2776 lines
189 KiB
Plaintext
2776 lines
189 KiB
Plaintext
I0428 01:11:00.749866 496 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210427-230857-0bd7/solver.prototxt
|
|
I0428 01:11:00.750097 496 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
|
|
W0428 01:11:00.750106 496 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
|
|
I0428 01:11:00.750209 496 caffe.cpp:218] Using GPUs 1
|
|
I0428 01:11:00.789594 496 caffe.cpp:223] GPU 1: GeForce RTX 2080
|
|
I0428 01:11:01.137974 496 solver.cpp:44] Initializing solver from parameters:
|
|
test_iter: 51
|
|
test_interval: 102
|
|
base_lr: 0.01
|
|
display: 7
|
|
max_iter: 3060
|
|
lr_policy: "exp"
|
|
gamma: 0.99934
|
|
momentum: 0.9
|
|
weight_decay: 0.0001
|
|
snapshot: 102
|
|
snapshot_prefix: "snapshot"
|
|
solver_mode: GPU
|
|
device_id: 1
|
|
net: "train_val.prototxt"
|
|
train_state {
|
|
level: 0
|
|
stage: ""
|
|
}
|
|
iter_size: 6
|
|
type: "SGD"
|
|
I0428 01:11:01.139006 496 solver.cpp:87] Creating training net from net file: train_val.prototxt
|
|
I0428 01:11:01.139894 496 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
|
|
I0428 01:11:01.139920 496 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
|
|
I0428 01:11:01.140202 496 net.cpp:51] Initializing net from parameters:
|
|
state {
|
|
phase: TRAIN
|
|
level: 0
|
|
stage: ""
|
|
}
|
|
layer {
|
|
name: "train-data"
|
|
type: "Data"
|
|
top: "data"
|
|
top: "label"
|
|
include {
|
|
phase: TRAIN
|
|
}
|
|
transform_param {
|
|
mirror: true
|
|
crop_size: 227
|
|
mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/mean.binaryproto"
|
|
}
|
|
data_param {
|
|
source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/train_db"
|
|
batch_size: 128
|
|
backend: LMDB
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv1"
|
|
type: "Convolution"
|
|
bottom: "data"
|
|
top: "conv1"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 96
|
|
kernel_size: 11
|
|
stride: 4
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu1"
|
|
type: "ReLU"
|
|
bottom: "conv1"
|
|
top: "conv1"
|
|
}
|
|
layer {
|
|
name: "norm1"
|
|
type: "LRN"
|
|
bottom: "conv1"
|
|
top: "norm1"
|
|
lrn_param {
|
|
local_size: 5
|
|
alpha: 0.0001
|
|
beta: 0.75
|
|
}
|
|
}
|
|
layer {
|
|
name: "pool1"
|
|
type: "Pooling"
|
|
bottom: "norm1"
|
|
top: "pool1"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv2"
|
|
type: "Convolution"
|
|
bottom: "pool1"
|
|
top: "conv2"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
pad: 2
|
|
kernel_size: 5
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu2"
|
|
type: "ReLU"
|
|
bottom: "conv2"
|
|
top: "conv2"
|
|
}
|
|
layer {
|
|
name: "norm2"
|
|
type: "LRN"
|
|
bottom: "conv2"
|
|
top: "norm2"
|
|
lrn_param {
|
|
local_size: 5
|
|
alpha: 0.0001
|
|
beta: 0.75
|
|
}
|
|
}
|
|
layer {
|
|
name: "pool2"
|
|
type: "Pooling"
|
|
bottom: "norm2"
|
|
top: "pool2"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv3"
|
|
type: "Convolution"
|
|
bottom: "pool2"
|
|
top: "conv3"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 384
|
|
pad: 1
|
|
kernel_size: 3
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu3"
|
|
type: "ReLU"
|
|
bottom: "conv3"
|
|
top: "conv3"
|
|
}
|
|
layer {
|
|
name: "conv4"
|
|
type: "Convolution"
|
|
bottom: "conv3"
|
|
top: "conv4"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 384
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu4"
|
|
type: "ReLU"
|
|
bottom: "conv4"
|
|
top: "conv4"
|
|
}
|
|
layer {
|
|
name: "conv5"
|
|
type: "Convolution"
|
|
bottom: "conv4"
|
|
top: "conv5"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu5"
|
|
type: "ReLU"
|
|
bottom: "conv5"
|
|
top: "conv5"
|
|
}
|
|
layer {
|
|
name: "pool5"
|
|
type: "Pooling"
|
|
bottom: "conv5"
|
|
top: "pool5"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc6"
|
|
type: "InnerProduct"
|
|
bottom: "pool5"
|
|
top: "fc6"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
inner_product_param {
|
|
num_output: 4096
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.005
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu6"
|
|
type: "ReLU"
|
|
bottom: "fc6"
|
|
top: "fc6"
|
|
}
|
|
layer {
|
|
name: "drop6"
|
|
type: "Dropout"
|
|
bottom: "fc6"
|
|
top: "fc6"
|
|
dropout_param {
|
|
dropout_ratio: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7"
|
|
type: "InnerProduct"
|
|
bottom: "fc6"
|
|
top: "fc7"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
inner_product_param {
|
|
num_output: 4096
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.005
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu7"
|
|
type: "ReLU"
|
|
bottom: "fc7"
|
|
top: "fc7"
|
|
}
|
|
layer {
|
|
name: "drop7"
|
|
type: "Dropout"
|
|
bottom: "fc7"
|
|
top: "fc7"
|
|
dropout_param {
|
|
dropout_ratio: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc8"
|
|
type: "InnerProduct"
|
|
bottom: "fc7"
|
|
top: "fc8"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
inner_product_param {
|
|
num_output: 196
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "loss"
|
|
type: "SoftmaxWithLoss"
|
|
bottom: "fc8"
|
|
bottom: "label"
|
|
top: "loss"
|
|
}
|
|
I0428 01:11:01.140363 496 layer_factory.hpp:77] Creating layer train-data
|
|
I0428 01:11:01.174301 496 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/train_db
|
|
I0428 01:11:01.222416 496 net.cpp:84] Creating Layer train-data
|
|
I0428 01:11:01.222448 496 net.cpp:380] train-data -> data
|
|
I0428 01:11:01.222478 496 net.cpp:380] train-data -> label
|
|
I0428 01:11:01.222496 496 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/mean.binaryproto
|
|
I0428 01:11:01.302656 496 data_layer.cpp:45] output data size: 128,3,227,227
|
|
I0428 01:11:01.439417 496 net.cpp:122] Setting up train-data
|
|
I0428 01:11:01.439440 496 net.cpp:129] Top shape: 128 3 227 227 (19787136)
|
|
I0428 01:11:01.439445 496 net.cpp:129] Top shape: 128 (128)
|
|
I0428 01:11:01.439447 496 net.cpp:137] Memory required for data: 79149056
|
|
I0428 01:11:01.439456 496 layer_factory.hpp:77] Creating layer conv1
|
|
I0428 01:11:01.439496 496 net.cpp:84] Creating Layer conv1
|
|
I0428 01:11:01.439502 496 net.cpp:406] conv1 <- data
|
|
I0428 01:11:01.439514 496 net.cpp:380] conv1 -> conv1
|
|
I0428 01:11:02.335304 496 net.cpp:122] Setting up conv1
|
|
I0428 01:11:02.335327 496 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
|
I0428 01:11:02.335332 496 net.cpp:137] Memory required for data: 227833856
|
|
I0428 01:11:02.335356 496 layer_factory.hpp:77] Creating layer relu1
|
|
I0428 01:11:02.335368 496 net.cpp:84] Creating Layer relu1
|
|
I0428 01:11:02.335373 496 net.cpp:406] relu1 <- conv1
|
|
I0428 01:11:02.335381 496 net.cpp:367] relu1 -> conv1 (in-place)
|
|
I0428 01:11:02.335793 496 net.cpp:122] Setting up relu1
|
|
I0428 01:11:02.335808 496 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
|
I0428 01:11:02.335811 496 net.cpp:137] Memory required for data: 376518656
|
|
I0428 01:11:02.335815 496 layer_factory.hpp:77] Creating layer norm1
|
|
I0428 01:11:02.335829 496 net.cpp:84] Creating Layer norm1
|
|
I0428 01:11:02.335834 496 net.cpp:406] norm1 <- conv1
|
|
I0428 01:11:02.335865 496 net.cpp:380] norm1 -> norm1
|
|
I0428 01:11:02.336549 496 net.cpp:122] Setting up norm1
|
|
I0428 01:11:02.336561 496 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
|
I0428 01:11:02.336565 496 net.cpp:137] Memory required for data: 525203456
|
|
I0428 01:11:02.336570 496 layer_factory.hpp:77] Creating layer pool1
|
|
I0428 01:11:02.336580 496 net.cpp:84] Creating Layer pool1
|
|
I0428 01:11:02.336583 496 net.cpp:406] pool1 <- norm1
|
|
I0428 01:11:02.336589 496 net.cpp:380] pool1 -> pool1
|
|
I0428 01:11:02.336627 496 net.cpp:122] Setting up pool1
|
|
I0428 01:11:02.336634 496 net.cpp:129] Top shape: 128 96 27 27 (8957952)
|
|
I0428 01:11:02.336637 496 net.cpp:137] Memory required for data: 561035264
|
|
I0428 01:11:02.336640 496 layer_factory.hpp:77] Creating layer conv2
|
|
I0428 01:11:02.336652 496 net.cpp:84] Creating Layer conv2
|
|
I0428 01:11:02.336654 496 net.cpp:406] conv2 <- pool1
|
|
I0428 01:11:02.336660 496 net.cpp:380] conv2 -> conv2
|
|
I0428 01:11:02.346978 496 net.cpp:122] Setting up conv2
|
|
I0428 01:11:02.346995 496 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
|
I0428 01:11:02.346998 496 net.cpp:137] Memory required for data: 656586752
|
|
I0428 01:11:02.347012 496 layer_factory.hpp:77] Creating layer relu2
|
|
I0428 01:11:02.347019 496 net.cpp:84] Creating Layer relu2
|
|
I0428 01:11:02.347023 496 net.cpp:406] relu2 <- conv2
|
|
I0428 01:11:02.347028 496 net.cpp:367] relu2 -> conv2 (in-place)
|
|
I0428 01:11:02.347687 496 net.cpp:122] Setting up relu2
|
|
I0428 01:11:02.347697 496 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
|
I0428 01:11:02.347700 496 net.cpp:137] Memory required for data: 752138240
|
|
I0428 01:11:02.347703 496 layer_factory.hpp:77] Creating layer norm2
|
|
I0428 01:11:02.347710 496 net.cpp:84] Creating Layer norm2
|
|
I0428 01:11:02.347713 496 net.cpp:406] norm2 <- conv2
|
|
I0428 01:11:02.347718 496 net.cpp:380] norm2 -> norm2
|
|
I0428 01:11:02.348042 496 net.cpp:122] Setting up norm2
|
|
I0428 01:11:02.348052 496 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
|
I0428 01:11:02.348054 496 net.cpp:137] Memory required for data: 847689728
|
|
I0428 01:11:02.348057 496 layer_factory.hpp:77] Creating layer pool2
|
|
I0428 01:11:02.348064 496 net.cpp:84] Creating Layer pool2
|
|
I0428 01:11:02.348068 496 net.cpp:406] pool2 <- norm2
|
|
I0428 01:11:02.348073 496 net.cpp:380] pool2 -> pool2
|
|
I0428 01:11:02.348098 496 net.cpp:122] Setting up pool2
|
|
I0428 01:11:02.348102 496 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
|
I0428 01:11:02.348105 496 net.cpp:137] Memory required for data: 869840896
|
|
I0428 01:11:02.348107 496 layer_factory.hpp:77] Creating layer conv3
|
|
I0428 01:11:02.348117 496 net.cpp:84] Creating Layer conv3
|
|
I0428 01:11:02.348119 496 net.cpp:406] conv3 <- pool2
|
|
I0428 01:11:02.348124 496 net.cpp:380] conv3 -> conv3
|
|
I0428 01:11:02.361608 496 net.cpp:122] Setting up conv3
|
|
I0428 01:11:02.361622 496 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0428 01:11:02.361626 496 net.cpp:137] Memory required for data: 903067648
|
|
I0428 01:11:02.361635 496 layer_factory.hpp:77] Creating layer relu3
|
|
I0428 01:11:02.361644 496 net.cpp:84] Creating Layer relu3
|
|
I0428 01:11:02.361646 496 net.cpp:406] relu3 <- conv3
|
|
I0428 01:11:02.361651 496 net.cpp:367] relu3 -> conv3 (in-place)
|
|
I0428 01:11:02.362491 496 net.cpp:122] Setting up relu3
|
|
I0428 01:11:02.362501 496 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0428 01:11:02.362504 496 net.cpp:137] Memory required for data: 936294400
|
|
I0428 01:11:02.362507 496 layer_factory.hpp:77] Creating layer conv4
|
|
I0428 01:11:02.362519 496 net.cpp:84] Creating Layer conv4
|
|
I0428 01:11:02.362521 496 net.cpp:406] conv4 <- conv3
|
|
I0428 01:11:02.362527 496 net.cpp:380] conv4 -> conv4
|
|
I0428 01:11:02.373270 496 net.cpp:122] Setting up conv4
|
|
I0428 01:11:02.373286 496 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0428 01:11:02.373288 496 net.cpp:137] Memory required for data: 969521152
|
|
I0428 01:11:02.373296 496 layer_factory.hpp:77] Creating layer relu4
|
|
I0428 01:11:02.373303 496 net.cpp:84] Creating Layer relu4
|
|
I0428 01:11:02.373325 496 net.cpp:406] relu4 <- conv4
|
|
I0428 01:11:02.373330 496 net.cpp:367] relu4 -> conv4 (in-place)
|
|
I0428 01:11:02.373804 496 net.cpp:122] Setting up relu4
|
|
I0428 01:11:02.373814 496 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0428 01:11:02.373817 496 net.cpp:137] Memory required for data: 1002747904
|
|
I0428 01:11:02.373821 496 layer_factory.hpp:77] Creating layer conv5
|
|
I0428 01:11:02.373829 496 net.cpp:84] Creating Layer conv5
|
|
I0428 01:11:02.373833 496 net.cpp:406] conv5 <- conv4
|
|
I0428 01:11:02.373838 496 net.cpp:380] conv5 -> conv5
|
|
I0428 01:11:02.383026 496 net.cpp:122] Setting up conv5
|
|
I0428 01:11:02.383041 496 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
|
I0428 01:11:02.383044 496 net.cpp:137] Memory required for data: 1024899072
|
|
I0428 01:11:02.383055 496 layer_factory.hpp:77] Creating layer relu5
|
|
I0428 01:11:02.383064 496 net.cpp:84] Creating Layer relu5
|
|
I0428 01:11:02.383069 496 net.cpp:406] relu5 <- conv5
|
|
I0428 01:11:02.383074 496 net.cpp:367] relu5 -> conv5 (in-place)
|
|
I0428 01:11:02.383620 496 net.cpp:122] Setting up relu5
|
|
I0428 01:11:02.383630 496 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
|
I0428 01:11:02.383632 496 net.cpp:137] Memory required for data: 1047050240
|
|
I0428 01:11:02.383635 496 layer_factory.hpp:77] Creating layer pool5
|
|
I0428 01:11:02.383641 496 net.cpp:84] Creating Layer pool5
|
|
I0428 01:11:02.383644 496 net.cpp:406] pool5 <- conv5
|
|
I0428 01:11:02.383651 496 net.cpp:380] pool5 -> pool5
|
|
I0428 01:11:02.383687 496 net.cpp:122] Setting up pool5
|
|
I0428 01:11:02.383693 496 net.cpp:129] Top shape: 128 256 6 6 (1179648)
|
|
I0428 01:11:02.383697 496 net.cpp:137] Memory required for data: 1051768832
|
|
I0428 01:11:02.383698 496 layer_factory.hpp:77] Creating layer fc6
|
|
I0428 01:11:02.383708 496 net.cpp:84] Creating Layer fc6
|
|
I0428 01:11:02.383711 496 net.cpp:406] fc6 <- pool5
|
|
I0428 01:11:02.383715 496 net.cpp:380] fc6 -> fc6
|
|
I0428 01:11:02.741923 496 net.cpp:122] Setting up fc6
|
|
I0428 01:11:02.741943 496 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 01:11:02.741947 496 net.cpp:137] Memory required for data: 1053865984
|
|
I0428 01:11:02.741956 496 layer_factory.hpp:77] Creating layer relu6
|
|
I0428 01:11:02.741966 496 net.cpp:84] Creating Layer relu6
|
|
I0428 01:11:02.741969 496 net.cpp:406] relu6 <- fc6
|
|
I0428 01:11:02.741976 496 net.cpp:367] relu6 -> fc6 (in-place)
|
|
I0428 01:11:02.742861 496 net.cpp:122] Setting up relu6
|
|
I0428 01:11:02.742873 496 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 01:11:02.742877 496 net.cpp:137] Memory required for data: 1055963136
|
|
I0428 01:11:02.742880 496 layer_factory.hpp:77] Creating layer drop6
|
|
I0428 01:11:02.742887 496 net.cpp:84] Creating Layer drop6
|
|
I0428 01:11:02.742890 496 net.cpp:406] drop6 <- fc6
|
|
I0428 01:11:02.742897 496 net.cpp:367] drop6 -> fc6 (in-place)
|
|
I0428 01:11:02.742928 496 net.cpp:122] Setting up drop6
|
|
I0428 01:11:02.742933 496 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 01:11:02.742934 496 net.cpp:137] Memory required for data: 1058060288
|
|
I0428 01:11:02.742938 496 layer_factory.hpp:77] Creating layer fc7
|
|
I0428 01:11:02.742946 496 net.cpp:84] Creating Layer fc7
|
|
I0428 01:11:02.742949 496 net.cpp:406] fc7 <- fc6
|
|
I0428 01:11:02.742955 496 net.cpp:380] fc7 -> fc7
|
|
I0428 01:11:02.902552 496 net.cpp:122] Setting up fc7
|
|
I0428 01:11:02.902570 496 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 01:11:02.902573 496 net.cpp:137] Memory required for data: 1060157440
|
|
I0428 01:11:02.902582 496 layer_factory.hpp:77] Creating layer relu7
|
|
I0428 01:11:02.902591 496 net.cpp:84] Creating Layer relu7
|
|
I0428 01:11:02.902599 496 net.cpp:406] relu7 <- fc7
|
|
I0428 01:11:02.902606 496 net.cpp:367] relu7 -> fc7 (in-place)
|
|
I0428 01:11:02.903097 496 net.cpp:122] Setting up relu7
|
|
I0428 01:11:02.903106 496 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 01:11:02.903108 496 net.cpp:137] Memory required for data: 1062254592
|
|
I0428 01:11:02.903111 496 layer_factory.hpp:77] Creating layer drop7
|
|
I0428 01:11:02.903117 496 net.cpp:84] Creating Layer drop7
|
|
I0428 01:11:02.903144 496 net.cpp:406] drop7 <- fc7
|
|
I0428 01:11:02.903151 496 net.cpp:367] drop7 -> fc7 (in-place)
|
|
I0428 01:11:02.903177 496 net.cpp:122] Setting up drop7
|
|
I0428 01:11:02.903182 496 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 01:11:02.903184 496 net.cpp:137] Memory required for data: 1064351744
|
|
I0428 01:11:02.903187 496 layer_factory.hpp:77] Creating layer fc8
|
|
I0428 01:11:02.903193 496 net.cpp:84] Creating Layer fc8
|
|
I0428 01:11:02.903196 496 net.cpp:406] fc8 <- fc7
|
|
I0428 01:11:02.903203 496 net.cpp:380] fc8 -> fc8
|
|
I0428 01:11:02.911270 496 net.cpp:122] Setting up fc8
|
|
I0428 01:11:02.911284 496 net.cpp:129] Top shape: 128 196 (25088)
|
|
I0428 01:11:02.911288 496 net.cpp:137] Memory required for data: 1064452096
|
|
I0428 01:11:02.911294 496 layer_factory.hpp:77] Creating layer loss
|
|
I0428 01:11:02.911301 496 net.cpp:84] Creating Layer loss
|
|
I0428 01:11:02.911304 496 net.cpp:406] loss <- fc8
|
|
I0428 01:11:02.911309 496 net.cpp:406] loss <- label
|
|
I0428 01:11:02.911319 496 net.cpp:380] loss -> loss
|
|
I0428 01:11:02.911327 496 layer_factory.hpp:77] Creating layer loss
|
|
I0428 01:11:02.912322 496 net.cpp:122] Setting up loss
|
|
I0428 01:11:02.912333 496 net.cpp:129] Top shape: (1)
|
|
I0428 01:11:02.912335 496 net.cpp:132] with loss weight 1
|
|
I0428 01:11:02.912351 496 net.cpp:137] Memory required for data: 1064452100
|
|
I0428 01:11:02.912355 496 net.cpp:198] loss needs backward computation.
|
|
I0428 01:11:02.912361 496 net.cpp:198] fc8 needs backward computation.
|
|
I0428 01:11:02.912364 496 net.cpp:198] drop7 needs backward computation.
|
|
I0428 01:11:02.912367 496 net.cpp:198] relu7 needs backward computation.
|
|
I0428 01:11:02.912370 496 net.cpp:198] fc7 needs backward computation.
|
|
I0428 01:11:02.912372 496 net.cpp:198] drop6 needs backward computation.
|
|
I0428 01:11:02.912375 496 net.cpp:198] relu6 needs backward computation.
|
|
I0428 01:11:02.912379 496 net.cpp:198] fc6 needs backward computation.
|
|
I0428 01:11:02.912381 496 net.cpp:198] pool5 needs backward computation.
|
|
I0428 01:11:02.912384 496 net.cpp:198] relu5 needs backward computation.
|
|
I0428 01:11:02.912386 496 net.cpp:198] conv5 needs backward computation.
|
|
I0428 01:11:02.912389 496 net.cpp:198] relu4 needs backward computation.
|
|
I0428 01:11:02.912392 496 net.cpp:198] conv4 needs backward computation.
|
|
I0428 01:11:02.912395 496 net.cpp:198] relu3 needs backward computation.
|
|
I0428 01:11:02.912397 496 net.cpp:198] conv3 needs backward computation.
|
|
I0428 01:11:02.912400 496 net.cpp:198] pool2 needs backward computation.
|
|
I0428 01:11:02.912403 496 net.cpp:198] norm2 needs backward computation.
|
|
I0428 01:11:02.912406 496 net.cpp:198] relu2 needs backward computation.
|
|
I0428 01:11:02.912408 496 net.cpp:198] conv2 needs backward computation.
|
|
I0428 01:11:02.912411 496 net.cpp:198] pool1 needs backward computation.
|
|
I0428 01:11:02.912415 496 net.cpp:198] norm1 needs backward computation.
|
|
I0428 01:11:02.912417 496 net.cpp:198] relu1 needs backward computation.
|
|
I0428 01:11:02.912420 496 net.cpp:198] conv1 needs backward computation.
|
|
I0428 01:11:02.912423 496 net.cpp:200] train-data does not need backward computation.
|
|
I0428 01:11:02.912425 496 net.cpp:242] This network produces output loss
|
|
I0428 01:11:02.912439 496 net.cpp:255] Network initialization done.
|
|
I0428 01:11:02.913663 496 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
|
|
I0428 01:11:02.913695 496 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
|
|
I0428 01:11:02.913830 496 net.cpp:51] Initializing net from parameters:
|
|
state {
|
|
phase: TEST
|
|
}
|
|
layer {
|
|
name: "val-data"
|
|
type: "Data"
|
|
top: "data"
|
|
top: "label"
|
|
include {
|
|
phase: TEST
|
|
}
|
|
transform_param {
|
|
crop_size: 227
|
|
mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/mean.binaryproto"
|
|
}
|
|
data_param {
|
|
source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/val_db"
|
|
batch_size: 32
|
|
backend: LMDB
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv1"
|
|
type: "Convolution"
|
|
bottom: "data"
|
|
top: "conv1"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 96
|
|
kernel_size: 11
|
|
stride: 4
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu1"
|
|
type: "ReLU"
|
|
bottom: "conv1"
|
|
top: "conv1"
|
|
}
|
|
layer {
|
|
name: "norm1"
|
|
type: "LRN"
|
|
bottom: "conv1"
|
|
top: "norm1"
|
|
lrn_param {
|
|
local_size: 5
|
|
alpha: 0.0001
|
|
beta: 0.75
|
|
}
|
|
}
|
|
layer {
|
|
name: "pool1"
|
|
type: "Pooling"
|
|
bottom: "norm1"
|
|
top: "pool1"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv2"
|
|
type: "Convolution"
|
|
bottom: "pool1"
|
|
top: "conv2"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
pad: 2
|
|
kernel_size: 5
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu2"
|
|
type: "ReLU"
|
|
bottom: "conv2"
|
|
top: "conv2"
|
|
}
|
|
layer {
|
|
name: "norm2"
|
|
type: "LRN"
|
|
bottom: "conv2"
|
|
top: "norm2"
|
|
lrn_param {
|
|
local_size: 5
|
|
alpha: 0.0001
|
|
beta: 0.75
|
|
}
|
|
}
|
|
layer {
|
|
name: "pool2"
|
|
type: "Pooling"
|
|
bottom: "norm2"
|
|
top: "pool2"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv3"
|
|
type: "Convolution"
|
|
bottom: "pool2"
|
|
top: "conv3"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 384
|
|
pad: 1
|
|
kernel_size: 3
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu3"
|
|
type: "ReLU"
|
|
bottom: "conv3"
|
|
top: "conv3"
|
|
}
|
|
layer {
|
|
name: "conv4"
|
|
type: "Convolution"
|
|
bottom: "conv3"
|
|
top: "conv4"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 384
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu4"
|
|
type: "ReLU"
|
|
bottom: "conv4"
|
|
top: "conv4"
|
|
}
|
|
layer {
|
|
name: "conv5"
|
|
type: "Convolution"
|
|
bottom: "conv4"
|
|
top: "conv5"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu5"
|
|
type: "ReLU"
|
|
bottom: "conv5"
|
|
top: "conv5"
|
|
}
|
|
layer {
|
|
name: "pool5"
|
|
type: "Pooling"
|
|
bottom: "conv5"
|
|
top: "pool5"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc6"
|
|
type: "InnerProduct"
|
|
bottom: "pool5"
|
|
top: "fc6"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
inner_product_param {
|
|
num_output: 4096
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.005
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu6"
|
|
type: "ReLU"
|
|
bottom: "fc6"
|
|
top: "fc6"
|
|
}
|
|
layer {
|
|
name: "drop6"
|
|
type: "Dropout"
|
|
bottom: "fc6"
|
|
top: "fc6"
|
|
dropout_param {
|
|
dropout_ratio: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7"
|
|
type: "InnerProduct"
|
|
bottom: "fc6"
|
|
top: "fc7"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
inner_product_param {
|
|
num_output: 4096
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.005
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu7"
|
|
type: "ReLU"
|
|
bottom: "fc7"
|
|
top: "fc7"
|
|
}
|
|
layer {
|
|
name: "drop7"
|
|
type: "Dropout"
|
|
bottom: "fc7"
|
|
top: "fc7"
|
|
dropout_param {
|
|
dropout_ratio: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc8"
|
|
type: "InnerProduct"
|
|
bottom: "fc7"
|
|
top: "fc8"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
inner_product_param {
|
|
num_output: 196
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "accuracy"
|
|
type: "Accuracy"
|
|
bottom: "fc8"
|
|
bottom: "label"
|
|
top: "accuracy"
|
|
include {
|
|
phase: TEST
|
|
}
|
|
}
|
|
layer {
|
|
name: "loss"
|
|
type: "SoftmaxWithLoss"
|
|
bottom: "fc8"
|
|
bottom: "label"
|
|
top: "loss"
|
|
}
|
|
I0428 01:11:02.913935 496 layer_factory.hpp:77] Creating layer val-data
|
|
I0428 01:11:02.974299 496 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/val_db
|
|
I0428 01:11:03.012056 496 net.cpp:84] Creating Layer val-data
|
|
I0428 01:11:03.012086 496 net.cpp:380] val-data -> data
|
|
I0428 01:11:03.012105 496 net.cpp:380] val-data -> label
|
|
I0428 01:11:03.012118 496 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/mean.binaryproto
|
|
I0428 01:11:03.022379 496 data_layer.cpp:45] output data size: 32,3,227,227
|
|
I0428 01:11:03.057547 496 net.cpp:122] Setting up val-data
|
|
I0428 01:11:03.057570 496 net.cpp:129] Top shape: 32 3 227 227 (4946784)
|
|
I0428 01:11:03.057574 496 net.cpp:129] Top shape: 32 (32)
|
|
I0428 01:11:03.057577 496 net.cpp:137] Memory required for data: 19787264
|
|
I0428 01:11:03.057583 496 layer_factory.hpp:77] Creating layer label_val-data_1_split
|
|
I0428 01:11:03.057595 496 net.cpp:84] Creating Layer label_val-data_1_split
|
|
I0428 01:11:03.057598 496 net.cpp:406] label_val-data_1_split <- label
|
|
I0428 01:11:03.057605 496 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
|
|
I0428 01:11:03.057613 496 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
|
|
I0428 01:11:03.057654 496 net.cpp:122] Setting up label_val-data_1_split
|
|
I0428 01:11:03.057659 496 net.cpp:129] Top shape: 32 (32)
|
|
I0428 01:11:03.057662 496 net.cpp:129] Top shape: 32 (32)
|
|
I0428 01:11:03.057664 496 net.cpp:137] Memory required for data: 19787520
|
|
I0428 01:11:03.057667 496 layer_factory.hpp:77] Creating layer conv1
|
|
I0428 01:11:03.057678 496 net.cpp:84] Creating Layer conv1
|
|
I0428 01:11:03.057682 496 net.cpp:406] conv1 <- data
|
|
I0428 01:11:03.057685 496 net.cpp:380] conv1 -> conv1
|
|
I0428 01:11:03.060904 496 net.cpp:122] Setting up conv1
|
|
I0428 01:11:03.060915 496 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0428 01:11:03.060918 496 net.cpp:137] Memory required for data: 56958720
|
|
I0428 01:11:03.060927 496 layer_factory.hpp:77] Creating layer relu1
|
|
I0428 01:11:03.060935 496 net.cpp:84] Creating Layer relu1
|
|
I0428 01:11:03.060937 496 net.cpp:406] relu1 <- conv1
|
|
I0428 01:11:03.060941 496 net.cpp:367] relu1 -> conv1 (in-place)
|
|
I0428 01:11:03.061269 496 net.cpp:122] Setting up relu1
|
|
I0428 01:11:03.061278 496 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0428 01:11:03.061281 496 net.cpp:137] Memory required for data: 94129920
|
|
I0428 01:11:03.061285 496 layer_factory.hpp:77] Creating layer norm1
|
|
I0428 01:11:03.061292 496 net.cpp:84] Creating Layer norm1
|
|
I0428 01:11:03.061295 496 net.cpp:406] norm1 <- conv1
|
|
I0428 01:11:03.061300 496 net.cpp:380] norm1 -> norm1
|
|
I0428 01:11:03.061812 496 net.cpp:122] Setting up norm1
|
|
I0428 01:11:03.061822 496 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0428 01:11:03.061825 496 net.cpp:137] Memory required for data: 131301120
|
|
I0428 01:11:03.061828 496 layer_factory.hpp:77] Creating layer pool1
|
|
I0428 01:11:03.061834 496 net.cpp:84] Creating Layer pool1
|
|
I0428 01:11:03.061837 496 net.cpp:406] pool1 <- norm1
|
|
I0428 01:11:03.061841 496 net.cpp:380] pool1 -> pool1
|
|
I0428 01:11:03.061867 496 net.cpp:122] Setting up pool1
|
|
I0428 01:11:03.061872 496 net.cpp:129] Top shape: 32 96 27 27 (2239488)
|
|
I0428 01:11:03.061874 496 net.cpp:137] Memory required for data: 140259072
|
|
I0428 01:11:03.061877 496 layer_factory.hpp:77] Creating layer conv2
|
|
I0428 01:11:03.061884 496 net.cpp:84] Creating Layer conv2
|
|
I0428 01:11:03.061887 496 net.cpp:406] conv2 <- pool1
|
|
I0428 01:11:03.061914 496 net.cpp:380] conv2 -> conv2
|
|
I0428 01:11:03.071472 496 net.cpp:122] Setting up conv2
|
|
I0428 01:11:03.071489 496 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
|
I0428 01:11:03.071492 496 net.cpp:137] Memory required for data: 164146944
|
|
I0428 01:11:03.071502 496 layer_factory.hpp:77] Creating layer relu2
|
|
I0428 01:11:03.071511 496 net.cpp:84] Creating Layer relu2
|
|
I0428 01:11:03.071514 496 net.cpp:406] relu2 <- conv2
|
|
I0428 01:11:03.071521 496 net.cpp:367] relu2 -> conv2 (in-place)
|
|
I0428 01:11:03.072082 496 net.cpp:122] Setting up relu2
|
|
I0428 01:11:03.072091 496 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
|
I0428 01:11:03.072094 496 net.cpp:137] Memory required for data: 188034816
|
|
I0428 01:11:03.072098 496 layer_factory.hpp:77] Creating layer norm2
|
|
I0428 01:11:03.072108 496 net.cpp:84] Creating Layer norm2
|
|
I0428 01:11:03.072118 496 net.cpp:406] norm2 <- conv2
|
|
I0428 01:11:03.072122 496 net.cpp:380] norm2 -> norm2
|
|
I0428 01:11:03.072894 496 net.cpp:122] Setting up norm2
|
|
I0428 01:11:03.072904 496 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
|
I0428 01:11:03.072907 496 net.cpp:137] Memory required for data: 211922688
|
|
I0428 01:11:03.072911 496 layer_factory.hpp:77] Creating layer pool2
|
|
I0428 01:11:03.072917 496 net.cpp:84] Creating Layer pool2
|
|
I0428 01:11:03.072921 496 net.cpp:406] pool2 <- norm2
|
|
I0428 01:11:03.072928 496 net.cpp:380] pool2 -> pool2
|
|
I0428 01:11:03.072958 496 net.cpp:122] Setting up pool2
|
|
I0428 01:11:03.072963 496 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
|
I0428 01:11:03.072966 496 net.cpp:137] Memory required for data: 217460480
|
|
I0428 01:11:03.072969 496 layer_factory.hpp:77] Creating layer conv3
|
|
I0428 01:11:03.072980 496 net.cpp:84] Creating Layer conv3
|
|
I0428 01:11:03.072983 496 net.cpp:406] conv3 <- pool2
|
|
I0428 01:11:03.072988 496 net.cpp:380] conv3 -> conv3
|
|
I0428 01:11:03.084776 496 net.cpp:122] Setting up conv3
|
|
I0428 01:11:03.084797 496 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0428 01:11:03.084800 496 net.cpp:137] Memory required for data: 225767168
|
|
I0428 01:11:03.084812 496 layer_factory.hpp:77] Creating layer relu3
|
|
I0428 01:11:03.084820 496 net.cpp:84] Creating Layer relu3
|
|
I0428 01:11:03.084825 496 net.cpp:406] relu3 <- conv3
|
|
I0428 01:11:03.084830 496 net.cpp:367] relu3 -> conv3 (in-place)
|
|
I0428 01:11:03.085424 496 net.cpp:122] Setting up relu3
|
|
I0428 01:11:03.085435 496 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0428 01:11:03.085438 496 net.cpp:137] Memory required for data: 234073856
|
|
I0428 01:11:03.085441 496 layer_factory.hpp:77] Creating layer conv4
|
|
I0428 01:11:03.085453 496 net.cpp:84] Creating Layer conv4
|
|
I0428 01:11:03.085456 496 net.cpp:406] conv4 <- conv3
|
|
I0428 01:11:03.085461 496 net.cpp:380] conv4 -> conv4
|
|
I0428 01:11:03.095793 496 net.cpp:122] Setting up conv4
|
|
I0428 01:11:03.095809 496 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0428 01:11:03.095813 496 net.cpp:137] Memory required for data: 242380544
|
|
I0428 01:11:03.095820 496 layer_factory.hpp:77] Creating layer relu4
|
|
I0428 01:11:03.095829 496 net.cpp:84] Creating Layer relu4
|
|
I0428 01:11:03.095834 496 net.cpp:406] relu4 <- conv4
|
|
I0428 01:11:03.095839 496 net.cpp:367] relu4 -> conv4 (in-place)
|
|
I0428 01:11:03.096223 496 net.cpp:122] Setting up relu4
|
|
I0428 01:11:03.096235 496 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0428 01:11:03.096236 496 net.cpp:137] Memory required for data: 250687232
|
|
I0428 01:11:03.096240 496 layer_factory.hpp:77] Creating layer conv5
|
|
I0428 01:11:03.096251 496 net.cpp:84] Creating Layer conv5
|
|
I0428 01:11:03.096256 496 net.cpp:406] conv5 <- conv4
|
|
I0428 01:11:03.096261 496 net.cpp:380] conv5 -> conv5
|
|
I0428 01:11:03.105993 496 net.cpp:122] Setting up conv5
|
|
I0428 01:11:03.106012 496 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
|
I0428 01:11:03.106016 496 net.cpp:137] Memory required for data: 256225024
|
|
I0428 01:11:03.106029 496 layer_factory.hpp:77] Creating layer relu5
|
|
I0428 01:11:03.106036 496 net.cpp:84] Creating Layer relu5
|
|
I0428 01:11:03.106040 496 net.cpp:406] relu5 <- conv5
|
|
I0428 01:11:03.106065 496 net.cpp:367] relu5 -> conv5 (in-place)
|
|
I0428 01:11:03.106638 496 net.cpp:122] Setting up relu5
|
|
I0428 01:11:03.106649 496 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
|
I0428 01:11:03.106652 496 net.cpp:137] Memory required for data: 261762816
|
|
I0428 01:11:03.106657 496 layer_factory.hpp:77] Creating layer pool5
|
|
I0428 01:11:03.106667 496 net.cpp:84] Creating Layer pool5
|
|
I0428 01:11:03.106669 496 net.cpp:406] pool5 <- conv5
|
|
I0428 01:11:03.106674 496 net.cpp:380] pool5 -> pool5
|
|
I0428 01:11:03.106712 496 net.cpp:122] Setting up pool5
|
|
I0428 01:11:03.106719 496 net.cpp:129] Top shape: 32 256 6 6 (294912)
|
|
I0428 01:11:03.106721 496 net.cpp:137] Memory required for data: 262942464
|
|
I0428 01:11:03.106724 496 layer_factory.hpp:77] Creating layer fc6
|
|
I0428 01:11:03.106730 496 net.cpp:84] Creating Layer fc6
|
|
I0428 01:11:03.106734 496 net.cpp:406] fc6 <- pool5
|
|
I0428 01:11:03.106739 496 net.cpp:380] fc6 -> fc6
|
|
I0428 01:11:03.465464 496 net.cpp:122] Setting up fc6
|
|
I0428 01:11:03.465482 496 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0428 01:11:03.465487 496 net.cpp:137] Memory required for data: 263466752
|
|
I0428 01:11:03.465495 496 layer_factory.hpp:77] Creating layer relu6
|
|
I0428 01:11:03.465503 496 net.cpp:84] Creating Layer relu6
|
|
I0428 01:11:03.465507 496 net.cpp:406] relu6 <- fc6
|
|
I0428 01:11:03.465515 496 net.cpp:367] relu6 -> fc6 (in-place)
|
|
I0428 01:11:03.466275 496 net.cpp:122] Setting up relu6
|
|
I0428 01:11:03.466286 496 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0428 01:11:03.466289 496 net.cpp:137] Memory required for data: 263991040
|
|
I0428 01:11:03.466292 496 layer_factory.hpp:77] Creating layer drop6
|
|
I0428 01:11:03.466298 496 net.cpp:84] Creating Layer drop6
|
|
I0428 01:11:03.466301 496 net.cpp:406] drop6 <- fc6
|
|
I0428 01:11:03.466306 496 net.cpp:367] drop6 -> fc6 (in-place)
|
|
I0428 01:11:03.466331 496 net.cpp:122] Setting up drop6
|
|
I0428 01:11:03.466334 496 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0428 01:11:03.466337 496 net.cpp:137] Memory required for data: 264515328
|
|
I0428 01:11:03.466341 496 layer_factory.hpp:77] Creating layer fc7
|
|
I0428 01:11:03.466347 496 net.cpp:84] Creating Layer fc7
|
|
I0428 01:11:03.466351 496 net.cpp:406] fc7 <- fc6
|
|
I0428 01:11:03.466354 496 net.cpp:380] fc7 -> fc7
|
|
I0428 01:11:03.626685 496 net.cpp:122] Setting up fc7
|
|
I0428 01:11:03.626706 496 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0428 01:11:03.626710 496 net.cpp:137] Memory required for data: 265039616
|
|
I0428 01:11:03.626718 496 layer_factory.hpp:77] Creating layer relu7
|
|
I0428 01:11:03.626726 496 net.cpp:84] Creating Layer relu7
|
|
I0428 01:11:03.626732 496 net.cpp:406] relu7 <- fc7
|
|
I0428 01:11:03.626737 496 net.cpp:367] relu7 -> fc7 (in-place)
|
|
I0428 01:11:03.627256 496 net.cpp:122] Setting up relu7
|
|
I0428 01:11:03.627267 496 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0428 01:11:03.627270 496 net.cpp:137] Memory required for data: 265563904
|
|
I0428 01:11:03.627274 496 layer_factory.hpp:77] Creating layer drop7
|
|
I0428 01:11:03.627282 496 net.cpp:84] Creating Layer drop7
|
|
I0428 01:11:03.627286 496 net.cpp:406] drop7 <- fc7
|
|
I0428 01:11:03.627291 496 net.cpp:367] drop7 -> fc7 (in-place)
|
|
I0428 01:11:03.627321 496 net.cpp:122] Setting up drop7
|
|
I0428 01:11:03.627326 496 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0428 01:11:03.627329 496 net.cpp:137] Memory required for data: 266088192
|
|
I0428 01:11:03.627331 496 layer_factory.hpp:77] Creating layer fc8
|
|
I0428 01:11:03.627338 496 net.cpp:84] Creating Layer fc8
|
|
I0428 01:11:03.627341 496 net.cpp:406] fc8 <- fc7
|
|
I0428 01:11:03.627347 496 net.cpp:380] fc8 -> fc8
|
|
I0428 01:11:03.635202 496 net.cpp:122] Setting up fc8
|
|
I0428 01:11:03.635215 496 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0428 01:11:03.635218 496 net.cpp:137] Memory required for data: 266113280
|
|
I0428 01:11:03.635224 496 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
|
|
I0428 01:11:03.635231 496 net.cpp:84] Creating Layer fc8_fc8_0_split
|
|
I0428 01:11:03.635234 496 net.cpp:406] fc8_fc8_0_split <- fc8
|
|
I0428 01:11:03.635262 496 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
|
|
I0428 01:11:03.635268 496 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
|
|
I0428 01:11:03.635300 496 net.cpp:122] Setting up fc8_fc8_0_split
|
|
I0428 01:11:03.635305 496 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0428 01:11:03.635308 496 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0428 01:11:03.635310 496 net.cpp:137] Memory required for data: 266163456
|
|
I0428 01:11:03.635313 496 layer_factory.hpp:77] Creating layer accuracy
|
|
I0428 01:11:03.635321 496 net.cpp:84] Creating Layer accuracy
|
|
I0428 01:11:03.635324 496 net.cpp:406] accuracy <- fc8_fc8_0_split_0
|
|
I0428 01:11:03.635329 496 net.cpp:406] accuracy <- label_val-data_1_split_0
|
|
I0428 01:11:03.635332 496 net.cpp:380] accuracy -> accuracy
|
|
I0428 01:11:03.635340 496 net.cpp:122] Setting up accuracy
|
|
I0428 01:11:03.635344 496 net.cpp:129] Top shape: (1)
|
|
I0428 01:11:03.635346 496 net.cpp:137] Memory required for data: 266163460
|
|
I0428 01:11:03.635349 496 layer_factory.hpp:77] Creating layer loss
|
|
I0428 01:11:03.635354 496 net.cpp:84] Creating Layer loss
|
|
I0428 01:11:03.635355 496 net.cpp:406] loss <- fc8_fc8_0_split_1
|
|
I0428 01:11:03.635360 496 net.cpp:406] loss <- label_val-data_1_split_1
|
|
I0428 01:11:03.635363 496 net.cpp:380] loss -> loss
|
|
I0428 01:11:03.635370 496 layer_factory.hpp:77] Creating layer loss
|
|
I0428 01:11:03.636126 496 net.cpp:122] Setting up loss
|
|
I0428 01:11:03.636134 496 net.cpp:129] Top shape: (1)
|
|
I0428 01:11:03.636137 496 net.cpp:132] with loss weight 1
|
|
I0428 01:11:03.636147 496 net.cpp:137] Memory required for data: 266163464
|
|
I0428 01:11:03.636150 496 net.cpp:198] loss needs backward computation.
|
|
I0428 01:11:03.636154 496 net.cpp:200] accuracy does not need backward computation.
|
|
I0428 01:11:03.636157 496 net.cpp:198] fc8_fc8_0_split needs backward computation.
|
|
I0428 01:11:03.636160 496 net.cpp:198] fc8 needs backward computation.
|
|
I0428 01:11:03.636163 496 net.cpp:198] drop7 needs backward computation.
|
|
I0428 01:11:03.636165 496 net.cpp:198] relu7 needs backward computation.
|
|
I0428 01:11:03.636168 496 net.cpp:198] fc7 needs backward computation.
|
|
I0428 01:11:03.636171 496 net.cpp:198] drop6 needs backward computation.
|
|
I0428 01:11:03.636173 496 net.cpp:198] relu6 needs backward computation.
|
|
I0428 01:11:03.636176 496 net.cpp:198] fc6 needs backward computation.
|
|
I0428 01:11:03.636180 496 net.cpp:198] pool5 needs backward computation.
|
|
I0428 01:11:03.636183 496 net.cpp:198] relu5 needs backward computation.
|
|
I0428 01:11:03.636186 496 net.cpp:198] conv5 needs backward computation.
|
|
I0428 01:11:03.636188 496 net.cpp:198] relu4 needs backward computation.
|
|
I0428 01:11:03.636193 496 net.cpp:198] conv4 needs backward computation.
|
|
I0428 01:11:03.636194 496 net.cpp:198] relu3 needs backward computation.
|
|
I0428 01:11:03.636199 496 net.cpp:198] conv3 needs backward computation.
|
|
I0428 01:11:03.636202 496 net.cpp:198] pool2 needs backward computation.
|
|
I0428 01:11:03.636205 496 net.cpp:198] norm2 needs backward computation.
|
|
I0428 01:11:03.636209 496 net.cpp:198] relu2 needs backward computation.
|
|
I0428 01:11:03.636210 496 net.cpp:198] conv2 needs backward computation.
|
|
I0428 01:11:03.636214 496 net.cpp:198] pool1 needs backward computation.
|
|
I0428 01:11:03.636216 496 net.cpp:198] norm1 needs backward computation.
|
|
I0428 01:11:03.636219 496 net.cpp:198] relu1 needs backward computation.
|
|
I0428 01:11:03.636222 496 net.cpp:198] conv1 needs backward computation.
|
|
I0428 01:11:03.636225 496 net.cpp:200] label_val-data_1_split does not need backward computation.
|
|
I0428 01:11:03.636229 496 net.cpp:200] val-data does not need backward computation.
|
|
I0428 01:11:03.636231 496 net.cpp:242] This network produces output accuracy
|
|
I0428 01:11:03.636234 496 net.cpp:242] This network produces output loss
|
|
I0428 01:11:03.636250 496 net.cpp:255] Network initialization done.
|
|
I0428 01:11:03.636327 496 solver.cpp:56] Solver scaffolding done.
|
|
I0428 01:11:03.636667 496 caffe.cpp:248] Starting Optimization
|
|
I0428 01:11:03.636675 496 solver.cpp:272] Solving
|
|
I0428 01:11:03.636691 496 solver.cpp:273] Learning Rate Policy: exp
|
|
I0428 01:11:03.638283 496 solver.cpp:330] Iteration 0, Testing net (#0)
|
|
I0428 01:11:03.638291 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:11:03.724237 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 01:11:08.692765 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:11:08.741227 496 solver.cpp:397] Test net output #0: accuracy = 0.00612745
|
|
I0428 01:11:08.741274 496 solver.cpp:397] Test net output #1: loss = 5.28036 (* 1 = 5.28036 loss)
|
|
I0428 01:11:09.801909 496 solver.cpp:218] Iteration 0 (-2.95066e-33 iter/s, 6.16518s/7 iters), loss = 5.28045
|
|
I0428 01:11:09.801949 496 solver.cpp:237] Train net output #0: loss = 5.2835 (* 1 = 5.2835 loss)
|
|
I0428 01:11:09.801970 496 sgd_solver.cpp:105] Iteration 0, lr = 0.01
|
|
I0428 01:11:27.623409 496 solver.cpp:218] Iteration 7 (0.392784 iter/s, 17.8215s/7 iters), loss = 5.28121
|
|
I0428 01:11:27.623454 496 solver.cpp:237] Train net output #0: loss = 5.28205 (* 1 = 5.28205 loss)
|
|
I0428 01:11:27.623462 496 sgd_solver.cpp:105] Iteration 7, lr = 0.00995389
|
|
I0428 01:11:46.279408 496 solver.cpp:218] Iteration 14 (0.375215 iter/s, 18.656s/7 iters), loss = 5.27978
|
|
I0428 01:11:46.279489 496 solver.cpp:237] Train net output #0: loss = 5.27735 (* 1 = 5.27735 loss)
|
|
I0428 01:11:46.279498 496 sgd_solver.cpp:105] Iteration 14, lr = 0.00990799
|
|
I0428 01:12:03.632752 496 solver.cpp:218] Iteration 21 (0.403382 iter/s, 17.3533s/7 iters), loss = 5.28401
|
|
I0428 01:12:03.632799 496 solver.cpp:237] Train net output #0: loss = 5.29605 (* 1 = 5.29605 loss)
|
|
I0428 01:12:03.632807 496 sgd_solver.cpp:105] Iteration 21, lr = 0.00986231
|
|
I0428 01:12:25.421008 496 solver.cpp:218] Iteration 28 (0.321274 iter/s, 21.7882s/7 iters), loss = 5.28831
|
|
I0428 01:12:25.421125 496 solver.cpp:237] Train net output #0: loss = 5.27989 (* 1 = 5.27989 loss)
|
|
I0428 01:12:25.421134 496 sgd_solver.cpp:105] Iteration 28, lr = 0.00981684
|
|
I0428 01:12:49.297096 496 solver.cpp:218] Iteration 35 (0.293181 iter/s, 23.876s/7 iters), loss = 5.28085
|
|
I0428 01:12:49.297135 496 solver.cpp:237] Train net output #0: loss = 5.27331 (* 1 = 5.27331 loss)
|
|
I0428 01:12:49.297143 496 sgd_solver.cpp:105] Iteration 35, lr = 0.00977157
|
|
I0428 01:13:07.919312 496 solver.cpp:218] Iteration 42 (0.375895 iter/s, 18.6222s/7 iters), loss = 5.26338
|
|
I0428 01:13:07.919426 496 solver.cpp:237] Train net output #0: loss = 5.28481 (* 1 = 5.28481 loss)
|
|
I0428 01:13:07.919435 496 sgd_solver.cpp:105] Iteration 42, lr = 0.00972652
|
|
I0428 01:13:26.557934 496 solver.cpp:218] Iteration 49 (0.375566 iter/s, 18.6385s/7 iters), loss = 5.2794
|
|
I0428 01:13:26.557991 496 solver.cpp:237] Train net output #0: loss = 5.28713 (* 1 = 5.28713 loss)
|
|
I0428 01:13:26.558005 496 sgd_solver.cpp:105] Iteration 49, lr = 0.00968167
|
|
I0428 01:13:44.198202 496 solver.cpp:218] Iteration 56 (0.39682 iter/s, 17.6402s/7 iters), loss = 5.26834
|
|
I0428 01:13:44.198316 496 solver.cpp:237] Train net output #0: loss = 5.2562 (* 1 = 5.2562 loss)
|
|
I0428 01:13:44.198325 496 sgd_solver.cpp:105] Iteration 56, lr = 0.00963703
|
|
I0428 01:14:02.158489 496 solver.cpp:218] Iteration 63 (0.389751 iter/s, 17.9602s/7 iters), loss = 5.27342
|
|
I0428 01:14:02.158551 496 solver.cpp:237] Train net output #0: loss = 5.2677 (* 1 = 5.2677 loss)
|
|
I0428 01:14:02.158562 496 sgd_solver.cpp:105] Iteration 63, lr = 0.00959259
|
|
I0428 01:14:27.774633 496 solver.cpp:218] Iteration 70 (0.273265 iter/s, 25.6161s/7 iters), loss = 5.2787
|
|
I0428 01:14:27.774777 496 solver.cpp:237] Train net output #0: loss = 5.26131 (* 1 = 5.26131 loss)
|
|
I0428 01:14:27.774788 496 sgd_solver.cpp:105] Iteration 70, lr = 0.00954836
|
|
I0428 01:14:46.093144 496 solver.cpp:218] Iteration 77 (0.38213 iter/s, 18.3184s/7 iters), loss = 5.27838
|
|
I0428 01:14:46.093189 496 solver.cpp:237] Train net output #0: loss = 5.29972 (* 1 = 5.29972 loss)
|
|
I0428 01:14:46.093196 496 sgd_solver.cpp:105] Iteration 77, lr = 0.00950434
|
|
I0428 01:15:04.697149 496 solver.cpp:218] Iteration 84 (0.376264 iter/s, 18.604s/7 iters), loss = 5.26818
|
|
I0428 01:15:04.697309 496 solver.cpp:237] Train net output #0: loss = 5.26755 (* 1 = 5.26755 loss)
|
|
I0428 01:15:04.697320 496 sgd_solver.cpp:105] Iteration 84, lr = 0.00946051
|
|
I0428 01:15:22.450385 496 solver.cpp:218] Iteration 91 (0.394297 iter/s, 17.7531s/7 iters), loss = 5.2681
|
|
I0428 01:15:22.450428 496 solver.cpp:237] Train net output #0: loss = 5.24737 (* 1 = 5.24737 loss)
|
|
I0428 01:15:22.450435 496 sgd_solver.cpp:105] Iteration 91, lr = 0.00941689
|
|
I0428 01:15:39.815907 496 solver.cpp:218] Iteration 98 (0.403098 iter/s, 17.3655s/7 iters), loss = 5.26053
|
|
I0428 01:15:39.816032 496 solver.cpp:237] Train net output #0: loss = 5.26727 (* 1 = 5.26727 loss)
|
|
I0428 01:15:39.816045 496 sgd_solver.cpp:105] Iteration 98, lr = 0.00937347
|
|
I0428 01:15:45.085377 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:15:47.144604 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
|
|
I0428 01:15:50.705797 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
|
|
I0428 01:15:57.291401 496 solver.cpp:330] Iteration 102, Testing net (#0)
|
|
I0428 01:15:57.291419 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:16:01.687880 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:16:01.767536 496 solver.cpp:397] Test net output #0: accuracy = 0.0067402
|
|
I0428 01:16:01.767588 496 solver.cpp:397] Test net output #1: loss = 5.26411 (* 1 = 5.26411 loss)
|
|
I0428 01:16:10.928591 496 solver.cpp:218] Iteration 105 (0.224989 iter/s, 31.1126s/7 iters), loss = 5.2487
|
|
I0428 01:16:10.928661 496 solver.cpp:237] Train net output #0: loss = 5.23697 (* 1 = 5.23697 loss)
|
|
I0428 01:16:10.928670 496 sgd_solver.cpp:105] Iteration 105, lr = 0.00933025
|
|
I0428 01:16:28.613188 496 solver.cpp:218] Iteration 112 (0.395826 iter/s, 17.6845s/7 iters), loss = 5.24328
|
|
I0428 01:16:28.613229 496 solver.cpp:237] Train net output #0: loss = 5.25891 (* 1 = 5.25891 loss)
|
|
I0428 01:16:28.613236 496 sgd_solver.cpp:105] Iteration 112, lr = 0.00928723
|
|
I0428 01:16:46.454741 496 solver.cpp:218] Iteration 119 (0.392343 iter/s, 17.8415s/7 iters), loss = 5.21365
|
|
I0428 01:16:46.454809 496 solver.cpp:237] Train net output #0: loss = 5.20859 (* 1 = 5.20859 loss)
|
|
I0428 01:16:46.454818 496 sgd_solver.cpp:105] Iteration 119, lr = 0.00924441
|
|
I0428 01:17:03.622932 496 solver.cpp:218] Iteration 126 (0.407732 iter/s, 17.1681s/7 iters), loss = 5.18521
|
|
I0428 01:17:03.622972 496 solver.cpp:237] Train net output #0: loss = 5.16909 (* 1 = 5.16909 loss)
|
|
I0428 01:17:03.622979 496 sgd_solver.cpp:105] Iteration 126, lr = 0.00920178
|
|
I0428 01:17:20.625844 496 solver.cpp:218] Iteration 133 (0.411695 iter/s, 17.0029s/7 iters), loss = 5.16541
|
|
I0428 01:17:20.625957 496 solver.cpp:237] Train net output #0: loss = 5.13851 (* 1 = 5.13851 loss)
|
|
I0428 01:17:20.625967 496 sgd_solver.cpp:105] Iteration 133, lr = 0.00915936
|
|
I0428 01:17:37.486328 496 solver.cpp:218] Iteration 140 (0.415174 iter/s, 16.8604s/7 iters), loss = 5.14536
|
|
I0428 01:17:37.486372 496 solver.cpp:237] Train net output #0: loss = 5.1125 (* 1 = 5.1125 loss)
|
|
I0428 01:17:37.486380 496 sgd_solver.cpp:105] Iteration 140, lr = 0.00911712
|
|
I0428 01:17:54.409246 496 solver.cpp:218] Iteration 147 (0.413641 iter/s, 16.9229s/7 iters), loss = 5.13176
|
|
I0428 01:17:54.409358 496 solver.cpp:237] Train net output #0: loss = 5.15218 (* 1 = 5.15218 loss)
|
|
I0428 01:17:54.409368 496 sgd_solver.cpp:105] Iteration 147, lr = 0.00907508
|
|
I0428 01:18:04.023937 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 01:18:11.243376 496 solver.cpp:218] Iteration 154 (0.415824 iter/s, 16.834s/7 iters), loss = 5.13183
|
|
I0428 01:18:11.243419 496 solver.cpp:237] Train net output #0: loss = 5.0611 (* 1 = 5.0611 loss)
|
|
I0428 01:18:11.243428 496 sgd_solver.cpp:105] Iteration 154, lr = 0.00903324
|
|
I0428 01:18:28.257375 496 solver.cpp:218] Iteration 161 (0.411426 iter/s, 17.014s/7 iters), loss = 5.09681
|
|
I0428 01:18:28.257499 496 solver.cpp:237] Train net output #0: loss = 5.14413 (* 1 = 5.14413 loss)
|
|
I0428 01:18:28.257510 496 sgd_solver.cpp:105] Iteration 161, lr = 0.00899159
|
|
I0428 01:18:45.167769 496 solver.cpp:218] Iteration 168 (0.413949 iter/s, 16.9103s/7 iters), loss = 5.15189
|
|
I0428 01:18:45.167814 496 solver.cpp:237] Train net output #0: loss = 5.22895 (* 1 = 5.22895 loss)
|
|
I0428 01:18:45.167821 496 sgd_solver.cpp:105] Iteration 168, lr = 0.00895013
|
|
I0428 01:19:02.317842 496 solver.cpp:218] Iteration 175 (0.408162 iter/s, 17.1501s/7 iters), loss = 5.08524
|
|
I0428 01:19:02.317914 496 solver.cpp:237] Train net output #0: loss = 5.05043 (* 1 = 5.05043 loss)
|
|
I0428 01:19:02.317924 496 sgd_solver.cpp:105] Iteration 175, lr = 0.00890886
|
|
I0428 01:19:19.174727 496 solver.cpp:218] Iteration 182 (0.415262 iter/s, 16.8568s/7 iters), loss = 5.01561
|
|
I0428 01:19:19.174769 496 solver.cpp:237] Train net output #0: loss = 4.99762 (* 1 = 4.99762 loss)
|
|
I0428 01:19:19.174777 496 sgd_solver.cpp:105] Iteration 182, lr = 0.00886778
|
|
I0428 01:19:36.097733 496 solver.cpp:218] Iteration 189 (0.413638 iter/s, 16.923s/7 iters), loss = 5.08473
|
|
I0428 01:19:36.097811 496 solver.cpp:237] Train net output #0: loss = 5.11571 (* 1 = 5.11571 loss)
|
|
I0428 01:19:36.097821 496 sgd_solver.cpp:105] Iteration 189, lr = 0.0088269
|
|
I0428 01:19:53.415668 496 solver.cpp:218] Iteration 196 (0.404206 iter/s, 17.3179s/7 iters), loss = 5.06203
|
|
I0428 01:19:53.415711 496 solver.cpp:237] Train net output #0: loss = 5.11049 (* 1 = 5.11049 loss)
|
|
I0428 01:19:53.415720 496 sgd_solver.cpp:105] Iteration 196, lr = 0.0087862
|
|
I0428 01:20:06.210268 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:20:10.357723 496 solver.cpp:218] Iteration 203 (0.413173 iter/s, 16.942s/7 iters), loss = 5.02825
|
|
I0428 01:20:10.357764 496 solver.cpp:237] Train net output #0: loss = 5.03724 (* 1 = 5.03724 loss)
|
|
I0428 01:20:10.357770 496 sgd_solver.cpp:105] Iteration 203, lr = 0.00874568
|
|
I0428 01:20:10.357951 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
|
|
I0428 01:20:13.361074 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
|
|
I0428 01:20:16.157435 496 solver.cpp:330] Iteration 204, Testing net (#0)
|
|
I0428 01:20:16.157462 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:20:20.715462 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:20:20.842686 496 solver.cpp:397] Test net output #0: accuracy = 0.026348
|
|
I0428 01:20:20.842736 496 solver.cpp:397] Test net output #1: loss = 5.04658 (* 1 = 5.04658 loss)
|
|
I0428 01:20:36.683859 496 solver.cpp:218] Iteration 210 (0.265895 iter/s, 26.3262s/7 iters), loss = 5.03198
|
|
I0428 01:20:36.683977 496 solver.cpp:237] Train net output #0: loss = 4.96781 (* 1 = 4.96781 loss)
|
|
I0428 01:20:36.683987 496 sgd_solver.cpp:105] Iteration 210, lr = 0.00870536
|
|
I0428 01:20:53.573169 496 solver.cpp:218] Iteration 217 (0.414466 iter/s, 16.8892s/7 iters), loss = 5.02235
|
|
I0428 01:20:53.573216 496 solver.cpp:237] Train net output #0: loss = 5.05059 (* 1 = 5.05059 loss)
|
|
I0428 01:20:53.573225 496 sgd_solver.cpp:105] Iteration 217, lr = 0.00866522
|
|
I0428 01:21:10.488755 496 solver.cpp:218] Iteration 224 (0.41382 iter/s, 16.9156s/7 iters), loss = 5.0104
|
|
I0428 01:21:10.488879 496 solver.cpp:237] Train net output #0: loss = 5.06317 (* 1 = 5.06317 loss)
|
|
I0428 01:21:10.488888 496 sgd_solver.cpp:105] Iteration 224, lr = 0.00862527
|
|
I0428 01:21:27.617530 496 solver.cpp:218] Iteration 231 (0.408671 iter/s, 17.1287s/7 iters), loss = 4.98388
|
|
I0428 01:21:27.617574 496 solver.cpp:237] Train net output #0: loss = 4.98093 (* 1 = 4.98093 loss)
|
|
I0428 01:21:27.617583 496 sgd_solver.cpp:105] Iteration 231, lr = 0.0085855
|
|
I0428 01:21:44.534250 496 solver.cpp:218] Iteration 238 (0.413792 iter/s, 16.9167s/7 iters), loss = 5.0516
|
|
I0428 01:21:44.534406 496 solver.cpp:237] Train net output #0: loss = 5.00622 (* 1 = 5.00622 loss)
|
|
I0428 01:21:44.534416 496 sgd_solver.cpp:105] Iteration 238, lr = 0.00854591
|
|
I0428 01:22:01.437669 496 solver.cpp:218] Iteration 245 (0.41412 iter/s, 16.9033s/7 iters), loss = 5.00044
|
|
I0428 01:22:01.437716 496 solver.cpp:237] Train net output #0: loss = 5.03527 (* 1 = 5.03527 loss)
|
|
I0428 01:22:01.437724 496 sgd_solver.cpp:105] Iteration 245, lr = 0.0085065
|
|
I0428 01:22:18.362073 496 solver.cpp:218] Iteration 252 (0.413604 iter/s, 16.9244s/7 iters), loss = 4.99326
|
|
I0428 01:22:18.362164 496 solver.cpp:237] Train net output #0: loss = 4.97906 (* 1 = 4.97906 loss)
|
|
I0428 01:22:18.362174 496 sgd_solver.cpp:105] Iteration 252, lr = 0.00846728
|
|
I0428 01:22:35.326684 496 solver.cpp:218] Iteration 259 (0.412625 iter/s, 16.9646s/7 iters), loss = 4.96034
|
|
I0428 01:22:35.326735 496 solver.cpp:237] Train net output #0: loss = 5.00658 (* 1 = 5.00658 loss)
|
|
I0428 01:22:35.326742 496 sgd_solver.cpp:105] Iteration 259, lr = 0.00842824
|
|
I0428 01:22:52.234360 496 solver.cpp:218] Iteration 266 (0.414014 iter/s, 16.9077s/7 iters), loss = 4.98093
|
|
I0428 01:22:52.234480 496 solver.cpp:237] Train net output #0: loss = 4.98008 (* 1 = 4.98008 loss)
|
|
I0428 01:22:52.234489 496 sgd_solver.cpp:105] Iteration 266, lr = 0.00838938
|
|
I0428 01:23:09.130735 496 solver.cpp:218] Iteration 273 (0.414292 iter/s, 16.8963s/7 iters), loss = 4.94812
|
|
I0428 01:23:09.130784 496 solver.cpp:237] Train net output #0: loss = 4.97023 (* 1 = 4.97023 loss)
|
|
I0428 01:23:09.130791 496 sgd_solver.cpp:105] Iteration 273, lr = 0.0083507
|
|
I0428 01:23:26.063889 496 solver.cpp:218] Iteration 280 (0.413391 iter/s, 16.9331s/7 iters), loss = 4.93641
|
|
I0428 01:23:26.064018 496 solver.cpp:237] Train net output #0: loss = 4.83205 (* 1 = 4.83205 loss)
|
|
I0428 01:23:26.064028 496 sgd_solver.cpp:105] Iteration 280, lr = 0.00831219
|
|
I0428 01:23:42.988143 496 solver.cpp:218] Iteration 287 (0.41361 iter/s, 16.9242s/7 iters), loss = 4.92222
|
|
I0428 01:23:42.988183 496 solver.cpp:237] Train net output #0: loss = 4.86408 (* 1 = 4.86408 loss)
|
|
I0428 01:23:42.988191 496 sgd_solver.cpp:105] Iteration 287, lr = 0.00827387
|
|
I0428 01:23:59.932536 496 solver.cpp:218] Iteration 294 (0.413116 iter/s, 16.9444s/7 iters), loss = 4.86591
|
|
I0428 01:23:59.932655 496 solver.cpp:237] Train net output #0: loss = 4.87711 (* 1 = 4.87711 loss)
|
|
I0428 01:23:59.932665 496 sgd_solver.cpp:105] Iteration 294, lr = 0.00823572
|
|
I0428 01:24:16.840698 496 solver.cpp:218] Iteration 301 (0.414003 iter/s, 16.9081s/7 iters), loss = 4.87753
|
|
I0428 01:24:16.840739 496 solver.cpp:237] Train net output #0: loss = 4.86081 (* 1 = 4.86081 loss)
|
|
I0428 01:24:16.840747 496 sgd_solver.cpp:105] Iteration 301, lr = 0.00819774
|
|
I0428 01:24:20.314559 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:24:26.436420 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
|
|
I0428 01:24:29.531064 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
|
|
I0428 01:24:31.904630 496 solver.cpp:330] Iteration 306, Testing net (#0)
|
|
I0428 01:24:31.904723 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:24:34.998643 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 01:24:36.522146 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:24:36.697383 496 solver.cpp:397] Test net output #0: accuracy = 0.0373775
|
|
I0428 01:24:36.697423 496 solver.cpp:397] Test net output #1: loss = 4.94907 (* 1 = 4.94907 loss)
|
|
I0428 01:24:42.965698 496 solver.cpp:218] Iteration 308 (0.267942 iter/s, 26.125s/7 iters), loss = 4.92653
|
|
I0428 01:24:42.965739 496 solver.cpp:237] Train net output #0: loss = 4.92464 (* 1 = 4.92464 loss)
|
|
I0428 01:24:42.965746 496 sgd_solver.cpp:105] Iteration 308, lr = 0.00815994
|
|
I0428 01:24:59.847754 496 solver.cpp:218] Iteration 315 (0.414642 iter/s, 16.882s/7 iters), loss = 4.86733
|
|
I0428 01:24:59.847795 496 solver.cpp:237] Train net output #0: loss = 4.74624 (* 1 = 4.74624 loss)
|
|
I0428 01:24:59.847803 496 sgd_solver.cpp:105] Iteration 315, lr = 0.00812232
|
|
I0428 01:25:16.783012 496 solver.cpp:218] Iteration 322 (0.413339 iter/s, 16.9352s/7 iters), loss = 4.8885
|
|
I0428 01:25:16.783138 496 solver.cpp:237] Train net output #0: loss = 4.85107 (* 1 = 4.85107 loss)
|
|
I0428 01:25:16.783149 496 sgd_solver.cpp:105] Iteration 322, lr = 0.00808487
|
|
I0428 01:25:33.675649 496 solver.cpp:218] Iteration 329 (0.414384 iter/s, 16.8925s/7 iters), loss = 4.83365
|
|
I0428 01:25:33.675695 496 solver.cpp:237] Train net output #0: loss = 4.88838 (* 1 = 4.88838 loss)
|
|
I0428 01:25:33.675704 496 sgd_solver.cpp:105] Iteration 329, lr = 0.00804759
|
|
I0428 01:25:50.605185 496 solver.cpp:218] Iteration 336 (0.413479 iter/s, 16.9295s/7 iters), loss = 4.86183
|
|
I0428 01:25:50.605301 496 solver.cpp:237] Train net output #0: loss = 5.01873 (* 1 = 5.01873 loss)
|
|
I0428 01:25:50.605310 496 sgd_solver.cpp:105] Iteration 336, lr = 0.00801048
|
|
I0428 01:26:07.524737 496 solver.cpp:218] Iteration 343 (0.413725 iter/s, 16.9195s/7 iters), loss = 4.8101
|
|
I0428 01:26:07.524778 496 solver.cpp:237] Train net output #0: loss = 4.87633 (* 1 = 4.87633 loss)
|
|
I0428 01:26:07.524786 496 sgd_solver.cpp:105] Iteration 343, lr = 0.00797355
|
|
I0428 01:26:24.427215 496 solver.cpp:218] Iteration 350 (0.414141 iter/s, 16.9025s/7 iters), loss = 4.84942
|
|
I0428 01:26:24.427337 496 solver.cpp:237] Train net output #0: loss = 4.78325 (* 1 = 4.78325 loss)
|
|
I0428 01:26:24.427347 496 sgd_solver.cpp:105] Iteration 350, lr = 0.00793678
|
|
I0428 01:26:41.339651 496 solver.cpp:218] Iteration 357 (0.413899 iter/s, 16.9123s/7 iters), loss = 4.89169
|
|
I0428 01:26:41.339689 496 solver.cpp:237] Train net output #0: loss = 4.85728 (* 1 = 4.85728 loss)
|
|
I0428 01:26:41.339697 496 sgd_solver.cpp:105] Iteration 357, lr = 0.00790019
|
|
I0428 01:26:58.256618 496 solver.cpp:218] Iteration 364 (0.413786 iter/s, 16.917s/7 iters), loss = 4.7934
|
|
I0428 01:26:58.256745 496 solver.cpp:237] Train net output #0: loss = 4.87204 (* 1 = 4.87204 loss)
|
|
I0428 01:26:58.256755 496 sgd_solver.cpp:105] Iteration 364, lr = 0.00786376
|
|
I0428 01:27:15.204950 496 solver.cpp:218] Iteration 371 (0.413022 iter/s, 16.9482s/7 iters), loss = 4.85624
|
|
I0428 01:27:15.204993 496 solver.cpp:237] Train net output #0: loss = 4.86532 (* 1 = 4.86532 loss)
|
|
I0428 01:27:15.205000 496 sgd_solver.cpp:105] Iteration 371, lr = 0.0078275
|
|
I0428 01:27:32.085604 496 solver.cpp:218] Iteration 378 (0.414676 iter/s, 16.8806s/7 iters), loss = 4.82749
|
|
I0428 01:27:32.085731 496 solver.cpp:237] Train net output #0: loss = 4.79682 (* 1 = 4.79682 loss)
|
|
I0428 01:27:32.085741 496 sgd_solver.cpp:105] Iteration 378, lr = 0.00779141
|
|
I0428 01:27:49.002899 496 solver.cpp:218] Iteration 385 (0.41378 iter/s, 16.9172s/7 iters), loss = 4.85213
|
|
I0428 01:27:49.002943 496 solver.cpp:237] Train net output #0: loss = 4.90163 (* 1 = 4.90163 loss)
|
|
I0428 01:27:49.002952 496 sgd_solver.cpp:105] Iteration 385, lr = 0.00775548
|
|
I0428 01:28:05.959952 496 solver.cpp:218] Iteration 392 (0.412808 iter/s, 16.957s/7 iters), loss = 4.72697
|
|
I0428 01:28:05.960084 496 solver.cpp:237] Train net output #0: loss = 4.85834 (* 1 = 4.85834 loss)
|
|
I0428 01:28:05.960093 496 sgd_solver.cpp:105] Iteration 392, lr = 0.00771973
|
|
I0428 01:28:22.852102 496 solver.cpp:218] Iteration 399 (0.414396 iter/s, 16.8921s/7 iters), loss = 4.66117
|
|
I0428 01:28:22.852146 496 solver.cpp:237] Train net output #0: loss = 4.67171 (* 1 = 4.67171 loss)
|
|
I0428 01:28:22.852154 496 sgd_solver.cpp:105] Iteration 399, lr = 0.00768413
|
|
I0428 01:28:33.967104 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:28:39.803539 496 solver.cpp:218] Iteration 406 (0.412945 iter/s, 16.9514s/7 iters), loss = 4.67941
|
|
I0428 01:28:39.803673 496 solver.cpp:237] Train net output #0: loss = 4.63874 (* 1 = 4.63874 loss)
|
|
I0428 01:28:39.803683 496 sgd_solver.cpp:105] Iteration 406, lr = 0.0076487
|
|
I0428 01:28:42.194558 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
|
|
I0428 01:28:47.351819 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
|
|
I0428 01:28:50.756876 496 solver.cpp:330] Iteration 408, Testing net (#0)
|
|
I0428 01:28:50.756896 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:28:55.321024 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:28:55.553933 496 solver.cpp:397] Test net output #0: accuracy = 0.0594363
|
|
I0428 01:28:55.553979 496 solver.cpp:397] Test net output #1: loss = 4.77451 (* 1 = 4.77451 loss)
|
|
I0428 01:29:08.941728 496 solver.cpp:218] Iteration 413 (0.240235 iter/s, 29.1381s/7 iters), loss = 4.67976
|
|
I0428 01:29:08.941771 496 solver.cpp:237] Train net output #0: loss = 4.63123 (* 1 = 4.63123 loss)
|
|
I0428 01:29:08.941779 496 sgd_solver.cpp:105] Iteration 413, lr = 0.00761343
|
|
I0428 01:29:25.828980 496 solver.cpp:218] Iteration 420 (0.414514 iter/s, 16.8872s/7 iters), loss = 4.73319
|
|
I0428 01:29:25.829064 496 solver.cpp:237] Train net output #0: loss = 4.75189 (* 1 = 4.75189 loss)
|
|
I0428 01:29:25.829073 496 sgd_solver.cpp:105] Iteration 420, lr = 0.00757833
|
|
I0428 01:29:42.751729 496 solver.cpp:218] Iteration 427 (0.413646 iter/s, 16.9227s/7 iters), loss = 4.67157
|
|
I0428 01:29:42.751772 496 solver.cpp:237] Train net output #0: loss = 4.73438 (* 1 = 4.73438 loss)
|
|
I0428 01:29:42.751780 496 sgd_solver.cpp:105] Iteration 427, lr = 0.00754339
|
|
I0428 01:29:59.650010 496 solver.cpp:218] Iteration 434 (0.414244 iter/s, 16.8983s/7 iters), loss = 4.64195
|
|
I0428 01:29:59.650142 496 solver.cpp:237] Train net output #0: loss = 4.56506 (* 1 = 4.56506 loss)
|
|
I0428 01:29:59.650152 496 sgd_solver.cpp:105] Iteration 434, lr = 0.0075086
|
|
I0428 01:30:16.524369 496 solver.cpp:218] Iteration 441 (0.414833 iter/s, 16.8743s/7 iters), loss = 4.64466
|
|
I0428 01:30:16.524417 496 solver.cpp:237] Train net output #0: loss = 4.63573 (* 1 = 4.63573 loss)
|
|
I0428 01:30:16.524425 496 sgd_solver.cpp:105] Iteration 441, lr = 0.00747398
|
|
I0428 01:30:33.394217 496 solver.cpp:218] Iteration 448 (0.414942 iter/s, 16.8698s/7 iters), loss = 4.6456
|
|
I0428 01:30:33.394302 496 solver.cpp:237] Train net output #0: loss = 4.54227 (* 1 = 4.54227 loss)
|
|
I0428 01:30:33.394311 496 sgd_solver.cpp:105] Iteration 448, lr = 0.00743952
|
|
I0428 01:30:50.253746 496 solver.cpp:218] Iteration 455 (0.415197 iter/s, 16.8595s/7 iters), loss = 4.65176
|
|
I0428 01:30:50.253789 496 solver.cpp:237] Train net output #0: loss = 4.68025 (* 1 = 4.68025 loss)
|
|
I0428 01:30:50.253798 496 sgd_solver.cpp:105] Iteration 455, lr = 0.00740522
|
|
I0428 01:31:06.736773 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 01:31:07.182988 496 solver.cpp:218] Iteration 462 (0.413486 iter/s, 16.9292s/7 iters), loss = 4.60105
|
|
I0428 01:31:07.183032 496 solver.cpp:237] Train net output #0: loss = 4.61578 (* 1 = 4.61578 loss)
|
|
I0428 01:31:07.183040 496 sgd_solver.cpp:105] Iteration 462, lr = 0.00737107
|
|
I0428 01:31:24.072857 496 solver.cpp:218] Iteration 469 (0.41445 iter/s, 16.8899s/7 iters), loss = 4.61676
|
|
I0428 01:31:24.072902 496 solver.cpp:237] Train net output #0: loss = 4.56909 (* 1 = 4.56909 loss)
|
|
I0428 01:31:24.072911 496 sgd_solver.cpp:105] Iteration 469, lr = 0.00733709
|
|
I0428 01:31:41.007040 496 solver.cpp:218] Iteration 476 (0.413365 iter/s, 16.9342s/7 iters), loss = 4.55872
|
|
I0428 01:31:41.007153 496 solver.cpp:237] Train net output #0: loss = 4.51832 (* 1 = 4.51832 loss)
|
|
I0428 01:31:41.007162 496 sgd_solver.cpp:105] Iteration 476, lr = 0.00730326
|
|
I0428 01:31:57.916016 496 solver.cpp:218] Iteration 483 (0.413983 iter/s, 16.9089s/7 iters), loss = 4.51703
|
|
I0428 01:31:57.916059 496 solver.cpp:237] Train net output #0: loss = 4.42188 (* 1 = 4.42188 loss)
|
|
I0428 01:31:57.916066 496 sgd_solver.cpp:105] Iteration 483, lr = 0.00726958
|
|
I0428 01:32:14.913165 496 solver.cpp:218] Iteration 490 (0.411834 iter/s, 16.9971s/7 iters), loss = 4.46097
|
|
I0428 01:32:14.913347 496 solver.cpp:237] Train net output #0: loss = 4.53148 (* 1 = 4.53148 loss)
|
|
I0428 01:32:14.913357 496 sgd_solver.cpp:105] Iteration 490, lr = 0.00723606
|
|
I0428 01:32:31.804054 496 solver.cpp:218] Iteration 497 (0.414428 iter/s, 16.8907s/7 iters), loss = 4.58744
|
|
I0428 01:32:31.804096 496 solver.cpp:237] Train net output #0: loss = 4.40313 (* 1 = 4.40313 loss)
|
|
I0428 01:32:31.804105 496 sgd_solver.cpp:105] Iteration 497, lr = 0.0072027
|
|
I0428 01:32:48.725963 496 solver.cpp:218] Iteration 504 (0.413665 iter/s, 16.9219s/7 iters), loss = 4.54678
|
|
I0428 01:32:48.726076 496 solver.cpp:237] Train net output #0: loss = 4.48366 (* 1 = 4.48366 loss)
|
|
I0428 01:32:48.726085 496 sgd_solver.cpp:105] Iteration 504, lr = 0.00716949
|
|
I0428 01:32:50.540108 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:33:00.756827 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
|
|
I0428 01:33:04.784759 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
|
|
I0428 01:33:08.060051 496 solver.cpp:330] Iteration 510, Testing net (#0)
|
|
I0428 01:33:08.060077 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:33:12.382076 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:33:12.626528 496 solver.cpp:397] Test net output #0: accuracy = 0.0582108
|
|
I0428 01:33:12.626576 496 solver.cpp:397] Test net output #1: loss = 4.64048 (* 1 = 4.64048 loss)
|
|
I0428 01:33:16.441499 496 solver.cpp:218] Iteration 511 (0.252566 iter/s, 27.7155s/7 iters), loss = 4.52007
|
|
I0428 01:33:16.441541 496 solver.cpp:237] Train net output #0: loss = 4.49726 (* 1 = 4.49726 loss)
|
|
I0428 01:33:16.441550 496 sgd_solver.cpp:105] Iteration 511, lr = 0.00713643
|
|
I0428 01:33:33.401201 496 solver.cpp:218] Iteration 518 (0.412743 iter/s, 16.9597s/7 iters), loss = 4.45308
|
|
I0428 01:33:33.401268 496 solver.cpp:237] Train net output #0: loss = 4.26105 (* 1 = 4.26105 loss)
|
|
I0428 01:33:33.401275 496 sgd_solver.cpp:105] Iteration 518, lr = 0.00710352
|
|
I0428 01:33:50.283653 496 solver.cpp:218] Iteration 525 (0.414633 iter/s, 16.8824s/7 iters), loss = 4.49451
|
|
I0428 01:33:50.283694 496 solver.cpp:237] Train net output #0: loss = 4.54812 (* 1 = 4.54812 loss)
|
|
I0428 01:33:50.283702 496 sgd_solver.cpp:105] Iteration 525, lr = 0.00707077
|
|
I0428 01:34:07.211426 496 solver.cpp:218] Iteration 532 (0.413522 iter/s, 16.9278s/7 iters), loss = 4.38062
|
|
I0428 01:34:07.211563 496 solver.cpp:237] Train net output #0: loss = 4.44445 (* 1 = 4.44445 loss)
|
|
I0428 01:34:07.211578 496 sgd_solver.cpp:105] Iteration 532, lr = 0.00703817
|
|
I0428 01:34:24.480890 496 solver.cpp:218] Iteration 539 (0.405342 iter/s, 17.2694s/7 iters), loss = 4.41995
|
|
I0428 01:34:24.480954 496 solver.cpp:237] Train net output #0: loss = 4.29491 (* 1 = 4.29491 loss)
|
|
I0428 01:34:24.480967 496 sgd_solver.cpp:105] Iteration 539, lr = 0.00700572
|
|
I0428 01:34:41.928614 496 solver.cpp:218] Iteration 546 (0.401199 iter/s, 17.4477s/7 iters), loss = 4.3641
|
|
I0428 01:34:41.928716 496 solver.cpp:237] Train net output #0: loss = 4.20973 (* 1 = 4.20973 loss)
|
|
I0428 01:34:41.928726 496 sgd_solver.cpp:105] Iteration 546, lr = 0.00697341
|
|
I0428 01:34:59.852628 496 solver.cpp:218] Iteration 553 (0.390539 iter/s, 17.924s/7 iters), loss = 4.38816
|
|
I0428 01:34:59.852674 496 solver.cpp:237] Train net output #0: loss = 4.38755 (* 1 = 4.38755 loss)
|
|
I0428 01:34:59.852681 496 sgd_solver.cpp:105] Iteration 553, lr = 0.00694126
|
|
I0428 01:35:17.352443 496 solver.cpp:218] Iteration 560 (0.400004 iter/s, 17.4998s/7 iters), loss = 4.27115
|
|
I0428 01:35:17.352556 496 solver.cpp:237] Train net output #0: loss = 4.44986 (* 1 = 4.44986 loss)
|
|
I0428 01:35:17.352566 496 sgd_solver.cpp:105] Iteration 560, lr = 0.00690925
|
|
I0428 01:35:34.272430 496 solver.cpp:218] Iteration 567 (0.413714 iter/s, 16.9199s/7 iters), loss = 4.22339
|
|
I0428 01:35:34.272470 496 solver.cpp:237] Train net output #0: loss = 4.23757 (* 1 = 4.23757 loss)
|
|
I0428 01:35:34.272478 496 sgd_solver.cpp:105] Iteration 567, lr = 0.0068774
|
|
I0428 01:35:51.206923 496 solver.cpp:218] Iteration 574 (0.413358 iter/s, 16.9345s/7 iters), loss = 4.31264
|
|
I0428 01:35:51.207034 496 solver.cpp:237] Train net output #0: loss = 4.30314 (* 1 = 4.30314 loss)
|
|
I0428 01:35:51.207043 496 sgd_solver.cpp:105] Iteration 574, lr = 0.00684569
|
|
I0428 01:36:08.126533 496 solver.cpp:218] Iteration 581 (0.413723 iter/s, 16.9195s/7 iters), loss = 4.23495
|
|
I0428 01:36:08.126574 496 solver.cpp:237] Train net output #0: loss = 4.31326 (* 1 = 4.31326 loss)
|
|
I0428 01:36:08.126582 496 sgd_solver.cpp:105] Iteration 581, lr = 0.00681412
|
|
I0428 01:36:25.016271 496 solver.cpp:218] Iteration 588 (0.414453 iter/s, 16.8897s/7 iters), loss = 4.09847
|
|
I0428 01:36:25.016396 496 solver.cpp:237] Train net output #0: loss = 4.31714 (* 1 = 4.31714 loss)
|
|
I0428 01:36:25.016404 496 sgd_solver.cpp:105] Iteration 588, lr = 0.0067827
|
|
I0428 01:36:42.254822 496 solver.cpp:218] Iteration 595 (0.406069 iter/s, 17.2385s/7 iters), loss = 4.10692
|
|
I0428 01:36:42.254869 496 solver.cpp:237] Train net output #0: loss = 4.09652 (* 1 = 4.09652 loss)
|
|
I0428 01:36:42.254878 496 sgd_solver.cpp:105] Iteration 595, lr = 0.00675143
|
|
I0428 01:36:59.309861 496 solver.cpp:218] Iteration 602 (0.410436 iter/s, 17.055s/7 iters), loss = 4.05141
|
|
I0428 01:36:59.309988 496 solver.cpp:237] Train net output #0: loss = 3.95092 (* 1 = 3.95092 loss)
|
|
I0428 01:36:59.309998 496 sgd_solver.cpp:105] Iteration 602, lr = 0.0067203
|
|
I0428 01:37:08.947161 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:37:16.432962 496 solver.cpp:218] Iteration 609 (0.408806 iter/s, 17.123s/7 iters), loss = 4.07997
|
|
I0428 01:37:16.433007 496 solver.cpp:237] Train net output #0: loss = 4.06946 (* 1 = 4.06946 loss)
|
|
I0428 01:37:16.433015 496 sgd_solver.cpp:105] Iteration 609, lr = 0.00668931
|
|
I0428 01:37:21.356328 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
|
|
I0428 01:37:24.427904 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
|
|
I0428 01:37:29.008874 496 solver.cpp:330] Iteration 612, Testing net (#0)
|
|
I0428 01:37:29.008895 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:37:33.573766 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:37:33.887010 496 solver.cpp:397] Test net output #0: accuracy = 0.0900735
|
|
I0428 01:37:33.887048 496 solver.cpp:397] Test net output #1: loss = 4.40447 (* 1 = 4.40447 loss)
|
|
I0428 01:37:38.843907 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 01:37:44.940373 496 solver.cpp:218] Iteration 616 (0.24555 iter/s, 28.5074s/7 iters), loss = 4.13311
|
|
I0428 01:37:44.940424 496 solver.cpp:237] Train net output #0: loss = 4.01763 (* 1 = 4.01763 loss)
|
|
I0428 01:37:44.940433 496 sgd_solver.cpp:105] Iteration 616, lr = 0.00665847
|
|
I0428 01:38:01.893872 496 solver.cpp:218] Iteration 623 (0.412895 iter/s, 16.9535s/7 iters), loss = 4.0961
|
|
I0428 01:38:01.893913 496 solver.cpp:237] Train net output #0: loss = 4.12149 (* 1 = 4.12149 loss)
|
|
I0428 01:38:01.893921 496 sgd_solver.cpp:105] Iteration 623, lr = 0.00662777
|
|
I0428 01:38:18.793865 496 solver.cpp:218] Iteration 630 (0.414202 iter/s, 16.9s/7 iters), loss = 4.02607
|
|
I0428 01:38:18.794000 496 solver.cpp:237] Train net output #0: loss = 4.09849 (* 1 = 4.09849 loss)
|
|
I0428 01:38:18.794010 496 sgd_solver.cpp:105] Iteration 630, lr = 0.00659721
|
|
I0428 01:38:35.923338 496 solver.cpp:218] Iteration 637 (0.408655 iter/s, 17.1294s/7 iters), loss = 4.02421
|
|
I0428 01:38:35.923382 496 solver.cpp:237] Train net output #0: loss = 4.13763 (* 1 = 4.13763 loss)
|
|
I0428 01:38:35.923391 496 sgd_solver.cpp:105] Iteration 637, lr = 0.00656679
|
|
I0428 01:38:52.987391 496 solver.cpp:218] Iteration 644 (0.410219 iter/s, 17.064s/7 iters), loss = 4.03429
|
|
I0428 01:38:52.987550 496 solver.cpp:237] Train net output #0: loss = 4.07262 (* 1 = 4.07262 loss)
|
|
I0428 01:38:52.987558 496 sgd_solver.cpp:105] Iteration 644, lr = 0.00653651
|
|
I0428 01:39:09.891688 496 solver.cpp:218] Iteration 651 (0.414099 iter/s, 16.9042s/7 iters), loss = 4.02301
|
|
I0428 01:39:09.891731 496 solver.cpp:237] Train net output #0: loss = 4.04434 (* 1 = 4.04434 loss)
|
|
I0428 01:39:09.891739 496 sgd_solver.cpp:105] Iteration 651, lr = 0.00650637
|
|
I0428 01:39:26.809799 496 solver.cpp:218] Iteration 658 (0.413758 iter/s, 16.9181s/7 iters), loss = 3.89409
|
|
I0428 01:39:26.809937 496 solver.cpp:237] Train net output #0: loss = 4.00201 (* 1 = 4.00201 loss)
|
|
I0428 01:39:26.809947 496 sgd_solver.cpp:105] Iteration 658, lr = 0.00647637
|
|
I0428 01:39:43.696501 496 solver.cpp:218] Iteration 665 (0.41453 iter/s, 16.8866s/7 iters), loss = 3.85946
|
|
I0428 01:39:43.696542 496 solver.cpp:237] Train net output #0: loss = 3.96191 (* 1 = 3.96191 loss)
|
|
I0428 01:39:43.696550 496 sgd_solver.cpp:105] Iteration 665, lr = 0.00644651
|
|
I0428 01:40:00.777335 496 solver.cpp:218] Iteration 672 (0.409816 iter/s, 17.0808s/7 iters), loss = 3.85855
|
|
I0428 01:40:00.777467 496 solver.cpp:237] Train net output #0: loss = 4.06257 (* 1 = 4.06257 loss)
|
|
I0428 01:40:00.777477 496 sgd_solver.cpp:105] Iteration 672, lr = 0.00641678
|
|
I0428 01:40:17.772498 496 solver.cpp:218] Iteration 679 (0.411884 iter/s, 16.9951s/7 iters), loss = 3.79547
|
|
I0428 01:40:17.772548 496 solver.cpp:237] Train net output #0: loss = 3.81513 (* 1 = 3.81513 loss)
|
|
I0428 01:40:17.772557 496 sgd_solver.cpp:105] Iteration 679, lr = 0.0063872
|
|
I0428 01:40:34.672617 496 solver.cpp:218] Iteration 686 (0.414199 iter/s, 16.9001s/7 iters), loss = 3.83046
|
|
I0428 01:40:34.672732 496 solver.cpp:237] Train net output #0: loss = 4.03265 (* 1 = 4.03265 loss)
|
|
I0428 01:40:34.672741 496 sgd_solver.cpp:105] Iteration 686, lr = 0.00635775
|
|
I0428 01:40:51.609400 496 solver.cpp:218] Iteration 693 (0.413304 iter/s, 16.9367s/7 iters), loss = 3.78739
|
|
I0428 01:40:51.609445 496 solver.cpp:237] Train net output #0: loss = 3.98845 (* 1 = 3.98845 loss)
|
|
I0428 01:40:51.609453 496 sgd_solver.cpp:105] Iteration 693, lr = 0.00632843
|
|
I0428 01:41:08.525476 496 solver.cpp:218] Iteration 700 (0.413808 iter/s, 16.9161s/7 iters), loss = 3.85542
|
|
I0428 01:41:08.525566 496 solver.cpp:237] Train net output #0: loss = 3.81596 (* 1 = 3.81596 loss)
|
|
I0428 01:41:08.525576 496 sgd_solver.cpp:105] Iteration 700, lr = 0.00629925
|
|
I0428 01:41:25.497944 496 solver.cpp:218] Iteration 707 (0.412434 iter/s, 16.9724s/7 iters), loss = 3.87021
|
|
I0428 01:41:25.497988 496 solver.cpp:237] Train net output #0: loss = 3.71596 (* 1 = 3.71596 loss)
|
|
I0428 01:41:25.497997 496 sgd_solver.cpp:105] Iteration 707, lr = 0.00627021
|
|
I0428 01:41:25.671890 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:41:39.905506 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
|
|
I0428 01:41:43.921824 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
|
|
I0428 01:41:47.023366 496 solver.cpp:330] Iteration 714, Testing net (#0)
|
|
I0428 01:41:47.023389 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:41:51.322468 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:41:51.647917 496 solver.cpp:397] Test net output #0: accuracy = 0.121324
|
|
I0428 01:41:51.647962 496 solver.cpp:397] Test net output #1: loss = 4.20043 (* 1 = 4.20043 loss)
|
|
I0428 01:41:53.027726 496 solver.cpp:218] Iteration 714 (0.25427 iter/s, 27.5298s/7 iters), loss = 3.78137
|
|
I0428 01:41:53.027766 496 solver.cpp:237] Train net output #0: loss = 3.77309 (* 1 = 3.77309 loss)
|
|
I0428 01:41:53.027773 496 sgd_solver.cpp:105] Iteration 714, lr = 0.0062413
|
|
I0428 01:42:10.339187 496 solver.cpp:218] Iteration 721 (0.404357 iter/s, 17.3114s/7 iters), loss = 3.63005
|
|
I0428 01:42:10.339282 496 solver.cpp:237] Train net output #0: loss = 3.78058 (* 1 = 3.78058 loss)
|
|
I0428 01:42:10.339291 496 sgd_solver.cpp:105] Iteration 721, lr = 0.00621252
|
|
I0428 01:42:27.253293 496 solver.cpp:218] Iteration 728 (0.413857 iter/s, 16.914s/7 iters), loss = 3.75926
|
|
I0428 01:42:27.253336 496 solver.cpp:237] Train net output #0: loss = 3.52788 (* 1 = 3.52788 loss)
|
|
I0428 01:42:27.253345 496 sgd_solver.cpp:105] Iteration 728, lr = 0.00618387
|
|
I0428 01:42:44.142799 496 solver.cpp:218] Iteration 735 (0.414459 iter/s, 16.8895s/7 iters), loss = 3.67696
|
|
I0428 01:42:44.142916 496 solver.cpp:237] Train net output #0: loss = 3.67064 (* 1 = 3.67064 loss)
|
|
I0428 01:42:44.142925 496 sgd_solver.cpp:105] Iteration 735, lr = 0.00615536
|
|
I0428 01:43:01.101824 496 solver.cpp:218] Iteration 742 (0.412762 iter/s, 16.9589s/7 iters), loss = 3.64297
|
|
I0428 01:43:01.101868 496 solver.cpp:237] Train net output #0: loss = 3.59042 (* 1 = 3.59042 loss)
|
|
I0428 01:43:01.101876 496 sgd_solver.cpp:105] Iteration 742, lr = 0.00612698
|
|
I0428 01:43:18.023802 496 solver.cpp:218] Iteration 749 (0.413664 iter/s, 16.922s/7 iters), loss = 3.49873
|
|
I0428 01:43:18.023910 496 solver.cpp:237] Train net output #0: loss = 3.60823 (* 1 = 3.60823 loss)
|
|
I0428 01:43:18.023921 496 sgd_solver.cpp:105] Iteration 749, lr = 0.00609873
|
|
I0428 01:43:34.944739 496 solver.cpp:218] Iteration 756 (0.41369 iter/s, 16.9209s/7 iters), loss = 3.58972
|
|
I0428 01:43:34.944785 496 solver.cpp:237] Train net output #0: loss = 3.56842 (* 1 = 3.56842 loss)
|
|
I0428 01:43:34.944794 496 sgd_solver.cpp:105] Iteration 756, lr = 0.00607061
|
|
I0428 01:43:51.900400 496 solver.cpp:218] Iteration 763 (0.412842 iter/s, 16.9556s/7 iters), loss = 3.57103
|
|
I0428 01:43:51.900519 496 solver.cpp:237] Train net output #0: loss = 3.58629 (* 1 = 3.58629 loss)
|
|
I0428 01:43:51.900528 496 sgd_solver.cpp:105] Iteration 763, lr = 0.00604262
|
|
I0428 01:44:08.905730 496 solver.cpp:218] Iteration 770 (0.411638 iter/s, 17.0052s/7 iters), loss = 3.38982
|
|
I0428 01:44:08.905771 496 solver.cpp:237] Train net output #0: loss = 3.42509 (* 1 = 3.42509 loss)
|
|
I0428 01:44:08.905779 496 sgd_solver.cpp:105] Iteration 770, lr = 0.00601475
|
|
I0428 01:44:15.346577 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 01:44:25.890591 496 solver.cpp:218] Iteration 777 (0.412132 iter/s, 16.9849s/7 iters), loss = 3.29764
|
|
I0428 01:44:25.890715 496 solver.cpp:237] Train net output #0: loss = 3.41169 (* 1 = 3.41169 loss)
|
|
I0428 01:44:25.890725 496 sgd_solver.cpp:105] Iteration 777, lr = 0.00598702
|
|
I0428 01:44:42.841722 496 solver.cpp:218] Iteration 784 (0.412954 iter/s, 16.951s/7 iters), loss = 3.33008
|
|
I0428 01:44:42.841770 496 solver.cpp:237] Train net output #0: loss = 3.16421 (* 1 = 3.16421 loss)
|
|
I0428 01:44:42.841778 496 sgd_solver.cpp:105] Iteration 784, lr = 0.00595942
|
|
I0428 01:44:59.785517 496 solver.cpp:218] Iteration 791 (0.413131 iter/s, 16.9438s/7 iters), loss = 3.43304
|
|
I0428 01:44:59.785605 496 solver.cpp:237] Train net output #0: loss = 3.60153 (* 1 = 3.60153 loss)
|
|
I0428 01:44:59.785614 496 sgd_solver.cpp:105] Iteration 791, lr = 0.00593194
|
|
I0428 01:45:16.831629 496 solver.cpp:218] Iteration 798 (0.410652 iter/s, 17.046s/7 iters), loss = 3.28207
|
|
I0428 01:45:16.831696 496 solver.cpp:237] Train net output #0: loss = 3.44494 (* 1 = 3.44494 loss)
|
|
I0428 01:45:16.831708 496 sgd_solver.cpp:105] Iteration 798, lr = 0.00590459
|
|
I0428 01:45:33.743772 496 solver.cpp:218] Iteration 805 (0.413904 iter/s, 16.9121s/7 iters), loss = 3.23822
|
|
I0428 01:45:33.743901 496 solver.cpp:237] Train net output #0: loss = 3.34454 (* 1 = 3.34454 loss)
|
|
I0428 01:45:33.743911 496 sgd_solver.cpp:105] Iteration 805, lr = 0.00587736
|
|
I0428 01:45:41.577088 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:45:50.669639 496 solver.cpp:218] Iteration 812 (0.41357 iter/s, 16.9258s/7 iters), loss = 3.36102
|
|
I0428 01:45:50.669684 496 solver.cpp:237] Train net output #0: loss = 3.61773 (* 1 = 3.61773 loss)
|
|
I0428 01:45:50.669693 496 sgd_solver.cpp:105] Iteration 812, lr = 0.00585026
|
|
I0428 01:45:57.861950 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
|
|
I0428 01:46:00.959467 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
|
|
I0428 01:46:03.315454 496 solver.cpp:330] Iteration 816, Testing net (#0)
|
|
I0428 01:46:03.315471 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:46:07.887045 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:46:08.266746 496 solver.cpp:397] Test net output #0: accuracy = 0.137255
|
|
I0428 01:46:08.266794 496 solver.cpp:397] Test net output #1: loss = 4.03764 (* 1 = 4.03764 loss)
|
|
I0428 01:46:16.867082 496 solver.cpp:218] Iteration 819 (0.267202 iter/s, 26.1975s/7 iters), loss = 3.32808
|
|
I0428 01:46:16.867123 496 solver.cpp:237] Train net output #0: loss = 2.95132 (* 1 = 2.95132 loss)
|
|
I0428 01:46:16.867131 496 sgd_solver.cpp:105] Iteration 819, lr = 0.00582329
|
|
I0428 01:46:33.738404 496 solver.cpp:218] Iteration 826 (0.414906 iter/s, 16.8713s/7 iters), loss = 3.11665
|
|
I0428 01:46:33.738456 496 solver.cpp:237] Train net output #0: loss = 2.91793 (* 1 = 2.91793 loss)
|
|
I0428 01:46:33.738464 496 sgd_solver.cpp:105] Iteration 826, lr = 0.00579644
|
|
I0428 01:46:50.650656 496 solver.cpp:218] Iteration 833 (0.413902 iter/s, 16.9122s/7 iters), loss = 3.17931
|
|
I0428 01:46:50.650754 496 solver.cpp:237] Train net output #0: loss = 3.3029 (* 1 = 3.3029 loss)
|
|
I0428 01:46:50.650764 496 sgd_solver.cpp:105] Iteration 833, lr = 0.00576971
|
|
I0428 01:47:07.567824 496 solver.cpp:218] Iteration 840 (0.413782 iter/s, 16.9171s/7 iters), loss = 3.23882
|
|
I0428 01:47:07.567862 496 solver.cpp:237] Train net output #0: loss = 3.35703 (* 1 = 3.35703 loss)
|
|
I0428 01:47:07.567870 496 sgd_solver.cpp:105] Iteration 840, lr = 0.00574311
|
|
I0428 01:47:24.493628 496 solver.cpp:218] Iteration 847 (0.41357 iter/s, 16.9258s/7 iters), loss = 3.23412
|
|
I0428 01:47:24.493741 496 solver.cpp:237] Train net output #0: loss = 3.39766 (* 1 = 3.39766 loss)
|
|
I0428 01:47:24.493752 496 sgd_solver.cpp:105] Iteration 847, lr = 0.00571663
|
|
I0428 01:47:41.397025 496 solver.cpp:218] Iteration 854 (0.41412 iter/s, 16.9033s/7 iters), loss = 3.13895
|
|
I0428 01:47:41.397074 496 solver.cpp:237] Train net output #0: loss = 3.01734 (* 1 = 3.01734 loss)
|
|
I0428 01:47:41.397083 496 sgd_solver.cpp:105] Iteration 854, lr = 0.00569027
|
|
I0428 01:47:58.316519 496 solver.cpp:218] Iteration 861 (0.413724 iter/s, 16.9195s/7 iters), loss = 3.03529
|
|
I0428 01:47:58.316604 496 solver.cpp:237] Train net output #0: loss = 3.02595 (* 1 = 3.02595 loss)
|
|
I0428 01:47:58.316613 496 sgd_solver.cpp:105] Iteration 861, lr = 0.00566403
|
|
I0428 01:48:15.239316 496 solver.cpp:218] Iteration 868 (0.413644 iter/s, 16.9227s/7 iters), loss = 3.06402
|
|
I0428 01:48:15.239367 496 solver.cpp:237] Train net output #0: loss = 3.20461 (* 1 = 3.20461 loss)
|
|
I0428 01:48:15.239374 496 sgd_solver.cpp:105] Iteration 868, lr = 0.00563791
|
|
I0428 01:48:32.149765 496 solver.cpp:218] Iteration 875 (0.413946 iter/s, 16.9104s/7 iters), loss = 2.89162
|
|
I0428 01:48:32.149868 496 solver.cpp:237] Train net output #0: loss = 2.85035 (* 1 = 2.85035 loss)
|
|
I0428 01:48:32.149878 496 sgd_solver.cpp:105] Iteration 875, lr = 0.00561192
|
|
I0428 01:48:49.045625 496 solver.cpp:218] Iteration 882 (0.414305 iter/s, 16.8958s/7 iters), loss = 2.80484
|
|
I0428 01:48:49.045675 496 solver.cpp:237] Train net output #0: loss = 3.0024 (* 1 = 3.0024 loss)
|
|
I0428 01:48:49.045682 496 sgd_solver.cpp:105] Iteration 882, lr = 0.00558604
|
|
I0428 01:49:05.966301 496 solver.cpp:218] Iteration 889 (0.413695 iter/s, 16.9207s/7 iters), loss = 2.78818
|
|
I0428 01:49:05.966390 496 solver.cpp:237] Train net output #0: loss = 2.6637 (* 1 = 2.6637 loss)
|
|
I0428 01:49:05.966399 496 sgd_solver.cpp:105] Iteration 889, lr = 0.00556028
|
|
I0428 01:49:22.890033 496 solver.cpp:218] Iteration 896 (0.413622 iter/s, 16.9237s/7 iters), loss = 2.73459
|
|
I0428 01:49:22.890069 496 solver.cpp:237] Train net output #0: loss = 2.69907 (* 1 = 2.69907 loss)
|
|
I0428 01:49:22.890077 496 sgd_solver.cpp:105] Iteration 896, lr = 0.00553465
|
|
I0428 01:49:39.981084 496 solver.cpp:218] Iteration 903 (0.409571 iter/s, 17.091s/7 iters), loss = 2.79951
|
|
I0428 01:49:39.981243 496 solver.cpp:237] Train net output #0: loss = 2.57973 (* 1 = 2.57973 loss)
|
|
I0428 01:49:39.981253 496 sgd_solver.cpp:105] Iteration 903, lr = 0.00550913
|
|
I0428 01:49:55.677583 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:49:57.141047 496 solver.cpp:218] Iteration 910 (0.407929 iter/s, 17.1598s/7 iters), loss = 2.72891
|
|
I0428 01:49:57.141091 496 solver.cpp:237] Train net output #0: loss = 2.77877 (* 1 = 2.77877 loss)
|
|
I0428 01:49:57.141100 496 sgd_solver.cpp:105] Iteration 910, lr = 0.00548373
|
|
I0428 01:50:14.075830 496 solver.cpp:218] Iteration 917 (0.413351 iter/s, 16.9348s/7 iters), loss = 2.71019
|
|
I0428 01:50:14.075968 496 solver.cpp:237] Train net output #0: loss = 2.56876 (* 1 = 2.56876 loss)
|
|
I0428 01:50:14.075978 496 sgd_solver.cpp:105] Iteration 917, lr = 0.00545844
|
|
I0428 01:50:14.076177 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
|
|
I0428 01:50:18.386674 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
|
|
I0428 01:50:22.631876 496 solver.cpp:330] Iteration 918, Testing net (#0)
|
|
I0428 01:50:22.631894 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:50:26.966388 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:50:27.407347 496 solver.cpp:397] Test net output #0: accuracy = 0.181373
|
|
I0428 01:50:27.407395 496 solver.cpp:397] Test net output #1: loss = 3.9048 (* 1 = 3.9048 loss)
|
|
I0428 01:50:43.227612 496 solver.cpp:218] Iteration 924 (0.240123 iter/s, 29.1517s/7 iters), loss = 2.80228
|
|
I0428 01:50:43.227663 496 solver.cpp:237] Train net output #0: loss = 2.8164 (* 1 = 2.8164 loss)
|
|
I0428 01:50:43.227671 496 sgd_solver.cpp:105] Iteration 924, lr = 0.00543327
|
|
I0428 01:50:43.972131 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 01:51:00.551374 496 solver.cpp:218] Iteration 931 (0.40407 iter/s, 17.3237s/7 iters), loss = 2.78118
|
|
I0428 01:51:00.551465 496 solver.cpp:237] Train net output #0: loss = 2.8736 (* 1 = 2.8736 loss)
|
|
I0428 01:51:00.551476 496 sgd_solver.cpp:105] Iteration 931, lr = 0.00540822
|
|
I0428 01:51:17.609774 496 solver.cpp:218] Iteration 938 (0.410356 iter/s, 17.0583s/7 iters), loss = 2.71138
|
|
I0428 01:51:17.609817 496 solver.cpp:237] Train net output #0: loss = 2.74816 (* 1 = 2.74816 loss)
|
|
I0428 01:51:17.609824 496 sgd_solver.cpp:105] Iteration 938, lr = 0.00538328
|
|
I0428 01:51:34.616780 496 solver.cpp:218] Iteration 945 (0.411595 iter/s, 17.007s/7 iters), loss = 2.6282
|
|
I0428 01:51:34.616885 496 solver.cpp:237] Train net output #0: loss = 2.54036 (* 1 = 2.54036 loss)
|
|
I0428 01:51:34.616894 496 sgd_solver.cpp:105] Iteration 945, lr = 0.00535846
|
|
I0428 01:51:51.521257 496 solver.cpp:218] Iteration 952 (0.414093 iter/s, 16.9044s/7 iters), loss = 2.54961
|
|
I0428 01:51:51.521301 496 solver.cpp:237] Train net output #0: loss = 2.73945 (* 1 = 2.73945 loss)
|
|
I0428 01:51:51.521309 496 sgd_solver.cpp:105] Iteration 952, lr = 0.00533376
|
|
I0428 01:52:08.451112 496 solver.cpp:218] Iteration 959 (0.413471 iter/s, 16.9298s/7 iters), loss = 2.49555
|
|
I0428 01:52:08.451207 496 solver.cpp:237] Train net output #0: loss = 2.55579 (* 1 = 2.55579 loss)
|
|
I0428 01:52:08.451216 496 sgd_solver.cpp:105] Iteration 959, lr = 0.00530916
|
|
I0428 01:52:25.344679 496 solver.cpp:218] Iteration 966 (0.41436 iter/s, 16.8935s/7 iters), loss = 2.59311
|
|
I0428 01:52:25.344722 496 solver.cpp:237] Train net output #0: loss = 2.79515 (* 1 = 2.79515 loss)
|
|
I0428 01:52:25.344729 496 sgd_solver.cpp:105] Iteration 966, lr = 0.00528468
|
|
I0428 01:52:42.268299 496 solver.cpp:218] Iteration 973 (0.413623 iter/s, 16.9236s/7 iters), loss = 2.4448
|
|
I0428 01:52:42.268395 496 solver.cpp:237] Train net output #0: loss = 2.31738 (* 1 = 2.31738 loss)
|
|
I0428 01:52:42.268404 496 sgd_solver.cpp:105] Iteration 973, lr = 0.00526031
|
|
I0428 01:52:59.201141 496 solver.cpp:218] Iteration 980 (0.413399 iter/s, 16.9328s/7 iters), loss = 2.26632
|
|
I0428 01:52:59.201192 496 solver.cpp:237] Train net output #0: loss = 2.21692 (* 1 = 2.21692 loss)
|
|
I0428 01:52:59.201203 496 sgd_solver.cpp:105] Iteration 980, lr = 0.00523606
|
|
I0428 01:53:16.128599 496 solver.cpp:218] Iteration 987 (0.41353 iter/s, 16.9274s/7 iters), loss = 2.43555
|
|
I0428 01:53:16.128767 496 solver.cpp:237] Train net output #0: loss = 2.46483 (* 1 = 2.46483 loss)
|
|
I0428 01:53:16.128779 496 sgd_solver.cpp:105] Iteration 987, lr = 0.00521192
|
|
I0428 01:53:33.048774 496 solver.cpp:218] Iteration 994 (0.413711 iter/s, 16.92s/7 iters), loss = 2.206
|
|
I0428 01:53:33.048817 496 solver.cpp:237] Train net output #0: loss = 2.16573 (* 1 = 2.16573 loss)
|
|
I0428 01:53:33.048825 496 sgd_solver.cpp:105] Iteration 994, lr = 0.00518789
|
|
I0428 01:53:50.039058 496 solver.cpp:218] Iteration 1001 (0.412 iter/s, 16.9903s/7 iters), loss = 2.23874
|
|
I0428 01:53:50.039178 496 solver.cpp:237] Train net output #0: loss = 2.35107 (* 1 = 2.35107 loss)
|
|
I0428 01:53:50.039187 496 sgd_solver.cpp:105] Iteration 1001, lr = 0.00516397
|
|
I0428 01:54:06.943066 496 solver.cpp:218] Iteration 1008 (0.414105 iter/s, 16.9039s/7 iters), loss = 2.26949
|
|
I0428 01:54:06.943116 496 solver.cpp:237] Train net output #0: loss = 2.39248 (* 1 = 2.39248 loss)
|
|
I0428 01:54:06.943125 496 sgd_solver.cpp:105] Iteration 1008, lr = 0.00514015
|
|
I0428 01:54:13.099148 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:54:23.880230 496 solver.cpp:218] Iteration 1015 (0.413293 iter/s, 16.9372s/7 iters), loss = 2.30428
|
|
I0428 01:54:23.880336 496 solver.cpp:237] Train net output #0: loss = 2.33533 (* 1 = 2.33533 loss)
|
|
I0428 01:54:23.880345 496 sgd_solver.cpp:105] Iteration 1015, lr = 0.00511645
|
|
I0428 01:54:33.519155 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
|
|
I0428 01:54:43.258546 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
|
|
I0428 01:54:46.039844 496 solver.cpp:330] Iteration 1020, Testing net (#0)
|
|
I0428 01:54:46.039865 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:54:50.350014 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:54:50.826628 496 solver.cpp:397] Test net output #0: accuracy = 0.19424
|
|
I0428 01:54:50.826676 496 solver.cpp:397] Test net output #1: loss = 3.89869 (* 1 = 3.89869 loss)
|
|
I0428 01:54:57.180469 496 solver.cpp:218] Iteration 1022 (0.210209 iter/s, 33.3002s/7 iters), loss = 2.18172
|
|
I0428 01:54:57.180567 496 solver.cpp:237] Train net output #0: loss = 2.39966 (* 1 = 2.39966 loss)
|
|
I0428 01:54:57.180577 496 sgd_solver.cpp:105] Iteration 1022, lr = 0.00509286
|
|
I0428 01:55:14.062484 496 solver.cpp:218] Iteration 1029 (0.414644 iter/s, 16.8819s/7 iters), loss = 2.2754
|
|
I0428 01:55:14.062526 496 solver.cpp:237] Train net output #0: loss = 2.3877 (* 1 = 2.3877 loss)
|
|
I0428 01:55:14.062534 496 sgd_solver.cpp:105] Iteration 1029, lr = 0.00506938
|
|
I0428 01:55:31.038754 496 solver.cpp:218] Iteration 1036 (0.412341 iter/s, 16.9762s/7 iters), loss = 2.18779
|
|
I0428 01:55:31.038830 496 solver.cpp:237] Train net output #0: loss = 2.15263 (* 1 = 2.15263 loss)
|
|
I0428 01:55:31.038839 496 sgd_solver.cpp:105] Iteration 1036, lr = 0.00504601
|
|
I0428 01:55:47.916915 496 solver.cpp:218] Iteration 1043 (0.414738 iter/s, 16.8781s/7 iters), loss = 2.04419
|
|
I0428 01:55:47.916958 496 solver.cpp:237] Train net output #0: loss = 1.99978 (* 1 = 1.99978 loss)
|
|
I0428 01:55:47.916966 496 sgd_solver.cpp:105] Iteration 1043, lr = 0.00502274
|
|
I0428 01:56:04.820050 496 solver.cpp:218] Iteration 1050 (0.414125 iter/s, 16.9031s/7 iters), loss = 2.20712
|
|
I0428 01:56:04.820186 496 solver.cpp:237] Train net output #0: loss = 1.90674 (* 1 = 1.90674 loss)
|
|
I0428 01:56:04.820195 496 sgd_solver.cpp:105] Iteration 1050, lr = 0.00499958
|
|
I0428 01:56:21.791620 496 solver.cpp:218] Iteration 1057 (0.412457 iter/s, 16.9715s/7 iters), loss = 1.99234
|
|
I0428 01:56:21.791666 496 solver.cpp:237] Train net output #0: loss = 2.15938 (* 1 = 2.15938 loss)
|
|
I0428 01:56:21.791672 496 sgd_solver.cpp:105] Iteration 1057, lr = 0.00497653
|
|
I0428 01:56:38.724473 496 solver.cpp:218] Iteration 1064 (0.413398 iter/s, 16.9328s/7 iters), loss = 1.92872
|
|
I0428 01:56:38.724640 496 solver.cpp:237] Train net output #0: loss = 1.96624 (* 1 = 1.96624 loss)
|
|
I0428 01:56:38.724650 496 sgd_solver.cpp:105] Iteration 1064, lr = 0.00495358
|
|
I0428 01:56:55.648869 496 solver.cpp:218] Iteration 1071 (0.413607 iter/s, 16.9243s/7 iters), loss = 1.8973
|
|
I0428 01:56:55.648911 496 solver.cpp:237] Train net output #0: loss = 1.7249 (* 1 = 1.7249 loss)
|
|
I0428 01:56:55.648919 496 sgd_solver.cpp:105] Iteration 1071, lr = 0.00493074
|
|
I0428 01:57:12.577661 496 solver.cpp:218] Iteration 1078 (0.413497 iter/s, 16.9288s/7 iters), loss = 1.82805
|
|
I0428 01:57:12.577777 496 solver.cpp:237] Train net output #0: loss = 1.62142 (* 1 = 1.62142 loss)
|
|
I0428 01:57:12.577786 496 sgd_solver.cpp:105] Iteration 1078, lr = 0.00490801
|
|
I0428 01:57:25.880447 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 01:57:29.567874 496 solver.cpp:218] Iteration 1085 (0.412004 iter/s, 16.9901s/7 iters), loss = 1.85934
|
|
I0428 01:57:29.567919 496 solver.cpp:237] Train net output #0: loss = 1.69864 (* 1 = 1.69864 loss)
|
|
I0428 01:57:29.567929 496 sgd_solver.cpp:105] Iteration 1085, lr = 0.00488538
|
|
I0428 01:57:46.553045 496 solver.cpp:218] Iteration 1092 (0.412124 iter/s, 16.9852s/7 iters), loss = 1.77511
|
|
I0428 01:57:46.553165 496 solver.cpp:237] Train net output #0: loss = 1.8854 (* 1 = 1.8854 loss)
|
|
I0428 01:57:46.553174 496 sgd_solver.cpp:105] Iteration 1092, lr = 0.00486285
|
|
I0428 01:58:03.270916 496 solver.cpp:218] Iteration 1099 (0.418716 iter/s, 16.7178s/7 iters), loss = 1.66613
|
|
I0428 01:58:03.270958 496 solver.cpp:237] Train net output #0: loss = 1.63191 (* 1 = 1.63191 loss)
|
|
I0428 01:58:03.270967 496 sgd_solver.cpp:105] Iteration 1099, lr = 0.00484043
|
|
I0428 01:58:20.188400 496 solver.cpp:218] Iteration 1106 (0.413773 iter/s, 16.9175s/7 iters), loss = 1.75984
|
|
I0428 01:58:20.188530 496 solver.cpp:237] Train net output #0: loss = 2.02817 (* 1 = 2.02817 loss)
|
|
I0428 01:58:20.188539 496 sgd_solver.cpp:105] Iteration 1106, lr = 0.00481811
|
|
I0428 01:58:34.022039 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:58:37.165669 496 solver.cpp:218] Iteration 1113 (0.412318 iter/s, 16.9772s/7 iters), loss = 1.73699
|
|
I0428 01:58:37.165709 496 solver.cpp:237] Train net output #0: loss = 1.96808 (* 1 = 1.96808 loss)
|
|
I0428 01:58:37.165717 496 sgd_solver.cpp:105] Iteration 1113, lr = 0.00479589
|
|
I0428 01:58:54.061635 496 solver.cpp:218] Iteration 1120 (0.4143 iter/s, 16.896s/7 iters), loss = 1.98098
|
|
I0428 01:58:54.061764 496 solver.cpp:237] Train net output #0: loss = 1.92167 (* 1 = 1.92167 loss)
|
|
I0428 01:58:54.061772 496 sgd_solver.cpp:105] Iteration 1120, lr = 0.00477378
|
|
I0428 01:58:56.418071 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
|
|
I0428 01:59:01.579813 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
|
|
I0428 01:59:05.389115 496 solver.cpp:330] Iteration 1122, Testing net (#0)
|
|
I0428 01:59:05.389135 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 01:59:09.646956 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 01:59:10.173913 496 solver.cpp:397] Test net output #0: accuracy = 0.190564
|
|
I0428 01:59:10.173947 496 solver.cpp:397] Test net output #1: loss = 4.04 (* 1 = 4.04 loss)
|
|
I0428 01:59:23.569023 496 solver.cpp:218] Iteration 1127 (0.237229 iter/s, 29.5073s/7 iters), loss = 1.70744
|
|
I0428 01:59:23.569067 496 solver.cpp:237] Train net output #0: loss = 1.60922 (* 1 = 1.60922 loss)
|
|
I0428 01:59:23.569074 496 sgd_solver.cpp:105] Iteration 1127, lr = 0.00475177
|
|
I0428 01:59:40.491940 496 solver.cpp:218] Iteration 1134 (0.413641 iter/s, 16.9229s/7 iters), loss = 1.65189
|
|
I0428 01:59:40.492024 496 solver.cpp:237] Train net output #0: loss = 1.61514 (* 1 = 1.61514 loss)
|
|
I0428 01:59:40.492033 496 sgd_solver.cpp:105] Iteration 1134, lr = 0.00472986
|
|
I0428 01:59:57.395159 496 solver.cpp:218] Iteration 1141 (0.414124 iter/s, 16.9032s/7 iters), loss = 1.50722
|
|
I0428 01:59:57.395221 496 solver.cpp:237] Train net output #0: loss = 1.49659 (* 1 = 1.49659 loss)
|
|
I0428 01:59:57.395234 496 sgd_solver.cpp:105] Iteration 1141, lr = 0.00470805
|
|
I0428 02:00:14.323032 496 solver.cpp:218] Iteration 1148 (0.41352 iter/s, 16.9278s/7 iters), loss = 1.54865
|
|
I0428 02:00:14.323172 496 solver.cpp:237] Train net output #0: loss = 1.47521 (* 1 = 1.47521 loss)
|
|
I0428 02:00:14.323182 496 sgd_solver.cpp:105] Iteration 1148, lr = 0.00468634
|
|
I0428 02:00:31.245867 496 solver.cpp:218] Iteration 1155 (0.413645 iter/s, 16.9227s/7 iters), loss = 1.47908
|
|
I0428 02:00:31.245910 496 solver.cpp:237] Train net output #0: loss = 1.62732 (* 1 = 1.62732 loss)
|
|
I0428 02:00:31.245918 496 sgd_solver.cpp:105] Iteration 1155, lr = 0.00466473
|
|
I0428 02:00:48.209900 496 solver.cpp:218] Iteration 1162 (0.412638 iter/s, 16.964s/7 iters), loss = 1.56132
|
|
I0428 02:00:48.210034 496 solver.cpp:237] Train net output #0: loss = 1.6143 (* 1 = 1.6143 loss)
|
|
I0428 02:00:48.210043 496 sgd_solver.cpp:105] Iteration 1162, lr = 0.00464323
|
|
I0428 02:01:05.124229 496 solver.cpp:218] Iteration 1169 (0.413853 iter/s, 16.9142s/7 iters), loss = 1.50772
|
|
I0428 02:01:05.124274 496 solver.cpp:237] Train net output #0: loss = 1.32236 (* 1 = 1.32236 loss)
|
|
I0428 02:01:05.124282 496 sgd_solver.cpp:105] Iteration 1169, lr = 0.00462182
|
|
I0428 02:01:22.069499 496 solver.cpp:218] Iteration 1176 (0.413095 iter/s, 16.9453s/7 iters), loss = 1.53582
|
|
I0428 02:01:22.069614 496 solver.cpp:237] Train net output #0: loss = 1.54505 (* 1 = 1.54505 loss)
|
|
I0428 02:01:22.069623 496 sgd_solver.cpp:105] Iteration 1176, lr = 0.00460051
|
|
I0428 02:01:38.990854 496 solver.cpp:218] Iteration 1183 (0.41368 iter/s, 16.9213s/7 iters), loss = 1.36163
|
|
I0428 02:01:38.990895 496 solver.cpp:237] Train net output #0: loss = 1.4934 (* 1 = 1.4934 loss)
|
|
I0428 02:01:38.990902 496 sgd_solver.cpp:105] Iteration 1183, lr = 0.00457929
|
|
I0428 02:01:55.847548 496 solver.cpp:218] Iteration 1190 (0.415265 iter/s, 16.8567s/7 iters), loss = 1.52639
|
|
I0428 02:01:55.847641 496 solver.cpp:237] Train net output #0: loss = 1.62509 (* 1 = 1.62509 loss)
|
|
I0428 02:01:55.847651 496 sgd_solver.cpp:105] Iteration 1190, lr = 0.00455818
|
|
I0428 02:02:12.765255 496 solver.cpp:218] Iteration 1197 (0.413769 iter/s, 16.9177s/7 iters), loss = 1.41377
|
|
I0428 02:02:12.765297 496 solver.cpp:237] Train net output #0: loss = 1.42277 (* 1 = 1.42277 loss)
|
|
I0428 02:02:12.765305 496 sgd_solver.cpp:105] Iteration 1197, lr = 0.00453716
|
|
I0428 02:02:29.706970 496 solver.cpp:218] Iteration 1204 (0.413181 iter/s, 16.9417s/7 iters), loss = 1.6048
|
|
I0428 02:02:29.707056 496 solver.cpp:237] Train net output #0: loss = 1.72243 (* 1 = 1.72243 loss)
|
|
I0428 02:02:29.707065 496 sgd_solver.cpp:105] Iteration 1204, lr = 0.00451624
|
|
I0428 02:02:46.629930 496 solver.cpp:218] Iteration 1211 (0.41364 iter/s, 16.9229s/7 iters), loss = 1.26328
|
|
I0428 02:02:46.629973 496 solver.cpp:237] Train net output #0: loss = 1.31745 (* 1 = 1.31745 loss)
|
|
I0428 02:02:46.629982 496 sgd_solver.cpp:105] Iteration 1211, lr = 0.00449542
|
|
I0428 02:02:51.138864 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:03:03.579167 496 solver.cpp:218] Iteration 1218 (0.412998 iter/s, 16.9492s/7 iters), loss = 1.37496
|
|
I0428 02:03:03.579285 496 solver.cpp:237] Train net output #0: loss = 1.25008 (* 1 = 1.25008 loss)
|
|
I0428 02:03:03.579294 496 sgd_solver.cpp:105] Iteration 1218, lr = 0.00447469
|
|
I0428 02:03:15.673907 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
|
|
I0428 02:03:19.938843 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
|
|
I0428 02:03:23.212003 496 solver.cpp:330] Iteration 1224, Testing net (#0)
|
|
I0428 02:03:23.212019 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:03:27.708488 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:03:28.296161 496 solver.cpp:397] Test net output #0: accuracy = 0.234681
|
|
I0428 02:03:28.296208 496 solver.cpp:397] Test net output #1: loss = 3.97652 (* 1 = 3.97652 loss)
|
|
I0428 02:03:32.088289 496 solver.cpp:218] Iteration 1225 (0.245536 iter/s, 28.5091s/7 iters), loss = 1.40983
|
|
I0428 02:03:32.088332 496 solver.cpp:237] Train net output #0: loss = 1.59294 (* 1 = 1.59294 loss)
|
|
I0428 02:03:32.088340 496 sgd_solver.cpp:105] Iteration 1225, lr = 0.00445406
|
|
I0428 02:03:49.014535 496 solver.cpp:218] Iteration 1232 (0.413559 iter/s, 16.9262s/7 iters), loss = 1.222
|
|
I0428 02:03:49.014672 496 solver.cpp:237] Train net output #0: loss = 0.887204 (* 1 = 0.887204 loss)
|
|
I0428 02:03:49.014681 496 sgd_solver.cpp:105] Iteration 1232, lr = 0.00443352
|
|
I0428 02:03:56.611074 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 02:04:05.945820 496 solver.cpp:218] Iteration 1239 (0.413438 iter/s, 16.9312s/7 iters), loss = 1.35211
|
|
I0428 02:04:05.945868 496 solver.cpp:237] Train net output #0: loss = 1.47006 (* 1 = 1.47006 loss)
|
|
I0428 02:04:05.945878 496 sgd_solver.cpp:105] Iteration 1239, lr = 0.00441308
|
|
I0428 02:04:22.835219 496 solver.cpp:218] Iteration 1246 (0.414461 iter/s, 16.8894s/7 iters), loss = 1.22051
|
|
I0428 02:04:22.835319 496 solver.cpp:237] Train net output #0: loss = 1.13783 (* 1 = 1.13783 loss)
|
|
I0428 02:04:22.835328 496 sgd_solver.cpp:105] Iteration 1246, lr = 0.00439273
|
|
I0428 02:04:39.768318 496 solver.cpp:218] Iteration 1253 (0.413393 iter/s, 16.933s/7 iters), loss = 1.11596
|
|
I0428 02:04:39.768360 496 solver.cpp:237] Train net output #0: loss = 1.25489 (* 1 = 1.25489 loss)
|
|
I0428 02:04:39.768369 496 sgd_solver.cpp:105] Iteration 1253, lr = 0.00437248
|
|
I0428 02:04:56.718608 496 solver.cpp:218] Iteration 1260 (0.412973 iter/s, 16.9503s/7 iters), loss = 1.16041
|
|
I0428 02:04:56.718726 496 solver.cpp:237] Train net output #0: loss = 1.19601 (* 1 = 1.19601 loss)
|
|
I0428 02:04:56.718740 496 sgd_solver.cpp:105] Iteration 1260, lr = 0.00435231
|
|
I0428 02:05:13.869401 496 solver.cpp:218] Iteration 1267 (0.408146 iter/s, 17.1507s/7 iters), loss = 1.19482
|
|
I0428 02:05:13.869446 496 solver.cpp:237] Train net output #0: loss = 1.32632 (* 1 = 1.32632 loss)
|
|
I0428 02:05:13.869454 496 sgd_solver.cpp:105] Iteration 1267, lr = 0.00433225
|
|
I0428 02:05:31.032402 496 solver.cpp:218] Iteration 1274 (0.407854 iter/s, 17.163s/7 iters), loss = 1.16378
|
|
I0428 02:05:31.032526 496 solver.cpp:237] Train net output #0: loss = 1.07652 (* 1 = 1.07652 loss)
|
|
I0428 02:05:31.032536 496 sgd_solver.cpp:105] Iteration 1274, lr = 0.00431227
|
|
I0428 02:05:48.244383 496 solver.cpp:218] Iteration 1281 (0.406695 iter/s, 17.2119s/7 iters), loss = 1.07001
|
|
I0428 02:05:48.244427 496 solver.cpp:237] Train net output #0: loss = 1.21213 (* 1 = 1.21213 loss)
|
|
I0428 02:05:48.244436 496 sgd_solver.cpp:105] Iteration 1281, lr = 0.00429239
|
|
I0428 02:06:05.160315 496 solver.cpp:218] Iteration 1288 (0.413811 iter/s, 16.9159s/7 iters), loss = 0.998203
|
|
I0428 02:06:05.160415 496 solver.cpp:237] Train net output #0: loss = 0.859896 (* 1 = 0.859896 loss)
|
|
I0428 02:06:05.160425 496 sgd_solver.cpp:105] Iteration 1288, lr = 0.0042726
|
|
I0428 02:06:22.055058 496 solver.cpp:218] Iteration 1295 (0.414332 iter/s, 16.8947s/7 iters), loss = 1.11831
|
|
I0428 02:06:22.055102 496 solver.cpp:237] Train net output #0: loss = 1.20993 (* 1 = 1.20993 loss)
|
|
I0428 02:06:22.055109 496 sgd_solver.cpp:105] Iteration 1295, lr = 0.0042529
|
|
I0428 02:06:38.993360 496 solver.cpp:218] Iteration 1302 (0.413265 iter/s, 16.9383s/7 iters), loss = 0.958518
|
|
I0428 02:06:38.993489 496 solver.cpp:237] Train net output #0: loss = 0.850712 (* 1 = 0.850712 loss)
|
|
I0428 02:06:38.993499 496 sgd_solver.cpp:105] Iteration 1302, lr = 0.00423329
|
|
I0428 02:06:55.905544 496 solver.cpp:218] Iteration 1309 (0.413905 iter/s, 16.9121s/7 iters), loss = 1.02814
|
|
I0428 02:06:55.905586 496 solver.cpp:237] Train net output #0: loss = 1.17951 (* 1 = 1.17951 loss)
|
|
I0428 02:06:55.905594 496 sgd_solver.cpp:105] Iteration 1309, lr = 0.00421377
|
|
I0428 02:07:08.079466 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:07:12.842070 496 solver.cpp:218] Iteration 1316 (0.413308 iter/s, 16.9365s/7 iters), loss = 1.11037
|
|
I0428 02:07:12.842197 496 solver.cpp:237] Train net output #0: loss = 1.06846 (* 1 = 1.06846 loss)
|
|
I0428 02:07:12.842207 496 sgd_solver.cpp:105] Iteration 1316, lr = 0.00419434
|
|
I0428 02:07:29.751744 496 solver.cpp:218] Iteration 1323 (0.413967 iter/s, 16.9096s/7 iters), loss = 0.919928
|
|
I0428 02:07:29.751789 496 solver.cpp:237] Train net output #0: loss = 0.748314 (* 1 = 0.748314 loss)
|
|
I0428 02:07:29.751798 496 sgd_solver.cpp:105] Iteration 1323, lr = 0.004175
|
|
I0428 02:07:34.529986 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
|
|
I0428 02:07:37.571763 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
|
|
I0428 02:07:39.926434 496 solver.cpp:330] Iteration 1326, Testing net (#0)
|
|
I0428 02:07:39.926453 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:07:44.102370 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:07:44.719601 496 solver.cpp:397] Test net output #0: accuracy = 0.231618
|
|
I0428 02:07:44.719650 496 solver.cpp:397] Test net output #1: loss = 4.08427 (* 1 = 4.08427 loss)
|
|
I0428 02:07:55.723335 496 solver.cpp:218] Iteration 1330 (0.269525 iter/s, 25.9716s/7 iters), loss = 0.880477
|
|
I0428 02:07:55.723387 496 solver.cpp:237] Train net output #0: loss = 0.739334 (* 1 = 0.739334 loss)
|
|
I0428 02:07:55.723395 496 sgd_solver.cpp:105] Iteration 1330, lr = 0.00415575
|
|
I0428 02:08:12.732506 496 solver.cpp:218] Iteration 1337 (0.411543 iter/s, 17.0092s/7 iters), loss = 0.931405
|
|
I0428 02:08:12.732544 496 solver.cpp:237] Train net output #0: loss = 0.69731 (* 1 = 0.69731 loss)
|
|
I0428 02:08:12.732551 496 sgd_solver.cpp:105] Iteration 1337, lr = 0.00413659
|
|
I0428 02:08:29.711887 496 solver.cpp:218] Iteration 1344 (0.412265 iter/s, 16.9794s/7 iters), loss = 0.967005
|
|
I0428 02:08:29.712013 496 solver.cpp:237] Train net output #0: loss = 0.850073 (* 1 = 0.850073 loss)
|
|
I0428 02:08:29.712023 496 sgd_solver.cpp:105] Iteration 1344, lr = 0.00411751
|
|
I0428 02:08:46.705483 496 solver.cpp:218] Iteration 1351 (0.411922 iter/s, 16.9935s/7 iters), loss = 0.820595
|
|
I0428 02:08:46.705531 496 solver.cpp:237] Train net output #0: loss = 0.709741 (* 1 = 0.709741 loss)
|
|
I0428 02:08:46.705539 496 sgd_solver.cpp:105] Iteration 1351, lr = 0.00409853
|
|
I0428 02:09:03.641427 496 solver.cpp:218] Iteration 1358 (0.413323 iter/s, 16.9359s/7 iters), loss = 0.930007
|
|
I0428 02:09:03.641539 496 solver.cpp:237] Train net output #0: loss = 0.796324 (* 1 = 0.796324 loss)
|
|
I0428 02:09:03.641549 496 sgd_solver.cpp:105] Iteration 1358, lr = 0.00407963
|
|
I0428 02:09:20.579782 496 solver.cpp:218] Iteration 1365 (0.413266 iter/s, 16.9383s/7 iters), loss = 0.853926
|
|
I0428 02:09:20.579826 496 solver.cpp:237] Train net output #0: loss = 0.807968 (* 1 = 0.807968 loss)
|
|
I0428 02:09:20.579835 496 sgd_solver.cpp:105] Iteration 1365, lr = 0.00406082
|
|
I0428 02:09:37.492358 496 solver.cpp:218] Iteration 1372 (0.413894 iter/s, 16.9126s/7 iters), loss = 0.735624
|
|
I0428 02:09:37.492449 496 solver.cpp:237] Train net output #0: loss = 0.689301 (* 1 = 0.689301 loss)
|
|
I0428 02:09:37.492458 496 sgd_solver.cpp:105] Iteration 1372, lr = 0.00404209
|
|
I0428 02:09:54.391429 496 solver.cpp:218] Iteration 1379 (0.414226 iter/s, 16.899s/7 iters), loss = 0.838964
|
|
I0428 02:09:54.391485 496 solver.cpp:237] Train net output #0: loss = 1.03477 (* 1 = 1.03477 loss)
|
|
I0428 02:09:54.391494 496 sgd_solver.cpp:105] Iteration 1379, lr = 0.00402346
|
|
I0428 02:10:11.296185 496 solver.cpp:218] Iteration 1386 (0.414085 iter/s, 16.9047s/7 iters), loss = 0.898373
|
|
I0428 02:10:11.296275 496 solver.cpp:237] Train net output #0: loss = 0.884832 (* 1 = 0.884832 loss)
|
|
I0428 02:10:11.296284 496 sgd_solver.cpp:105] Iteration 1386, lr = 0.00400491
|
|
I0428 02:10:28.207285 496 solver.cpp:218] Iteration 1393 (0.413931 iter/s, 16.911s/7 iters), loss = 0.785317
|
|
I0428 02:10:28.207332 496 solver.cpp:237] Train net output #0: loss = 0.768592 (* 1 = 0.768592 loss)
|
|
I0428 02:10:28.207340 496 sgd_solver.cpp:105] Iteration 1393, lr = 0.00398644
|
|
I0428 02:10:31.367143 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 02:10:45.127948 496 solver.cpp:218] Iteration 1400 (0.413696 iter/s, 16.9206s/7 iters), loss = 0.794331
|
|
I0428 02:10:45.128078 496 solver.cpp:237] Train net output #0: loss = 0.775419 (* 1 = 0.775419 loss)
|
|
I0428 02:10:45.128088 496 sgd_solver.cpp:105] Iteration 1400, lr = 0.00396806
|
|
I0428 02:11:02.029500 496 solver.cpp:218] Iteration 1407 (0.414166 iter/s, 16.9014s/7 iters), loss = 0.670481
|
|
I0428 02:11:02.029546 496 solver.cpp:237] Train net output #0: loss = 0.638454 (* 1 = 0.638454 loss)
|
|
I0428 02:11:02.029554 496 sgd_solver.cpp:105] Iteration 1407, lr = 0.00394976
|
|
I0428 02:11:18.980072 496 solver.cpp:218] Iteration 1414 (0.412966 iter/s, 16.9506s/7 iters), loss = 0.748898
|
|
I0428 02:11:18.980178 496 solver.cpp:237] Train net output #0: loss = 0.614089 (* 1 = 0.614089 loss)
|
|
I0428 02:11:18.980187 496 sgd_solver.cpp:105] Iteration 1414, lr = 0.00393155
|
|
I0428 02:11:21.838716 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:11:35.878051 496 solver.cpp:218] Iteration 1421 (0.414253 iter/s, 16.8979s/7 iters), loss = 0.759271
|
|
I0428 02:11:35.878096 496 solver.cpp:237] Train net output #0: loss = 0.676858 (* 1 = 0.676858 loss)
|
|
I0428 02:11:35.878104 496 sgd_solver.cpp:105] Iteration 1421, lr = 0.00391342
|
|
I0428 02:11:50.353297 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
|
|
I0428 02:11:54.216141 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
|
|
I0428 02:11:58.004160 496 solver.cpp:330] Iteration 1428, Testing net (#0)
|
|
I0428 02:11:58.004186 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:12:02.191233 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:12:02.850778 496 solver.cpp:397] Test net output #0: accuracy = 0.248162
|
|
I0428 02:12:02.850813 496 solver.cpp:397] Test net output #1: loss = 4.30154 (* 1 = 4.30154 loss)
|
|
I0428 02:12:04.265476 496 solver.cpp:218] Iteration 1428 (0.246588 iter/s, 28.3874s/7 iters), loss = 0.770248
|
|
I0428 02:12:04.265524 496 solver.cpp:237] Train net output #0: loss = 0.830513 (* 1 = 0.830513 loss)
|
|
I0428 02:12:04.265532 496 sgd_solver.cpp:105] Iteration 1428, lr = 0.00389538
|
|
I0428 02:12:21.165052 496 solver.cpp:218] Iteration 1435 (0.414212 iter/s, 16.8995s/7 iters), loss = 0.681173
|
|
I0428 02:12:21.165169 496 solver.cpp:237] Train net output #0: loss = 0.762325 (* 1 = 0.762325 loss)
|
|
I0428 02:12:21.165179 496 sgd_solver.cpp:105] Iteration 1435, lr = 0.00387742
|
|
I0428 02:12:38.157568 496 solver.cpp:218] Iteration 1442 (0.411948 iter/s, 16.9924s/7 iters), loss = 0.734173
|
|
I0428 02:12:38.157609 496 solver.cpp:237] Train net output #0: loss = 0.776315 (* 1 = 0.776315 loss)
|
|
I0428 02:12:38.157618 496 sgd_solver.cpp:105] Iteration 1442, lr = 0.00385954
|
|
I0428 02:12:55.216962 496 solver.cpp:218] Iteration 1449 (0.410332 iter/s, 17.0594s/7 iters), loss = 0.651463
|
|
I0428 02:12:55.217072 496 solver.cpp:237] Train net output #0: loss = 0.685445 (* 1 = 0.685445 loss)
|
|
I0428 02:12:55.217080 496 sgd_solver.cpp:105] Iteration 1449, lr = 0.00384174
|
|
I0428 02:13:12.347568 496 solver.cpp:218] Iteration 1456 (0.408628 iter/s, 17.1305s/7 iters), loss = 0.63281
|
|
I0428 02:13:12.347611 496 solver.cpp:237] Train net output #0: loss = 0.597189 (* 1 = 0.597189 loss)
|
|
I0428 02:13:12.347620 496 sgd_solver.cpp:105] Iteration 1456, lr = 0.00382403
|
|
I0428 02:13:29.240134 496 solver.cpp:218] Iteration 1463 (0.414384 iter/s, 16.8925s/7 iters), loss = 0.708514
|
|
I0428 02:13:29.240247 496 solver.cpp:237] Train net output #0: loss = 0.65845 (* 1 = 0.65845 loss)
|
|
I0428 02:13:29.240257 496 sgd_solver.cpp:105] Iteration 1463, lr = 0.0038064
|
|
I0428 02:13:46.180454 496 solver.cpp:218] Iteration 1470 (0.413218 iter/s, 16.9402s/7 iters), loss = 0.712214
|
|
I0428 02:13:46.180498 496 solver.cpp:237] Train net output #0: loss = 0.827328 (* 1 = 0.827328 loss)
|
|
I0428 02:13:46.180507 496 sgd_solver.cpp:105] Iteration 1470, lr = 0.00378885
|
|
I0428 02:14:03.095818 496 solver.cpp:218] Iteration 1477 (0.413826 iter/s, 16.9153s/7 iters), loss = 0.688053
|
|
I0428 02:14:03.095976 496 solver.cpp:237] Train net output #0: loss = 0.690349 (* 1 = 0.690349 loss)
|
|
I0428 02:14:03.095985 496 sgd_solver.cpp:105] Iteration 1477, lr = 0.00377138
|
|
I0428 02:14:20.017227 496 solver.cpp:218] Iteration 1484 (0.41368 iter/s, 16.9213s/7 iters), loss = 0.558934
|
|
I0428 02:14:20.017272 496 solver.cpp:237] Train net output #0: loss = 0.458622 (* 1 = 0.458622 loss)
|
|
I0428 02:14:20.017282 496 sgd_solver.cpp:105] Iteration 1484, lr = 0.00375399
|
|
I0428 02:14:36.905499 496 solver.cpp:218] Iteration 1491 (0.414489 iter/s, 16.8883s/7 iters), loss = 0.595178
|
|
I0428 02:14:36.905593 496 solver.cpp:237] Train net output #0: loss = 0.705539 (* 1 = 0.705539 loss)
|
|
I0428 02:14:36.905602 496 sgd_solver.cpp:105] Iteration 1491, lr = 0.00373668
|
|
I0428 02:14:53.811275 496 solver.cpp:218] Iteration 1498 (0.414061 iter/s, 16.9057s/7 iters), loss = 0.539213
|
|
I0428 02:14:53.811317 496 solver.cpp:237] Train net output #0: loss = 0.620418 (* 1 = 0.620418 loss)
|
|
I0428 02:14:53.811326 496 sgd_solver.cpp:105] Iteration 1498, lr = 0.00371945
|
|
I0428 02:15:10.789561 496 solver.cpp:218] Iteration 1505 (0.412292 iter/s, 16.9783s/7 iters), loss = 0.612831
|
|
I0428 02:15:10.789671 496 solver.cpp:237] Train net output #0: loss = 0.68203 (* 1 = 0.68203 loss)
|
|
I0428 02:15:10.789680 496 sgd_solver.cpp:105] Iteration 1505, lr = 0.0037023
|
|
I0428 02:15:27.679369 496 solver.cpp:218] Iteration 1512 (0.414453 iter/s, 16.8897s/7 iters), loss = 0.710857
|
|
I0428 02:15:27.679412 496 solver.cpp:237] Train net output #0: loss = 0.527475 (* 1 = 0.527475 loss)
|
|
I0428 02:15:27.679421 496 sgd_solver.cpp:105] Iteration 1512, lr = 0.00368523
|
|
I0428 02:15:38.181313 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:15:44.611479 496 solver.cpp:218] Iteration 1519 (0.413416 iter/s, 16.9321s/7 iters), loss = 0.568361
|
|
I0428 02:15:44.611613 496 solver.cpp:237] Train net output #0: loss = 0.486161 (* 1 = 0.486161 loss)
|
|
I0428 02:15:44.611621 496 sgd_solver.cpp:105] Iteration 1519, lr = 0.00366824
|
|
I0428 02:16:01.520136 496 solver.cpp:218] Iteration 1526 (0.413992 iter/s, 16.9086s/7 iters), loss = 0.544302
|
|
I0428 02:16:01.520177 496 solver.cpp:237] Train net output #0: loss = 0.559134 (* 1 = 0.559134 loss)
|
|
I0428 02:16:01.520185 496 sgd_solver.cpp:105] Iteration 1526, lr = 0.00365132
|
|
I0428 02:16:08.715837 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
|
|
I0428 02:16:11.778362 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
|
|
I0428 02:16:15.700955 496 solver.cpp:330] Iteration 1530, Testing net (#0)
|
|
I0428 02:16:15.701066 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:16:19.805687 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:16:20.510093 496 solver.cpp:397] Test net output #0: accuracy = 0.239583
|
|
I0428 02:16:20.510141 496 solver.cpp:397] Test net output #1: loss = 4.30178 (* 1 = 4.30178 loss)
|
|
I0428 02:16:29.106508 496 solver.cpp:218] Iteration 1533 (0.253748 iter/s, 27.5864s/7 iters), loss = 0.560632
|
|
I0428 02:16:29.106555 496 solver.cpp:237] Train net output #0: loss = 0.648275 (* 1 = 0.648275 loss)
|
|
I0428 02:16:29.106564 496 sgd_solver.cpp:105] Iteration 1533, lr = 0.00363449
|
|
I0428 02:16:45.994347 496 solver.cpp:218] Iteration 1540 (0.4145 iter/s, 16.8878s/7 iters), loss = 0.506828
|
|
I0428 02:16:45.994496 496 solver.cpp:237] Train net output #0: loss = 0.428795 (* 1 = 0.428795 loss)
|
|
I0428 02:16:45.994506 496 sgd_solver.cpp:105] Iteration 1540, lr = 0.00361773
|
|
I0428 02:17:00.414105 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 02:17:02.897706 496 solver.cpp:218] Iteration 1547 (0.414122 iter/s, 16.9032s/7 iters), loss = 0.55715
|
|
I0428 02:17:02.897753 496 solver.cpp:237] Train net output #0: loss = 0.440974 (* 1 = 0.440974 loss)
|
|
I0428 02:17:02.897763 496 sgd_solver.cpp:105] Iteration 1547, lr = 0.00360105
|
|
I0428 02:17:19.805259 496 solver.cpp:218] Iteration 1554 (0.414017 iter/s, 16.9075s/7 iters), loss = 0.573917
|
|
I0428 02:17:19.805413 496 solver.cpp:237] Train net output #0: loss = 0.77377 (* 1 = 0.77377 loss)
|
|
I0428 02:17:19.805423 496 sgd_solver.cpp:105] Iteration 1554, lr = 0.00358444
|
|
I0428 02:17:36.743924 496 solver.cpp:218] Iteration 1561 (0.413259 iter/s, 16.9385s/7 iters), loss = 0.58188
|
|
I0428 02:17:36.743974 496 solver.cpp:237] Train net output #0: loss = 0.72943 (* 1 = 0.72943 loss)
|
|
I0428 02:17:36.743984 496 sgd_solver.cpp:105] Iteration 1561, lr = 0.00356792
|
|
I0428 02:17:53.558825 496 solver.cpp:218] Iteration 1568 (0.416298 iter/s, 16.8149s/7 iters), loss = 0.491186
|
|
I0428 02:17:53.558933 496 solver.cpp:237] Train net output #0: loss = 0.523398 (* 1 = 0.523398 loss)
|
|
I0428 02:17:53.558943 496 sgd_solver.cpp:105] Iteration 1568, lr = 0.00355146
|
|
I0428 02:18:10.476073 496 solver.cpp:218] Iteration 1575 (0.413781 iter/s, 16.9172s/7 iters), loss = 0.420908
|
|
I0428 02:18:10.476121 496 solver.cpp:237] Train net output #0: loss = 0.647445 (* 1 = 0.647445 loss)
|
|
I0428 02:18:10.476130 496 sgd_solver.cpp:105] Iteration 1575, lr = 0.00353509
|
|
I0428 02:18:27.399250 496 solver.cpp:218] Iteration 1582 (0.413634 iter/s, 16.9232s/7 iters), loss = 0.436287
|
|
I0428 02:18:27.399374 496 solver.cpp:237] Train net output #0: loss = 0.366398 (* 1 = 0.366398 loss)
|
|
I0428 02:18:27.399382 496 sgd_solver.cpp:105] Iteration 1582, lr = 0.00351879
|
|
I0428 02:18:44.293156 496 solver.cpp:218] Iteration 1589 (0.414353 iter/s, 16.8938s/7 iters), loss = 0.575176
|
|
I0428 02:18:44.293201 496 solver.cpp:237] Train net output #0: loss = 0.474005 (* 1 = 0.474005 loss)
|
|
I0428 02:18:44.293210 496 sgd_solver.cpp:105] Iteration 1589, lr = 0.00350256
|
|
I0428 02:19:01.160024 496 solver.cpp:218] Iteration 1596 (0.415015 iter/s, 16.8669s/7 iters), loss = 0.493396
|
|
I0428 02:19:01.160145 496 solver.cpp:237] Train net output #0: loss = 0.411729 (* 1 = 0.411729 loss)
|
|
I0428 02:19:01.160154 496 sgd_solver.cpp:105] Iteration 1596, lr = 0.00348641
|
|
I0428 02:19:18.152508 496 solver.cpp:218] Iteration 1603 (0.411949 iter/s, 16.9924s/7 iters), loss = 0.461353
|
|
I0428 02:19:18.152552 496 solver.cpp:237] Train net output #0: loss = 0.583411 (* 1 = 0.583411 loss)
|
|
I0428 02:19:18.152560 496 sgd_solver.cpp:105] Iteration 1603, lr = 0.00347034
|
|
I0428 02:19:35.162606 496 solver.cpp:218] Iteration 1610 (0.411521 iter/s, 17.0101s/7 iters), loss = 0.554504
|
|
I0428 02:19:35.162714 496 solver.cpp:237] Train net output #0: loss = 0.618049 (* 1 = 0.618049 loss)
|
|
I0428 02:19:35.162724 496 sgd_solver.cpp:105] Iteration 1610, lr = 0.00345434
|
|
I0428 02:19:52.483477 496 solver.cpp:218] Iteration 1617 (0.404138 iter/s, 17.3208s/7 iters), loss = 0.415097
|
|
I0428 02:19:52.483520 496 solver.cpp:237] Train net output #0: loss = 0.540624 (* 1 = 0.540624 loss)
|
|
I0428 02:19:52.483528 496 sgd_solver.cpp:105] Iteration 1617, lr = 0.00343841
|
|
I0428 02:19:53.594208 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:20:09.468813 496 solver.cpp:218] Iteration 1624 (0.41212 iter/s, 16.9853s/7 iters), loss = 0.478162
|
|
I0428 02:20:09.468931 496 solver.cpp:237] Train net output #0: loss = 0.631121 (* 1 = 0.631121 loss)
|
|
I0428 02:20:09.468940 496 sgd_solver.cpp:105] Iteration 1624, lr = 0.00342256
|
|
I0428 02:20:26.562194 496 solver.cpp:218] Iteration 1631 (0.409517 iter/s, 17.0933s/7 iters), loss = 0.431112
|
|
I0428 02:20:26.562242 496 solver.cpp:237] Train net output #0: loss = 0.33358 (* 1 = 0.33358 loss)
|
|
I0428 02:20:26.562250 496 sgd_solver.cpp:105] Iteration 1631, lr = 0.00340677
|
|
I0428 02:20:26.562443 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
|
|
I0428 02:20:29.645629 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
|
|
I0428 02:20:32.027523 496 solver.cpp:330] Iteration 1632, Testing net (#0)
|
|
I0428 02:20:32.027542 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:20:36.062000 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:20:36.805321 496 solver.cpp:397] Test net output #0: accuracy = 0.249387
|
|
I0428 02:20:36.805366 496 solver.cpp:397] Test net output #1: loss = 4.4434 (* 1 = 4.4434 loss)
|
|
I0428 02:20:52.747486 496 solver.cpp:218] Iteration 1638 (0.267326 iter/s, 26.1853s/7 iters), loss = 0.410066
|
|
I0428 02:20:52.747648 496 solver.cpp:237] Train net output #0: loss = 0.427673 (* 1 = 0.427673 loss)
|
|
I0428 02:20:52.747659 496 sgd_solver.cpp:105] Iteration 1638, lr = 0.00339107
|
|
I0428 02:21:09.644896 496 solver.cpp:218] Iteration 1645 (0.414268 iter/s, 16.8973s/7 iters), loss = 0.446775
|
|
I0428 02:21:09.644938 496 solver.cpp:237] Train net output #0: loss = 0.469407 (* 1 = 0.469407 loss)
|
|
I0428 02:21:09.644945 496 sgd_solver.cpp:105] Iteration 1645, lr = 0.00337543
|
|
I0428 02:21:26.579617 496 solver.cpp:218] Iteration 1652 (0.413352 iter/s, 16.9347s/7 iters), loss = 0.452584
|
|
I0428 02:21:26.579739 496 solver.cpp:237] Train net output #0: loss = 0.387722 (* 1 = 0.387722 loss)
|
|
I0428 02:21:26.579748 496 sgd_solver.cpp:105] Iteration 1652, lr = 0.00335987
|
|
I0428 02:21:43.506814 496 solver.cpp:218] Iteration 1659 (0.413538 iter/s, 16.9271s/7 iters), loss = 0.36788
|
|
I0428 02:21:43.506852 496 solver.cpp:237] Train net output #0: loss = 0.448364 (* 1 = 0.448364 loss)
|
|
I0428 02:21:43.506860 496 sgd_solver.cpp:105] Iteration 1659, lr = 0.00334438
|
|
I0428 02:22:00.411803 496 solver.cpp:218] Iteration 1666 (0.414079 iter/s, 16.905s/7 iters), loss = 0.325241
|
|
I0428 02:22:00.411921 496 solver.cpp:237] Train net output #0: loss = 0.292497 (* 1 = 0.292497 loss)
|
|
I0428 02:22:00.411931 496 sgd_solver.cpp:105] Iteration 1666, lr = 0.00332895
|
|
I0428 02:22:17.395602 496 solver.cpp:218] Iteration 1673 (0.41216 iter/s, 16.9837s/7 iters), loss = 0.400142
|
|
I0428 02:22:17.395648 496 solver.cpp:237] Train net output #0: loss = 0.486314 (* 1 = 0.486314 loss)
|
|
I0428 02:22:17.395658 496 sgd_solver.cpp:105] Iteration 1673, lr = 0.00331361
|
|
I0428 02:22:34.301115 496 solver.cpp:218] Iteration 1680 (0.414067 iter/s, 16.9055s/7 iters), loss = 0.473213
|
|
I0428 02:22:34.301225 496 solver.cpp:237] Train net output #0: loss = 0.538844 (* 1 = 0.538844 loss)
|
|
I0428 02:22:34.301235 496 sgd_solver.cpp:105] Iteration 1680, lr = 0.00329833
|
|
I0428 02:22:51.180953 496 solver.cpp:218] Iteration 1687 (0.414698 iter/s, 16.8798s/7 iters), loss = 0.316216
|
|
I0428 02:22:51.181001 496 solver.cpp:237] Train net output #0: loss = 0.251331 (* 1 = 0.251331 loss)
|
|
I0428 02:22:51.181010 496 sgd_solver.cpp:105] Iteration 1687, lr = 0.00328312
|
|
I0428 02:23:08.087246 496 solver.cpp:218] Iteration 1694 (0.414048 iter/s, 16.9063s/7 iters), loss = 0.422515
|
|
I0428 02:23:08.087386 496 solver.cpp:237] Train net output #0: loss = 0.311489 (* 1 = 0.311489 loss)
|
|
I0428 02:23:08.087407 496 sgd_solver.cpp:105] Iteration 1694, lr = 0.00326798
|
|
I0428 02:23:25.010660 496 solver.cpp:218] Iteration 1701 (0.413631 iter/s, 16.9233s/7 iters), loss = 0.402934
|
|
I0428 02:23:25.010704 496 solver.cpp:237] Train net output #0: loss = 0.460916 (* 1 = 0.460916 loss)
|
|
I0428 02:23:25.010711 496 sgd_solver.cpp:105] Iteration 1701, lr = 0.00325291
|
|
I0428 02:23:35.070371 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 02:23:41.959807 496 solver.cpp:218] Iteration 1708 (0.413001 iter/s, 16.9491s/7 iters), loss = 0.390985
|
|
I0428 02:23:41.959925 496 solver.cpp:237] Train net output #0: loss = 0.323939 (* 1 = 0.323939 loss)
|
|
I0428 02:23:41.959935 496 sgd_solver.cpp:105] Iteration 1708, lr = 0.00323791
|
|
I0428 02:23:58.864635 496 solver.cpp:218] Iteration 1715 (0.414085 iter/s, 16.9047s/7 iters), loss = 0.409521
|
|
I0428 02:23:58.864692 496 solver.cpp:237] Train net output #0: loss = 0.356142 (* 1 = 0.356142 loss)
|
|
I0428 02:23:58.864706 496 sgd_solver.cpp:105] Iteration 1715, lr = 0.00322298
|
|
I0428 02:24:07.695742 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:24:15.866592 496 solver.cpp:218] Iteration 1722 (0.411718 iter/s, 17.0019s/7 iters), loss = 0.372463
|
|
I0428 02:24:15.866816 496 solver.cpp:237] Train net output #0: loss = 0.41463 (* 1 = 0.41463 loss)
|
|
I0428 02:24:15.866832 496 sgd_solver.cpp:105] Iteration 1722, lr = 0.00320812
|
|
I0428 02:24:32.790285 496 solver.cpp:218] Iteration 1729 (0.413626 iter/s, 16.9235s/7 iters), loss = 0.355237
|
|
I0428 02:24:32.790331 496 solver.cpp:237] Train net output #0: loss = 0.332978 (* 1 = 0.332978 loss)
|
|
I0428 02:24:32.790339 496 sgd_solver.cpp:105] Iteration 1729, lr = 0.00319333
|
|
I0428 02:24:42.401904 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
|
|
I0428 02:24:46.658987 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
|
|
I0428 02:24:50.904078 496 solver.cpp:330] Iteration 1734, Testing net (#0)
|
|
I0428 02:24:50.904105 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:24:54.918289 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:24:55.696638 496 solver.cpp:397] Test net output #0: accuracy = 0.271446
|
|
I0428 02:24:55.696682 496 solver.cpp:397] Test net output #1: loss = 4.48245 (* 1 = 4.48245 loss)
|
|
I0428 02:25:01.914561 496 solver.cpp:218] Iteration 1736 (0.240349 iter/s, 29.1243s/7 iters), loss = 0.379199
|
|
I0428 02:25:01.914610 496 solver.cpp:237] Train net output #0: loss = 0.313514 (* 1 = 0.313514 loss)
|
|
I0428 02:25:01.914620 496 sgd_solver.cpp:105] Iteration 1736, lr = 0.00317861
|
|
I0428 02:25:18.799206 496 solver.cpp:218] Iteration 1743 (0.414578 iter/s, 16.8846s/7 iters), loss = 0.363574
|
|
I0428 02:25:18.799307 496 solver.cpp:237] Train net output #0: loss = 0.234868 (* 1 = 0.234868 loss)
|
|
I0428 02:25:18.799317 496 sgd_solver.cpp:105] Iteration 1743, lr = 0.00316395
|
|
I0428 02:25:35.664003 496 solver.cpp:218] Iteration 1750 (0.415068 iter/s, 16.8647s/7 iters), loss = 0.448273
|
|
I0428 02:25:35.664045 496 solver.cpp:237] Train net output #0: loss = 0.565879 (* 1 = 0.565879 loss)
|
|
I0428 02:25:35.664053 496 sgd_solver.cpp:105] Iteration 1750, lr = 0.00314936
|
|
I0428 02:25:52.492856 496 solver.cpp:218] Iteration 1757 (0.415953 iter/s, 16.8288s/7 iters), loss = 0.329641
|
|
I0428 02:25:52.492928 496 solver.cpp:237] Train net output #0: loss = 0.391813 (* 1 = 0.391813 loss)
|
|
I0428 02:25:52.492938 496 sgd_solver.cpp:105] Iteration 1757, lr = 0.00313484
|
|
I0428 02:26:09.410749 496 solver.cpp:218] Iteration 1764 (0.413764 iter/s, 16.9178s/7 iters), loss = 0.325191
|
|
I0428 02:26:09.410797 496 solver.cpp:237] Train net output #0: loss = 0.379462 (* 1 = 0.379462 loss)
|
|
I0428 02:26:09.410807 496 sgd_solver.cpp:105] Iteration 1764, lr = 0.00312039
|
|
I0428 02:26:26.368736 496 solver.cpp:218] Iteration 1771 (0.412785 iter/s, 16.958s/7 iters), loss = 0.362368
|
|
I0428 02:26:26.368858 496 solver.cpp:237] Train net output #0: loss = 0.20131 (* 1 = 0.20131 loss)
|
|
I0428 02:26:26.368868 496 sgd_solver.cpp:105] Iteration 1771, lr = 0.003106
|
|
I0428 02:26:43.238237 496 solver.cpp:218] Iteration 1778 (0.414952 iter/s, 16.8694s/7 iters), loss = 0.296179
|
|
I0428 02:26:43.238299 496 solver.cpp:237] Train net output #0: loss = 0.255115 (* 1 = 0.255115 loss)
|
|
I0428 02:26:43.238310 496 sgd_solver.cpp:105] Iteration 1778, lr = 0.00309168
|
|
I0428 02:27:00.134092 496 solver.cpp:218] Iteration 1785 (0.414304 iter/s, 16.8958s/7 iters), loss = 0.346493
|
|
I0428 02:27:00.134239 496 solver.cpp:237] Train net output #0: loss = 0.456831 (* 1 = 0.456831 loss)
|
|
I0428 02:27:00.134250 496 sgd_solver.cpp:105] Iteration 1785, lr = 0.00307742
|
|
I0428 02:27:17.040467 496 solver.cpp:218] Iteration 1792 (0.414048 iter/s, 16.9063s/7 iters), loss = 0.395458
|
|
I0428 02:27:17.040520 496 solver.cpp:237] Train net output #0: loss = 0.462556 (* 1 = 0.462556 loss)
|
|
I0428 02:27:17.040530 496 sgd_solver.cpp:105] Iteration 1792, lr = 0.00306323
|
|
I0428 02:27:33.941457 496 solver.cpp:218] Iteration 1799 (0.414178 iter/s, 16.901s/7 iters), loss = 0.351856
|
|
I0428 02:27:33.941639 496 solver.cpp:237] Train net output #0: loss = 0.258684 (* 1 = 0.258684 loss)
|
|
I0428 02:27:33.941650 496 sgd_solver.cpp:105] Iteration 1799, lr = 0.00304911
|
|
I0428 02:27:50.880496 496 solver.cpp:218] Iteration 1806 (0.41325 iter/s, 16.9389s/7 iters), loss = 0.318533
|
|
I0428 02:27:50.880561 496 solver.cpp:237] Train net output #0: loss = 0.291153 (* 1 = 0.291153 loss)
|
|
I0428 02:27:50.880573 496 sgd_solver.cpp:105] Iteration 1806, lr = 0.00303505
|
|
I0428 02:28:07.799897 496 solver.cpp:218] Iteration 1813 (0.413727 iter/s, 16.9194s/7 iters), loss = 0.328147
|
|
I0428 02:28:07.800017 496 solver.cpp:237] Train net output #0: loss = 0.349204 (* 1 = 0.349204 loss)
|
|
I0428 02:28:07.800029 496 sgd_solver.cpp:105] Iteration 1813, lr = 0.00302105
|
|
I0428 02:28:24.183766 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:28:24.711076 496 solver.cpp:218] Iteration 1820 (0.41393 iter/s, 16.9111s/7 iters), loss = 0.311147
|
|
I0428 02:28:24.711118 496 solver.cpp:237] Train net output #0: loss = 0.332502 (* 1 = 0.332502 loss)
|
|
I0428 02:28:24.711127 496 sgd_solver.cpp:105] Iteration 1820, lr = 0.00300712
|
|
I0428 02:28:41.665700 496 solver.cpp:218] Iteration 1827 (0.412867 iter/s, 16.9546s/7 iters), loss = 0.343414
|
|
I0428 02:28:41.665863 496 solver.cpp:237] Train net output #0: loss = 0.364796 (* 1 = 0.364796 loss)
|
|
I0428 02:28:41.665881 496 sgd_solver.cpp:105] Iteration 1827, lr = 0.00299326
|
|
I0428 02:28:58.818516 496 solver.cpp:218] Iteration 1834 (0.408099 iter/s, 17.1527s/7 iters), loss = 0.340131
|
|
I0428 02:28:58.818555 496 solver.cpp:237] Train net output #0: loss = 0.376663 (* 1 = 0.376663 loss)
|
|
I0428 02:28:58.818564 496 sgd_solver.cpp:105] Iteration 1834, lr = 0.00297946
|
|
I0428 02:29:01.172797 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
|
|
I0428 02:29:04.285095 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
|
|
I0428 02:29:06.665405 496 solver.cpp:330] Iteration 1836, Testing net (#0)
|
|
I0428 02:29:06.665431 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:29:10.632231 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:29:11.460917 496 solver.cpp:397] Test net output #0: accuracy = 0.257966
|
|
I0428 02:29:11.460963 496 solver.cpp:397] Test net output #1: loss = 4.64686 (* 1 = 4.64686 loss)
|
|
I0428 02:29:24.940646 496 solver.cpp:218] Iteration 1841 (0.267972 iter/s, 26.1221s/7 iters), loss = 0.291957
|
|
I0428 02:29:24.940768 496 solver.cpp:237] Train net output #0: loss = 0.248553 (* 1 = 0.248553 loss)
|
|
I0428 02:29:24.940776 496 sgd_solver.cpp:105] Iteration 1841, lr = 0.00296572
|
|
I0428 02:29:41.838143 496 solver.cpp:218] Iteration 1848 (0.414265 iter/s, 16.8974s/7 iters), loss = 0.321201
|
|
I0428 02:29:41.838186 496 solver.cpp:237] Train net output #0: loss = 0.336619 (* 1 = 0.336619 loss)
|
|
I0428 02:29:41.838193 496 sgd_solver.cpp:105] Iteration 1848, lr = 0.00295205
|
|
I0428 02:29:58.662164 496 solver.cpp:218] Iteration 1855 (0.416072 iter/s, 16.824s/7 iters), loss = 0.334628
|
|
I0428 02:29:58.662290 496 solver.cpp:237] Train net output #0: loss = 0.32855 (* 1 = 0.32855 loss)
|
|
I0428 02:29:58.662300 496 sgd_solver.cpp:105] Iteration 1855, lr = 0.00293843
|
|
I0428 02:30:03.053354 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 02:30:15.668135 496 solver.cpp:218] Iteration 1862 (0.411622 iter/s, 17.0059s/7 iters), loss = 0.270244
|
|
I0428 02:30:15.668179 496 solver.cpp:237] Train net output #0: loss = 0.266779 (* 1 = 0.266779 loss)
|
|
I0428 02:30:15.668186 496 sgd_solver.cpp:105] Iteration 1862, lr = 0.00292489
|
|
I0428 02:30:32.617610 496 solver.cpp:218] Iteration 1869 (0.412993 iter/s, 16.9495s/7 iters), loss = 0.341191
|
|
I0428 02:30:32.617702 496 solver.cpp:237] Train net output #0: loss = 0.34539 (* 1 = 0.34539 loss)
|
|
I0428 02:30:32.617712 496 sgd_solver.cpp:105] Iteration 1869, lr = 0.0029114
|
|
I0428 02:30:49.538733 496 solver.cpp:218] Iteration 1876 (0.413686 iter/s, 16.9211s/7 iters), loss = 0.342462
|
|
I0428 02:30:49.538784 496 solver.cpp:237] Train net output #0: loss = 0.335971 (* 1 = 0.335971 loss)
|
|
I0428 02:30:49.538792 496 sgd_solver.cpp:105] Iteration 1876, lr = 0.00289797
|
|
I0428 02:31:06.659898 496 solver.cpp:218] Iteration 1883 (0.408851 iter/s, 17.1211s/7 iters), loss = 0.27691
|
|
I0428 02:31:06.660055 496 solver.cpp:237] Train net output #0: loss = 0.339631 (* 1 = 0.339631 loss)
|
|
I0428 02:31:06.660065 496 sgd_solver.cpp:105] Iteration 1883, lr = 0.00288461
|
|
I0428 02:31:23.564066 496 solver.cpp:218] Iteration 1890 (0.414102 iter/s, 16.904s/7 iters), loss = 0.234004
|
|
I0428 02:31:23.564108 496 solver.cpp:237] Train net output #0: loss = 0.200005 (* 1 = 0.200005 loss)
|
|
I0428 02:31:23.564116 496 sgd_solver.cpp:105] Iteration 1890, lr = 0.00287131
|
|
I0428 02:31:40.465626 496 solver.cpp:218] Iteration 1897 (0.414163 iter/s, 16.9015s/7 iters), loss = 0.257409
|
|
I0428 02:31:40.465724 496 solver.cpp:237] Train net output #0: loss = 0.285993 (* 1 = 0.285993 loss)
|
|
I0428 02:31:40.465734 496 sgd_solver.cpp:105] Iteration 1897, lr = 0.00285807
|
|
I0428 02:31:57.401985 496 solver.cpp:218] Iteration 1904 (0.413314 iter/s, 16.9363s/7 iters), loss = 0.313441
|
|
I0428 02:31:57.402029 496 solver.cpp:237] Train net output #0: loss = 0.238467 (* 1 = 0.238467 loss)
|
|
I0428 02:31:57.402036 496 sgd_solver.cpp:105] Iteration 1904, lr = 0.00284489
|
|
I0428 02:32:14.303835 496 solver.cpp:218] Iteration 1911 (0.414156 iter/s, 16.9018s/7 iters), loss = 0.347166
|
|
I0428 02:32:14.303975 496 solver.cpp:237] Train net output #0: loss = 0.483865 (* 1 = 0.483865 loss)
|
|
I0428 02:32:14.303987 496 sgd_solver.cpp:105] Iteration 1911, lr = 0.00283178
|
|
I0428 02:32:31.232061 496 solver.cpp:218] Iteration 1918 (0.413513 iter/s, 16.9281s/7 iters), loss = 0.301432
|
|
I0428 02:32:31.232120 496 solver.cpp:237] Train net output #0: loss = 0.345731 (* 1 = 0.345731 loss)
|
|
I0428 02:32:31.232131 496 sgd_solver.cpp:105] Iteration 1918, lr = 0.00281872
|
|
I0428 02:32:38.321444 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:32:48.169098 496 solver.cpp:218] Iteration 1925 (0.413296 iter/s, 16.937s/7 iters), loss = 0.249848
|
|
I0428 02:32:48.169242 496 solver.cpp:237] Train net output #0: loss = 0.266367 (* 1 = 0.266367 loss)
|
|
I0428 02:32:48.169255 496 sgd_solver.cpp:105] Iteration 1925, lr = 0.00280572
|
|
I0428 02:33:05.124823 496 solver.cpp:218] Iteration 1932 (0.412843 iter/s, 16.9556s/7 iters), loss = 0.219419
|
|
I0428 02:33:05.124867 496 solver.cpp:237] Train net output #0: loss = 0.210951 (* 1 = 0.210951 loss)
|
|
I0428 02:33:05.124876 496 sgd_solver.cpp:105] Iteration 1932, lr = 0.00279279
|
|
I0428 02:33:17.119441 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
|
|
I0428 02:33:20.987717 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
|
|
I0428 02:33:24.599686 496 solver.cpp:330] Iteration 1938, Testing net (#0)
|
|
I0428 02:33:24.599707 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:33:28.459247 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:33:29.258200 496 solver.cpp:397] Test net output #0: accuracy = 0.249387
|
|
I0428 02:33:29.258244 496 solver.cpp:397] Test net output #1: loss = 4.62138 (* 1 = 4.62138 loss)
|
|
I0428 02:33:33.106462 496 solver.cpp:218] Iteration 1939 (0.250164 iter/s, 27.9817s/7 iters), loss = 0.257712
|
|
I0428 02:33:33.106504 496 solver.cpp:237] Train net output #0: loss = 0.312764 (* 1 = 0.312764 loss)
|
|
I0428 02:33:33.106513 496 sgd_solver.cpp:105] Iteration 1939, lr = 0.00277991
|
|
I0428 02:33:50.141506 496 solver.cpp:218] Iteration 1946 (0.410918 iter/s, 17.035s/7 iters), loss = 0.263418
|
|
I0428 02:33:50.141551 496 solver.cpp:237] Train net output #0: loss = 0.197031 (* 1 = 0.197031 loss)
|
|
I0428 02:33:50.141559 496 sgd_solver.cpp:105] Iteration 1946, lr = 0.00276709
|
|
I0428 02:34:07.043093 496 solver.cpp:218] Iteration 1953 (0.414163 iter/s, 16.9016s/7 iters), loss = 0.293458
|
|
I0428 02:34:07.043243 496 solver.cpp:237] Train net output #0: loss = 0.337609 (* 1 = 0.337609 loss)
|
|
I0428 02:34:07.043253 496 sgd_solver.cpp:105] Iteration 1953, lr = 0.00275433
|
|
I0428 02:34:24.189913 496 solver.cpp:218] Iteration 1960 (0.408242 iter/s, 17.1467s/7 iters), loss = 0.28593
|
|
I0428 02:34:24.189957 496 solver.cpp:237] Train net output #0: loss = 0.259232 (* 1 = 0.259232 loss)
|
|
I0428 02:34:24.189966 496 sgd_solver.cpp:105] Iteration 1960, lr = 0.00274163
|
|
I0428 02:34:41.128247 496 solver.cpp:218] Iteration 1967 (0.413264 iter/s, 16.9383s/7 iters), loss = 0.270626
|
|
I0428 02:34:41.128366 496 solver.cpp:237] Train net output #0: loss = 0.276048 (* 1 = 0.276048 loss)
|
|
I0428 02:34:41.128376 496 sgd_solver.cpp:105] Iteration 1967, lr = 0.00272899
|
|
I0428 02:34:58.056855 496 solver.cpp:218] Iteration 1974 (0.413503 iter/s, 16.9285s/7 iters), loss = 0.178111
|
|
I0428 02:34:58.056902 496 solver.cpp:237] Train net output #0: loss = 0.129646 (* 1 = 0.129646 loss)
|
|
I0428 02:34:58.056911 496 sgd_solver.cpp:105] Iteration 1974, lr = 0.00271641
|
|
I0428 02:35:14.947247 496 solver.cpp:218] Iteration 1981 (0.414437 iter/s, 16.8904s/7 iters), loss = 0.21817
|
|
I0428 02:35:14.947347 496 solver.cpp:237] Train net output #0: loss = 0.237157 (* 1 = 0.237157 loss)
|
|
I0428 02:35:14.947357 496 sgd_solver.cpp:105] Iteration 1981, lr = 0.00270388
|
|
I0428 02:35:31.888782 496 solver.cpp:218] Iteration 1988 (0.413187 iter/s, 16.9415s/7 iters), loss = 0.262
|
|
I0428 02:35:31.888823 496 solver.cpp:237] Train net output #0: loss = 0.257719 (* 1 = 0.257719 loss)
|
|
I0428 02:35:31.888832 496 sgd_solver.cpp:105] Iteration 1988, lr = 0.00269142
|
|
I0428 02:35:48.763800 496 solver.cpp:218] Iteration 1995 (0.414815 iter/s, 16.875s/7 iters), loss = 0.215281
|
|
I0428 02:35:48.763892 496 solver.cpp:237] Train net output #0: loss = 0.205735 (* 1 = 0.205735 loss)
|
|
I0428 02:35:48.763901 496 sgd_solver.cpp:105] Iteration 1995, lr = 0.00267901
|
|
I0428 02:36:05.673625 496 solver.cpp:218] Iteration 2002 (0.413962 iter/s, 16.9098s/7 iters), loss = 0.266338
|
|
I0428 02:36:05.673671 496 solver.cpp:237] Train net output #0: loss = 0.304696 (* 1 = 0.304696 loss)
|
|
I0428 02:36:05.673679 496 sgd_solver.cpp:105] Iteration 2002, lr = 0.00266665
|
|
I0428 02:36:22.697410 496 solver.cpp:218] Iteration 2009 (0.41119 iter/s, 17.0238s/7 iters), loss = 0.251268
|
|
I0428 02:36:22.697551 496 solver.cpp:237] Train net output #0: loss = 0.235665 (* 1 = 0.235665 loss)
|
|
I0428 02:36:22.697561 496 sgd_solver.cpp:105] Iteration 2009, lr = 0.00265436
|
|
I0428 02:36:39.617324 496 solver.cpp:218] Iteration 2016 (0.413716 iter/s, 16.9198s/7 iters), loss = 0.237356
|
|
I0428 02:36:39.617364 496 solver.cpp:237] Train net output #0: loss = 0.280932 (* 1 = 0.280932 loss)
|
|
I0428 02:36:39.617373 496 sgd_solver.cpp:105] Iteration 2016, lr = 0.00264212
|
|
I0428 02:36:39.617609 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 02:36:54.387303 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:36:56.564041 496 solver.cpp:218] Iteration 2023 (0.41306 iter/s, 16.9467s/7 iters), loss = 0.188233
|
|
I0428 02:36:56.564086 496 solver.cpp:237] Train net output #0: loss = 0.120472 (* 1 = 0.120472 loss)
|
|
I0428 02:36:56.564095 496 sgd_solver.cpp:105] Iteration 2023, lr = 0.00262994
|
|
I0428 02:37:13.484082 496 solver.cpp:218] Iteration 2030 (0.413711 iter/s, 16.92s/7 iters), loss = 0.187576
|
|
I0428 02:37:13.484129 496 solver.cpp:237] Train net output #0: loss = 0.236417 (* 1 = 0.236417 loss)
|
|
I0428 02:37:13.484138 496 sgd_solver.cpp:105] Iteration 2030, lr = 0.00261781
|
|
I0428 02:37:30.393193 496 solver.cpp:218] Iteration 2037 (0.413978 iter/s, 16.9091s/7 iters), loss = 0.203533
|
|
I0428 02:37:30.393296 496 solver.cpp:237] Train net output #0: loss = 0.214305 (* 1 = 0.214305 loss)
|
|
I0428 02:37:30.393306 496 sgd_solver.cpp:105] Iteration 2037, lr = 0.00260574
|
|
I0428 02:37:35.155911 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
|
|
I0428 02:37:38.211899 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
|
|
I0428 02:37:42.476603 496 solver.cpp:330] Iteration 2040, Testing net (#0)
|
|
I0428 02:37:42.476621 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:37:46.344576 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:37:47.260846 496 solver.cpp:397] Test net output #0: accuracy = 0.265319
|
|
I0428 02:37:47.260893 496 solver.cpp:397] Test net output #1: loss = 4.72275 (* 1 = 4.72275 loss)
|
|
I0428 02:37:58.299140 496 solver.cpp:218] Iteration 2044 (0.250843 iter/s, 27.9059s/7 iters), loss = 0.263884
|
|
I0428 02:37:58.299180 496 solver.cpp:237] Train net output #0: loss = 0.293706 (* 1 = 0.293706 loss)
|
|
I0428 02:37:58.299188 496 sgd_solver.cpp:105] Iteration 2044, lr = 0.00259373
|
|
I0428 02:38:15.214679 496 solver.cpp:218] Iteration 2051 (0.413821 iter/s, 16.9155s/7 iters), loss = 0.308734
|
|
I0428 02:38:15.214828 496 solver.cpp:237] Train net output #0: loss = 0.245845 (* 1 = 0.245845 loss)
|
|
I0428 02:38:15.214838 496 sgd_solver.cpp:105] Iteration 2051, lr = 0.00258177
|
|
I0428 02:38:32.101384 496 solver.cpp:218] Iteration 2058 (0.41453 iter/s, 16.8866s/7 iters), loss = 0.227317
|
|
I0428 02:38:32.101426 496 solver.cpp:237] Train net output #0: loss = 0.212654 (* 1 = 0.212654 loss)
|
|
I0428 02:38:32.101434 496 sgd_solver.cpp:105] Iteration 2058, lr = 0.00256986
|
|
I0428 02:38:49.011976 496 solver.cpp:218] Iteration 2065 (0.413942 iter/s, 16.9106s/7 iters), loss = 0.221238
|
|
I0428 02:38:49.012100 496 solver.cpp:237] Train net output #0: loss = 0.305908 (* 1 = 0.305908 loss)
|
|
I0428 02:38:49.012109 496 sgd_solver.cpp:105] Iteration 2065, lr = 0.00255801
|
|
I0428 02:39:05.933986 496 solver.cpp:218] Iteration 2072 (0.413665 iter/s, 16.9219s/7 iters), loss = 0.213799
|
|
I0428 02:39:05.934026 496 solver.cpp:237] Train net output #0: loss = 0.140264 (* 1 = 0.140264 loss)
|
|
I0428 02:39:05.934036 496 sgd_solver.cpp:105] Iteration 2072, lr = 0.00254622
|
|
I0428 02:39:22.835744 496 solver.cpp:218] Iteration 2079 (0.414158 iter/s, 16.9017s/7 iters), loss = 0.225346
|
|
I0428 02:39:22.835876 496 solver.cpp:237] Train net output #0: loss = 0.171698 (* 1 = 0.171698 loss)
|
|
I0428 02:39:22.835891 496 sgd_solver.cpp:105] Iteration 2079, lr = 0.00253448
|
|
I0428 02:39:39.781778 496 solver.cpp:218] Iteration 2086 (0.413078 iter/s, 16.9459s/7 iters), loss = 0.219193
|
|
I0428 02:39:39.781821 496 solver.cpp:237] Train net output #0: loss = 0.214939 (* 1 = 0.214939 loss)
|
|
I0428 02:39:39.781829 496 sgd_solver.cpp:105] Iteration 2086, lr = 0.00252279
|
|
I0428 02:39:56.625926 496 solver.cpp:218] Iteration 2093 (0.415575 iter/s, 16.8441s/7 iters), loss = 0.252291
|
|
I0428 02:39:56.626039 496 solver.cpp:237] Train net output #0: loss = 0.186117 (* 1 = 0.186117 loss)
|
|
I0428 02:39:56.626049 496 sgd_solver.cpp:105] Iteration 2093, lr = 0.00251116
|
|
I0428 02:40:13.580627 496 solver.cpp:218] Iteration 2100 (0.412867 iter/s, 16.9546s/7 iters), loss = 0.180146
|
|
I0428 02:40:13.580667 496 solver.cpp:237] Train net output #0: loss = 0.141183 (* 1 = 0.141183 loss)
|
|
I0428 02:40:13.580674 496 sgd_solver.cpp:105] Iteration 2100, lr = 0.00249958
|
|
I0428 02:40:30.570345 496 solver.cpp:218] Iteration 2107 (0.412014 iter/s, 16.9897s/7 iters), loss = 0.167614
|
|
I0428 02:40:30.570468 496 solver.cpp:237] Train net output #0: loss = 0.108396 (* 1 = 0.108396 loss)
|
|
I0428 02:40:30.570478 496 sgd_solver.cpp:105] Iteration 2107, lr = 0.00248806
|
|
I0428 02:40:47.731917 496 solver.cpp:218] Iteration 2114 (0.40789 iter/s, 17.1615s/7 iters), loss = 0.195553
|
|
I0428 02:40:47.731981 496 solver.cpp:237] Train net output #0: loss = 0.161019 (* 1 = 0.161019 loss)
|
|
I0428 02:40:47.731995 496 sgd_solver.cpp:105] Iteration 2114, lr = 0.00247658
|
|
I0428 02:41:04.687494 496 solver.cpp:218] Iteration 2121 (0.412844 iter/s, 16.9556s/7 iters), loss = 0.130916
|
|
I0428 02:41:04.687600 496 solver.cpp:237] Train net output #0: loss = 0.200489 (* 1 = 0.200489 loss)
|
|
I0428 02:41:04.687609 496 sgd_solver.cpp:105] Iteration 2121, lr = 0.00246516
|
|
I0428 02:41:10.135260 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:41:21.586768 496 solver.cpp:218] Iteration 2128 (0.414221 iter/s, 16.8992s/7 iters), loss = 0.232133
|
|
I0428 02:41:21.586808 496 solver.cpp:237] Train net output #0: loss = 0.153806 (* 1 = 0.153806 loss)
|
|
I0428 02:41:21.586817 496 sgd_solver.cpp:105] Iteration 2128, lr = 0.0024538
|
|
I0428 02:41:38.480938 496 solver.cpp:218] Iteration 2135 (0.414344 iter/s, 16.8942s/7 iters), loss = 0.21315
|
|
I0428 02:41:38.481101 496 solver.cpp:237] Train net output #0: loss = 0.154429 (* 1 = 0.154429 loss)
|
|
I0428 02:41:38.481110 496 sgd_solver.cpp:105] Iteration 2135, lr = 0.00244248
|
|
I0428 02:41:52.885581 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
|
|
I0428 02:41:56.988214 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
|
|
I0428 02:42:00.065551 496 solver.cpp:330] Iteration 2142, Testing net (#0)
|
|
I0428 02:42:00.065570 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:42:03.696143 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:42:04.583695 496 solver.cpp:397] Test net output #0: accuracy = 0.265931
|
|
I0428 02:42:04.583729 496 solver.cpp:397] Test net output #1: loss = 4.76874 (* 1 = 4.76874 loss)
|
|
I0428 02:42:05.927300 496 solver.cpp:218] Iteration 2142 (0.255044 iter/s, 27.4463s/7 iters), loss = 0.19031
|
|
I0428 02:42:05.927341 496 solver.cpp:237] Train net output #0: loss = 0.198082 (* 1 = 0.198082 loss)
|
|
I0428 02:42:05.927350 496 sgd_solver.cpp:105] Iteration 2142, lr = 0.00243122
|
|
I0428 02:42:23.409667 496 solver.cpp:218] Iteration 2149 (0.400404 iter/s, 17.4823s/7 iters), loss = 0.212181
|
|
I0428 02:42:23.409744 496 solver.cpp:237] Train net output #0: loss = 0.237029 (* 1 = 0.237029 loss)
|
|
I0428 02:42:23.409752 496 sgd_solver.cpp:105] Iteration 2149, lr = 0.00242001
|
|
I0428 02:42:41.570703 496 solver.cpp:218] Iteration 2156 (0.385442 iter/s, 18.161s/7 iters), loss = 0.168084
|
|
I0428 02:42:41.570742 496 solver.cpp:237] Train net output #0: loss = 0.166915 (* 1 = 0.166915 loss)
|
|
I0428 02:42:41.570750 496 sgd_solver.cpp:105] Iteration 2156, lr = 0.00240885
|
|
I0428 02:42:59.062629 496 solver.cpp:218] Iteration 2163 (0.400185 iter/s, 17.4919s/7 iters), loss = 0.176157
|
|
I0428 02:42:59.062714 496 solver.cpp:237] Train net output #0: loss = 0.183203 (* 1 = 0.183203 loss)
|
|
I0428 02:42:59.062723 496 sgd_solver.cpp:105] Iteration 2163, lr = 0.00239775
|
|
I0428 02:43:10.769457 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 02:43:16.602876 496 solver.cpp:218] Iteration 2170 (0.399083 iter/s, 17.5402s/7 iters), loss = 0.222567
|
|
I0428 02:43:16.602919 496 solver.cpp:237] Train net output #0: loss = 0.196028 (* 1 = 0.196028 loss)
|
|
I0428 02:43:16.602927 496 sgd_solver.cpp:105] Iteration 2170, lr = 0.00238669
|
|
I0428 02:43:34.065188 496 solver.cpp:218] Iteration 2177 (0.400863 iter/s, 17.4623s/7 iters), loss = 0.190119
|
|
I0428 02:43:34.065294 496 solver.cpp:237] Train net output #0: loss = 0.22614 (* 1 = 0.22614 loss)
|
|
I0428 02:43:34.065304 496 sgd_solver.cpp:105] Iteration 2177, lr = 0.00237569
|
|
I0428 02:43:52.003315 496 solver.cpp:218] Iteration 2184 (0.390231 iter/s, 17.9381s/7 iters), loss = 0.176026
|
|
I0428 02:43:52.003358 496 solver.cpp:237] Train net output #0: loss = 0.173333 (* 1 = 0.173333 loss)
|
|
I0428 02:43:52.003367 496 sgd_solver.cpp:105] Iteration 2184, lr = 0.00236473
|
|
I0428 02:44:08.948895 496 solver.cpp:218] Iteration 2191 (0.413087 iter/s, 16.9456s/7 iters), loss = 0.185784
|
|
I0428 02:44:08.949033 496 solver.cpp:237] Train net output #0: loss = 0.12667 (* 1 = 0.12667 loss)
|
|
I0428 02:44:08.949045 496 sgd_solver.cpp:105] Iteration 2191, lr = 0.00235383
|
|
I0428 02:44:25.816006 496 solver.cpp:218] Iteration 2198 (0.415011 iter/s, 16.867s/7 iters), loss = 0.167933
|
|
I0428 02:44:25.816048 496 solver.cpp:237] Train net output #0: loss = 0.171007 (* 1 = 0.171007 loss)
|
|
I0428 02:44:25.816056 496 sgd_solver.cpp:105] Iteration 2198, lr = 0.00234297
|
|
I0428 02:44:42.731585 496 solver.cpp:218] Iteration 2205 (0.413819 iter/s, 16.9156s/7 iters), loss = 0.172729
|
|
I0428 02:44:42.731751 496 solver.cpp:237] Train net output #0: loss = 0.214747 (* 1 = 0.214747 loss)
|
|
I0428 02:44:42.731761 496 sgd_solver.cpp:105] Iteration 2205, lr = 0.00233217
|
|
I0428 02:44:59.679632 496 solver.cpp:218] Iteration 2212 (0.41303 iter/s, 16.9479s/7 iters), loss = 0.143429
|
|
I0428 02:44:59.679674 496 solver.cpp:237] Train net output #0: loss = 0.156836 (* 1 = 0.156836 loss)
|
|
I0428 02:44:59.679682 496 sgd_solver.cpp:105] Iteration 2212, lr = 0.00232142
|
|
I0428 02:45:16.539875 496 solver.cpp:218] Iteration 2219 (0.415178 iter/s, 16.8603s/7 iters), loss = 0.187411
|
|
I0428 02:45:16.539966 496 solver.cpp:237] Train net output #0: loss = 0.276476 (* 1 = 0.276476 loss)
|
|
I0428 02:45:16.539976 496 sgd_solver.cpp:105] Iteration 2219, lr = 0.00231071
|
|
I0428 02:45:29.651413 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:45:33.468557 496 solver.cpp:218] Iteration 2226 (0.4135 iter/s, 16.9286s/7 iters), loss = 0.16927
|
|
I0428 02:45:33.468595 496 solver.cpp:237] Train net output #0: loss = 0.165903 (* 1 = 0.165903 loss)
|
|
I0428 02:45:33.468603 496 sgd_solver.cpp:105] Iteration 2226, lr = 0.00230006
|
|
I0428 02:45:50.384953 496 solver.cpp:218] Iteration 2233 (0.413799 iter/s, 16.9164s/7 iters), loss = 0.151685
|
|
I0428 02:45:50.385046 496 solver.cpp:237] Train net output #0: loss = 0.22339 (* 1 = 0.22339 loss)
|
|
I0428 02:45:50.385056 496 sgd_solver.cpp:105] Iteration 2233, lr = 0.00228945
|
|
I0428 02:46:07.323627 496 solver.cpp:218] Iteration 2240 (0.413256 iter/s, 16.9386s/7 iters), loss = 0.154684
|
|
I0428 02:46:07.323669 496 solver.cpp:237] Train net output #0: loss = 0.101289 (* 1 = 0.101289 loss)
|
|
I0428 02:46:07.323678 496 sgd_solver.cpp:105] Iteration 2240, lr = 0.0022789
|
|
I0428 02:46:14.686987 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
|
|
I0428 02:46:17.806905 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
|
|
I0428 02:46:20.158885 496 solver.cpp:330] Iteration 2244, Testing net (#0)
|
|
I0428 02:46:20.158907 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:46:23.859289 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:46:24.867987 496 solver.cpp:397] Test net output #0: accuracy = 0.264706
|
|
I0428 02:46:24.868028 496 solver.cpp:397] Test net output #1: loss = 4.81054 (* 1 = 4.81054 loss)
|
|
I0428 02:46:33.473491 496 solver.cpp:218] Iteration 2247 (0.267687 iter/s, 26.1499s/7 iters), loss = 0.149464
|
|
I0428 02:46:33.473532 496 solver.cpp:237] Train net output #0: loss = 0.180647 (* 1 = 0.180647 loss)
|
|
I0428 02:46:33.473541 496 sgd_solver.cpp:105] Iteration 2247, lr = 0.00226839
|
|
I0428 02:46:50.341351 496 solver.cpp:218] Iteration 2254 (0.41499 iter/s, 16.8679s/7 iters), loss = 0.144027
|
|
I0428 02:46:50.341396 496 solver.cpp:237] Train net output #0: loss = 0.161886 (* 1 = 0.161886 loss)
|
|
I0428 02:46:50.341404 496 sgd_solver.cpp:105] Iteration 2254, lr = 0.00225793
|
|
I0428 02:47:07.254130 496 solver.cpp:218] Iteration 2261 (0.413888 iter/s, 16.9128s/7 iters), loss = 0.169873
|
|
I0428 02:47:07.254240 496 solver.cpp:237] Train net output #0: loss = 0.119914 (* 1 = 0.119914 loss)
|
|
I0428 02:47:07.254249 496 sgd_solver.cpp:105] Iteration 2261, lr = 0.00224752
|
|
I0428 02:47:24.170641 496 solver.cpp:218] Iteration 2268 (0.413798 iter/s, 16.9165s/7 iters), loss = 0.16147
|
|
I0428 02:47:24.170680 496 solver.cpp:237] Train net output #0: loss = 0.171966 (* 1 = 0.171966 loss)
|
|
I0428 02:47:24.170687 496 sgd_solver.cpp:105] Iteration 2268, lr = 0.00223716
|
|
I0428 02:47:41.106521 496 solver.cpp:218] Iteration 2275 (0.413324 iter/s, 16.9359s/7 iters), loss = 0.159347
|
|
I0428 02:47:41.106621 496 solver.cpp:237] Train net output #0: loss = 0.110914 (* 1 = 0.110914 loss)
|
|
I0428 02:47:41.106631 496 sgd_solver.cpp:105] Iteration 2275, lr = 0.00222684
|
|
I0428 02:47:58.010712 496 solver.cpp:218] Iteration 2282 (0.4141 iter/s, 16.9041s/7 iters), loss = 0.164327
|
|
I0428 02:47:58.010751 496 solver.cpp:237] Train net output #0: loss = 0.220743 (* 1 = 0.220743 loss)
|
|
I0428 02:47:58.010759 496 sgd_solver.cpp:105] Iteration 2282, lr = 0.00221657
|
|
I0428 02:48:14.933782 496 solver.cpp:218] Iteration 2289 (0.413636 iter/s, 16.9231s/7 iters), loss = 0.140517
|
|
I0428 02:48:14.933923 496 solver.cpp:237] Train net output #0: loss = 0.057682 (* 1 = 0.057682 loss)
|
|
I0428 02:48:14.933933 496 sgd_solver.cpp:105] Iteration 2289, lr = 0.00220635
|
|
I0428 02:48:31.847535 496 solver.cpp:218] Iteration 2296 (0.413867 iter/s, 16.9137s/7 iters), loss = 0.159588
|
|
I0428 02:48:31.847575 496 solver.cpp:237] Train net output #0: loss = 0.175999 (* 1 = 0.175999 loss)
|
|
I0428 02:48:31.847584 496 sgd_solver.cpp:105] Iteration 2296, lr = 0.00219618
|
|
I0428 02:48:48.758621 496 solver.cpp:218] Iteration 2303 (0.413929 iter/s, 16.9111s/7 iters), loss = 0.174553
|
|
I0428 02:48:48.758724 496 solver.cpp:237] Train net output #0: loss = 0.14792 (* 1 = 0.14792 loss)
|
|
I0428 02:48:48.758733 496 sgd_solver.cpp:105] Iteration 2303, lr = 0.00218605
|
|
I0428 02:49:05.654592 496 solver.cpp:218] Iteration 2310 (0.414301 iter/s, 16.8959s/7 iters), loss = 0.153403
|
|
I0428 02:49:05.654637 496 solver.cpp:237] Train net output #0: loss = 0.114529 (* 1 = 0.114529 loss)
|
|
I0428 02:49:05.654645 496 sgd_solver.cpp:105] Iteration 2310, lr = 0.00217597
|
|
I0428 02:49:22.568917 496 solver.cpp:218] Iteration 2317 (0.41385 iter/s, 16.9143s/7 iters), loss = 0.140274
|
|
I0428 02:49:22.569026 496 solver.cpp:237] Train net output #0: loss = 0.0961447 (* 1 = 0.0961447 loss)
|
|
I0428 02:49:22.569034 496 sgd_solver.cpp:105] Iteration 2317, lr = 0.00216594
|
|
I0428 02:49:39.412910 496 solver.cpp:218] Iteration 2324 (0.41558 iter/s, 16.8439s/7 iters), loss = 0.151239
|
|
I0428 02:49:39.412950 496 solver.cpp:237] Train net output #0: loss = 0.263806 (* 1 = 0.263806 loss)
|
|
I0428 02:49:39.412956 496 sgd_solver.cpp:105] Iteration 2324, lr = 0.00215595
|
|
I0428 02:49:43.193501 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:49:46.171950 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 02:49:56.237807 496 solver.cpp:218] Iteration 2331 (0.41605 iter/s, 16.8249s/7 iters), loss = 0.153484
|
|
I0428 02:49:56.237929 496 solver.cpp:237] Train net output #0: loss = 0.116582 (* 1 = 0.116582 loss)
|
|
I0428 02:49:56.237939 496 sgd_solver.cpp:105] Iteration 2331, lr = 0.00214601
|
|
I0428 02:50:13.127209 496 solver.cpp:218] Iteration 2338 (0.414463 iter/s, 16.8893s/7 iters), loss = 0.18336
|
|
I0428 02:50:13.127251 496 solver.cpp:237] Train net output #0: loss = 0.110041 (* 1 = 0.110041 loss)
|
|
I0428 02:50:13.127259 496 sgd_solver.cpp:105] Iteration 2338, lr = 0.00213612
|
|
I0428 02:50:29.964452 496 solver.cpp:218] Iteration 2345 (0.415745 iter/s, 16.8372s/7 iters), loss = 0.162009
|
|
I0428 02:50:29.964576 496 solver.cpp:237] Train net output #0: loss = 0.170206 (* 1 = 0.170206 loss)
|
|
I0428 02:50:29.964584 496 sgd_solver.cpp:105] Iteration 2345, lr = 0.00212627
|
|
I0428 02:50:29.964771 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
|
|
I0428 02:50:32.963454 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
|
|
I0428 02:50:35.320158 496 solver.cpp:330] Iteration 2346, Testing net (#0)
|
|
I0428 02:50:35.320176 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:50:39.057767 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:50:40.100348 496 solver.cpp:397] Test net output #0: accuracy = 0.265319
|
|
I0428 02:50:40.100397 496 solver.cpp:397] Test net output #1: loss = 4.89454 (* 1 = 4.89454 loss)
|
|
I0428 02:50:56.001158 496 solver.cpp:218] Iteration 2352 (0.268852 iter/s, 26.0366s/7 iters), loss = 0.132633
|
|
I0428 02:50:56.001207 496 solver.cpp:237] Train net output #0: loss = 0.0908654 (* 1 = 0.0908654 loss)
|
|
I0428 02:50:56.001215 496 sgd_solver.cpp:105] Iteration 2352, lr = 0.00211647
|
|
I0428 02:51:12.928472 496 solver.cpp:218] Iteration 2359 (0.413533 iter/s, 16.9273s/7 iters), loss = 0.191395
|
|
I0428 02:51:12.928635 496 solver.cpp:237] Train net output #0: loss = 0.20467 (* 1 = 0.20467 loss)
|
|
I0428 02:51:12.928645 496 sgd_solver.cpp:105] Iteration 2359, lr = 0.00210671
|
|
I0428 02:51:29.844671 496 solver.cpp:218] Iteration 2366 (0.413807 iter/s, 16.9161s/7 iters), loss = 0.157319
|
|
I0428 02:51:29.844715 496 solver.cpp:237] Train net output #0: loss = 0.14455 (* 1 = 0.14455 loss)
|
|
I0428 02:51:29.844724 496 sgd_solver.cpp:105] Iteration 2366, lr = 0.00209699
|
|
I0428 02:51:46.752671 496 solver.cpp:218] Iteration 2373 (0.414005 iter/s, 16.908s/7 iters), loss = 0.153913
|
|
I0428 02:51:46.752784 496 solver.cpp:237] Train net output #0: loss = 0.171568 (* 1 = 0.171568 loss)
|
|
I0428 02:51:46.752794 496 sgd_solver.cpp:105] Iteration 2373, lr = 0.00208732
|
|
I0428 02:52:03.665386 496 solver.cpp:218] Iteration 2380 (0.413892 iter/s, 16.9126s/7 iters), loss = 0.147669
|
|
I0428 02:52:03.665431 496 solver.cpp:237] Train net output #0: loss = 0.142527 (* 1 = 0.142527 loss)
|
|
I0428 02:52:03.665439 496 sgd_solver.cpp:105] Iteration 2380, lr = 0.0020777
|
|
I0428 02:52:20.579051 496 solver.cpp:218] Iteration 2387 (0.413867 iter/s, 16.9137s/7 iters), loss = 0.156137
|
|
I0428 02:52:20.579156 496 solver.cpp:237] Train net output #0: loss = 0.218903 (* 1 = 0.218903 loss)
|
|
I0428 02:52:20.579166 496 sgd_solver.cpp:105] Iteration 2387, lr = 0.00206812
|
|
I0428 02:52:37.482390 496 solver.cpp:218] Iteration 2394 (0.414121 iter/s, 16.9033s/7 iters), loss = 0.136767
|
|
I0428 02:52:37.482429 496 solver.cpp:237] Train net output #0: loss = 0.144959 (* 1 = 0.144959 loss)
|
|
I0428 02:52:37.482437 496 sgd_solver.cpp:105] Iteration 2394, lr = 0.00205858
|
|
I0428 02:52:54.400454 496 solver.cpp:218] Iteration 2401 (0.413759 iter/s, 16.9181s/7 iters), loss = 0.111246
|
|
I0428 02:52:54.400578 496 solver.cpp:237] Train net output #0: loss = 0.188795 (* 1 = 0.188795 loss)
|
|
I0428 02:52:54.400588 496 sgd_solver.cpp:105] Iteration 2401, lr = 0.00204909
|
|
I0428 02:53:11.304282 496 solver.cpp:218] Iteration 2408 (0.414109 iter/s, 16.9038s/7 iters), loss = 0.1315
|
|
I0428 02:53:11.304322 496 solver.cpp:237] Train net output #0: loss = 0.20898 (* 1 = 0.20898 loss)
|
|
I0428 02:53:11.304330 496 sgd_solver.cpp:105] Iteration 2408, lr = 0.00203964
|
|
I0428 02:53:28.262051 496 solver.cpp:218] Iteration 2415 (0.41279 iter/s, 16.9578s/7 iters), loss = 0.145075
|
|
I0428 02:53:28.262166 496 solver.cpp:237] Train net output #0: loss = 0.119379 (* 1 = 0.119379 loss)
|
|
I0428 02:53:28.262176 496 sgd_solver.cpp:105] Iteration 2415, lr = 0.00203024
|
|
I0428 02:53:45.144248 496 solver.cpp:218] Iteration 2422 (0.41464 iter/s, 16.8821s/7 iters), loss = 0.139448
|
|
I0428 02:53:45.144290 496 solver.cpp:237] Train net output #0: loss = 0.145061 (* 1 = 0.145061 loss)
|
|
I0428 02:53:45.144299 496 sgd_solver.cpp:105] Iteration 2422, lr = 0.00202088
|
|
I0428 02:53:56.598659 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:54:02.009650 496 solver.cpp:218] Iteration 2429 (0.415051 iter/s, 16.8654s/7 iters), loss = 0.169017
|
|
I0428 02:54:02.009775 496 solver.cpp:237] Train net output #0: loss = 0.234034 (* 1 = 0.234034 loss)
|
|
I0428 02:54:02.009785 496 sgd_solver.cpp:105] Iteration 2429, lr = 0.00201156
|
|
I0428 02:54:18.988612 496 solver.cpp:218] Iteration 2436 (0.412277 iter/s, 16.9789s/7 iters), loss = 0.165557
|
|
I0428 02:54:18.988656 496 solver.cpp:237] Train net output #0: loss = 0.106189 (* 1 = 0.106189 loss)
|
|
I0428 02:54:18.988663 496 sgd_solver.cpp:105] Iteration 2436, lr = 0.00200228
|
|
I0428 02:54:35.903551 496 solver.cpp:218] Iteration 2443 (0.413835 iter/s, 16.9149s/7 iters), loss = 0.124743
|
|
I0428 02:54:35.903671 496 solver.cpp:237] Train net output #0: loss = 0.13811 (* 1 = 0.13811 loss)
|
|
I0428 02:54:35.903679 496 sgd_solver.cpp:105] Iteration 2443, lr = 0.00199305
|
|
I0428 02:54:45.526077 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
|
|
I0428 02:54:48.606765 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
|
|
I0428 02:54:50.964135 496 solver.cpp:330] Iteration 2448, Testing net (#0)
|
|
I0428 02:54:50.964162 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:54:54.665964 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:54:55.748525 496 solver.cpp:397] Test net output #0: accuracy = 0.265319
|
|
I0428 02:54:55.748574 496 solver.cpp:397] Test net output #1: loss = 4.89021 (* 1 = 4.89021 loss)
|
|
I0428 02:55:01.933661 496 solver.cpp:218] Iteration 2450 (0.26892 iter/s, 26.0301s/7 iters), loss = 0.154366
|
|
I0428 02:55:01.933702 496 solver.cpp:237] Train net output #0: loss = 0.22526 (* 1 = 0.22526 loss)
|
|
I0428 02:55:01.933710 496 sgd_solver.cpp:105] Iteration 2450, lr = 0.00198386
|
|
I0428 02:55:18.803455 496 solver.cpp:218] Iteration 2457 (0.414943 iter/s, 16.8698s/7 iters), loss = 0.142392
|
|
I0428 02:55:18.803580 496 solver.cpp:237] Train net output #0: loss = 0.2322 (* 1 = 0.2322 loss)
|
|
I0428 02:55:18.803591 496 sgd_solver.cpp:105] Iteration 2457, lr = 0.00197472
|
|
I0428 02:55:35.715644 496 solver.cpp:218] Iteration 2464 (0.413905 iter/s, 16.9121s/7 iters), loss = 0.13958
|
|
I0428 02:55:35.715685 496 solver.cpp:237] Train net output #0: loss = 0.106117 (* 1 = 0.106117 loss)
|
|
I0428 02:55:35.715692 496 sgd_solver.cpp:105] Iteration 2464, lr = 0.00196561
|
|
I0428 02:55:52.636559 496 solver.cpp:218] Iteration 2471 (0.413689 iter/s, 16.9209s/7 iters), loss = 0.16281
|
|
I0428 02:55:52.636690 496 solver.cpp:237] Train net output #0: loss = 0.219099 (* 1 = 0.219099 loss)
|
|
I0428 02:55:52.636699 496 sgd_solver.cpp:105] Iteration 2471, lr = 0.00195655
|
|
I0428 02:56:09.564802 496 solver.cpp:218] Iteration 2478 (0.413512 iter/s, 16.9282s/7 iters), loss = 0.149839
|
|
I0428 02:56:09.564849 496 solver.cpp:237] Train net output #0: loss = 0.119505 (* 1 = 0.119505 loss)
|
|
I0428 02:56:09.564858 496 sgd_solver.cpp:105] Iteration 2478, lr = 0.00194753
|
|
I0428 02:56:10.708505 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 02:56:26.459163 496 solver.cpp:218] Iteration 2485 (0.414339 iter/s, 16.8944s/7 iters), loss = 0.147774
|
|
I0428 02:56:26.459272 496 solver.cpp:237] Train net output #0: loss = 0.195268 (* 1 = 0.195268 loss)
|
|
I0428 02:56:26.459281 496 sgd_solver.cpp:105] Iteration 2485, lr = 0.00193855
|
|
I0428 02:56:43.367607 496 solver.cpp:218] Iteration 2492 (0.413996 iter/s, 16.9084s/7 iters), loss = 0.124796
|
|
I0428 02:56:43.367650 496 solver.cpp:237] Train net output #0: loss = 0.115549 (* 1 = 0.115549 loss)
|
|
I0428 02:56:43.367658 496 sgd_solver.cpp:105] Iteration 2492, lr = 0.00192961
|
|
I0428 02:57:00.275715 496 solver.cpp:218] Iteration 2499 (0.414003 iter/s, 16.9081s/7 iters), loss = 0.153646
|
|
I0428 02:57:00.275826 496 solver.cpp:237] Train net output #0: loss = 0.165452 (* 1 = 0.165452 loss)
|
|
I0428 02:57:00.275836 496 sgd_solver.cpp:105] Iteration 2499, lr = 0.00192071
|
|
I0428 02:57:17.087610 496 solver.cpp:218] Iteration 2506 (0.416374 iter/s, 16.8118s/7 iters), loss = 0.118189
|
|
I0428 02:57:17.087653 496 solver.cpp:237] Train net output #0: loss = 0.137527 (* 1 = 0.137527 loss)
|
|
I0428 02:57:17.087662 496 sgd_solver.cpp:105] Iteration 2506, lr = 0.00191185
|
|
I0428 02:57:33.916141 496 solver.cpp:218] Iteration 2513 (0.41596 iter/s, 16.8285s/7 iters), loss = 0.129782
|
|
I0428 02:57:33.916260 496 solver.cpp:237] Train net output #0: loss = 0.113461 (* 1 = 0.113461 loss)
|
|
I0428 02:57:33.916268 496 sgd_solver.cpp:105] Iteration 2513, lr = 0.00190304
|
|
I0428 02:57:50.828243 496 solver.cpp:218] Iteration 2520 (0.413907 iter/s, 16.912s/7 iters), loss = 0.147877
|
|
I0428 02:57:50.828282 496 solver.cpp:237] Train net output #0: loss = 0.156663 (* 1 = 0.156663 loss)
|
|
I0428 02:57:50.828290 496 sgd_solver.cpp:105] Iteration 2520, lr = 0.00189426
|
|
I0428 02:58:07.788254 496 solver.cpp:218] Iteration 2527 (0.412736 iter/s, 16.96s/7 iters), loss = 0.126537
|
|
I0428 02:58:07.788530 496 solver.cpp:237] Train net output #0: loss = 0.135428 (* 1 = 0.135428 loss)
|
|
I0428 02:58:07.788539 496 sgd_solver.cpp:105] Iteration 2527, lr = 0.00188553
|
|
I0428 02:58:09.922963 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:58:24.682410 496 solver.cpp:218] Iteration 2534 (0.41435 iter/s, 16.8939s/7 iters), loss = 0.15618
|
|
I0428 02:58:24.682456 496 solver.cpp:237] Train net output #0: loss = 0.132812 (* 1 = 0.132812 loss)
|
|
I0428 02:58:24.682464 496 sgd_solver.cpp:105] Iteration 2534, lr = 0.00187684
|
|
I0428 02:58:41.595010 496 solver.cpp:218] Iteration 2541 (0.413893 iter/s, 16.9126s/7 iters), loss = 0.130385
|
|
I0428 02:58:41.595144 496 solver.cpp:237] Train net output #0: loss = 0.121258 (* 1 = 0.121258 loss)
|
|
I0428 02:58:41.595155 496 sgd_solver.cpp:105] Iteration 2541, lr = 0.00186818
|
|
I0428 02:58:58.515789 496 solver.cpp:218] Iteration 2548 (0.413695 iter/s, 16.9207s/7 iters), loss = 0.139255
|
|
I0428 02:58:58.515832 496 solver.cpp:237] Train net output #0: loss = 0.153999 (* 1 = 0.153999 loss)
|
|
I0428 02:58:58.515841 496 sgd_solver.cpp:105] Iteration 2548, lr = 0.00185957
|
|
I0428 02:59:00.869469 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
|
|
I0428 02:59:03.981941 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
|
|
I0428 02:59:07.191112 496 solver.cpp:330] Iteration 2550, Testing net (#0)
|
|
I0428 02:59:07.191128 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 02:59:10.749735 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 02:59:11.794488 496 solver.cpp:397] Test net output #0: accuracy = 0.269608
|
|
I0428 02:59:11.794642 496 solver.cpp:397] Test net output #1: loss = 4.91025 (* 1 = 4.91025 loss)
|
|
I0428 02:59:25.686702 496 solver.cpp:218] Iteration 2555 (0.257628 iter/s, 27.1709s/7 iters), loss = 0.0993361
|
|
I0428 02:59:25.686748 496 solver.cpp:237] Train net output #0: loss = 0.0780722 (* 1 = 0.0780722 loss)
|
|
I0428 02:59:25.686755 496 sgd_solver.cpp:105] Iteration 2555, lr = 0.00185099
|
|
I0428 02:59:42.679409 496 solver.cpp:218] Iteration 2562 (0.411942 iter/s, 16.9927s/7 iters), loss = 0.13188
|
|
I0428 02:59:42.679539 496 solver.cpp:237] Train net output #0: loss = 0.213706 (* 1 = 0.213706 loss)
|
|
I0428 02:59:42.679549 496 sgd_solver.cpp:105] Iteration 2562, lr = 0.00184246
|
|
I0428 02:59:59.583648 496 solver.cpp:218] Iteration 2569 (0.414099 iter/s, 16.9042s/7 iters), loss = 0.106155
|
|
I0428 02:59:59.583690 496 solver.cpp:237] Train net output #0: loss = 0.0600662 (* 1 = 0.0600662 loss)
|
|
I0428 02:59:59.583698 496 sgd_solver.cpp:105] Iteration 2569, lr = 0.00183396
|
|
I0428 03:00:16.445294 496 solver.cpp:218] Iteration 2576 (0.415143 iter/s, 16.8616s/7 iters), loss = 0.139303
|
|
I0428 03:00:16.445384 496 solver.cpp:237] Train net output #0: loss = 0.0609307 (* 1 = 0.0609307 loss)
|
|
I0428 03:00:16.445392 496 sgd_solver.cpp:105] Iteration 2576, lr = 0.00182551
|
|
I0428 03:00:33.355691 496 solver.cpp:218] Iteration 2583 (0.413948 iter/s, 16.9103s/7 iters), loss = 0.0888633
|
|
I0428 03:00:33.355732 496 solver.cpp:237] Train net output #0: loss = 0.0547488 (* 1 = 0.0547488 loss)
|
|
I0428 03:00:33.355741 496 sgd_solver.cpp:105] Iteration 2583, lr = 0.00181709
|
|
I0428 03:00:50.266860 496 solver.cpp:218] Iteration 2590 (0.413928 iter/s, 16.9112s/7 iters), loss = 0.153928
|
|
I0428 03:00:50.266966 496 solver.cpp:237] Train net output #0: loss = 0.122166 (* 1 = 0.122166 loss)
|
|
I0428 03:00:50.266980 496 sgd_solver.cpp:105] Iteration 2590, lr = 0.00180871
|
|
I0428 03:01:07.197419 496 solver.cpp:218] Iteration 2597 (0.413455 iter/s, 16.9305s/7 iters), loss = 0.145291
|
|
I0428 03:01:07.197460 496 solver.cpp:237] Train net output #0: loss = 0.131407 (* 1 = 0.131407 loss)
|
|
I0428 03:01:07.197468 496 sgd_solver.cpp:105] Iteration 2597, lr = 0.00180037
|
|
I0428 03:01:24.095414 496 solver.cpp:218] Iteration 2604 (0.41425 iter/s, 16.898s/7 iters), loss = 0.119835
|
|
I0428 03:01:24.095522 496 solver.cpp:237] Train net output #0: loss = 0.11649 (* 1 = 0.11649 loss)
|
|
I0428 03:01:24.095532 496 sgd_solver.cpp:105] Iteration 2604, lr = 0.00179207
|
|
I0428 03:01:40.996822 496 solver.cpp:218] Iteration 2611 (0.414168 iter/s, 16.9013s/7 iters), loss = 0.115933
|
|
I0428 03:01:40.996865 496 solver.cpp:237] Train net output #0: loss = 0.0429976 (* 1 = 0.0429976 loss)
|
|
I0428 03:01:40.996873 496 sgd_solver.cpp:105] Iteration 2611, lr = 0.00178381
|
|
I0428 03:01:57.930086 496 solver.cpp:218] Iteration 2618 (0.413388 iter/s, 16.9333s/7 iters), loss = 0.175195
|
|
I0428 03:01:57.930199 496 solver.cpp:237] Train net output #0: loss = 0.183487 (* 1 = 0.183487 loss)
|
|
I0428 03:01:57.930209 496 sgd_solver.cpp:105] Iteration 2618, lr = 0.00177558
|
|
I0428 03:02:14.837628 496 solver.cpp:218] Iteration 2625 (0.414018 iter/s, 16.9075s/7 iters), loss = 0.130417
|
|
I0428 03:02:14.837671 496 solver.cpp:237] Train net output #0: loss = 0.141955 (* 1 = 0.141955 loss)
|
|
I0428 03:02:14.837678 496 sgd_solver.cpp:105] Iteration 2625, lr = 0.0017674
|
|
I0428 03:02:24.626941 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:02:31.755198 496 solver.cpp:218] Iteration 2632 (0.413771 iter/s, 16.9176s/7 iters), loss = 0.139365
|
|
I0428 03:02:31.755319 496 solver.cpp:237] Train net output #0: loss = 0.0763997 (* 1 = 0.0763997 loss)
|
|
I0428 03:02:31.755329 496 sgd_solver.cpp:105] Iteration 2632, lr = 0.00175925
|
|
I0428 03:02:45.395700 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 03:02:48.633435 496 solver.cpp:218] Iteration 2639 (0.414737 iter/s, 16.8781s/7 iters), loss = 0.106814
|
|
I0428 03:02:48.633477 496 solver.cpp:237] Train net output #0: loss = 0.181523 (* 1 = 0.181523 loss)
|
|
I0428 03:02:48.633486 496 sgd_solver.cpp:105] Iteration 2639, lr = 0.00175113
|
|
I0428 03:03:05.582751 496 solver.cpp:218] Iteration 2646 (0.412996 iter/s, 16.9493s/7 iters), loss = 0.123714
|
|
I0428 03:03:05.582880 496 solver.cpp:237] Train net output #0: loss = 0.133028 (* 1 = 0.133028 loss)
|
|
I0428 03:03:05.582890 496 sgd_solver.cpp:105] Iteration 2646, lr = 0.00174306
|
|
I0428 03:03:17.610880 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
|
|
I0428 03:03:20.668918 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
|
|
I0428 03:03:23.033656 496 solver.cpp:330] Iteration 2652, Testing net (#0)
|
|
I0428 03:03:23.033675 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 03:03:26.648689 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:03:27.821957 496 solver.cpp:397] Test net output #0: accuracy = 0.273284
|
|
I0428 03:03:27.822005 496 solver.cpp:397] Test net output #1: loss = 5.01314 (* 1 = 5.01314 loss)
|
|
I0428 03:03:31.595331 496 solver.cpp:218] Iteration 2653 (0.269101 iter/s, 26.0125s/7 iters), loss = 0.114376
|
|
I0428 03:03:31.595378 496 solver.cpp:237] Train net output #0: loss = 0.105059 (* 1 = 0.105059 loss)
|
|
I0428 03:03:31.595386 496 sgd_solver.cpp:105] Iteration 2653, lr = 0.00173502
|
|
I0428 03:03:48.448490 496 solver.cpp:218] Iteration 2660 (0.415353 iter/s, 16.8531s/7 iters), loss = 0.0765719
|
|
I0428 03:03:48.448607 496 solver.cpp:237] Train net output #0: loss = 0.0792665 (* 1 = 0.0792665 loss)
|
|
I0428 03:03:48.448616 496 sgd_solver.cpp:105] Iteration 2660, lr = 0.00172702
|
|
I0428 03:04:05.347779 496 solver.cpp:218] Iteration 2667 (0.414221 iter/s, 16.8992s/7 iters), loss = 0.119828
|
|
I0428 03:04:05.347826 496 solver.cpp:237] Train net output #0: loss = 0.0629039 (* 1 = 0.0629039 loss)
|
|
I0428 03:04:05.347833 496 sgd_solver.cpp:105] Iteration 2667, lr = 0.00171906
|
|
I0428 03:04:22.265667 496 solver.cpp:218] Iteration 2674 (0.413764 iter/s, 16.9179s/7 iters), loss = 0.103004
|
|
I0428 03:04:22.265810 496 solver.cpp:237] Train net output #0: loss = 0.116182 (* 1 = 0.116182 loss)
|
|
I0428 03:04:22.265823 496 sgd_solver.cpp:105] Iteration 2674, lr = 0.00171113
|
|
I0428 03:04:39.187232 496 solver.cpp:218] Iteration 2681 (0.413676 iter/s, 16.9215s/7 iters), loss = 0.131707
|
|
I0428 03:04:39.187273 496 solver.cpp:237] Train net output #0: loss = 0.194305 (* 1 = 0.194305 loss)
|
|
I0428 03:04:39.187281 496 sgd_solver.cpp:105] Iteration 2681, lr = 0.00170324
|
|
I0428 03:04:56.093335 496 solver.cpp:218] Iteration 2688 (0.414052 iter/s, 16.9061s/7 iters), loss = 0.155917
|
|
I0428 03:04:56.093497 496 solver.cpp:237] Train net output #0: loss = 0.160258 (* 1 = 0.160258 loss)
|
|
I0428 03:04:56.093506 496 sgd_solver.cpp:105] Iteration 2688, lr = 0.00169539
|
|
I0428 03:05:13.001633 496 solver.cpp:218] Iteration 2695 (0.414001 iter/s, 16.9082s/7 iters), loss = 0.0896492
|
|
I0428 03:05:13.001682 496 solver.cpp:237] Train net output #0: loss = 0.122482 (* 1 = 0.122482 loss)
|
|
I0428 03:05:13.001693 496 sgd_solver.cpp:105] Iteration 2695, lr = 0.00168757
|
|
I0428 03:05:29.921365 496 solver.cpp:218] Iteration 2702 (0.413718 iter/s, 16.9197s/7 iters), loss = 0.109748
|
|
I0428 03:05:29.921500 496 solver.cpp:237] Train net output #0: loss = 0.125474 (* 1 = 0.125474 loss)
|
|
I0428 03:05:29.921509 496 sgd_solver.cpp:105] Iteration 2702, lr = 0.00167979
|
|
I0428 03:05:46.815964 496 solver.cpp:218] Iteration 2709 (0.414336 iter/s, 16.8945s/7 iters), loss = 0.0895508
|
|
I0428 03:05:46.816009 496 solver.cpp:237] Train net output #0: loss = 0.0565647 (* 1 = 0.0565647 loss)
|
|
I0428 03:05:46.816017 496 sgd_solver.cpp:105] Iteration 2709, lr = 0.00167205
|
|
I0428 03:06:03.748169 496 solver.cpp:218] Iteration 2716 (0.413414 iter/s, 16.9322s/7 iters), loss = 0.121615
|
|
I0428 03:06:03.748236 496 solver.cpp:237] Train net output #0: loss = 0.128646 (* 1 = 0.128646 loss)
|
|
I0428 03:06:03.748245 496 sgd_solver.cpp:105] Iteration 2716, lr = 0.00166434
|
|
I0428 03:06:20.668542 496 solver.cpp:218] Iteration 2723 (0.413703 iter/s, 16.9203s/7 iters), loss = 0.106858
|
|
I0428 03:06:20.668581 496 solver.cpp:237] Train net output #0: loss = 0.0698151 (* 1 = 0.0698151 loss)
|
|
I0428 03:06:20.668591 496 sgd_solver.cpp:105] Iteration 2723, lr = 0.00165666
|
|
I0428 03:06:37.564235 496 solver.cpp:218] Iteration 2730 (0.414307 iter/s, 16.8957s/7 iters), loss = 0.120595
|
|
I0428 03:06:37.564349 496 solver.cpp:237] Train net output #0: loss = 0.0547622 (* 1 = 0.0547622 loss)
|
|
I0428 03:06:37.564358 496 sgd_solver.cpp:105] Iteration 2730, lr = 0.00164902
|
|
I0428 03:06:38.059689 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:06:54.482884 496 solver.cpp:218] Iteration 2737 (0.413747 iter/s, 16.9186s/7 iters), loss = 0.0918095
|
|
I0428 03:06:54.482926 496 solver.cpp:237] Train net output #0: loss = 0.139169 (* 1 = 0.139169 loss)
|
|
I0428 03:06:54.482934 496 sgd_solver.cpp:105] Iteration 2737, lr = 0.00164142
|
|
I0428 03:07:11.391465 496 solver.cpp:218] Iteration 2744 (0.413991 iter/s, 16.9086s/7 iters), loss = 0.0960929
|
|
I0428 03:07:11.391583 496 solver.cpp:237] Train net output #0: loss = 0.0837009 (* 1 = 0.0837009 loss)
|
|
I0428 03:07:11.391593 496 sgd_solver.cpp:105] Iteration 2744, lr = 0.00163385
|
|
I0428 03:07:28.319963 496 solver.cpp:218] Iteration 2751 (0.413506 iter/s, 16.9284s/7 iters), loss = 0.103595
|
|
I0428 03:07:28.320003 496 solver.cpp:237] Train net output #0: loss = 0.108171 (* 1 = 0.108171 loss)
|
|
I0428 03:07:28.320010 496 sgd_solver.cpp:105] Iteration 2751, lr = 0.00162632
|
|
I0428 03:07:33.088595 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
|
|
I0428 03:07:36.137984 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
|
|
I0428 03:07:38.489485 496 solver.cpp:330] Iteration 2754, Testing net (#0)
|
|
I0428 03:07:38.489503 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 03:07:41.803133 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:07:43.020836 496 solver.cpp:397] Test net output #0: accuracy = 0.268995
|
|
I0428 03:07:43.020884 496 solver.cpp:397] Test net output #1: loss = 5.02551 (* 1 = 5.02551 loss)
|
|
I0428 03:07:54.157024 496 solver.cpp:218] Iteration 2758 (0.270928 iter/s, 25.8371s/7 iters), loss = 0.136217
|
|
I0428 03:07:54.157065 496 solver.cpp:237] Train net output #0: loss = 0.170047 (* 1 = 0.170047 loss)
|
|
I0428 03:07:54.157078 496 sgd_solver.cpp:105] Iteration 2758, lr = 0.00161882
|
|
I0428 03:08:10.973397 496 solver.cpp:218] Iteration 2765 (0.416261 iter/s, 16.8164s/7 iters), loss = 0.0967222
|
|
I0428 03:08:10.973443 496 solver.cpp:237] Train net output #0: loss = 0.0236076 (* 1 = 0.0236076 loss)
|
|
I0428 03:08:10.973451 496 sgd_solver.cpp:105] Iteration 2765, lr = 0.00161136
|
|
I0428 03:08:27.962834 496 solver.cpp:218] Iteration 2772 (0.412021 iter/s, 16.9894s/7 iters), loss = 0.120488
|
|
I0428 03:08:27.963022 496 solver.cpp:237] Train net output #0: loss = 0.121983 (* 1 = 0.121983 loss)
|
|
I0428 03:08:27.963037 496 sgd_solver.cpp:105] Iteration 2772, lr = 0.00160393
|
|
I0428 03:08:44.829118 496 solver.cpp:218] Iteration 2779 (0.415033 iter/s, 16.8661s/7 iters), loss = 0.134915
|
|
I0428 03:08:44.829160 496 solver.cpp:237] Train net output #0: loss = 0.141697 (* 1 = 0.141697 loss)
|
|
I0428 03:08:44.829167 496 sgd_solver.cpp:105] Iteration 2779, lr = 0.00159653
|
|
I0428 03:09:01.739972 496 solver.cpp:218] Iteration 2786 (0.413935 iter/s, 16.9109s/7 iters), loss = 0.117903
|
|
I0428 03:09:01.740103 496 solver.cpp:237] Train net output #0: loss = 0.110502 (* 1 = 0.110502 loss)
|
|
I0428 03:09:01.740113 496 sgd_solver.cpp:105] Iteration 2786, lr = 0.00158917
|
|
I0428 03:09:09.737257 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 03:09:18.650027 496 solver.cpp:218] Iteration 2793 (0.413957 iter/s, 16.91s/7 iters), loss = 0.121076
|
|
I0428 03:09:18.650070 496 solver.cpp:237] Train net output #0: loss = 0.176701 (* 1 = 0.176701 loss)
|
|
I0428 03:09:18.650077 496 sgd_solver.cpp:105] Iteration 2793, lr = 0.00158184
|
|
I0428 03:09:35.575843 496 solver.cpp:218] Iteration 2800 (0.41357 iter/s, 16.9258s/7 iters), loss = 0.083221
|
|
I0428 03:09:35.575973 496 solver.cpp:237] Train net output #0: loss = 0.0846528 (* 1 = 0.0846528 loss)
|
|
I0428 03:09:35.575982 496 sgd_solver.cpp:105] Iteration 2800, lr = 0.00157455
|
|
I0428 03:09:52.462894 496 solver.cpp:218] Iteration 2807 (0.414521 iter/s, 16.887s/7 iters), loss = 0.106581
|
|
I0428 03:09:52.462939 496 solver.cpp:237] Train net output #0: loss = 0.111419 (* 1 = 0.111419 loss)
|
|
I0428 03:09:52.462947 496 sgd_solver.cpp:105] Iteration 2807, lr = 0.00156729
|
|
I0428 03:10:09.371112 496 solver.cpp:218] Iteration 2814 (0.414 iter/s, 16.9082s/7 iters), loss = 0.100728
|
|
I0428 03:10:09.371227 496 solver.cpp:237] Train net output #0: loss = 0.0766083 (* 1 = 0.0766083 loss)
|
|
I0428 03:10:09.371237 496 sgd_solver.cpp:105] Iteration 2814, lr = 0.00156006
|
|
I0428 03:10:26.291831 496 solver.cpp:218] Iteration 2821 (0.413696 iter/s, 16.9206s/7 iters), loss = 0.111109
|
|
I0428 03:10:26.291870 496 solver.cpp:237] Train net output #0: loss = 0.111604 (* 1 = 0.111604 loss)
|
|
I0428 03:10:26.291879 496 sgd_solver.cpp:105] Iteration 2821, lr = 0.00155287
|
|
I0428 03:10:43.112154 496 solver.cpp:218] Iteration 2828 (0.416163 iter/s, 16.8203s/7 iters), loss = 0.129876
|
|
I0428 03:10:43.112278 496 solver.cpp:237] Train net output #0: loss = 0.0877259 (* 1 = 0.0877259 loss)
|
|
I0428 03:10:43.112287 496 sgd_solver.cpp:105] Iteration 2828, lr = 0.00154571
|
|
I0428 03:10:51.257759 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:11:00.024394 496 solver.cpp:218] Iteration 2835 (0.413903 iter/s, 16.9122s/7 iters), loss = 0.0706012
|
|
I0428 03:11:00.024439 496 solver.cpp:237] Train net output #0: loss = 0.0266102 (* 1 = 0.0266102 loss)
|
|
I0428 03:11:00.024447 496 sgd_solver.cpp:105] Iteration 2835, lr = 0.00153858
|
|
I0428 03:11:16.922503 496 solver.cpp:218] Iteration 2842 (0.414248 iter/s, 16.8981s/7 iters), loss = 0.0725492
|
|
I0428 03:11:16.922590 496 solver.cpp:237] Train net output #0: loss = 0.062797 (* 1 = 0.062797 loss)
|
|
I0428 03:11:16.922602 496 sgd_solver.cpp:105] Iteration 2842, lr = 0.00153149
|
|
I0428 03:11:33.765506 496 solver.cpp:218] Iteration 2849 (0.415604 iter/s, 16.843s/7 iters), loss = 0.101306
|
|
I0428 03:11:33.765552 496 solver.cpp:237] Train net output #0: loss = 0.0422625 (* 1 = 0.0422625 loss)
|
|
I0428 03:11:33.765560 496 sgd_solver.cpp:105] Iteration 2849, lr = 0.00152443
|
|
I0428 03:11:48.224298 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
|
|
I0428 03:11:51.285231 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
|
|
I0428 03:11:53.630162 496 solver.cpp:330] Iteration 2856, Testing net (#0)
|
|
I0428 03:11:53.630182 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 03:11:57.309684 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:11:58.489486 496 solver.cpp:397] Test net output #0: accuracy = 0.270833
|
|
I0428 03:11:58.489535 496 solver.cpp:397] Test net output #1: loss = 5.03115 (* 1 = 5.03115 loss)
|
|
I0428 03:11:59.847936 496 solver.cpp:218] Iteration 2856 (0.26838 iter/s, 26.0824s/7 iters), loss = 0.099744
|
|
I0428 03:11:59.847981 496 solver.cpp:237] Train net output #0: loss = 0.100257 (* 1 = 0.100257 loss)
|
|
I0428 03:11:59.847990 496 sgd_solver.cpp:105] Iteration 2856, lr = 0.0015174
|
|
I0428 03:12:16.659448 496 solver.cpp:218] Iteration 2863 (0.416382 iter/s, 16.8115s/7 iters), loss = 0.0965703
|
|
I0428 03:12:16.659492 496 solver.cpp:237] Train net output #0: loss = 0.051367 (* 1 = 0.051367 loss)
|
|
I0428 03:12:16.659499 496 sgd_solver.cpp:105] Iteration 2863, lr = 0.0015104
|
|
I0428 03:12:33.582782 496 solver.cpp:218] Iteration 2870 (0.41363 iter/s, 16.9233s/7 iters), loss = 0.0681517
|
|
I0428 03:12:33.582911 496 solver.cpp:237] Train net output #0: loss = 0.0473179 (* 1 = 0.0473179 loss)
|
|
I0428 03:12:33.582921 496 sgd_solver.cpp:105] Iteration 2870, lr = 0.00150344
|
|
I0428 03:12:50.493901 496 solver.cpp:218] Iteration 2877 (0.413931 iter/s, 16.911s/7 iters), loss = 0.103352
|
|
I0428 03:12:50.493943 496 solver.cpp:237] Train net output #0: loss = 0.128656 (* 1 = 0.128656 loss)
|
|
I0428 03:12:50.493952 496 sgd_solver.cpp:105] Iteration 2877, lr = 0.0014965
|
|
I0428 03:13:07.415284 496 solver.cpp:218] Iteration 2884 (0.413678 iter/s, 16.9214s/7 iters), loss = 0.104405
|
|
I0428 03:13:07.415407 496 solver.cpp:237] Train net output #0: loss = 0.150603 (* 1 = 0.150603 loss)
|
|
I0428 03:13:07.415417 496 sgd_solver.cpp:105] Iteration 2884, lr = 0.0014896
|
|
I0428 03:13:24.323870 496 solver.cpp:218] Iteration 2891 (0.413993 iter/s, 16.9085s/7 iters), loss = 0.0883768
|
|
I0428 03:13:24.323910 496 solver.cpp:237] Train net output #0: loss = 0.0880725 (* 1 = 0.0880725 loss)
|
|
I0428 03:13:24.323918 496 sgd_solver.cpp:105] Iteration 2891, lr = 0.00148274
|
|
I0428 03:13:41.236070 496 solver.cpp:218] Iteration 2898 (0.413902 iter/s, 16.9122s/7 iters), loss = 0.141385
|
|
I0428 03:13:41.236186 496 solver.cpp:237] Train net output #0: loss = 0.126237 (* 1 = 0.126237 loss)
|
|
I0428 03:13:41.236193 496 sgd_solver.cpp:105] Iteration 2898, lr = 0.0014759
|
|
I0428 03:13:58.147058 496 solver.cpp:218] Iteration 2905 (0.413934 iter/s, 16.9109s/7 iters), loss = 0.109918
|
|
I0428 03:13:58.147096 496 solver.cpp:237] Train net output #0: loss = 0.0940852 (* 1 = 0.0940852 loss)
|
|
I0428 03:13:58.147104 496 sgd_solver.cpp:105] Iteration 2905, lr = 0.00146909
|
|
I0428 03:14:15.059119 496 solver.cpp:218] Iteration 2912 (0.413906 iter/s, 16.9121s/7 iters), loss = 0.10602
|
|
I0428 03:14:15.059242 496 solver.cpp:237] Train net output #0: loss = 0.0430778 (* 1 = 0.0430778 loss)
|
|
I0428 03:14:15.059252 496 sgd_solver.cpp:105] Iteration 2912, lr = 0.00146232
|
|
I0428 03:14:31.995987 496 solver.cpp:218] Iteration 2919 (0.413302 iter/s, 16.9368s/7 iters), loss = 0.116032
|
|
I0428 03:14:31.996028 496 solver.cpp:237] Train net output #0: loss = 0.159236 (* 1 = 0.159236 loss)
|
|
I0428 03:14:31.996037 496 sgd_solver.cpp:105] Iteration 2919, lr = 0.00145558
|
|
I0428 03:14:48.823339 496 solver.cpp:218] Iteration 2926 (0.41599 iter/s, 16.8273s/7 iters), loss = 0.105781
|
|
I0428 03:14:48.823475 496 solver.cpp:237] Train net output #0: loss = 0.105454 (* 1 = 0.105454 loss)
|
|
I0428 03:14:48.823490 496 sgd_solver.cpp:105] Iteration 2926, lr = 0.00144887
|
|
I0428 03:15:04.558362 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:15:05.698613 496 solver.cpp:218] Iteration 2933 (0.41481 iter/s, 16.8752s/7 iters), loss = 0.0655381
|
|
I0428 03:15:05.698654 496 solver.cpp:237] Train net output #0: loss = 0.038609 (* 1 = 0.038609 loss)
|
|
I0428 03:15:05.698662 496 sgd_solver.cpp:105] Iteration 2933, lr = 0.00144219
|
|
I0428 03:15:22.584431 496 solver.cpp:218] Iteration 2940 (0.414549 iter/s, 16.8858s/7 iters), loss = 0.0925482
|
|
I0428 03:15:22.584703 496 solver.cpp:237] Train net output #0: loss = 0.126869 (* 1 = 0.126869 loss)
|
|
I0428 03:15:22.584712 496 sgd_solver.cpp:105] Iteration 2940, lr = 0.00143554
|
|
I0428 03:15:39.484478 496 solver.cpp:218] Iteration 2947 (0.414206 iter/s, 16.8998s/7 iters), loss = 0.111471
|
|
I0428 03:15:39.484525 496 solver.cpp:237] Train net output #0: loss = 0.11464 (* 1 = 0.11464 loss)
|
|
I0428 03:15:39.484534 496 sgd_solver.cpp:105] Iteration 2947, lr = 0.00142892
|
|
I0428 03:15:43.071859 496 blocking_queue.cpp:49] Waiting for data
|
|
I0428 03:15:56.536370 496 solver.cpp:218] Iteration 2954 (0.410512 iter/s, 17.0519s/7 iters), loss = 0.127201
|
|
I0428 03:15:56.536490 496 solver.cpp:237] Train net output #0: loss = 0.300966 (* 1 = 0.300966 loss)
|
|
I0428 03:15:56.536499 496 sgd_solver.cpp:105] Iteration 2954, lr = 0.00142233
|
|
I0428 03:16:03.717808 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
|
|
I0428 03:16:06.816707 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
|
|
I0428 03:16:09.171767 496 solver.cpp:330] Iteration 2958, Testing net (#0)
|
|
I0428 03:16:09.171792 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 03:16:12.641891 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:16:13.944278 496 solver.cpp:397] Test net output #0: accuracy = 0.276961
|
|
I0428 03:16:13.944325 496 solver.cpp:397] Test net output #1: loss = 5.13222 (* 1 = 5.13222 loss)
|
|
I0428 03:16:22.567975 496 solver.cpp:218] Iteration 2961 (0.268904 iter/s, 26.0316s/7 iters), loss = 0.122846
|
|
I0428 03:16:22.568012 496 solver.cpp:237] Train net output #0: loss = 0.156011 (* 1 = 0.156011 loss)
|
|
I0428 03:16:22.568020 496 sgd_solver.cpp:105] Iteration 2961, lr = 0.00141577
|
|
I0428 03:16:39.464411 496 solver.cpp:218] Iteration 2968 (0.414289 iter/s, 16.8964s/7 iters), loss = 0.13307
|
|
I0428 03:16:39.464545 496 solver.cpp:237] Train net output #0: loss = 0.0963316 (* 1 = 0.0963316 loss)
|
|
I0428 03:16:39.464555 496 sgd_solver.cpp:105] Iteration 2968, lr = 0.00140924
|
|
I0428 03:16:56.456892 496 solver.cpp:218] Iteration 2975 (0.411949 iter/s, 16.9924s/7 iters), loss = 0.101599
|
|
I0428 03:16:56.456933 496 solver.cpp:237] Train net output #0: loss = 0.0870283 (* 1 = 0.0870283 loss)
|
|
I0428 03:16:56.456941 496 sgd_solver.cpp:105] Iteration 2975, lr = 0.00140274
|
|
I0428 03:17:13.308156 496 solver.cpp:218] Iteration 2982 (0.415399 iter/s, 16.8513s/7 iters), loss = 0.1148
|
|
I0428 03:17:13.308274 496 solver.cpp:237] Train net output #0: loss = 0.0574747 (* 1 = 0.0574747 loss)
|
|
I0428 03:17:13.308282 496 sgd_solver.cpp:105] Iteration 2982, lr = 0.00139628
|
|
I0428 03:17:30.199385 496 solver.cpp:218] Iteration 2989 (0.414418 iter/s, 16.8911s/7 iters), loss = 0.106351
|
|
I0428 03:17:30.199431 496 solver.cpp:237] Train net output #0: loss = 0.119056 (* 1 = 0.119056 loss)
|
|
I0428 03:17:30.199438 496 sgd_solver.cpp:105] Iteration 2989, lr = 0.00138984
|
|
I0428 03:17:47.110884 496 solver.cpp:218] Iteration 2996 (0.41392 iter/s, 16.9115s/7 iters), loss = 0.0710455
|
|
I0428 03:17:47.111013 496 solver.cpp:237] Train net output #0: loss = 0.0292164 (* 1 = 0.0292164 loss)
|
|
I0428 03:17:47.111023 496 sgd_solver.cpp:105] Iteration 2996, lr = 0.00138343
|
|
I0428 03:18:04.020857 496 solver.cpp:218] Iteration 3003 (0.413959 iter/s, 16.9099s/7 iters), loss = 0.103955
|
|
I0428 03:18:04.020902 496 solver.cpp:237] Train net output #0: loss = 0.10327 (* 1 = 0.10327 loss)
|
|
I0428 03:18:04.020911 496 sgd_solver.cpp:105] Iteration 3003, lr = 0.00137705
|
|
I0428 03:18:20.923072 496 solver.cpp:218] Iteration 3010 (0.414147 iter/s, 16.9022s/7 iters), loss = 0.0969675
|
|
I0428 03:18:20.923234 496 solver.cpp:237] Train net output #0: loss = 0.110959 (* 1 = 0.110959 loss)
|
|
I0428 03:18:20.923243 496 sgd_solver.cpp:105] Iteration 3010, lr = 0.0013707
|
|
I0428 03:18:37.843812 496 solver.cpp:218] Iteration 3017 (0.413697 iter/s, 16.9206s/7 iters), loss = 0.0841062
|
|
I0428 03:18:37.843853 496 solver.cpp:237] Train net output #0: loss = 0.139468 (* 1 = 0.139468 loss)
|
|
I0428 03:18:37.843861 496 sgd_solver.cpp:105] Iteration 3017, lr = 0.00136438
|
|
I0428 03:18:54.758467 496 solver.cpp:218] Iteration 3024 (0.413843 iter/s, 16.9146s/7 iters), loss = 0.0725638
|
|
I0428 03:18:54.758605 496 solver.cpp:237] Train net output #0: loss = 0.0911432 (* 1 = 0.0911432 loss)
|
|
I0428 03:18:54.758615 496 sgd_solver.cpp:105] Iteration 3024, lr = 0.00135809
|
|
I0428 03:19:11.676406 496 solver.cpp:218] Iteration 3031 (0.413764 iter/s, 16.9178s/7 iters), loss = 0.101492
|
|
I0428 03:19:11.676450 496 solver.cpp:237] Train net output #0: loss = 0.0963166 (* 1 = 0.0963166 loss)
|
|
I0428 03:19:11.676456 496 sgd_solver.cpp:105] Iteration 3031, lr = 0.00135183
|
|
I0428 03:19:18.173115 504 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:19:28.526414 496 solver.cpp:218] Iteration 3038 (0.41543 iter/s, 16.85s/7 iters), loss = 0.0793213
|
|
I0428 03:19:28.526541 496 solver.cpp:237] Train net output #0: loss = 0.0902533 (* 1 = 0.0902533 loss)
|
|
I0428 03:19:28.526551 496 sgd_solver.cpp:105] Iteration 3038, lr = 0.00134559
|
|
I0428 03:19:45.361680 496 solver.cpp:218] Iteration 3045 (0.415796 iter/s, 16.8352s/7 iters), loss = 0.0973375
|
|
I0428 03:19:45.361723 496 solver.cpp:237] Train net output #0: loss = 0.0851774 (* 1 = 0.0851774 loss)
|
|
I0428 03:19:45.361732 496 sgd_solver.cpp:105] Iteration 3045, lr = 0.00133939
|
|
I0428 03:20:02.164052 496 solver.cpp:218] Iteration 3052 (0.416608 iter/s, 16.8024s/7 iters), loss = 0.0909548
|
|
I0428 03:20:02.164144 496 solver.cpp:237] Train net output #0: loss = 0.153217 (* 1 = 0.153217 loss)
|
|
I0428 03:20:02.164152 496 sgd_solver.cpp:105] Iteration 3052, lr = 0.00133321
|
|
I0428 03:20:19.039079 496 solver.cpp:218] Iteration 3059 (0.414815 iter/s, 16.875s/7 iters), loss = 0.10293
|
|
I0428 03:20:19.039120 496 solver.cpp:237] Train net output #0: loss = 0.0672725 (* 1 = 0.0672725 loss)
|
|
I0428 03:20:19.039129 496 sgd_solver.cpp:105] Iteration 3059, lr = 0.00132707
|
|
I0428 03:20:19.039309 496 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
|
|
I0428 03:20:22.051429 496 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
|
|
I0428 03:20:24.400766 496 solver.cpp:330] Iteration 3060, Testing net (#0)
|
|
I0428 03:20:24.400789 496 net.cpp:676] Ignoring source layer train-data
|
|
I0428 03:20:27.834311 514 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 03:20:29.176218 496 solver.cpp:397] Test net output #0: accuracy = 0.273897
|
|
I0428 03:20:29.176262 496 solver.cpp:397] Test net output #1: loss = 5.06661 (* 1 = 5.06661 loss)
|
|
I0428 03:20:29.176272 496 solver.cpp:315] Optimization Done.
|
|
I0428 03:20:29.176280 496 caffe.cpp:259] Optimization Done.
|