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caffe.log
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Log file created at: 2015/08/21 09:21:08
Running on machine: hkk-Z97-HD3
Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg
I0821 09:21:08.874228 5405 caffe.cpp:113] Use GPU with device ID 0
I0821 09:21:09.034922 5405 caffe.cpp:121] Starting Optimization
I0821 09:21:09.035015 5405 solver.cpp:38] Initializing solver from parameters:
test_iter: 1
test_interval: 500
base_lr: 0.003
display: 100
max_iter: 20000
lr_policy: "fixed"
gamma: 0.0001
power: 0.75
momentum: 0.9
weight_decay: 0.0005
stepsize: 100000
snapshot: 5000
snapshot_prefix: "examples/kaggle_prototxt/model/fkp"
solver_mode: GPU
net: "examples/kaggle_prototxt/fkp_net.prototxt"
solver_type: NESTEROV
I0821 09:21:09.035078 5405 solver.cpp:80] Creating training net from net file: examples/kaggle_prototxt/fkp_net.prototxt
I0821 09:21:09.035431 5405 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: examples/kaggle_prototxt/fkp_net.prototxt
E0821 09:21:09.035465 5405 upgrade_proto.cpp:636] Input NetParameter to be upgraded already specifies 'layer' fields; these will be ignored for the upgrade.
E0821 09:21:09.051756 5405 upgrade_proto.cpp:623] Warning: had one or more problems upgrading V1LayerParameter (see above); continuing anyway.
I0821 09:21:09.051867 5405 net.cpp:339] The NetState phase (0) differed from the phase (1) specified by a rule in layer MyData
I0821 09:21:09.051990 5405 net.cpp:50] Initializing net from parameters:
name: "fkp_net"
state {
phase: TRAIN
}
layer {
name: "MyData"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
hdf5_data_param {
source: "data/kaggle_fkp_data/train_data_list.txt"
batch_size: 128
shuffle: true
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 48
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "fc5"
type: "InnerProduct"
bottom: "conv3"
top: "fc5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "fc5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 30
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "fc6"
bottom: "label"
top: "loss"
}
I0821 09:21:09.052466 5405 layer_factory.hpp:75] Creating layer MyData
I0821 09:21:09.052503 5405 net.cpp:110] Creating Layer MyData
I0821 09:21:09.052513 5405 net.cpp:432] MyData -> data
I0821 09:21:09.052549 5405 net.cpp:432] MyData -> label
I0821 09:21:09.052574 5405 hdf5_data_layer.cpp:80] Loading list of HDF5 filenames from: data/kaggle_fkp_data/train_data_list.txt
I0821 09:21:09.052621 5405 hdf5_data_layer.cpp:94] Number of HDF5 files: 1
I0821 09:21:09.093633 5405 net.cpp:155] Setting up MyData
I0821 09:21:09.093675 5405 net.cpp:163] Top shape: 128 1 96 96 (1179648)
I0821 09:21:09.093685 5405 net.cpp:163] Top shape: 128 30 (3840)
I0821 09:21:09.093698 5405 layer_factory.hpp:75] Creating layer conv1
I0821 09:21:09.093719 5405 net.cpp:110] Creating Layer conv1
I0821 09:21:09.093730 5405 net.cpp:476] conv1 <- data
I0821 09:21:09.093744 5405 net.cpp:432] conv1 -> conv1
I0821 09:21:09.130288 5405 net.cpp:155] Setting up conv1
I0821 09:21:09.130329 5405 net.cpp:163] Top shape: 128 20 92 92 (21667840)
I0821 09:21:09.130367 5405 layer_factory.hpp:75] Creating layer relu1
I0821 09:21:09.130381 5405 net.cpp:110] Creating Layer relu1
I0821 09:21:09.130390 5405 net.cpp:476] relu1 <- conv1
I0821 09:21:09.130399 5405 net.cpp:419] relu1 -> conv1 (in-place)
I0821 09:21:09.130460 5405 net.cpp:155] Setting up relu1
I0821 09:21:09.130480 5405 net.cpp:163] Top shape: 128 20 92 92 (21667840)
I0821 09:21:09.130486 5405 layer_factory.hpp:75] Creating layer pool1
I0821 09:21:09.130496 5405 net.cpp:110] Creating Layer pool1
I0821 09:21:09.130502 5405 net.cpp:476] pool1 <- conv1
I0821 09:21:09.130511 5405 net.cpp:432] pool1 -> pool1
I0821 09:21:09.130664 5405 net.cpp:155] Setting up pool1
I0821 09:21:09.130679 5405 net.cpp:163] Top shape: 128 20 46 46 (5416960)
I0821 09:21:09.130686 5405 layer_factory.hpp:75] Creating layer conv2
I0821 09:21:09.130697 5405 net.cpp:110] Creating Layer conv2
I0821 09:21:09.130703 5405 net.cpp:476] conv2 <- pool1
I0821 09:21:09.130712 5405 net.cpp:432] conv2 -> conv2
I0821 09:21:09.131453 5405 net.cpp:155] Setting up conv2
I0821 09:21:09.131470 5405 net.cpp:163] Top shape: 128 48 42 42 (10838016)
I0821 09:21:09.131484 5405 layer_factory.hpp:75] Creating layer relu2
I0821 09:21:09.131491 5405 net.cpp:110] Creating Layer relu2
I0821 09:21:09.131497 5405 net.cpp:476] relu2 <- conv2
I0821 09:21:09.131505 5405 net.cpp:419] relu2 -> conv2 (in-place)
I0821 09:21:09.131557 5405 net.cpp:155] Setting up relu2
I0821 09:21:09.131567 5405 net.cpp:163] Top shape: 128 48 42 42 (10838016)
I0821 09:21:09.131573 5405 layer_factory.hpp:75] Creating layer pool2
I0821 09:21:09.131582 5405 net.cpp:110] Creating Layer pool2
I0821 09:21:09.131587 5405 net.cpp:476] pool2 <- conv2
I0821 09:21:09.131594 5405 net.cpp:432] pool2 -> pool2
I0821 09:21:09.131644 5405 net.cpp:155] Setting up pool2
I0821 09:21:09.131654 5405 net.cpp:163] Top shape: 128 48 21 21 (2709504)
I0821 09:21:09.131660 5405 layer_factory.hpp:75] Creating layer conv3
I0821 09:21:09.131669 5405 net.cpp:110] Creating Layer conv3
I0821 09:21:09.131675 5405 net.cpp:476] conv3 <- pool2
I0821 09:21:09.131682 5405 net.cpp:432] conv3 -> conv3
I0821 09:21:09.132164 5405 net.cpp:155] Setting up conv3
I0821 09:21:09.132180 5405 net.cpp:163] Top shape: 128 64 19 19 (2957312)
I0821 09:21:09.132192 5405 layer_factory.hpp:75] Creating layer relu3
I0821 09:21:09.132202 5405 net.cpp:110] Creating Layer relu3
I0821 09:21:09.132208 5405 net.cpp:476] relu3 <- conv3
I0821 09:21:09.132216 5405 net.cpp:419] relu3 -> conv3 (in-place)
I0821 09:21:09.132264 5405 net.cpp:155] Setting up relu3
I0821 09:21:09.132274 5405 net.cpp:163] Top shape: 128 64 19 19 (2957312)
I0821 09:21:09.132280 5405 layer_factory.hpp:75] Creating layer fc5
I0821 09:21:09.132290 5405 net.cpp:110] Creating Layer fc5
I0821 09:21:09.132297 5405 net.cpp:476] fc5 <- conv3
I0821 09:21:09.132303 5405 net.cpp:432] fc5 -> fc5
I0821 09:21:09.230295 5405 net.cpp:155] Setting up fc5
I0821 09:21:09.230332 5405 net.cpp:163] Top shape: 128 500 (64000)
I0821 09:21:09.230346 5405 layer_factory.hpp:75] Creating layer fc6
I0821 09:21:09.230360 5405 net.cpp:110] Creating Layer fc6
I0821 09:21:09.230370 5405 net.cpp:476] fc6 <- fc5
I0821 09:21:09.230381 5405 net.cpp:432] fc6 -> fc6
I0821 09:21:09.230572 5405 net.cpp:155] Setting up fc6
I0821 09:21:09.230584 5405 net.cpp:163] Top shape: 128 30 (3840)
I0821 09:21:09.230595 5405 layer_factory.hpp:75] Creating layer loss
I0821 09:21:09.230604 5405 net.cpp:110] Creating Layer loss
I0821 09:21:09.230609 5405 net.cpp:476] loss <- fc6
I0821 09:21:09.230617 5405 net.cpp:476] loss <- label
I0821 09:21:09.230625 5405 net.cpp:432] loss -> loss
I0821 09:21:09.230646 5405 net.cpp:155] Setting up loss
I0821 09:21:09.230655 5405 net.cpp:163] Top shape: (1)
I0821 09:21:09.230660 5405 net.cpp:168] with loss weight 1
I0821 09:21:09.230677 5405 net.cpp:236] loss needs backward computation.
I0821 09:21:09.230684 5405 net.cpp:236] fc6 needs backward computation.
I0821 09:21:09.230690 5405 net.cpp:236] fc5 needs backward computation.
I0821 09:21:09.230695 5405 net.cpp:236] relu3 needs backward computation.
I0821 09:21:09.230701 5405 net.cpp:236] conv3 needs backward computation.
I0821 09:21:09.230707 5405 net.cpp:236] pool2 needs backward computation.
I0821 09:21:09.230713 5405 net.cpp:236] relu2 needs backward computation.
I0821 09:21:09.230718 5405 net.cpp:236] conv2 needs backward computation.
I0821 09:21:09.230726 5405 net.cpp:236] pool1 needs backward computation.
I0821 09:21:09.230731 5405 net.cpp:236] relu1 needs backward computation.
I0821 09:21:09.230736 5405 net.cpp:236] conv1 needs backward computation.
I0821 09:21:09.230743 5405 net.cpp:240] MyData does not need backward computation.
I0821 09:21:09.230748 5405 net.cpp:283] This network produces output loss
I0821 09:21:09.230759 5405 net.cpp:297] Network initialization done.
I0821 09:21:09.230765 5405 net.cpp:298] Memory required for data: 321216516
I0821 09:21:09.231151 5405 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: examples/kaggle_prototxt/fkp_net.prototxt
E0821 09:21:09.231201 5405 upgrade_proto.cpp:636] Input NetParameter to be upgraded already specifies 'layer' fields; these will be ignored for the upgrade.
E0821 09:21:09.231246 5405 upgrade_proto.cpp:623] Warning: had one or more problems upgrading V1LayerParameter (see above); continuing anyway.
I0821 09:21:09.231272 5405 solver.cpp:170] Creating test net (#0) specified by net file: examples/kaggle_prototxt/fkp_net.prototxt
I0821 09:21:09.231295 5405 net.cpp:339] The NetState phase (1) differed from the phase (0) specified by a rule in layer MyData
I0821 09:21:09.231384 5405 net.cpp:50] Initializing net from parameters:
name: "fkp_net"
state {
phase: TEST
}
layer {
name: "MyData"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TEST
}
hdf5_data_param {
source: "data/kaggle_fkp_data/val_data_list.txt"
batch_size: 540
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 48
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 64
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "fc5"
type: "InnerProduct"
bottom: "conv3"
top: "fc5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "fc5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 30
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "fc6"
bottom: "label"
top: "loss"
}
I0821 09:21:09.231801 5405 layer_factory.hpp:75] Creating layer MyData
I0821 09:21:09.231812 5405 net.cpp:110] Creating Layer MyData
I0821 09:21:09.231818 5405 net.cpp:432] MyData -> data
I0821 09:21:09.231828 5405 net.cpp:432] MyData -> label
I0821 09:21:09.231837 5405 hdf5_data_layer.cpp:80] Loading list of HDF5 filenames from: data/kaggle_fkp_data/val_data_list.txt
I0821 09:21:09.231856 5405 hdf5_data_layer.cpp:94] Number of HDF5 files: 1
I0821 09:21:09.245085 5405 net.cpp:155] Setting up MyData
I0821 09:21:09.245139 5405 net.cpp:163] Top shape: 540 1 96 96 (4976640)
I0821 09:21:09.245147 5405 net.cpp:163] Top shape: 540 30 (16200)
I0821 09:21:09.245158 5405 layer_factory.hpp:75] Creating layer conv1
I0821 09:21:09.245179 5405 net.cpp:110] Creating Layer conv1
I0821 09:21:09.245187 5405 net.cpp:476] conv1 <- data
I0821 09:21:09.245198 5405 net.cpp:432] conv1 -> conv1
I0821 09:21:09.245630 5405 net.cpp:155] Setting up conv1
I0821 09:21:09.245646 5405 net.cpp:163] Top shape: 540 20 92 92 (91411200)
I0821 09:21:09.245661 5405 layer_factory.hpp:75] Creating layer relu1
I0821 09:21:09.245671 5405 net.cpp:110] Creating Layer relu1
I0821 09:21:09.245676 5405 net.cpp:476] relu1 <- conv1
I0821 09:21:09.245684 5405 net.cpp:419] relu1 -> conv1 (in-place)
I0821 09:21:09.245818 5405 net.cpp:155] Setting up relu1
I0821 09:21:09.245833 5405 net.cpp:163] Top shape: 540 20 92 92 (91411200)
I0821 09:21:09.245839 5405 layer_factory.hpp:75] Creating layer pool1
I0821 09:21:09.245848 5405 net.cpp:110] Creating Layer pool1
I0821 09:21:09.245854 5405 net.cpp:476] pool1 <- conv1
I0821 09:21:09.245862 5405 net.cpp:432] pool1 -> pool1
I0821 09:21:09.245914 5405 net.cpp:155] Setting up pool1
I0821 09:21:09.245924 5405 net.cpp:163] Top shape: 540 20 46 46 (22852800)
I0821 09:21:09.245930 5405 layer_factory.hpp:75] Creating layer conv2
I0821 09:21:09.245940 5405 net.cpp:110] Creating Layer conv2
I0821 09:21:09.245946 5405 net.cpp:476] conv2 <- pool1
I0821 09:21:09.245954 5405 net.cpp:432] conv2 -> conv2
I0821 09:21:09.246404 5405 net.cpp:155] Setting up conv2
I0821 09:21:09.246420 5405 net.cpp:163] Top shape: 540 48 42 42 (45722880)
I0821 09:21:09.246431 5405 layer_factory.hpp:75] Creating layer relu2
I0821 09:21:09.246440 5405 net.cpp:110] Creating Layer relu2
I0821 09:21:09.246446 5405 net.cpp:476] relu2 <- conv2
I0821 09:21:09.246454 5405 net.cpp:419] relu2 -> conv2 (in-place)
I0821 09:21:09.246505 5405 net.cpp:155] Setting up relu2
I0821 09:21:09.246513 5405 net.cpp:163] Top shape: 540 48 42 42 (45722880)
I0821 09:21:09.246520 5405 layer_factory.hpp:75] Creating layer pool2
I0821 09:21:09.246527 5405 net.cpp:110] Creating Layer pool2
I0821 09:21:09.246533 5405 net.cpp:476] pool2 <- conv2
I0821 09:21:09.246541 5405 net.cpp:432] pool2 -> pool2
I0821 09:21:09.246588 5405 net.cpp:155] Setting up pool2
I0821 09:21:09.246598 5405 net.cpp:163] Top shape: 540 48 21 21 (11430720)
I0821 09:21:09.246604 5405 layer_factory.hpp:75] Creating layer conv3
I0821 09:21:09.246631 5405 net.cpp:110] Creating Layer conv3
I0821 09:21:09.246639 5405 net.cpp:476] conv3 <- pool2
I0821 09:21:09.246645 5405 net.cpp:432] conv3 -> conv3
I0821 09:21:09.247123 5405 net.cpp:155] Setting up conv3
I0821 09:21:09.247138 5405 net.cpp:163] Top shape: 540 64 19 19 (12476160)
I0821 09:21:09.247148 5405 layer_factory.hpp:75] Creating layer relu3
I0821 09:21:09.247158 5405 net.cpp:110] Creating Layer relu3
I0821 09:21:09.247164 5405 net.cpp:476] relu3 <- conv3
I0821 09:21:09.247172 5405 net.cpp:419] relu3 -> conv3 (in-place)
I0821 09:21:09.247300 5405 net.cpp:155] Setting up relu3
I0821 09:21:09.247314 5405 net.cpp:163] Top shape: 540 64 19 19 (12476160)
I0821 09:21:09.247320 5405 layer_factory.hpp:75] Creating layer fc5
I0821 09:21:09.247330 5405 net.cpp:110] Creating Layer fc5
I0821 09:21:09.247336 5405 net.cpp:476] fc5 <- conv3
I0821 09:21:09.247344 5405 net.cpp:432] fc5 -> fc5
I0821 09:21:09.345037 5405 net.cpp:155] Setting up fc5
I0821 09:21:09.345082 5405 net.cpp:163] Top shape: 540 500 (270000)
I0821 09:21:09.345100 5405 layer_factory.hpp:75] Creating layer fc6
I0821 09:21:09.345118 5405 net.cpp:110] Creating Layer fc6
I0821 09:21:09.345127 5405 net.cpp:476] fc6 <- fc5
I0821 09:21:09.345139 5405 net.cpp:432] fc6 -> fc6
I0821 09:21:09.345325 5405 net.cpp:155] Setting up fc6
I0821 09:21:09.345337 5405 net.cpp:163] Top shape: 540 30 (16200)
I0821 09:21:09.345350 5405 layer_factory.hpp:75] Creating layer loss
I0821 09:21:09.345358 5405 net.cpp:110] Creating Layer loss
I0821 09:21:09.345365 5405 net.cpp:476] loss <- fc6
I0821 09:21:09.345372 5405 net.cpp:476] loss <- label
I0821 09:21:09.345381 5405 net.cpp:432] loss -> loss
I0821 09:21:09.345405 5405 net.cpp:155] Setting up loss
I0821 09:21:09.345414 5405 net.cpp:163] Top shape: (1)
I0821 09:21:09.345420 5405 net.cpp:168] with loss weight 1
I0821 09:21:09.345432 5405 net.cpp:236] loss needs backward computation.
I0821 09:21:09.345439 5405 net.cpp:236] fc6 needs backward computation.
I0821 09:21:09.345445 5405 net.cpp:236] fc5 needs backward computation.
I0821 09:21:09.345451 5405 net.cpp:236] relu3 needs backward computation.
I0821 09:21:09.345456 5405 net.cpp:236] conv3 needs backward computation.
I0821 09:21:09.345463 5405 net.cpp:236] pool2 needs backward computation.
I0821 09:21:09.345468 5405 net.cpp:236] relu2 needs backward computation.
I0821 09:21:09.345474 5405 net.cpp:236] conv2 needs backward computation.
I0821 09:21:09.345479 5405 net.cpp:236] pool1 needs backward computation.
I0821 09:21:09.345485 5405 net.cpp:236] relu1 needs backward computation.
I0821 09:21:09.345490 5405 net.cpp:236] conv1 needs backward computation.
I0821 09:21:09.345496 5405 net.cpp:240] MyData does not need backward computation.
I0821 09:21:09.345502 5405 net.cpp:283] This network produces output loss
I0821 09:21:09.345512 5405 net.cpp:297] Network initialization done.
I0821 09:21:09.345521 5405 net.cpp:298] Memory required for data: 1355132164
I0821 09:21:09.345595 5405 solver.cpp:49] Solver scaffolding done.
I0821 09:21:09.345623 5405 solver.cpp:265] Solving fkp_net
I0821 09:21:09.345628 5405 solver.cpp:266] Learning Rate Policy: fixed
I0821 09:21:09.346159 5405 solver.cpp:310] Iteration 0, Testing net (#0)
I0821 09:21:09.431867 5405 solver.cpp:359] Test net output #0: loss = 2.65418 (* 1 = 2.65418 loss)
I0821 09:21:09.451231 5405 solver.cpp:222] Iteration 0, loss = 2.6409
I0821 09:21:09.451259 5405 solver.cpp:238] Train net output #0: loss = 2.6409 (* 1 = 2.6409 loss)
I0821 09:21:09.451272 5405 solver.cpp:517] Iteration 0, lr = 0.003
I0821 09:21:15.397500 5405 solver.cpp:222] Iteration 100, loss = 0.0677099
I0821 09:21:15.397542 5405 solver.cpp:238] Train net output #0: loss = 0.06771 (* 1 = 0.06771 loss)
I0821 09:21:15.397552 5405 solver.cpp:517] Iteration 100, lr = 0.003
I0821 09:21:21.350584 5405 solver.cpp:222] Iteration 200, loss = 0.0578064
I0821 09:21:21.350623 5405 solver.cpp:238] Train net output #0: loss = 0.0578064 (* 1 = 0.0578064 loss)
I0821 09:21:21.350633 5405 solver.cpp:517] Iteration 200, lr = 0.003
I0821 09:21:27.310050 5405 solver.cpp:222] Iteration 300, loss = 0.0569765
I0821 09:21:27.310091 5405 solver.cpp:238] Train net output #0: loss = 0.0569765 (* 1 = 0.0569765 loss)
I0821 09:21:27.310101 5405 solver.cpp:517] Iteration 300, lr = 0.003
I0821 09:21:33.274334 5405 solver.cpp:222] Iteration 400, loss = 0.0390148
I0821 09:21:33.274381 5405 solver.cpp:238] Train net output #0: loss = 0.0390149 (* 1 = 0.0390149 loss)
I0821 09:21:33.274392 5405 solver.cpp:517] Iteration 400, lr = 0.003
I0821 09:21:39.185999 5405 solver.cpp:310] Iteration 500, Testing net (#0)
I0821 09:21:39.301501 5405 solver.cpp:359] Test net output #0: loss = 0.0346191 (* 1 = 0.0346191 loss)
I0821 09:21:39.319154 5405 solver.cpp:222] Iteration 500, loss = 0.0321405
I0821 09:21:39.319178 5405 solver.cpp:238] Train net output #0: loss = 0.0321406 (* 1 = 0.0321406 loss)
I0821 09:21:39.319188 5405 solver.cpp:517] Iteration 500, lr = 0.003
I0821 09:21:45.295368 5405 solver.cpp:222] Iteration 600, loss = 0.0274621
I0821 09:21:45.295408 5405 solver.cpp:238] Train net output #0: loss = 0.0274621 (* 1 = 0.0274621 loss)
I0821 09:21:45.295418 5405 solver.cpp:517] Iteration 600, lr = 0.003
I0821 09:21:51.266793 5405 solver.cpp:222] Iteration 700, loss = 0.0216737
I0821 09:21:51.266830 5405 solver.cpp:238] Train net output #0: loss = 0.0216738 (* 1 = 0.0216738 loss)
I0821 09:21:51.266840 5405 solver.cpp:517] Iteration 700, lr = 0.003
I0821 09:21:57.242305 5405 solver.cpp:222] Iteration 800, loss = 0.0218998
I0821 09:21:57.242347 5405 solver.cpp:238] Train net output #0: loss = 0.0218998 (* 1 = 0.0218998 loss)
I0821 09:21:57.242357 5405 solver.cpp:517] Iteration 800, lr = 0.003
I0821 09:22:03.214617 5405 solver.cpp:222] Iteration 900, loss = 0.0180164
I0821 09:22:03.214664 5405 solver.cpp:238] Train net output #0: loss = 0.0180164 (* 1 = 0.0180164 loss)
I0821 09:22:03.214675 5405 solver.cpp:517] Iteration 900, lr = 0.003
I0821 09:22:09.128273 5405 solver.cpp:310] Iteration 1000, Testing net (#0)
I0821 09:22:09.245307 5405 solver.cpp:359] Test net output #0: loss = 0.0244892 (* 1 = 0.0244892 loss)
I0821 09:22:09.263103 5405 solver.cpp:222] Iteration 1000, loss = 0.0226538
I0821 09:22:09.263128 5405 solver.cpp:238] Train net output #0: loss = 0.0226538 (* 1 = 0.0226538 loss)
I0821 09:22:09.263137 5405 solver.cpp:517] Iteration 1000, lr = 0.003
I0821 09:22:15.232853 5405 solver.cpp:222] Iteration 1100, loss = 0.0191192
I0821 09:22:15.232893 5405 solver.cpp:238] Train net output #0: loss = 0.0191192 (* 1 = 0.0191192 loss)
I0821 09:22:15.232903 5405 solver.cpp:517] Iteration 1100, lr = 0.003
I0821 09:22:21.198551 5405 solver.cpp:222] Iteration 1200, loss = 0.0171242
I0821 09:22:21.198588 5405 solver.cpp:238] Train net output #0: loss = 0.0171243 (* 1 = 0.0171243 loss)
I0821 09:22:21.198597 5405 solver.cpp:517] Iteration 1200, lr = 0.003
I0821 09:22:27.165140 5405 solver.cpp:222] Iteration 1300, loss = 0.026392
I0821 09:22:27.165179 5405 solver.cpp:238] Train net output #0: loss = 0.0263921 (* 1 = 0.0263921 loss)
I0821 09:22:27.165187 5405 solver.cpp:517] Iteration 1300, lr = 0.003
I0821 09:22:33.130661 5405 solver.cpp:222] Iteration 1400, loss = 0.0194519
I0821 09:22:33.130699 5405 solver.cpp:238] Train net output #0: loss = 0.019452 (* 1 = 0.019452 loss)
I0821 09:22:33.130708 5405 solver.cpp:517] Iteration 1400, lr = 0.003
I0821 09:22:39.035050 5405 solver.cpp:310] Iteration 1500, Testing net (#0)
I0821 09:22:39.152415 5405 solver.cpp:359] Test net output #0: loss = 0.0219909 (* 1 = 0.0219909 loss)
I0821 09:22:39.170099 5405 solver.cpp:222] Iteration 1500, loss = 0.0190458
I0821 09:22:39.170130 5405 solver.cpp:238] Train net output #0: loss = 0.0190458 (* 1 = 0.0190458 loss)
I0821 09:22:39.170140 5405 solver.cpp:517] Iteration 1500, lr = 0.003
I0821 09:22:45.141404 5405 solver.cpp:222] Iteration 1600, loss = 0.0183552
I0821 09:22:45.141543 5405 solver.cpp:238] Train net output #0: loss = 0.0183553 (* 1 = 0.0183553 loss)
I0821 09:22:45.141568 5405 solver.cpp:517] Iteration 1600, lr = 0.003
I0821 09:22:51.113435 5405 solver.cpp:222] Iteration 1700, loss = 0.0157709
I0821 09:22:51.113473 5405 solver.cpp:238] Train net output #0: loss = 0.015771 (* 1 = 0.015771 loss)
I0821 09:22:51.113482 5405 solver.cpp:517] Iteration 1700, lr = 0.003
I0821 09:22:57.083899 5405 solver.cpp:222] Iteration 1800, loss = 0.0182063
I0821 09:22:57.083935 5405 solver.cpp:238] Train net output #0: loss = 0.0182063 (* 1 = 0.0182063 loss)
I0821 09:22:57.083945 5405 solver.cpp:517] Iteration 1800, lr = 0.003
I0821 09:23:03.050568 5405 solver.cpp:222] Iteration 1900, loss = 0.0139108
I0821 09:23:03.050606 5405 solver.cpp:238] Train net output #0: loss = 0.0139109 (* 1 = 0.0139109 loss)
I0821 09:23:03.050616 5405 solver.cpp:517] Iteration 1900, lr = 0.003
I0821 09:23:08.960155 5405 solver.cpp:310] Iteration 2000, Testing net (#0)
I0821 09:23:09.074678 5405 solver.cpp:359] Test net output #0: loss = 0.0200152 (* 1 = 0.0200152 loss)
I0821 09:23:09.092224 5405 solver.cpp:222] Iteration 2000, loss = 0.0163429
I0821 09:23:09.092249 5405 solver.cpp:238] Train net output #0: loss = 0.0163429 (* 1 = 0.0163429 loss)
I0821 09:23:09.092258 5405 solver.cpp:517] Iteration 2000, lr = 0.003
I0821 09:23:15.062953 5405 solver.cpp:222] Iteration 2100, loss = 0.0153085
I0821 09:23:15.062991 5405 solver.cpp:238] Train net output #0: loss = 0.0153086 (* 1 = 0.0153086 loss)
I0821 09:23:15.063001 5405 solver.cpp:517] Iteration 2100, lr = 0.003
I0821 09:23:21.030874 5405 solver.cpp:222] Iteration 2200, loss = 0.0138345
I0821 09:23:21.031029 5405 solver.cpp:238] Train net output #0: loss = 0.0138346 (* 1 = 0.0138346 loss)
I0821 09:23:21.031044 5405 solver.cpp:517] Iteration 2200, lr = 0.003
I0821 09:23:26.997225 5405 solver.cpp:222] Iteration 2300, loss = 0.0142276
I0821 09:23:26.997263 5405 solver.cpp:238] Train net output #0: loss = 0.0142277 (* 1 = 0.0142277 loss)
I0821 09:23:26.997273 5405 solver.cpp:517] Iteration 2300, lr = 0.003
I0821 09:23:32.966337 5405 solver.cpp:222] Iteration 2400, loss = 0.0132374
I0821 09:23:32.966375 5405 solver.cpp:238] Train net output #0: loss = 0.0132375 (* 1 = 0.0132375 loss)
I0821 09:23:32.966385 5405 solver.cpp:517] Iteration 2400, lr = 0.003
I0821 09:23:38.874579 5405 solver.cpp:310] Iteration 2500, Testing net (#0)
I0821 09:23:38.989516 5405 solver.cpp:359] Test net output #0: loss = 0.0189302 (* 1 = 0.0189302 loss)
I0821 09:23:39.007253 5405 solver.cpp:222] Iteration 2500, loss = 0.0169627
I0821 09:23:39.007279 5405 solver.cpp:238] Train net output #0: loss = 0.0169627 (* 1 = 0.0169627 loss)
I0821 09:23:39.007289 5405 solver.cpp:517] Iteration 2500, lr = 0.003
I0821 09:23:44.977813 5405 solver.cpp:222] Iteration 2600, loss = 0.0147983
I0821 09:23:44.977852 5405 solver.cpp:238] Train net output #0: loss = 0.0147983 (* 1 = 0.0147983 loss)
I0821 09:23:44.977861 5405 solver.cpp:517] Iteration 2600, lr = 0.003
I0821 09:23:50.950896 5405 solver.cpp:222] Iteration 2700, loss = 0.0118159
I0821 09:23:50.950933 5405 solver.cpp:238] Train net output #0: loss = 0.011816 (* 1 = 0.011816 loss)
I0821 09:23:50.950942 5405 solver.cpp:517] Iteration 2700, lr = 0.003
I0821 09:23:56.924351 5405 solver.cpp:222] Iteration 2800, loss = 0.0140216
I0821 09:23:56.924501 5405 solver.cpp:238] Train net output #0: loss = 0.0140216 (* 1 = 0.0140216 loss)
I0821 09:23:56.924515 5405 solver.cpp:517] Iteration 2800, lr = 0.003
I0821 09:24:02.895431 5405 solver.cpp:222] Iteration 2900, loss = 0.0149531
I0821 09:24:02.895472 5405 solver.cpp:238] Train net output #0: loss = 0.0149532 (* 1 = 0.0149532 loss)
I0821 09:24:02.895480 5405 solver.cpp:517] Iteration 2900, lr = 0.003
I0821 09:24:08.807488 5405 solver.cpp:310] Iteration 3000, Testing net (#0)
I0821 09:24:08.921578 5405 solver.cpp:359] Test net output #0: loss = 0.0182427 (* 1 = 0.0182427 loss)
I0821 09:24:08.939262 5405 solver.cpp:222] Iteration 3000, loss = 0.0123936
I0821 09:24:08.939285 5405 solver.cpp:238] Train net output #0: loss = 0.0123936 (* 1 = 0.0123936 loss)
I0821 09:24:08.939296 5405 solver.cpp:517] Iteration 3000, lr = 0.003
I0821 09:24:14.906126 5405 solver.cpp:222] Iteration 3100, loss = 0.0119733
I0821 09:24:14.906163 5405 solver.cpp:238] Train net output #0: loss = 0.0119734 (* 1 = 0.0119734 loss)
I0821 09:24:14.906173 5405 solver.cpp:517] Iteration 3100, lr = 0.003
I0821 09:24:20.871377 5405 solver.cpp:222] Iteration 3200, loss = 0.0127372
I0821 09:24:20.871414 5405 solver.cpp:238] Train net output #0: loss = 0.0127373 (* 1 = 0.0127373 loss)
I0821 09:24:20.871423 5405 solver.cpp:517] Iteration 3200, lr = 0.003
I0821 09:24:26.841256 5405 solver.cpp:222] Iteration 3300, loss = 0.0119742
I0821 09:24:26.841295 5405 solver.cpp:238] Train net output #0: loss = 0.0119743 (* 1 = 0.0119743 loss)
I0821 09:24:26.841303 5405 solver.cpp:517] Iteration 3300, lr = 0.003
I0821 09:24:32.808125 5405 solver.cpp:222] Iteration 3400, loss = 0.0105186
I0821 09:24:32.808296 5405 solver.cpp:238] Train net output #0: loss = 0.0105187 (* 1 = 0.0105187 loss)
I0821 09:24:32.808311 5405 solver.cpp:517] Iteration 3400, lr = 0.003
I0821 09:24:38.714227 5405 solver.cpp:310] Iteration 3500, Testing net (#0)
I0821 09:24:38.828912 5405 solver.cpp:359] Test net output #0: loss = 0.0174317 (* 1 = 0.0174317 loss)
I0821 09:24:38.846992 5405 solver.cpp:222] Iteration 3500, loss = 0.0102381
I0821 09:24:38.847017 5405 solver.cpp:238] Train net output #0: loss = 0.0102382 (* 1 = 0.0102382 loss)
I0821 09:24:38.847025 5405 solver.cpp:517] Iteration 3500, lr = 0.003
I0821 09:24:44.818449 5405 solver.cpp:222] Iteration 3600, loss = 0.0105526
I0821 09:24:44.818486 5405 solver.cpp:238] Train net output #0: loss = 0.0105527 (* 1 = 0.0105527 loss)
I0821 09:24:44.818496 5405 solver.cpp:517] Iteration 3600, lr = 0.003
I0821 09:24:50.793851 5405 solver.cpp:222] Iteration 3700, loss = 0.0120274
I0821 09:24:50.793889 5405 solver.cpp:238] Train net output #0: loss = 0.0120274 (* 1 = 0.0120274 loss)
I0821 09:24:50.793900 5405 solver.cpp:517] Iteration 3700, lr = 0.003
I0821 09:24:56.771097 5405 solver.cpp:222] Iteration 3800, loss = 0.0143736
I0821 09:24:56.771136 5405 solver.cpp:238] Train net output #0: loss = 0.0143737 (* 1 = 0.0143737 loss)
I0821 09:24:56.771144 5405 solver.cpp:517] Iteration 3800, lr = 0.003
I0821 09:25:02.750058 5405 solver.cpp:222] Iteration 3900, loss = 0.0110788
I0821 09:25:02.750095 5405 solver.cpp:238] Train net output #0: loss = 0.0110789 (* 1 = 0.0110789 loss)
I0821 09:25:02.750107 5405 solver.cpp:517] Iteration 3900, lr = 0.003
I0821 09:25:08.666034 5405 solver.cpp:310] Iteration 4000, Testing net (#0)
I0821 09:25:08.782320 5405 solver.cpp:359] Test net output #0: loss = 0.0169053 (* 1 = 0.0169053 loss)
I0821 09:25:08.799872 5405 solver.cpp:222] Iteration 4000, loss = 0.011261
I0821 09:25:08.799898 5405 solver.cpp:238] Train net output #0: loss = 0.011261 (* 1 = 0.011261 loss)
I0821 09:25:08.799907 5405 solver.cpp:517] Iteration 4000, lr = 0.003
I0821 09:25:14.772166 5405 solver.cpp:222] Iteration 4100, loss = 0.0110039
I0821 09:25:14.772203 5405 solver.cpp:238] Train net output #0: loss = 0.011004 (* 1 = 0.011004 loss)
I0821 09:25:14.772212 5405 solver.cpp:517] Iteration 4100, lr = 0.003
I0821 09:25:20.738042 5405 solver.cpp:222] Iteration 4200, loss = 0.0113851
I0821 09:25:20.738080 5405 solver.cpp:238] Train net output #0: loss = 0.0113851 (* 1 = 0.0113851 loss)
I0821 09:25:20.738090 5405 solver.cpp:517] Iteration 4200, lr = 0.003
I0821 09:25:26.708467 5405 solver.cpp:222] Iteration 4300, loss = 0.0116973
I0821 09:25:26.708505 5405 solver.cpp:238] Train net output #0: loss = 0.0116974 (* 1 = 0.0116974 loss)
I0821 09:25:26.708515 5405 solver.cpp:517] Iteration 4300, lr = 0.003
I0821 09:25:32.676429 5405 solver.cpp:222] Iteration 4400, loss = 0.00980553
I0821 09:25:32.676468 5405 solver.cpp:238] Train net output #0: loss = 0.00980559 (* 1 = 0.00980559 loss)
I0821 09:25:32.676477 5405 solver.cpp:517] Iteration 4400, lr = 0.003
I0821 09:25:38.584980 5405 solver.cpp:310] Iteration 4500, Testing net (#0)
I0821 09:25:38.700592 5405 solver.cpp:359] Test net output #0: loss = 0.0165697 (* 1 = 0.0165697 loss)
I0821 09:25:38.718366 5405 solver.cpp:222] Iteration 4500, loss = 0.00898773
I0821 09:25:38.718402 5405 solver.cpp:238] Train net output #0: loss = 0.00898779 (* 1 = 0.00898779 loss)
I0821 09:25:38.718412 5405 solver.cpp:517] Iteration 4500, lr = 0.003
I0821 09:25:44.688573 5405 solver.cpp:222] Iteration 4600, loss = 0.0141232
I0821 09:25:44.688612 5405 solver.cpp:238] Train net output #0: loss = 0.0141232 (* 1 = 0.0141232 loss)
I0821 09:25:44.688622 5405 solver.cpp:517] Iteration 4600, lr = 0.003
I0821 09:25:50.662116 5405 solver.cpp:222] Iteration 4700, loss = 0.00957009
I0821 09:25:50.662155 5405 solver.cpp:238] Train net output #0: loss = 0.00957015 (* 1 = 0.00957015 loss)
I0821 09:25:50.662164 5405 solver.cpp:517] Iteration 4700, lr = 0.003
I0821 09:25:56.635241 5405 solver.cpp:222] Iteration 4800, loss = 0.00924356
I0821 09:25:56.635279 5405 solver.cpp:238] Train net output #0: loss = 0.00924362 (* 1 = 0.00924362 loss)
I0821 09:25:56.635289 5405 solver.cpp:517] Iteration 4800, lr = 0.003
I0821 09:26:02.610262 5405 solver.cpp:222] Iteration 4900, loss = 0.00954319
I0821 09:26:02.610301 5405 solver.cpp:238] Train net output #0: loss = 0.00954325 (* 1 = 0.00954325 loss)
I0821 09:26:02.610309 5405 solver.cpp:517] Iteration 4900, lr = 0.003
I0821 09:26:08.530220 5405 solver.cpp:395] Snapshotting to binary proto file examples/kaggle_prototxt/model/fkp_iter_5000.caffemodel
I0821 09:26:08.853508 5405 solver.cpp:680] Snapshotting solver state to binary proto fileexamples/kaggle_prototxt/model/fkp_iter_5000.solverstate
I0821 09:26:08.912705 5405 solver.cpp:310] Iteration 5000, Testing net (#0)
I0821 09:26:08.987784 5405 solver.cpp:359] Test net output #0: loss = 0.0163085 (* 1 = 0.0163085 loss)
I0821 09:26:09.006322 5405 solver.cpp:222] Iteration 5000, loss = 0.0105399
I0821 09:26:09.006355 5405 solver.cpp:238] Train net output #0: loss = 0.01054 (* 1 = 0.01054 loss)
I0821 09:26:09.006364 5405 solver.cpp:517] Iteration 5000, lr = 0.003
I0821 09:26:15.284273 5405 solver.cpp:222] Iteration 5100, loss = 0.0100736
I0821 09:26:15.284313 5405 solver.cpp:238] Train net output #0: loss = 0.0100737 (* 1 = 0.0100737 loss)
I0821 09:26:15.284323 5405 solver.cpp:517] Iteration 5100, lr = 0.003
I0821 09:26:21.463059 5405 solver.cpp:222] Iteration 5200, loss = 0.00850314
I0821 09:26:21.463124 5405 solver.cpp:238] Train net output #0: loss = 0.0085032 (* 1 = 0.0085032 loss)
I0821 09:26:21.463135 5405 solver.cpp:517] Iteration 5200, lr = 0.003
I0821 09:26:27.451169 5405 solver.cpp:222] Iteration 5300, loss = 0.00939321
I0821 09:26:27.451210 5405 solver.cpp:238] Train net output #0: loss = 0.00939327 (* 1 = 0.00939327 loss)
I0821 09:26:27.451220 5405 solver.cpp:517] Iteration 5300, lr = 0.003
I0821 09:26:33.423916 5405 solver.cpp:222] Iteration 5400, loss = 0.00815288
I0821 09:26:33.423959 5405 solver.cpp:238] Train net output #0: loss = 0.00815294 (* 1 = 0.00815294 loss)
I0821 09:26:33.423969 5405 solver.cpp:517] Iteration 5400, lr = 0.003
I0821 09:26:39.340683 5405 solver.cpp:310] Iteration 5500, Testing net (#0)
I0821 09:26:39.460067 5405 solver.cpp:359] Test net output #0: loss = 0.0161006 (* 1 = 0.0161006 loss)
I0821 09:26:39.478009 5405 solver.cpp:222] Iteration 5500, loss = 0.00801718
I0821 09:26:39.478047 5405 solver.cpp:238] Train net output #0: loss = 0.00801724 (* 1 = 0.00801724 loss)
I0821 09:26:39.478057 5405 solver.cpp:517] Iteration 5500, lr = 0.003
I0821 09:26:45.453423 5405 solver.cpp:222] Iteration 5600, loss = 0.00812415
I0821 09:26:45.453462 5405 solver.cpp:238] Train net output #0: loss = 0.00812421 (* 1 = 0.00812421 loss)
I0821 09:26:45.453472 5405 solver.cpp:517] Iteration 5600, lr = 0.003
I0821 09:26:51.427427 5405 solver.cpp:222] Iteration 5700, loss = 0.00817025
I0821 09:26:51.427467 5405 solver.cpp:238] Train net output #0: loss = 0.00817031 (* 1 = 0.00817031 loss)
I0821 09:26:51.427477 5405 solver.cpp:517] Iteration 5700, lr = 0.003
I0821 09:26:57.401399 5405 solver.cpp:222] Iteration 5800, loss = 0.00846917
I0821 09:26:57.401440 5405 solver.cpp:238] Train net output #0: loss = 0.00846923 (* 1 = 0.00846923 loss)
I0821 09:26:57.401450 5405 solver.cpp:517] Iteration 5800, lr = 0.003
I0821 09:27:03.381294 5405 solver.cpp:222] Iteration 5900, loss = 0.00744153
I0821 09:27:03.381335 5405 solver.cpp:238] Train net output #0: loss = 0.00744159 (* 1 = 0.00744159 loss)
I0821 09:27:03.381345 5405 solver.cpp:517] Iteration 5900, lr = 0.003
I0821 09:27:09.308066 5405 solver.cpp:310] Iteration 6000, Testing net (#0)
I0821 09:27:09.423012 5405 solver.cpp:359] Test net output #0: loss = 0.0158573 (* 1 = 0.0158573 loss)
I0821 09:27:09.441123 5405 solver.cpp:222] Iteration 6000, loss = 0.00872323
I0821 09:27:09.441153 5405 solver.cpp:238] Train net output #0: loss = 0.00872329 (* 1 = 0.00872329 loss)
I0821 09:27:09.441162 5405 solver.cpp:517] Iteration 6000, lr = 0.003
I0821 09:27:15.418184 5405 solver.cpp:222] Iteration 6100, loss = 0.00863231
I0821 09:27:15.418225 5405 solver.cpp:238] Train net output #0: loss = 0.00863236 (* 1 = 0.00863236 loss)
I0821 09:27:15.418233 5405 solver.cpp:517] Iteration 6100, lr = 0.003
I0821 09:27:21.392982 5405 solver.cpp:222] Iteration 6200, loss = 0.00914046
I0821 09:27:21.393023 5405 solver.cpp:238] Train net output #0: loss = 0.00914052 (* 1 = 0.00914052 loss)
I0821 09:27:21.393033 5405 solver.cpp:517] Iteration 6200, lr = 0.003
I0821 09:27:27.367136 5405 solver.cpp:222] Iteration 6300, loss = 0.00850551
I0821 09:27:27.367178 5405 solver.cpp:238] Train net output #0: loss = 0.00850557 (* 1 = 0.00850557 loss)
I0821 09:27:27.367188 5405 solver.cpp:517] Iteration 6300, lr = 0.003
I0821 09:27:33.345049 5405 solver.cpp:222] Iteration 6400, loss = 0.00727864
I0821 09:27:33.345090 5405 solver.cpp:238] Train net output #0: loss = 0.0072787 (* 1 = 0.0072787 loss)
I0821 09:27:33.345100 5405 solver.cpp:517] Iteration 6400, lr = 0.003
I0821 09:27:39.261961 5405 solver.cpp:310] Iteration 6500, Testing net (#0)
I0821 09:27:39.377586 5405 solver.cpp:359] Test net output #0: loss = 0.015684 (* 1 = 0.015684 loss)
I0821 09:27:39.395418 5405 solver.cpp:222] Iteration 6500, loss = 0.00803039
I0821 09:27:39.395443 5405 solver.cpp:238] Train net output #0: loss = 0.00803045 (* 1 = 0.00803045 loss)
I0821 09:27:39.395452 5405 solver.cpp:517] Iteration 6500, lr = 0.003
I0821 09:27:45.368875 5405 solver.cpp:222] Iteration 6600, loss = 0.00712564
I0821 09:27:45.369035 5405 solver.cpp:238] Train net output #0: loss = 0.0071257 (* 1 = 0.0071257 loss)
I0821 09:27:45.369050 5405 solver.cpp:517] Iteration 6600, lr = 0.003
I0821 09:27:51.345132 5405 solver.cpp:222] Iteration 6700, loss = 0.00923779
I0821 09:27:51.345175 5405 solver.cpp:238] Train net output #0: loss = 0.00923785 (* 1 = 0.00923785 loss)
I0821 09:27:51.345185 5405 solver.cpp:517] Iteration 6700, lr = 0.003
I0821 09:27:57.322093 5405 solver.cpp:222] Iteration 6800, loss = 0.00799755
I0821 09:27:57.322134 5405 solver.cpp:238] Train net output #0: loss = 0.00799761 (* 1 = 0.00799761 loss)
I0821 09:27:57.322144 5405 solver.cpp:517] Iteration 6800, lr = 0.003
I0821 09:28:03.303073 5405 solver.cpp:222] Iteration 6900, loss = 0.00862558
I0821 09:28:03.303115 5405 solver.cpp:238] Train net output #0: loss = 0.00862564 (* 1 = 0.00862564 loss)
I0821 09:28:03.303125 5405 solver.cpp:517] Iteration 6900, lr = 0.003
I0821 09:28:09.222786 5405 solver.cpp:310] Iteration 7000, Testing net (#0)
I0821 09:28:09.338865 5405 solver.cpp:359] Test net output #0: loss = 0.0155955 (* 1 = 0.0155955 loss)
I0821 09:28:09.356801 5405 solver.cpp:222] Iteration 7000, loss = 0.00775897
I0821 09:28:09.356825 5405 solver.cpp:238] Train net output #0: loss = 0.00775903 (* 1 = 0.00775903 loss)
I0821 09:28:09.356835 5405 solver.cpp:517] Iteration 7000, lr = 0.003
I0821 09:28:15.338407 5405 solver.cpp:222] Iteration 7100, loss = 0.00667488
I0821 09:28:15.338448 5405 solver.cpp:238] Train net output #0: loss = 0.00667494 (* 1 = 0.00667494 loss)
I0821 09:28:15.338457 5405 solver.cpp:517] Iteration 7100, lr = 0.003
I0821 09:28:21.322612 5405 solver.cpp:222] Iteration 7200, loss = 0.00673126
I0821 09:28:21.322813 5405 solver.cpp:238] Train net output #0: loss = 0.00673132 (* 1 = 0.00673132 loss)
I0821 09:28:21.322836 5405 solver.cpp:517] Iteration 7200, lr = 0.003
I0821 09:28:27.305656 5405 solver.cpp:222] Iteration 7300, loss = 0.00663566
I0821 09:28:27.305698 5405 solver.cpp:238] Train net output #0: loss = 0.00663571 (* 1 = 0.00663571 loss)
I0821 09:28:27.305707 5405 solver.cpp:517] Iteration 7300, lr = 0.003
I0821 09:28:33.287670 5405 solver.cpp:222] Iteration 7400, loss = 0.00715954
I0821 09:28:33.287713 5405 solver.cpp:238] Train net output #0: loss = 0.0071596 (* 1 = 0.0071596 loss)
I0821 09:28:33.287722 5405 solver.cpp:517] Iteration 7400, lr = 0.003
I0821 09:28:39.210067 5405 solver.cpp:310] Iteration 7500, Testing net (#0)
I0821 09:28:39.327250 5405 solver.cpp:359] Test net output #0: loss = 0.0154833 (* 1 = 0.0154833 loss)
I0821 09:28:39.345356 5405 solver.cpp:222] Iteration 7500, loss = 0.00660352
I0821 09:28:39.345383 5405 solver.cpp:238] Train net output #0: loss = 0.00660358 (* 1 = 0.00660358 loss)
I0821 09:28:39.345393 5405 solver.cpp:517] Iteration 7500, lr = 0.003
I0821 09:28:45.320777 5405 solver.cpp:222] Iteration 7600, loss = 0.00753852
I0821 09:28:45.320819 5405 solver.cpp:238] Train net output #0: loss = 0.00753858 (* 1 = 0.00753858 loss)
I0821 09:28:45.320830 5405 solver.cpp:517] Iteration 7600, lr = 0.003
I0821 09:28:51.298321 5405 solver.cpp:222] Iteration 7700, loss = 0.00617193
I0821 09:28:51.298363 5405 solver.cpp:238] Train net output #0: loss = 0.00617199 (* 1 = 0.00617199 loss)
I0821 09:28:51.298373 5405 solver.cpp:517] Iteration 7700, lr = 0.003
I0821 09:28:57.276605 5405 solver.cpp:222] Iteration 7800, loss = 0.00616212
I0821 09:28:57.276760 5405 solver.cpp:238] Train net output #0: loss = 0.00616218 (* 1 = 0.00616218 loss)
I0821 09:28:57.276774 5405 solver.cpp:517] Iteration 7800, lr = 0.003
I0821 09:29:03.255952 5405 solver.cpp:222] Iteration 7900, loss = 0.00705638
I0821 09:29:03.255993 5405 solver.cpp:238] Train net output #0: loss = 0.00705643 (* 1 = 0.00705643 loss)
I0821 09:29:03.256002 5405 solver.cpp:517] Iteration 7900, lr = 0.003
I0821 09:29:09.173471 5405 solver.cpp:310] Iteration 8000, Testing net (#0)
I0821 09:29:09.288182 5405 solver.cpp:359] Test net output #0: loss = 0.0153834 (* 1 = 0.0153834 loss)
I0821 09:29:09.306103 5405 solver.cpp:222] Iteration 8000, loss = 0.00693637
I0821 09:29:09.306128 5405 solver.cpp:238] Train net output #0: loss = 0.00693643 (* 1 = 0.00693643 loss)
I0821 09:29:09.306138 5405 solver.cpp:517] Iteration 8000, lr = 0.003
I0821 09:29:15.288807 5405 solver.cpp:222] Iteration 8100, loss = 0.00683235
I0821 09:29:15.288846 5405 solver.cpp:238] Train net output #0: loss = 0.00683241 (* 1 = 0.00683241 loss)
I0821 09:29:15.288854 5405 solver.cpp:517] Iteration 8100, lr = 0.003
I0821 09:29:21.270164 5405 solver.cpp:222] Iteration 8200, loss = 0.00806424
I0821 09:29:21.270205 5405 solver.cpp:238] Train net output #0: loss = 0.0080643 (* 1 = 0.0080643 loss)
I0821 09:29:21.270215 5405 solver.cpp:517] Iteration 8200, lr = 0.003
I0821 09:29:27.246574 5405 solver.cpp:222] Iteration 8300, loss = 0.00650411
I0821 09:29:27.246614 5405 solver.cpp:238] Train net output #0: loss = 0.00650417 (* 1 = 0.00650417 loss)
I0821 09:29:27.246624 5405 solver.cpp:517] Iteration 8300, lr = 0.003
I0821 09:29:33.221951 5405 solver.cpp:222] Iteration 8400, loss = 0.0054461
I0821 09:29:33.222102 5405 solver.cpp:238] Train net output #0: loss = 0.00544616 (* 1 = 0.00544616 loss)
I0821 09:29:33.222117 5405 solver.cpp:517] Iteration 8400, lr = 0.003
I0821 09:29:39.138664 5405 solver.cpp:310] Iteration 8500, Testing net (#0)
I0821 09:29:39.253290 5405 solver.cpp:359] Test net output #0: loss = 0.015358 (* 1 = 0.015358 loss)
I0821 09:29:39.271179 5405 solver.cpp:222] Iteration 8500, loss = 0.00620731
I0821 09:29:39.271206 5405 solver.cpp:238] Train net output #0: loss = 0.00620736 (* 1 = 0.00620736 loss)
I0821 09:29:39.271215 5405 solver.cpp:517] Iteration 8500, lr = 0.003
I0821 09:29:45.250445 5405 solver.cpp:222] Iteration 8600, loss = 0.00615218
I0821 09:29:45.250484 5405 solver.cpp:238] Train net output #0: loss = 0.00615224 (* 1 = 0.00615224 loss)
I0821 09:29:45.250494 5405 solver.cpp:517] Iteration 8600, lr = 0.003
I0821 09:29:51.226456 5405 solver.cpp:222] Iteration 8700, loss = 0.00645303
I0821 09:29:51.226498 5405 solver.cpp:238] Train net output #0: loss = 0.00645309 (* 1 = 0.00645309 loss)
I0821 09:29:51.226507 5405 solver.cpp:517] Iteration 8700, lr = 0.003
I0821 09:29:57.202085 5405 solver.cpp:222] Iteration 8800, loss = 0.0054876
I0821 09:29:57.202126 5405 solver.cpp:238] Train net output #0: loss = 0.00548766 (* 1 = 0.00548766 loss)
I0821 09:29:57.202136 5405 solver.cpp:517] Iteration 8800, lr = 0.003
I0821 09:30:03.177438 5405 solver.cpp:222] Iteration 8900, loss = 0.00669858
I0821 09:30:03.177489 5405 solver.cpp:238] Train net output #0: loss = 0.00669864 (* 1 = 0.00669864 loss)
I0821 09:30:03.177498 5405 solver.cpp:517] Iteration 8900, lr = 0.003
I0821 09:30:09.092192 5405 solver.cpp:310] Iteration 9000, Testing net (#0)
I0821 09:30:09.207533 5405 solver.cpp:359] Test net output #0: loss = 0.0152394 (* 1 = 0.0152394 loss)
I0821 09:30:09.225407 5405 solver.cpp:222] Iteration 9000, loss = 0.00628588
I0821 09:30:09.225432 5405 solver.cpp:238] Train net output #0: loss = 0.00628594 (* 1 = 0.00628594 loss)
I0821 09:30:09.225441 5405 solver.cpp:517] Iteration 9000, lr = 0.003
I0821 09:30:15.201637 5405 solver.cpp:222] Iteration 9100, loss = 0.00608225
I0821 09:30:15.201678 5405 solver.cpp:238] Train net output #0: loss = 0.00608231 (* 1 = 0.00608231 loss)
I0821 09:30:15.201688 5405 solver.cpp:517] Iteration 9100, lr = 0.003
I0821 09:30:21.177304 5405 solver.cpp:222] Iteration 9200, loss = 0.00592738
I0821 09:30:21.177347 5405 solver.cpp:238] Train net output #0: loss = 0.00592744 (* 1 = 0.00592744 loss)
I0821 09:30:21.177357 5405 solver.cpp:517] Iteration 9200, lr = 0.003
I0821 09:30:27.152261 5405 solver.cpp:222] Iteration 9300, loss = 0.00558138
I0821 09:30:27.152303 5405 solver.cpp:238] Train net output #0: loss = 0.00558144 (* 1 = 0.00558144 loss)
I0821 09:30:27.152312 5405 solver.cpp:517] Iteration 9300, lr = 0.003
I0821 09:30:33.130055 5405 solver.cpp:222] Iteration 9400, loss = 0.00506704
I0821 09:30:33.130097 5405 solver.cpp:238] Train net output #0: loss = 0.0050671 (* 1 = 0.0050671 loss)
I0821 09:30:33.130106 5405 solver.cpp:517] Iteration 9400, lr = 0.003
I0821 09:30:39.051370 5405 solver.cpp:310] Iteration 9500, Testing net (#0)
I0821 09:30:39.169031 5405 solver.cpp:359] Test net output #0: loss = 0.0151962 (* 1 = 0.0151962 loss)
I0821 09:30:39.186969 5405 solver.cpp:222] Iteration 9500, loss = 0.00507795
I0821 09:30:39.186995 5405 solver.cpp:238] Train net output #0: loss = 0.00507801 (* 1 = 0.00507801 loss)
I0821 09:30:39.187005 5405 solver.cpp:517] Iteration 9500, lr = 0.003
I0821 09:30:45.167737 5405 solver.cpp:222] Iteration 9600, loss = 0.00510779
I0821 09:30:45.167778 5405 solver.cpp:238] Train net output #0: loss = 0.00510785 (* 1 = 0.00510785 loss)
I0821 09:30:45.167788 5405 solver.cpp:517] Iteration 9600, lr = 0.003
I0821 09:30:51.145262 5405 solver.cpp:222] Iteration 9700, loss = 0.0052944
I0821 09:30:51.145303 5405 solver.cpp:238] Train net output #0: loss = 0.00529446 (* 1 = 0.00529446 loss)
I0821 09:30:51.145313 5405 solver.cpp:517] Iteration 9700, lr = 0.003
I0821 09:30:57.122956 5405 solver.cpp:222] Iteration 9800, loss = 0.00531247
I0821 09:30:57.122997 5405 solver.cpp:238] Train net output #0: loss = 0.00531253 (* 1 = 0.00531253 loss)
I0821 09:30:57.123006 5405 solver.cpp:517] Iteration 9800, lr = 0.003
I0821 09:31:03.099100 5405 solver.cpp:222] Iteration 9900, loss = 0.0052005
I0821 09:31:03.099140 5405 solver.cpp:238] Train net output #0: loss = 0.00520056 (* 1 = 0.00520056 loss)
I0821 09:31:03.099150 5405 solver.cpp:517] Iteration 9900, lr = 0.003
I0821 09:31:09.016680 5405 solver.cpp:395] Snapshotting to binary proto file examples/kaggle_prototxt/model/fkp_iter_10000.caffemodel
I0821 09:31:09.402175 5405 solver.cpp:680] Snapshotting solver state to binary proto fileexamples/kaggle_prototxt/model/fkp_iter_10000.solverstate
I0821 09:31:09.560963 5405 solver.cpp:310] Iteration 10000, Testing net (#0)
I0821 09:31:09.635921 5405 solver.cpp:359] Test net output #0: loss = 0.0151747 (* 1 = 0.0151747 loss)
I0821 09:31:09.653828 5405 solver.cpp:222] Iteration 10000, loss = 0.00504647
I0821 09:31:09.653854 5405 solver.cpp:238] Train net output #0: loss = 0.00504653 (* 1 = 0.00504653 loss)
I0821 09:31:09.653864 5405 solver.cpp:517] Iteration 10000, lr = 0.003
I0821 09:31:15.635051 5405 solver.cpp:222] Iteration 10100, loss = 0.00499782
I0821 09:31:15.635093 5405 solver.cpp:238] Train net output #0: loss = 0.00499788 (* 1 = 0.00499788 loss)
I0821 09:31:15.635104 5405 solver.cpp:517] Iteration 10100, lr = 0.003
I0821 09:31:21.619710 5405 solver.cpp:222] Iteration 10200, loss = 0.0048512
I0821 09:31:21.619753 5405 solver.cpp:238] Train net output #0: loss = 0.00485126 (* 1 = 0.00485126 loss)
I0821 09:31:21.619763 5405 solver.cpp:517] Iteration 10200, lr = 0.003
I0821 09:31:27.605221 5405 solver.cpp:222] Iteration 10300, loss = 0.00491596
I0821 09:31:27.605265 5405 solver.cpp:238] Train net output #0: loss = 0.00491602 (* 1 = 0.00491602 loss)
I0821 09:31:27.605276 5405 solver.cpp:517] Iteration 10300, lr = 0.003
I0821 09:31:33.590059 5405 solver.cpp:222] Iteration 10400, loss = 0.00438961
I0821 09:31:33.590101 5405 solver.cpp:238] Train net output #0: loss = 0.00438967 (* 1 = 0.00438967 loss)
I0821 09:31:33.590111 5405 solver.cpp:517] Iteration 10400, lr = 0.003
I0821 09:31:39.510354 5405 solver.cpp:310] Iteration 10500, Testing net (#0)
I0821 09:31:39.629614 5405 solver.cpp:359] Test net output #0: loss = 0.0151113 (* 1 = 0.0151113 loss)
I0821 09:31:39.647598 5405 solver.cpp:222] Iteration 10500, loss = 0.00482305
I0821 09:31:39.647624 5405 solver.cpp:238] Train net output #0: loss = 0.00482311 (* 1 = 0.00482311 loss)
I0821 09:31:39.647634 5405 solver.cpp:517] Iteration 10500, lr = 0.003
I0821 09:31:45.628831 5405 solver.cpp:222] Iteration 10600, loss = 0.00526693
I0821 09:31:45.628875 5405 solver.cpp:238] Train net output #0: loss = 0.00526699 (* 1 = 0.00526699 loss)
I0821 09:31:45.628885 5405 solver.cpp:517] Iteration 10600, lr = 0.003
I0821 09:31:51.610636 5405 solver.cpp:222] Iteration 10700, loss = 0.00463706
I0821 09:31:51.610678 5405 solver.cpp:238] Train net output #0: loss = 0.00463711 (* 1 = 0.00463711 loss)
I0821 09:31:51.610688 5405 solver.cpp:517] Iteration 10700, lr = 0.003
I0821 09:31:57.589089 5405 solver.cpp:222] Iteration 10800, loss = 0.00492762
I0821 09:31:57.589130 5405 solver.cpp:238] Train net output #0: loss = 0.00492768 (* 1 = 0.00492768 loss)
I0821 09:31:57.589140 5405 solver.cpp:517] Iteration 10800, lr = 0.003
I0821 09:32:03.572361 5405 solver.cpp:222] Iteration 10900, loss = 0.00471643
I0821 09:32:03.572402 5405 solver.cpp:238] Train net output #0: loss = 0.00471649 (* 1 = 0.00471649 loss)
I0821 09:32:03.572410 5405 solver.cpp:517] Iteration 10900, lr = 0.003
I0821 09:32:09.495825 5405 solver.cpp:310] Iteration 11000, Testing net (#0)
I0821 09:32:09.613101 5405 solver.cpp:359] Test net output #0: loss = 0.0150806 (* 1 = 0.0150806 loss)
I0821 09:32:09.631310 5405 solver.cpp:222] Iteration 11000, loss = 0.00537777
I0821 09:32:09.631340 5405 solver.cpp:238] Train net output #0: loss = 0.00537783 (* 1 = 0.00537783 loss)
I0821 09:32:09.631351 5405 solver.cpp:517] Iteration 11000, lr = 0.003
I0821 09:32:15.609299 5405 solver.cpp:222] Iteration 11100, loss = 0.00514921
I0821 09:32:15.609338 5405 solver.cpp:238] Train net output #0: loss = 0.00514927 (* 1 = 0.00514927 loss)
I0821 09:32:15.609349 5405 solver.cpp:517] Iteration 11100, lr = 0.003
I0821 09:32:21.591207 5405 solver.cpp:222] Iteration 11200, loss = 0.00452562
I0821 09:32:21.591251 5405 solver.cpp:238] Train net output #0: loss = 0.00452568 (* 1 = 0.00452568 loss)
I0821 09:32:21.591262 5405 solver.cpp:517] Iteration 11200, lr = 0.003
I0821 09:32:27.570739 5405 solver.cpp:222] Iteration 11300, loss = 0.00478911
I0821 09:32:27.570783 5405 solver.cpp:238] Train net output #0: loss = 0.00478917 (* 1 = 0.00478917 loss)
I0821 09:32:27.570793 5405 solver.cpp:517] Iteration 11300, lr = 0.003
I0821 09:32:33.550648 5405 solver.cpp:222] Iteration 11400, loss = 0.00521347
I0821 09:32:33.550689 5405 solver.cpp:238] Train net output #0: loss = 0.00521353 (* 1 = 0.00521353 loss)
I0821 09:32:33.550698 5405 solver.cpp:517] Iteration 11400, lr = 0.003
I0821 09:32:39.469004 5405 solver.cpp:310] Iteration 11500, Testing net (#0)
I0821 09:32:39.585669 5405 solver.cpp:359] Test net output #0: loss = 0.0150304 (* 1 = 0.0150304 loss)
I0821 09:32:39.603516 5405 solver.cpp:222] Iteration 11500, loss = 0.00476115
I0821 09:32:39.603539 5405 solver.cpp:238] Train net output #0: loss = 0.00476121 (* 1 = 0.00476121 loss)
I0821 09:32:39.603549 5405 solver.cpp:517] Iteration 11500, lr = 0.003