331 11:06:45.933135 24302 solver.cpp:404] Test net output #0: accuracy = 0.6946
I0331 11:06:45.933156 24302 solver.cpp:404] Test net output #1: loss = 0.898484 (* 1 = 0.898484 loss)
I0331 11:06:45.936143 24302 solver.cpp:228] Iteration 3000, loss = 0.690615
I0331 11:06:45.936157 24302 solver.cpp:244] Train net output #0: loss = 0.69061 (* 1 = 0.69061 loss)
I0331 11:06:45.936163 24302 sgd_solver.cpp:106] Iteration 3000, lr = 0.001
I0331 11:06:46.273326 24302 solver.cpp:228] Iteration 3100, loss = 0.794096
I0331 11:06:46.273349 24302 solver.cpp:244] Train net output #0: loss = 0.79409 (* 1 = 0.79409 loss)
I0331 11:06:46.273353 24302 sgd_solver.cpp:106] Iteration 3100, lr = 0.001
I0331 11:06:46.607097 24302 solver.cpp:228] Iteration 3200, loss = 0.695419
I0331 11:06:46.607121 24302 solver.cpp:244] Train net output #0: loss = 0.695413 (* 1 = 0.695413 loss)
I0331 11:06:46.607129 24302 sgd_solver.cpp:106] Iteration 3200, lr = 0.001
I0331 11:06:46.940327 24302 solver.cpp:228] Iteration 3300, loss = 0.636181
I0331 11:06:46.940351 24302 solver.cpp:244] Train net output #0: loss = 0.636175 (* 1 = 0.636175 loss)
I0331 11:06:46.940356 24302 sgd_solver.cpp:106] Iteration 3300, lr = 0.001
I0331 11:06:47.278142 24302 solver.cpp:228] Iteration 3400, loss = 0.686613
I0331 11:06:47.278164 24302 solver.cpp:244] Train net output #0: loss = 0.686607 (* 1 = 0.686607 loss)
I0331 11:06:47.278169 24302 sgd_solver.cpp:106] Iteration 3400, lr = 0.001
I0331 11:06:47.610103 24302 solver.cpp:337] Iteration 3500, Testing net (#0)
I0331 11:06:47.792064 24302 solver.cpp:404] Test net output #0: accuracy = 0.6998
I0331 11:06:47.792084 24302 solver.cpp:404] Test net output #1: loss = 0.88248 (* 1 = 0.88248 loss)
I0331 11:06:47.795045 24302 solver.cpp:228] Iteration 3500, loss = 0.638955
I0331 11:06:47.795059 24302 solver.cpp:244] Train net output #0: loss = 0.63895 (* 1 = 0.63895 loss)
I0331 11:06:47.795064 24302 sgd_solver.cpp:106] Iteration 3500, lr = 0.001
I0331 11:06:48.126653 24302 solver.cpp:228] Iteration 3600, loss = 0.733167
I0331 11:06:48.126678 24302 solver.cpp:244] Train net output #0: loss = 0.733161 (* 1 = 0.733161 loss)
I0331 11:06:48.126683 24302 sgd_solver.cpp:106] Iteration 3600, lr = 0.001
I0331 11:06:48.459372 24302 solver.cpp:228] Iteration 3700, loss = 0.649733
I0331 11:06:48.459393 24302 solver.cpp:244] Train net output #0: loss = 0.649728 (* 1 = 0.649728 loss)
I0331 11:06:48.459398 24302 sgd_solver.cpp:106] Iteration 3700, lr = 0.001
I0331 11:06:48.793612 24302 solver.cpp:228] Iteration 3800, loss = 0.619229
I0331 11:06:48.793632 24302 solver.cpp:244] Train net output #0: loss = 0.619223 (* 1 = 0.619223 loss)
I0331 11:06:48.793637 24302 sgd_solver.cpp:106] Iteration 3800, lr = 0.001
I0331 11:06:49.126967 24302 solver.cpp:228] Iteration 3900, loss = 0.657576
I0331 11:06:49.126991 24302 solver.cpp:244] Train net output #0: loss = 0.65757 (* 1 = 0.65757 loss)
I0331 11:06:49.126996 24302 sgd_solver.cpp:106] Iteration 3900, lr = 0.001
I0331 11:06:49.457964 24302 solver.cpp:464] Snapshotting to HDF5 file examples/cifar10/cifar10_quick_iter_4000.caffemodel.h5
I0331 11:06:49.691646 24302 sgd_solver.cpp:283] Snapshotting solver state to HDF5 file examples/cifar10/cifar10_quick_iter_4000.solverstate.h5
I0331 11:06:49.697353 24302 solver.cpp:317] Iteration 4000, loss = 0.569912
I0331 11:06:49.697367 24302 solver.cpp:337] Iteration 4000, Testing net (#0)
I0331 11:06:49.878609 24302 solver.cpp:404] Test net output #0: accuracy = 0.7054
I0331 11:06:49.878630 24302 solver.cpp:404] Test net output #1: loss = 0.863621 (* 1 = 0.863621 loss)
I0331 11:06:49.878636 24302 solver.cpp:322] Optimization Done.
I0331 11:06:49.878639 24302 caffe.cpp:254] Optimization Done.
References
- hipCaffe ROCm Offical https://rocm.github.io/ROCmHipCaffeQuickstart.html
- TensorFlor-ROCm / HipCaffe / PyTorch-ROCm / Caffe2 installation https://rocm-documentation.readthedocs.io/en/latest/Deep_learning/Deep-learning.html
- ROCm https://github.com/ROCmSoftwarePlatform
- MIOpen https://gpuopen.com/compute-product/miopen/
- GPUEater summarized ROCm-Caffe https://github.com/aieater/rocm_caffe_informations
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