diff --git a/INSTALL.md b/INSTALL.md
index bd713cd1..8d0ba4aa 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -1,3 +1,4 @@
+
 # Installation
 
 See http://caffe.berkeleyvision.org/installation.html for the latest
@@ -14,27 +15,109 @@ $ git clone https://github.com/NVIDIA/caffe
 ```
 
 
-2. 
+2.
 
-```bash
-$ cd caffe
-$ mkdir build
+Edit ~/.bashrc
+
+
+```diff
++ export LIBRARY_PATH=${GFLAGS_DIR}/lib:${LIBRARY_PATH}
++ export LD_LIBRARY_PATH=${GFLAGS_DIR}/lib:${LD_LIBRARY_PATH}
 ```
 
-modify cmake/Cuda.cmake
-modify Makefile
-modify cmake/Modules/FindAtlas.cmake
+3.
 
-```bash
-$ cd build
-$ CMAKE_PREFIX_PATH=$NV_CAFFE_HOME/share \
-  cmake \
-  -DCMAKE_INSTALL_PREFIX=$NV_CAFFE_HOME \
-  -DCUDA_TOOLKIT_ROOT_DIR=$CUDA_HOME\ #required >=8.0
-  -DBLAS=$Atlas_LAPACK_LIBRARY \
-  -DCUDNN_LIBRARY=$CUDNN_LIBRARY \ #required >=6.0
-  -DCUDNN_INCLUDE=$CUDNN_INCLUDE \ #required >=6.0
-  ..
-
-$ make all -j 8
-$
+Edit some Cmake Files
+
+```diff
+--- a/cmake/Cuda.cmake
++++ b/cmake/Cuda.cmake
+@@ -251,6 +251,9 @@ if(USE_CUDNN)
+   endif()
+ endif()
+ 
++list(APPEND CUDA_NVCC_FLAGS "--pre-include $ENV{CUDNN6_HOME}/include/cudnn.h")
++include_directories(BEFORE "$ENV{CUDNN6_HOME}/include")
++
+ if(UNIX OR APPLE)
+   list(APPEND CUDA_NVCC_FLAGS -std=c++11;-Xcompiler;-fPIC)
+ endif()
+```
+
+```diff
+diff --git a/cmake/Modules/FindAtlas.cmake b/cmake/Modules/FindAtlas.cmake
+index 6e15643..e83fbeb 100644
+--- a/cmake/Modules/FindAtlas.cmake
++++ b/cmake/Modules/FindAtlas.cmake
+@@ -28,7 +28,7 @@ find_path(Atlas_CLAPACK_INCLUDE_DIR NAMES clapack.h PATHS ${Atlas_INCLUDE_SEARCH
+ 
+ find_library(Atlas_CBLAS_LIBRARY NAMES  ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
+ find_library(Atlas_BLAS_LIBRARY NAMES   atlas_r   atlas                 PATHS ${Atlas_LIB_SEARCH_PATHS})
+-find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas  PATHS ${Atlas_LIB_SEARCH_PATHS})
++find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r lapack lapack_atlas  PATHS ${Atlas_LIB_SEARCH_PATHS})
+ 
+ set(LOOKED_FOR
+   Atlas_CBLAS_INCLUDE_DIR
+diff --git a/cmake/Modules/FindNCCL.cmake b/cmake/Modules/FindNCCL.cmake
+index 1f7d97c..73f816c 100644
+--- a/cmake/Modules/FindNCCL.cmake
++++ b/cmake/Modules/FindNCCL.cmake
+@@ -20,6 +20,7 @@ find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIR NCCL_LIBRARY
+ 
+ if(NCCL_FOUND)
+   message(STATUS "Found NCCL (include: ${NCCL_INCLUDE_DIR}, library: ${NCCL_LIBRARY})")
++  include_directories(${NCCL_INCLUDE_DIR})
+   mark_as_advanced(NCCL_INCLUDE_DIR NCCL_LIBRARY)
+ endif()
+``` 
+
+```diff
+diff --git a/cmake/Modules/FindNVML.cmake b/cmake/Modules/FindNVML.cmake
+index 8747ab3..2a8b3e7 100644
+--- a/cmake/Modules/FindNVML.cmake
++++ b/cmake/Modules/FindNVML.cmake
+@@ -14,8 +14,16 @@ find_path(NVML_INCLUDE_DIR NAMES nvml.h
+     PATHS  ${CUDA_INCLUDE_DIRS} ${NVML_ROOT_DIR}/include
+     )
+ 
++set(MLPATH "/usr/lib64/nvidia")
++
+ find_library(NVML_LIBRARY nvidia-ml PATHS ${MLPATH} ${NVML_ROOT_DIR}/lib ${NVML_ROOT_DIR}/lib64)
+ 
++
++message(STATUS "CUDA_INCLUDE_DIRS: ${CUDA_INCLUDE_DIRS}")
++message(STATUS "NVML_INCLUDE_DIR: ${NVML_INCLUDE_DIR}")
++message(STATUS "NVML_LIBRARY: ${NVML_LIBRARY}")
++
++
+ include(FindPackageHandleStandardArgs)
+ find_package_handle_standard_args(NVML DEFAULT_MSG NVML_INCLUDE_DIR NVML_LIBRARY)
+```
+
+4. 
+Then you can do CMAKE
+
+```
+cmake -DCMAKE_INSTALL_PREFIX=${CAFFE_0.16_HOME} -DCUDA_TOOLKIT_ROOT_DIR=${CUDA8_HOME}  ..
+```
+
+console output seems like 
+
+
+```
+-- Found gflags  (include: /usr/include, library: /usr/lib64/libgflags.so)
+```
+
+after that ,you should do make command
+
+```
+$ make all -j 64
+$ make install -j 64
+```
+
+However, if you check library dependency by using ldd command
+
+```
+$ldd ${CAFFE_HOME}/build/tools/caffe
+libgflags.so.2.2 => ${GFLAGS_DIR}libs//libgflags.so.2.2 (0x0000XXXXXXXXXX)
+```
diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index e912ac43..1c55c5bb 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -2,12 +2,12 @@
 # Create the imagenet lmdb inputs
 # N.B. set the path to the imagenet train + val data dirs
 
-EXAMPLE=examples/imagenet
-DATA=data/ilsvrc12
-TOOLS=build/tools
+EXAMPLE=/mnt/nas100/share/data/imagenet/caffe_tool_dir
+DATA=/mnt/nas100/share/data/imagenet/caffe_tool_dir/txt_dir
+TOOLS=/home/hiroki11x/dl/NV-Caffe/build/tools
 
-TRAIN_DATA_ROOT=/path/to/imagenet/train/
-VAL_DATA_ROOT=/path/to/imagenet/val/
+TRAIN_DATA_ROOT=/mnt/nas100/share/data/imagenet/train/
+VAL_DATA_ROOT=/mnt/nas100/share/data/imagenet/val/
 
 # Set RESIZE=true to resize the images to 256x256. Leave as false if images have
 # already been resized using another tool.
diff --git a/install.sh b/install.sh
new file mode 100755
index 00000000..8083fd87
--- /dev/null
+++ b/install.sh
@@ -0,0 +1,3 @@
+make all -j 64
+make test -j 64
+make runtest -j 64
diff --git a/models/bvlc_alexnet/train_val_fp16.prototxt b/models/bvlc_alexnet/train_val_fp16.prototxt
index d2383884..3eb6bf8f 100644
--- a/models/bvlc_alexnet/train_val_fp16.prototxt
+++ b/models/bvlc_alexnet/train_val_fp16.prototxt
@@ -13,10 +13,17 @@ layer {
   transform_param {
     mirror: true
     crop_size: 227
+<<<<<<< HEAD
     mean_file: "/home/hiroki11x/ImageNet/imagenet_mean.binaryproto"
   }
   data_param {
     source: "/home/hiroki11x/ImageNet/ilsvrc12_train_lmdb"
+=======
+    mean_file: "/mnt/nas101/hiroki11x/imagenet_mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/nas101/hiroki11x/ilsvrc12_train_lmdb"
+>>>>>>> 5c1efbba51c185a619be77a215179bf2b04d9e7a
     batch_size: 256
 #    batch_size: 1024  # DGX1
     backend: LMDB
@@ -31,11 +38,19 @@ layer {
   transform_param {
     mirror: false
     crop_size: 227
+<<<<<<< HEAD
     mean_file: "/home/hiroki11x/ImageNet/imagenet_mean.binaryproto"
   }
   data_param {
     source: "/home/hiroki11x/ImageNet/ilsvrc12_val_lmdb"
     batch_size: 10
+=======
+    mean_file: "/mnt/nas101/hiroki11x/imagenet_mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/nas101/hiroki11x/ilsvrc12_val_lmdb"
+    batch_size: 32
+>>>>>>> 5c1efbba51c185a619be77a215179bf2b04d9e7a
 #    batch_size: 256  # DGx1
     backend: LMDB
   }
diff --git a/run.sh b/run.sh
index ca9c633d..52b2daf1 100755
--- a/run.sh
+++ b/run.sh
@@ -27,11 +27,17 @@ PREFIX_PATH=$PREFIX_PATH:$LOCAL/$s
 done
 
 
+for file in `git grep -l '"/home/hiroki11x/env/local/cuda/include/cudnn.h"'`; do
+sed -i -e \
+'s@"/home/hiroki11x/env/local/cuda/include/cudnn.h"@<cudnn.h>@g' \
+"$file"
+done
 
 CMAKE_PREFIX_PATH=$PYTHON_INCLUDE:$PYTHON_LIB:$HDF5_HL_LIBRARIES:$PREFIX_PATH cmake \
 -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda \
--DCMAKE_INSTALL_PREFIX=/home/hiroki11x/dl/nvcaffe \
+-DCMAKE_INSTALL_PREFIX=/home/hiroki11x/dl/nvcaffe/local \
 -DUSE_NCCL=ON \
+-DAtlas_LAPACK_LIBRARY=/home/hiroki11x/env/local/ATLAS \
 -DUSE_LEVELDB=OFF \
 .. | tee configure.log
 
diff --git a/share/Caffe/CaffeConfig.cmake b/share/Caffe/CaffeConfig.cmake
deleted file mode 100644
index ed7f711f..00000000
--- a/share/Caffe/CaffeConfig.cmake
+++ /dev/null
@@ -1,65 +0,0 @@
-# Config file for the Caffe package.
-#
-# Note:
-#   Caffe and this config file depends on opencv,
-#   so put `find_package(OpenCV)` before searching Caffe
-#   via `find_package(Caffe)`. All other lib/includes
-#   dependencies are hard coded in the file
-#
-# After successful configuration the following variables
-# will be defined:
-#
-#   Caffe_INCLUDE_DIRS - Caffe include directories
-#   Caffe_LIBRARIES    - libraries to link against
-#   Caffe_DEFINITIONS  - a list of definitions to pass to compiler
-#
-#   Caffe_HAVE_CUDA    - signals about CUDA support
-#   Caffe_HAVE_CUDNN   - signals about cuDNN support
-# 
-#
-#
-# OpenCV dependency
-
-if(ON)
-  if(NOT OpenCV_FOUND)
-    set(Caffe_OpenCV_CONFIG_PATH "/home/hiroki11/env/local/opencv_2.4.13/share/OpenCV")
-    if(Caffe_OpenCV_CONFIG_PATH)
-      get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
-
-      if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
-        message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
-        include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
-      endif()
-
-    else()
-      find_package(OpenCV REQUIRED)
-    endif()
-    unset(Caffe_OpenCV_CONFIG_PATH)
-  endif()
-endif()
-
-# Compute paths
-get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
-set(Caffe_INCLUDE_DIRS "/home/hiroki11/env/local/cuda/include;/home/hiroki11/env/local/boost_1.63.0/include;/home/hiroki11/env/local/glog_0.3.4/include;/usr/include;/home/hiroki11/env/local/hdf5_1.10.0/include;/home/hiroki11/env/local/lmdb_0.9.18/include;/home/hiroki11/env/local/leveldb/include;/home/hiroki11/env/local/snappy_1.1.4/include;/usr/local/cuda-8.0/include;/home/hiroki11/env/local/opencv_2.4.13/include/opencv;/home/hiroki11/env/local/opencv_2.4.13/include;/home/hiroki11/env/local/atlas_3.10.3/include;/home/hiroki11/env/local/nccl_1.3.4/include")
-
-get_filename_component(__caffe_include "${Caffe_CMAKE_DIR}/../../include" ABSOLUTE)
-list(APPEND Caffe_INCLUDE_DIRS ${__caffe_include})
-unset(__caffe_include)
-
-
-# Our library dependencies
-if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
-  include("${Caffe_CMAKE_DIR}/CaffeTargets.cmake")
-endif()
-
-# List of IMPORTED libs created by CaffeTargets.cmake
-set(Caffe_LIBRARIES caffe)
-
-# Definitions
-set(Caffe_DEFINITIONS "-DUSE_OPENCV;-DUSE_LMDB;-DUSE_LEVELDB;-DUSE_NCCL")
-
-# Cuda support variables
-set(Caffe_CPU_ONLY OFF)
-set(Caffe_HAVE_CUDA TRUE)
-set(Caffe_HAVE_CUDNN TRUE)
-
diff --git a/share/Caffe/CaffeTargets-release.cmake b/share/Caffe/CaffeTargets-release.cmake
deleted file mode 100644
index 2241bb62..00000000
--- a/share/Caffe/CaffeTargets-release.cmake
+++ /dev/null
@@ -1,30 +0,0 @@
-#----------------------------------------------------------------
-# Generated CMake target import file for configuration "Release".
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Import target "caffe" for configuration "Release"
-set_property(TARGET caffe APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(caffe PROPERTIES
-  IMPORTED_LINK_INTERFACE_LIBRARIES_RELEASE "proto;proto;/home/hiroki11/env/local/boost_1.63.0/lib/libboost_system.so;/home/hiroki11/env/local/boost_1.63.0/lib/libboost_thread.so;/home/hiroki11/env/local/boost_1.63.0/lib/libboost_filesystem.so;-lpthread;/home/hiroki11/env/local/glog_0.3.4/lib/libglog.so;/usr/lib64/libgflags.so;/usr/lib64/libprotobuf.so;-lpthread;/home/hiroki11/env/local/hdf5_1.10.0/lib/libhdf5_hl.so;/home/hiroki11/env/local/hdf5_1.10.0/lib/libhdf5.so;/home/hiroki11/env/local/hdf5_1.10.0/lib/libhdf5_hl.so;/home/hiroki11/env/local/hdf5_1.10.0/lib/libhdf5.so;/home/hiroki11/env/local/lmdb_0.9.18/lib/liblmdb.so;/home/hiroki11/env/local/leveldb/lib/libleveldb.so;/home/hiroki11/env/local/snappy_1.1.4/lib/libsnappy.so;/usr/local/cuda-8.0/lib64/libcudart.so;/usr/local/cuda-8.0/lib64/libcurand.so;/usr/local/cuda-8.0/lib64/libcublas.so;/home/hiroki11/env/local/cuda/lib64/libcudnn.so;opencv_core;opencv_highgui;opencv_imgproc;/home/hiroki11/env/local/atlas_3.10.3/lib/liblapack.a;/home/hiroki11/env/local/atlas_3.10.3/lib/libptcblas.a;/home/hiroki11/env/local/atlas_3.10.3/lib/libatlas.a;/usr/lib64/libpython2.7.so;/home/hiroki11/env/local/boost_1.63.0/lib/libboost_python.so;/home/hiroki11/env/local/nccl_1.3.4/lib/libnccl.so;/usr/lib64/nvidia/libnvidia-ml.so"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libcaffe-nv.so.0.16.1"
-  IMPORTED_SONAME_RELEASE "libcaffe-nv.so.0.16"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS caffe )
-list(APPEND _IMPORT_CHECK_FILES_FOR_caffe "${_IMPORT_PREFIX}/lib/libcaffe-nv.so.0.16.1" )
-
-# Import target "proto" for configuration "Release"
-set_property(TARGET proto APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(proto PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libproto.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS proto )
-list(APPEND _IMPORT_CHECK_FILES_FOR_proto "${_IMPORT_PREFIX}/lib/libproto.a" )
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/share/Caffe/CaffeTargets.cmake b/share/Caffe/CaffeTargets.cmake
deleted file mode 100644
index 4723a92a..00000000
--- a/share/Caffe/CaffeTargets.cmake
+++ /dev/null
@@ -1,86 +0,0 @@
-# Generated by CMake 2.8.12.2
-
-if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.5)
-   message(FATAL_ERROR "CMake >= 2.6.0 required")
-endif()
-cmake_policy(PUSH)
-cmake_policy(VERSION 2.6)
-#----------------------------------------------------------------
-# Generated CMake target import file.
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Protect against multiple inclusion, which would fail when already imported targets are added once more.
-set(_targetsDefined)
-set(_targetsNotDefined)
-set(_expectedTargets)
-foreach(_expectedTarget caffe proto)
-  list(APPEND _expectedTargets ${_expectedTarget})
-  if(NOT TARGET ${_expectedTarget})
-    list(APPEND _targetsNotDefined ${_expectedTarget})
-  endif()
-  if(TARGET ${_expectedTarget})
-    list(APPEND _targetsDefined ${_expectedTarget})
-  endif()
-endforeach()
-if("${_targetsDefined}" STREQUAL "${_expectedTargets}")
-  set(CMAKE_IMPORT_FILE_VERSION)
-  cmake_policy(POP)
-  return()
-endif()
-if(NOT "${_targetsDefined}" STREQUAL "")
-  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_targetsDefined}\nTargets not yet defined: ${_targetsNotDefined}\n")
-endif()
-unset(_targetsDefined)
-unset(_targetsNotDefined)
-unset(_expectedTargets)
-
-
-# Compute the installation prefix relative to this file.
-get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-
-# Create imported target caffe
-add_library(caffe SHARED IMPORTED)
-
-# Create imported target proto
-add_library(proto STATIC IMPORTED)
-
-# Load information for each installed configuration.
-get_filename_component(_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
-file(GLOB CONFIG_FILES "${_DIR}/CaffeTargets-*.cmake")
-foreach(f ${CONFIG_FILES})
-  include(${f})
-endforeach()
-
-# Cleanup temporary variables.
-set(_IMPORT_PREFIX)
-
-# Loop over all imported files and verify that they actually exist
-foreach(target ${_IMPORT_CHECK_TARGETS} )
-  foreach(file ${_IMPORT_CHECK_FILES_FOR_${target}} )
-    if(NOT EXISTS "${file}" )
-      message(FATAL_ERROR "The imported target \"${target}\" references the file
-   \"${file}\"
-but this file does not exist.  Possible reasons include:
-* The file was deleted, renamed, or moved to another location.
-* An install or uninstall procedure did not complete successfully.
-* The installation package was faulty and contained
-   \"${CMAKE_CURRENT_LIST_FILE}\"
-but not all the files it references.
-")
-    endif()
-  endforeach()
-  unset(_IMPORT_CHECK_FILES_FOR_${target})
-endforeach()
-unset(_IMPORT_CHECK_TARGETS)
-
-# This file does not depend on other imported targets which have
-# been exported from the same project but in a separate export set.
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
-cmake_policy(POP)
diff --git a/tools/extra/README.md b/tools/extra/README.md
new file mode 100644
index 00000000..83e314b8
--- /dev/null
+++ b/tools/extra/README.md
@@ -0,0 +1,5 @@
+gnuplot test_plot
+-> create Test accuracy vs. training time Graph
+
+gnuplot train_plot
+-> create Training loss vs. training time Graph
diff --git a/tools/extra/alexnet_fp16_test_plot b/tools/extra/alexnet_fp16_test_plot
new file mode 100755
index 00000000..05b894d1
--- /dev/null
+++ b/tools/extra/alexnet_fp16_test_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "alexnet_fp16_test.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Test accuracy vs. training time"
+set xlabel "Training time"
+set ylabel "Test accuracy"
+plot "alexnet_fp16.log.test" using 1:3 title "alexnet_fp16"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/alexnet_fp16_train_plot b/tools/extra/alexnet_fp16_train_plot
new file mode 100755
index 00000000..dc4d45b0
--- /dev/null
+++ b/tools/extra/alexnet_fp16_train_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "alexnet_fp16_train.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Training loss vs. training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
+plot "alexnet_fp16.log.train" using 1:3 title "alexnet_fp16"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/alexnet_test_plot b/tools/extra/alexnet_test_plot
new file mode 100755
index 00000000..1402ef26
--- /dev/null
+++ b/tools/extra/alexnet_test_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "alexnet_test.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Test accuracy vs. training time"
+set xlabel "Training time"
+set ylabel "Test accuracy"
+plot "alexnet.log.test" using 1:3 title "alexnet"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/alexnet_train_plot b/tools/extra/alexnet_train_plot
new file mode 100755
index 00000000..759d9e06
--- /dev/null
+++ b/tools/extra/alexnet_train_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "alexnet_train.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Training loss vs. training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
+plot "alexnet.log.train" using 1:3 title "alexnet"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/basic_plot/test_plot b/tools/extra/basic_plot/test_plot
new file mode 100755
index 00000000..6d0298a7
--- /dev/null
+++ b/tools/extra/basic_plot/test_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "alexnet_fp16_test.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Test accuracy vs. training time"
+set xlabel "Training time"
+set ylabel "Test accuracy"
+plot "train_alexnet_fp16.out.test" using 1:3 title "alexnet_fp16"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/basic_plot/train_plot b/tools/extra/basic_plot/train_plot
new file mode 100755
index 00000000..187f7563
--- /dev/null
+++ b/tools/extra/basic_plot/train_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "alexnet_fp16_training.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Training loss vs. training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
+plot "train_alexnet_fp16.out.train" using 1:3 title "alexnet_fp16"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/googlenet_fp16_test_plot b/tools/extra/googlenet_fp16_test_plot
new file mode 100755
index 00000000..7c78009a
--- /dev/null
+++ b/tools/extra/googlenet_fp16_test_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "googlenet_fp16_test.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Test accuracy vs. training time"
+set xlabel "Training time"
+set ylabel "Test accuracy"
+plot "googlenet_fp16.log.test" using 1:3 title "googlenet"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/googlenet_fp16_train_plot b/tools/extra/googlenet_fp16_train_plot
new file mode 100755
index 00000000..c21eed6b
--- /dev/null
+++ b/tools/extra/googlenet_fp16_train_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "googlenet_fp16_train.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Training loss vs. training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
+plot "googlenet_fp16.log.train" using 1:3 title "googlenet_fp16"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/googlenet_test_plot b/tools/extra/googlenet_test_plot
new file mode 100755
index 00000000..f6655ebf
--- /dev/null
+++ b/tools/extra/googlenet_test_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "googlenet_test.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Test accuracy vs. training time"
+set xlabel "Training time"
+set ylabel "Test accuracy"
+plot "googlenet.log.test" using 1:3 title "googlenet"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/googlenet_train_plot b/tools/extra/googlenet_train_plot
new file mode 100755
index 00000000..ec60fa81
--- /dev/null
+++ b/tools/extra/googlenet_train_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "googlenet_train.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Training loss vs. training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
+plot "googlenet.log.train" using 1:3 title "googlenet"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/img_12h/alexnet_fp16_test.png b/tools/extra/img_12h/alexnet_fp16_test.png
new file mode 100644
index 00000000..6645851d
Binary files /dev/null and b/tools/extra/img_12h/alexnet_fp16_test.png differ
diff --git a/tools/extra/img_12h/alexnet_fp16_train.png b/tools/extra/img_12h/alexnet_fp16_train.png
new file mode 100644
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diff --git a/tools/extra/resnet50_fp16_test_plot b/tools/extra/resnet50_fp16_test_plot
new file mode 100755
index 00000000..b24d005a
--- /dev/null
+++ b/tools/extra/resnet50_fp16_test_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "resnet50_fp16_test.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Test accuracy vs. training time"
+set xlabel "Training time"
+set ylabel "Test accuracy"
+plot "resnet50_fp16.log.test" using 1:3 title "resnet50_fp16"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/resnet50_fp16_train_plot b/tools/extra/resnet50_fp16_train_plot
new file mode 100755
index 00000000..ce85c260
--- /dev/null
+++ b/tools/extra/resnet50_fp16_train_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "resnet50_fp16_train.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Training loss vs. training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
+plot "resnet50_fp16.log.train" using 1:3 title "resnet50_fp16"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/resnet50_test_plot b/tools/extra/resnet50_test_plot
new file mode 100755
index 00000000..b8cc998d
--- /dev/null
+++ b/tools/extra/resnet50_test_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "resnet50_test.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Test accuracy vs. training time"
+set xlabel "Training time"
+set ylabel "Test accuracy"
+plot "resnet50.log.test" using 1:3 title "resnet50"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"
diff --git a/tools/extra/resnet50_train_plot b/tools/extra/resnet50_train_plot
new file mode 100755
index 00000000..096dfb6f
--- /dev/null
+++ b/tools/extra/resnet50_train_plot
@@ -0,0 +1,69 @@
+# These snippets serve only as basic examples.
+# Customization is a must.
+# You can copy, paste, edit them in whatever way you want.
+# Be warned that the fields in the training log may change in the future.
+# You had better check the data files before designing your own plots.
+
+# Please generate the neccessary data files with 
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
+# Example usage: 
+#     ./parse_log.sh mnist.log
+# Now you have mnist.log.train and mnist.log.test.
+#     gnuplot mnist.gnuplot
+
+# The fields present in the data files that are usually proper to plot along
+# the y axis are test accuracy, test loss, training loss, and learning rate.
+# Those should plot along the x axis are training iterations and seconds.
+# Possible combinations:
+# 1. Test accuracy (test score 0) vs. training iterations / time;
+# 2. Test loss (test score 1) time;
+# 3. Training loss vs. training iterations / time;
+# 4. Learning rate vs. training iterations / time;
+# A rarer one: Training time vs. iterations.
+
+# What is the difference between plotting against iterations and time?
+# If the overhead in one iteration is too high, one algorithm might appear
+# to be faster in terms of progress per iteration and slower when measured
+# against time. And the reverse case is not entirely impossible. Thus, some
+# papers chose to only publish the more favorable type. It is your freedom
+# to decide what to plot.
+
+reset
+set terminal png
+set output "resnet50_train.png"
+set style data lines
+set key right
+
+###### Fields in the data file your_log_name.log.train are
+###### Iters Seconds TrainingLoss LearningRate
+
+# Training loss vs. training iterations
+set title "Training loss vs. training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
+plot "resnet50.log.train" using 1:3 title "resnet50"
+
+# Training loss vs. training time
+# plot "mnist.log.train" using 2:3 title "mnist"
+
+# Learning rate vs. training iterations;
+# plot "mnist.log.train" using 1:4 title "mnist"
+
+# Learning rate vs. training time;
+# plot "mnist.log.train" using 2:4 title "mnist"
+
+
+###### Fields in the data file your_log_name.log.test are
+###### Iters Seconds TestAccuracy TestLoss
+
+# Test loss vs. training iterations
+# plot "mnist.log.test" using 1:4 title "mnist"
+
+# Test accuracy vs. training iterations
+# plot "mnist.log.test" using 1:3 title "mnist"
+
+# Test loss vs. training time
+# plot "mnist.log.test" using 2:4 title "mnist"
+
+# Test accuracy vs. training time
+# plot "mnist.log.test" using 2:3 title "mnist"