@tags caffe
前面根据train_lenet.sh改写了train_lenet.py后，在根目录下执行它，得到一系列输出，内容如下：
I1013 10:05:16.721294 1684 caffe.cpp:218] Using GPUs 0
I1013 10:05:17.525264 1684 caffe.cpp:223] GPU 0: GeForce GTX 970M
I1013 10:05:17.790920 1684 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I1013 10:05:17.806543 1684 solver.cpp:48] Initializing solver from parameters:
test_iter: 100
test_interval: 500
base_lr: 0.01
display: 100
max_iter: 10000
lr_policy: "inv"
gamma: 0.0001
power: 0.75
momentum: 0.9
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
solver_mode: GPU
device_id: 0
net: "examples/mnist/lenet_train_test.prototxt"
train_state {
level: 0
stage: ""
}
I1013 10:05:17.806543 1684 solver.cpp:91] Creating training net from net file: examples/mnist/lenet_train_test.prototxt
I1013 10:05:17.806543 1684 net.cpp:332] The NetState phase (0) differed from the phase (1) specified by a rule in layer mnist
I1013 10:05:17.806543 1684 net.cpp:332] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I1013 10:05:17.806543 1684 net.cpp:58] Initializing net from parameters:
name: "LeNet"
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_train_lmdb"
batch_size: 64
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
I1013 10:05:17.822134 1684 layer_factory.hpp:77] Creating layer mnist
I1013 10:05:17.853427 1684 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I1013 10:05:17.853427 1684 net.cpp:100] Creating Layer mnist
I1013 10:05:17.853427 1684 net.cpp:418] mnist -> data
I1013 10:05:17.853427 1684 net.cpp:418] mnist -> label
I1013 10:05:17.853427 10084 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I1013 10:05:17.900490 10084 db_lmdb.cpp:40] Opened lmdb examples/mnist/mnist_train_lmdb
I1013 10:05:17.978623 1684 data_layer.cpp:41] output data size: 64,1,28,28
I1013 10:05:17.978623 1684 net.cpp:150] Setting up mnist
I1013 10:05:17.978623 1684 net.cpp:157] Top shape: 64 1 28 28 (50176)
I1013 10:05:17.978623 824 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I1013 10:05:17.978623 1684 net.cpp:157] Top shape: 64 (64)
I1013 10:05:17.978623 1684 net.cpp:165] Memory required for data: 200960
I1013 10:05:17.978623 1684 layer_factory.hpp:77] Creating layer conv1
I1013 10:05:17.978623 1684 net.cpp:100] Creating Layer conv1
I1013 10:05:17.978623 1684 net.cpp:444] conv1 <- data
I1013 10:05:17.978623 1684 net.cpp:418] conv1 -> conv1
I1013 10:05:17.994026 1684 net.cpp:150] Setting up conv1
I1013 10:05:17.994026 1684 net.cpp:157] Top shape: 64 20 24 24 (737280)
I1013 10:05:17.994026 1684 net.cpp:165] Memory required for data: 3150080
I1013 10:05:17.994026 1684 layer_factory.hpp:77] Creating layer pool1
I1013 10:05:17.994026 1684 net.cpp:100] Creating Layer pool1
I1013 10:05:17.994026 1684 net.cpp:444] pool1 <- conv1
I1013 10:05:17.994026 1684 net.cpp:418] pool1 -> pool1
I1013 10:05:17.994026 1684 net.cpp:150] Setting up pool1
I1013 10:05:17.994026 1684 net.cpp:157] Top shape: 64 20 12 12 (184320)
I1013 10:05:17.994026 1684 net.cpp:165] Memory required for data: 3887360
I1013 10:05:18.009652 1684 layer_factory.hpp:77] Creating layer conv2
I1013 10:05:18.009652 1684 net.cpp:100] Creating Layer conv2
I1013 10:05:18.009652 1684 net.cpp:444] conv2 <- pool1
I1013 10:05:18.025316 1684 net.cpp:418] conv2 -> conv2
I1013 10:05:18.025316 1684 net.cpp:150] Setting up conv2
I1013 10:05:18.025316 1684 net.cpp:157] Top shape: 64 50 8 8 (204800)
I1013 10:05:18.025316 1684 net.cpp:165] Memory required for data: 4706560
I1013 10:05:18.025316 1684 layer_factory.hpp:77] Creating layer pool2
I1013 10:05:18.040946 1684 net.cpp:100] Creating Layer pool2
I1013 10:05:18.040946 1684 net.cpp:444] pool2 <- conv2
I1013 10:05:18.040946 1684 net.cpp:418] pool2 -> pool2
I1013 10:05:18.040946 1684 net.cpp:150] Setting up pool2
I1013 10:05:18.040946 1684 net.cpp:157] Top shape: 64 50 4 4 (51200)
I1013 10:05:18.040946 1684 net.cpp:165] Memory required for data: 4911360
I1013 10:05:18.056536 1684 layer_factory.hpp:77] Creating layer ip1
I1013 10:05:18.056536 1684 net.cpp:100] Creating Layer ip1
I1013 10:05:18.056536 1684 net.cpp:444] ip1 <- pool2
I1013 10:05:18.056536 1684 net.cpp:418] ip1 -> ip1
I1013 10:05:18.087842 1684 net.cpp:150] Setting up ip1
I1013 10:05:18.087842 1684 net.cpp:157] Top shape: 64 500 (32000)
I1013 10:05:18.087842 1684 net.cpp:165] Memory required for data: 5039360
I1013 10:05:18.087842 1684 layer_factory.hpp:77] Creating layer relu1
I1013 10:05:18.087842 1684 net.cpp:100] Creating Layer relu1
I1013 10:05:18.103415 1684 net.cpp:444] relu1 <- ip1
I1013 10:05:18.103415 1684 net.cpp:405] relu1 -> ip1 (in-place)
I1013 10:05:18.103415 1684 net.cpp:150] Setting up relu1
I1013 10:05:18.103415 1684 net.cpp:157] Top shape: 64 500 (32000)
I1013 10:05:18.103415 1684 net.cpp:165] Memory required for data: 5167360
I1013 10:05:18.119084 1684 layer_factory.hpp:77] Creating layer ip2
I1013 10:05:18.119084 1684 net.cpp:100] Creating Layer ip2
I1013 10:05:18.119084 1684 net.cpp:444] ip2 <- ip1
I1013 10:05:18.119084 1684 net.cpp:418] ip2 -> ip2
I1013 10:05:18.134666 1684 net.cpp:150] Setting up ip2
I1013 10:05:18.134666 1684 net.cpp:157] Top shape: 64 10 (640)
I1013 10:05:18.134666 1684 net.cpp:165] Memory required for data: 5169920
I1013 10:05:18.134666 1684 layer_factory.hpp:77] Creating layer loss
I1013 10:05:18.134666 1684 net.cpp:100] Creating Layer loss
I1013 10:05:18.150292 1684 net.cpp:444] loss <- ip2
I1013 10:05:18.150292 1684 net.cpp:444] loss <- label
I1013 10:05:18.150292 1684 net.cpp:418] loss -> loss
I1013 10:05:18.150292 1684 layer_factory.hpp:77] Creating layer loss
I1013 10:05:18.150292 1684 net.cpp:150] Setting up loss
I1013 10:05:18.165921 1684 net.cpp:157] Top shape: (1)
I1013 10:05:18.165921 1684 net.cpp:160] with loss weight 1
I1013 10:05:18.165921 1684 net.cpp:165] Memory required for data: 5169924
I1013 10:05:18.165921 1684 net.cpp:226] loss needs backward computation.
I1013 10:05:18.181591 1684 net.cpp:226] ip2 needs backward computation.
I1013 10:05:18.181591 1684 net.cpp:226] relu1 needs backward computation.
I1013 10:05:18.181591 1684 net.cpp:226] ip1 needs backward computation.
I1013 10:05:18.181591 1684 net.cpp:226] pool2 needs backward computation.
I1013 10:05:18.197201 1684 net.cpp:226] conv2 needs backward computation.
I1013 10:05:18.197201 1684 net.cpp:226] pool1 needs backward computation.
I1013 10:05:18.197201 1684 net.cpp:226] conv1 needs backward computation.
I1013 10:05:18.197201 1684 net.cpp:228] mnist does not need backward computation.
I1013 10:05:18.212836 1684 net.cpp:270] This network produces output loss
I1013 10:05:18.212836 1684 net.cpp:283] Network initialization done.
I1013 10:05:18.212836 1684 solver.cpp:181] Creating test net (#0) specified by net file: examples/mnist/lenet_train_test.prototxt
I1013 10:05:18.228471 1684 net.cpp:332] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist
I1013 10:05:18.228471 1684 net.cpp:58] Initializing net from parameters:
name: "LeNet"
state {
phase: TEST
}
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_test_lmdb"
batch_size: 100
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
I1013 10:05:18.275310 1684 layer_factory.hpp:77] Creating layer mnist
I1013 10:05:18.291010 1684 net.cpp:100] Creating Layer mnist
I1013 10:05:18.291010 1684 net.cpp:418] mnist -> data
I1013 10:05:18.291010 1684 net.cpp:418] mnist -> label
I1013 10:05:18.291010 7500 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I1013 10:05:18.369072 7500 db_lmdb.cpp:40] Opened lmdb examples/mnist/mnist_test_lmdb
I1013 10:05:18.369072 1684 data_layer.cpp:41] output data size: 100,1,28,28
I1013 10:05:18.384691 1684 net.cpp:150] Setting up mnist
I1013 10:05:18.384691 1684 net.cpp:157] Top shape: 100 1 28 28 (78400)
I1013 10:05:18.384691 1684 net.cpp:157] Top shape: 100 (100)
I1013 10:05:18.384691 1684 net.cpp:165] Memory required for data: 314000
I1013 10:05:18.384691 1684 layer_factory.hpp:77] Creating layer label_mnist_1_split
I1013 10:05:18.384691 2420 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I1013 10:05:18.384691 1684 net.cpp:100] Creating Layer label_mnist_1_split
I1013 10:05:18.384691 1684 net.cpp:444] label_mnist_1_split <- label
I1013 10:05:18.400321 1684 net.cpp:418] label_mnist_1_split -> label_mnist_1_split_0
I1013 10:05:18.400321 1684 net.cpp:418] label_mnist_1_split -> label_mnist_1_split_1
I1013 10:05:18.400321 1684 net.cpp:150] Setting up label_mnist_1_split
I1013 10:05:18.400321 1684 net.cpp:157] Top shape: 100 (100)
I1013 10:05:18.400321 1684 net.cpp:157] Top shape: 100 (100)
I1013 10:05:18.400321 1684 net.cpp:165] Memory required for data: 314800
I1013 10:05:18.400321 1684 layer_factory.hpp:77] Creating layer conv1
I1013 10:05:18.400321 1684 net.cpp:100] Creating Layer conv1
I1013 10:05:18.400321 1684 net.cpp:444] conv1 <- data
I1013 10:05:18.415946 1684 net.cpp:418] conv1 -> conv1
I1013 10:05:18.415946 1684 net.cpp:150] Setting up conv1
I1013 10:05:18.415946 1684 net.cpp:157] Top shape: 100 20 24 24 (1152000)
I1013 10:05:18.415946 1684 net.cpp:165] Memory required for data: 4922800
I1013 10:05:18.415946 1684 layer_factory.hpp:77] Creating layer pool1
I1013 10:05:18.415946 1684 net.cpp:100] Creating Layer pool1
I1013 10:05:18.415946 1684 net.cpp:444] pool1 <- conv1
I1013 10:05:18.415946 1684 net.cpp:418] pool1 -> pool1
I1013 10:05:18.415946 1684 net.cpp:150] Setting up pool1
I1013 10:05:18.415946 1684 net.cpp:157] Top shape: 100 20 12 12 (288000)
I1013 10:05:18.431571 1684 net.cpp:165] Memory required for data: 6074800
I1013 10:05:18.431571 1684 layer_factory.hpp:77] Creating layer conv2
I1013 10:05:18.431571 1684 net.cpp:100] Creating Layer conv2
I1013 10:05:18.431571 1684 net.cpp:444] conv2 <- pool1
I1013 10:05:18.431571 1684 net.cpp:418] conv2 -> conv2
I1013 10:05:18.431571 1684 net.cpp:150] Setting up conv2
I1013 10:05:18.431571 1684 net.cpp:157] Top shape: 100 50 8 8 (320000)
I1013 10:05:18.431571 1684 net.cpp:165] Memory required for data: 7354800
I1013 10:05:18.431571 1684 layer_factory.hpp:77] Creating layer pool2
I1013 10:05:18.431571 1684 net.cpp:100] Creating Layer pool2
I1013 10:05:18.431571 1684 net.cpp:444] pool2 <- conv2
I1013 10:05:18.447198 1684 net.cpp:418] pool2 -> pool2
I1013 10:05:18.447198 1684 net.cpp:150] Setting up pool2
I1013 10:05:18.447198 1684 net.cpp:157] Top shape: 100 50 4 4 (80000)
I1013 10:05:18.447198 1684 net.cpp:165] Memory required for data: 7674800
I1013 10:05:18.447198 1684 layer_factory.hpp:77] Creating layer ip1
I1013 10:05:18.447198 1684 net.cpp:100] Creating Layer ip1
I1013 10:05:18.447198 1684 net.cpp:444] ip1 <- pool2
I1013 10:05:18.447198 1684 net.cpp:418] ip1 -> ip1
I1013 10:05:18.462826 1684 net.cpp:150] Setting up ip1
I1013 10:05:18.462826 1684 net.cpp:157] Top shape: 100 500 (50000)
I1013 10:05:18.462826 1684 net.cpp:165] Memory required for data: 7874800
I1013 10:05:18.462826 1684 layer_factory.hpp:77] Creating layer relu1
I1013 10:05:18.462826 1684 net.cpp:100] Creating Layer relu1
I1013 10:05:18.462826 1684 net.cpp:444] relu1 <- ip1
I1013 10:05:18.462826 1684 net.cpp:405] relu1 -> ip1 (in-place)
I1013 10:05:18.462826 1684 net.cpp:150] Setting up relu1
I1013 10:05:18.462826 1684 net.cpp:157] Top shape: 100 500 (50000)
I1013 10:05:18.462826 1684 net.cpp:165] Memory required for data: 8074800
I1013 10:05:18.462826 1684 layer_factory.hpp:77] Creating layer ip2
I1013 10:05:18.478452 1684 net.cpp:100] Creating Layer ip2
I1013 10:05:18.478452 1684 net.cpp:444] ip2 <- ip1
I1013 10:05:18.478452 1684 net.cpp:418] ip2 -> ip2
I1013 10:05:18.478452 1684 net.cpp:150] Setting up ip2
I1013 10:05:18.478452 1684 net.cpp:157] Top shape: 100 10 (1000)
I1013 10:05:18.478452 1684 net.cpp:165] Memory required for data: 8078800
I1013 10:05:18.478452 1684 layer_factory.hpp:77] Creating layer ip2_ip2_0_split
I1013 10:05:18.494081 1684 net.cpp:100] Creating Layer ip2_ip2_0_split
I1013 10:05:18.494081 1684 net.cpp:444] ip2_ip2_0_split <- ip2
I1013 10:05:18.494081 1684 net.cpp:418] ip2_ip2_0_split -> ip2_ip2_0_split_0
I1013 10:05:18.494081 1684 net.cpp:418] ip2_ip2_0_split -> ip2_ip2_0_split_1
I1013 10:05:18.494081 1684 net.cpp:150] Setting up ip2_ip2_0_split
I1013 10:05:18.494081 1684 net.cpp:157] Top shape: 100 10 (1000)
I1013 10:05:18.494081 1684 net.cpp:157] Top shape: 100 10 (1000)
I1013 10:05:18.494081 1684 net.cpp:165] Memory required for data: 8086800
I1013 10:05:18.509729 1684 layer_factory.hpp:77] Creating layer accuracy
I1013 10:05:18.509729 1684 net.cpp:100] Creating Layer accuracy
I1013 10:05:18.509729 1684 net.cpp:444] accuracy <- ip2_ip2_0_split_0
I1013 10:05:18.509729 1684 net.cpp:444] accuracy <- label_mnist_1_split_0
I1013 10:05:18.509729 1684 net.cpp:418] accuracy -> accuracy
I1013 10:05:18.509729 1684 net.cpp:150] Setting up accuracy
I1013 10:05:18.509729 1684 net.cpp:157] Top shape: (1)
I1013 10:05:18.509729 1684 net.cpp:165] Memory required for data: 8086804
I1013 10:05:18.509729 1684 layer_factory.hpp:77] Creating layer loss
I1013 10:05:18.509729 1684 net.cpp:100] Creating Layer loss
I1013 10:05:18.509729 1684 net.cpp:444] loss <- ip2_ip2_0_split_1
I1013 10:05:18.525331 1684 net.cpp:444] loss <- label_mnist_1_split_1
I1013 10:05:18.525331 1684 net.cpp:418] loss -> loss
I1013 10:05:18.525331 1684 layer_factory.hpp:77] Creating layer loss
I1013 10:05:18.525331 1684 net.cpp:150] Setting up loss
I1013 10:05:18.525331 1684 net.cpp:157] Top shape: (1)
I1013 10:05:18.525331 1684 net.cpp:160] with loss weight 1
I1013 10:05:18.525331 1684 net.cpp:165] Memory required for data: 8086808
I1013 10:05:18.525331 1684 net.cpp:226] loss needs backward computation.
I1013 10:05:18.525331 1684 net.cpp:228] accuracy does not need backward computation.
I1013 10:05:18.525331 1684 net.cpp:226] ip2_ip2_0_split needs backward computation.
I1013 10:05:18.540958 1684 net.cpp:226] ip2 needs backward computation.
I1013 10:05:18.540958 1684 net.cpp:226] relu1 needs backward computation.
I1013 10:05:18.540958 1684 net.cpp:226] ip1 needs backward computation.
I1013 10:05:18.540958 1684 net.cpp:226] pool2 needs backward computation.
I1013 10:05:18.540958 1684 net.cpp:226] conv2 needs backward computation.
I1013 10:05:18.540958 1684 net.cpp:226] pool1 needs backward computation.
I1013 10:05:18.540958 1684 net.cpp:226] conv1 needs backward computation.
I1013 10:05:18.556589 1684 net.cpp:228] label_mnist_1_split does not need backward computation.
I1013 10:05:18.556589 1684 net.cpp:228] mnist does not need backward computation.
I1013 10:05:18.556589 1684 net.cpp:270] This network produces output accuracy
I1013 10:05:18.556589 1684 net.cpp:270] This network produces output loss
I1013 10:05:18.556589 1684 net.cpp:283] Network initialization done.
I1013 10:05:18.572244 1684 solver.cpp:60] Solver scaffolding done.
I1013 10:05:18.572244 1684 caffe.cpp:252] Starting Optimization
I1013 10:05:18.572244 1684 solver.cpp:279] Solving LeNet
I1013 10:05:18.572244 1684 solver.cpp:280] Learning Rate Policy: inv
I1013 10:05:18.572244 1684 solver.cpp:337] Iteration 0, Testing net (#0)
I1013 10:05:19.978624 1684 solver.cpp:404] Test net output #0: accuracy = 0.0789
I1013 10:05:19.978624 1684 solver.cpp:404] Test net output #1: loss = 2.36376 (* 1 = 2.36376 loss)
I1013 10:05:20.009863 1684 solver.cpp:228] Iteration 0, loss = 2.34559
I1013 10:05:20.009863 1684 solver.cpp:244] Train net output #0: loss = 2.34559 (* 1 = 2.34559 loss)
I1013 10:05:20.009863 1684 sgd_solver.cpp:106] Iteration 0, lr = 0.01
I1013 10:05:22.134766 1684 solver.cpp:228] Iteration 100, loss = 0.226693
I1013 10:05:22.136801 1684 solver.cpp:244] Train net output #0: loss = 0.226693 (* 1 = 0.226693 loss)
I1013 10:05:22.137765 1684 sgd_solver.cpp:106] Iteration 100, lr = 0.00992565
I1013 10:05:24.268718 1684 solver.cpp:228] Iteration 200, loss = 0.142792
I1013 10:05:24.270691 1684 solver.cpp:244] Train net output #0: loss = 0.142792 (* 1 = 0.142792 loss)
I1013 10:05:24.272729 1684 sgd_solver.cpp:106] Iteration 200, lr = 0.00985258
I1013 10:05:26.396376 1684 solver.cpp:228] Iteration 300, loss = 0.192766
I1013 10:05:26.399351 1684 solver.cpp:244] Train net output #0: loss = 0.192766 (* 1 = 0.192766 loss)
I1013 10:05:26.400354 1684 sgd_solver.cpp:106] Iteration 300, lr = 0.00978075
I1013 10:05:28.526006 1684 solver.cpp:228] Iteration 400, loss = 0.0834785
I1013 10:05:28.528012 1684 solver.cpp:244] Train net output #0: loss = 0.0834785 (* 1 = 0.0834785 loss)
I1013 10:05:28.531019 1684 sgd_solver.cpp:106] Iteration 400, lr = 0.00971013
I1013 10:05:30.658334 1684 solver.cpp:337] Iteration 500, Testing net (#0)
I1013 10:05:32.030649 1684 solver.cpp:404] Test net output #0: accuracy = 0.9678
I1013 10:05:32.031683 1684 solver.cpp:404] Test net output #1: loss = 0.0990599 (* 1 = 0.0990599 loss)
I1013 10:05:32.044724 1684 solver.cpp:228] Iteration 500, loss = 0.112297
I1013 10:05:32.045688 1684 solver.cpp:244] Train net output #0: loss = 0.112297 (* 1 = 0.112297 loss)
I1013 10:05:32.049700 1684 sgd_solver.cpp:106] Iteration 500, lr = 0.00964069
I1013 10:05:34.181881 1684 solver.cpp:228] Iteration 600, loss = 0.101184
I1013 10:05:34.182885 1684 solver.cpp:244] Train net output #0: loss = 0.101184 (* 1 = 0.101184 loss)
I1013 10:05:34.183862 1684 sgd_solver.cpp:106] Iteration 600, lr = 0.0095724
I1013 10:05:36.311400 1684 solver.cpp:228] Iteration 700, loss = 0.179369
I1013 10:05:36.312403 1684 solver.cpp:244] Train net output #0: loss = 0.179369 (* 1 = 0.179369 loss)
I1013 10:05:36.314407 1684 sgd_solver.cpp:106] Iteration 700, lr = 0.00950522
I1013 10:05:38.447108 1684 solver.cpp:228] Iteration 800, loss = 0.209864
I1013 10:05:38.449084 1684 solver.cpp:244] Train net output #0: loss = 0.209864 (* 1 = 0.209864 loss)
I1013 10:05:38.450114 1684 sgd_solver.cpp:106] Iteration 800, lr = 0.00943913
I1013 10:05:40.575814 1684 solver.cpp:228] Iteration 900, loss = 0.142768
I1013 10:05:40.575814 1684 solver.cpp:244] Train net output #0: loss = 0.142768 (* 1 = 0.142768 loss)
I1013 10:05:40.575814 1684 sgd_solver.cpp:106] Iteration 900, lr = 0.00937411
I1013 10:05:42.700186 1684 solver.cpp:337] Iteration 1000, Testing net (#0)
I1013 10:05:44.075335 1684 solver.cpp:404] Test net output #0: accuracy = 0.9808
I1013 10:05:44.075335 1684 solver.cpp:404] Test net output #1: loss = 0.0613375 (* 1 = 0.0613375 loss)
I1013 10:05:44.090960 1684 solver.cpp:228] Iteration 1000, loss = 0.0704594
I1013 10:05:44.090960 1684 solver.cpp:244] Train net output #0: loss = 0.0704594 (* 1 = 0.0704594 loss)
I1013 10:05:44.090960 1684 sgd_solver.cpp:106] Iteration 1000, lr = 0.00931012
I1013 10:05:46.231811 1684 solver.cpp:228] Iteration 1100, loss = 0.00886345
I1013 10:05:46.231811 1684 solver.cpp:244] Train net output #0: loss = 0.00886345 (* 1 = 0.00886345 loss)
I1013 10:05:46.231811 1684 sgd_solver.cpp:106] Iteration 1100, lr = 0.00924715
I1013 10:05:48.372705 1684 solver.cpp:228] Iteration 1200, loss = 0.0159409
I1013 10:05:48.372705 1684 solver.cpp:244] Train net output #0: loss = 0.0159409 (* 1 = 0.0159409 loss)
I1013 10:05:48.372705 1684 sgd_solver.cpp:106] Iteration 1200, lr = 0.00918515
I1013 10:05:50.513516 1684 solver.cpp:228] Iteration 1300, loss = 0.0102466
I1013 10:05:50.513516 1684 solver.cpp:244] Train net output #0: loss = 0.0102465 (* 1 = 0.0102465 loss)
I1013 10:05:50.513516 1684 sgd_solver.cpp:106] Iteration 1300, lr = 0.00912412
I1013 10:05:52.639024 1684 solver.cpp:228] Iteration 1400, loss = 0.00691616
I1013 10:05:52.639024 1684 solver.cpp:244] Train net output #0: loss = 0.00691615 (* 1 = 0.00691615 loss)
I1013 10:05:52.639024 1684 sgd_solver.cpp:106] Iteration 1400, lr = 0.00906403
I1013 10:05:54.748378 1684 solver.cpp:337] Iteration 1500, Testing net (#0)
I1013 10:05:56.123487 1684 solver.cpp:404] Test net output #0: accuracy = 0.9824
I1013 10:05:56.123487 1684 solver.cpp:404] Test net output #1: loss = 0.0558028 (* 1 = 0.0558028 loss)
I1013 10:05:56.139156 1684 solver.cpp:228] Iteration 1500, loss = 0.0770894
I1013 10:05:56.139156 1684 solver.cpp:244] Train net output #0: loss = 0.0770894 (* 1 = 0.0770894 loss)
I1013 10:05:56.139156 1684 sgd_solver.cpp:106] Iteration 1500, lr = 0.00900485
I1013 10:05:58.279999 1684 solver.cpp:228] Iteration 1600, loss = 0.08424
I1013 10:05:58.279999 1684 solver.cpp:244] Train net output #0: loss = 0.0842399 (* 1 = 0.0842399 loss)
I1013 10:05:58.279999 1684 sgd_solver.cpp:106] Iteration 1600, lr = 0.00894657
I1013 10:06:00.405194 1684 solver.cpp:228] Iteration 1700, loss = 0.0452077
I1013 10:06:00.405194 1684 solver.cpp:244] Train net output #0: loss = 0.0452077 (* 1 = 0.0452077 loss)
I1013 10:06:00.405194 1684 sgd_solver.cpp:106] Iteration 1700, lr = 0.00888916
I1013 10:06:02.546080 1684 solver.cpp:228] Iteration 1800, loss = 0.0248114
I1013 10:06:02.546080 1684 solver.cpp:244] Train net output #0: loss = 0.0248114 (* 1 = 0.0248114 loss)
I1013 10:06:02.546080 1684 sgd_solver.cpp:106] Iteration 1800, lr = 0.0088326
I1013 10:06:04.671310 1684 solver.cpp:228] Iteration 1900, loss = 0.114547
I1013 10:06:04.671310 1684 solver.cpp:244] Train net output #0: loss = 0.114547 (* 1 = 0.114547 loss)
I1013 10:06:04.686897 1684 sgd_solver.cpp:106] Iteration 1900, lr = 0.00877687
I1013 10:06:06.796535 1684 solver.cpp:337] Iteration 2000, Testing net (#0)
I1013 10:06:08.171643 1684 solver.cpp:404] Test net output #0: accuracy = 0.9841
I1013 10:06:08.187270 1684 solver.cpp:404] Test net output #1: loss = 0.0490052 (* 1 = 0.0490052 loss)
I1013 10:06:08.187270 1684 solver.cpp:228] Iteration 2000, loss = 0.00911095
I1013 10:06:08.202911 1684 solver.cpp:244] Train net output #0: loss = 0.0091109 (* 1 = 0.0091109 loss)
I1013 10:06:08.202911 1684 sgd_solver.cpp:106] Iteration 2000, lr = 0.00872196
I1013 10:06:10.328163 1684 solver.cpp:228] Iteration 2100, loss = 0.0175512
I1013 10:06:10.328163 1684 solver.cpp:244] Train net output #0: loss = 0.0175512 (* 1 = 0.0175512 loss)
I1013 10:06:10.328163 1684 sgd_solver.cpp:106] Iteration 2100, lr = 0.00866784
I1013 10:06:12.456619 1684 solver.cpp:228] Iteration 2200, loss = 0.0182508
I1013 10:06:12.456619 1684 solver.cpp:244] Train net output #0: loss = 0.0182508 (* 1 = 0.0182508 loss)
I1013 10:06:12.472260 1684 sgd_solver.cpp:106] Iteration 2200, lr = 0.0086145
I1013 10:06:14.597468 1684 solver.cpp:228] Iteration 2300, loss = 0.0929874
I1013 10:06:14.597468 1684 solver.cpp:244] Train net output #0: loss = 0.0929874 (* 1 = 0.0929874 loss)
I1013 10:06:14.597468 1684 sgd_solver.cpp:106] Iteration 2300, lr = 0.00856192
I1013 10:06:16.738363 1684 solver.cpp:228] Iteration 2400, loss = 0.0156817
I1013 10:06:16.738363 1684 solver.cpp:244] Train net output #0: loss = 0.0156816 (* 1 = 0.0156816 loss)
I1013 10:06:16.738363 1684 sgd_solver.cpp:106] Iteration 2400, lr = 0.00851008
I1013 10:06:18.847921 1684 solver.cpp:337] Iteration 2500, Testing net (#0)
I1013 10:06:20.223072 1684 solver.cpp:404] Test net output #0: accuracy = 0.9853
I1013 10:06:20.223072 1684 solver.cpp:404] Test net output #1: loss = 0.0476141 (* 1 = 0.0476141 loss)
I1013 10:06:20.238706 1684 solver.cpp:228] Iteration 2500, loss = 0.0254326
I1013 10:06:20.238706 1684 solver.cpp:244] Train net output #0: loss = 0.0254326 (* 1 = 0.0254326 loss)
I1013 10:06:20.238706 1684 sgd_solver.cpp:106] Iteration 2500, lr = 0.00845897
I1013 10:06:22.379546 1684 solver.cpp:228] Iteration 2600, loss = 0.0614191
I1013 10:06:22.379546 1684 solver.cpp:244] Train net output #0: loss = 0.061419 (* 1 = 0.061419 loss)
I1013 10:06:22.379546 1684 sgd_solver.cpp:106] Iteration 2600, lr = 0.00840857
I1013 10:06:24.520401 1684 solver.cpp:228] Iteration 2700, loss = 0.0625541
I1013 10:06:24.520401 1684 solver.cpp:244] Train net output #0: loss = 0.062554 (* 1 = 0.062554 loss)
I1013 10:06:24.520401 1684 sgd_solver.cpp:106] Iteration 2700, lr = 0.00835886
I1013 10:06:26.645644 1684 solver.cpp:228] Iteration 2800, loss = 0.00305949
I1013 10:06:26.645644 1684 solver.cpp:244] Train net output #0: loss = 0.00305946 (* 1 = 0.00305946 loss)
I1013 10:06:26.645644 1684 sgd_solver.cpp:106] Iteration 2800, lr = 0.00830984
I1013 10:06:28.786510 1684 solver.cpp:228] Iteration 2900, loss = 0.0252702
I1013 10:06:28.786510 1684 solver.cpp:244] Train net output #0: loss = 0.0252702 (* 1 = 0.0252702 loss)
I1013 10:06:28.786510 1684 sgd_solver.cpp:106] Iteration 2900, lr = 0.00826148
I1013 10:06:30.896109 1684 solver.cpp:337] Iteration 3000, Testing net (#0)
I1013 10:06:32.271224 1684 solver.cpp:404] Test net output #0: accuracy = 0.9861
I1013 10:06:32.271224 1684 solver.cpp:404] Test net output #1: loss = 0.0419692 (* 1 = 0.0419692 loss)
I1013 10:06:32.286850 1684 solver.cpp:228] Iteration 3000, loss = 0.00504212
I1013 10:06:32.286850 1684 solver.cpp:244] Train net output #0: loss = 0.00504212 (* 1 = 0.00504212 loss)
I1013 10:06:32.286850 1684 sgd_solver.cpp:106] Iteration 3000, lr = 0.00821377
I1013 10:06:34.412075 1684 solver.cpp:228] Iteration 3100, loss = 0.0165952
I1013 10:06:34.412075 1684 solver.cpp:244] Train net output #0: loss = 0.0165953 (* 1 = 0.0165953 loss)
I1013 10:06:34.427702 1684 sgd_solver.cpp:106] Iteration 3100, lr = 0.0081667
I1013 10:06:36.552963 1684 solver.cpp:228] Iteration 3200, loss = 0.0144548
I1013 10:06:36.552963 1684 solver.cpp:244] Train net output #0: loss = 0.0144548 (* 1 = 0.0144548 loss)
I1013 10:06:36.552963 1684 sgd_solver.cpp:106] Iteration 3200, lr = 0.00812025
I1013 10:06:38.693781 1684 solver.cpp:228] Iteration 3300, loss = 0.0481921
I1013 10:06:38.693781 1684 solver.cpp:244] Train net output #0: loss = 0.0481921 (* 1 = 0.0481921 loss)
I1013 10:06:38.693781 1684 sgd_solver.cpp:106] Iteration 3300, lr = 0.00807442
I1013 10:06:40.834671 1684 solver.cpp:228] Iteration 3400, loss = 0.0168258
I1013 10:06:40.834671 1684 solver.cpp:244] Train net output #0: loss = 0.0168259 (* 1 = 0.0168259 loss)
I1013 10:06:40.834671 1684 sgd_solver.cpp:106] Iteration 3400, lr = 0.00802918
I1013 10:06:42.944270 1684 solver.cpp:337] Iteration 3500, Testing net (#0)
I1013 10:06:44.303789 1684 solver.cpp:404] Test net output #0: accuracy = 0.9863
I1013 10:06:44.319380 1684 solver.cpp:404] Test net output #1: loss = 0.043148 (* 1 = 0.043148 loss)
I1013 10:06:44.335008 1684 solver.cpp:228] Iteration 3500, loss = 0.00682415
I1013 10:06:44.335008 1684 solver.cpp:244] Train net output #0: loss = 0.00682418 (* 1 = 0.00682418 loss)
I1013 10:06:44.335008 1684 sgd_solver.cpp:106] Iteration 3500, lr = 0.00798454
I1013 10:06:46.460263 1684 solver.cpp:228] Iteration 3600, loss = 0.0317525
I1013 10:06:46.460263 1684 solver.cpp:244] Train net output #0: loss = 0.0317526 (* 1 = 0.0317526 loss)
I1013 10:06:46.460263 1684 sgd_solver.cpp:106] Iteration 3600, lr = 0.00794046
I1013 10:06:48.601121 1684 solver.cpp:228] Iteration 3700, loss = 0.0246315
I1013 10:06:48.601121 1684 solver.cpp:244] Train net output #0: loss = 0.0246315 (* 1 = 0.0246315 loss)
I1013 10:06:48.601121 1684 sgd_solver.cpp:106] Iteration 3700, lr = 0.00789695
I1013 10:06:50.726347 1684 solver.cpp:228] Iteration 3800, loss = 0.00837651
I1013 10:06:50.726347 1684 solver.cpp:244] Train net output #0: loss = 0.00837653 (* 1 = 0.00837653 loss)
I1013 10:06:50.726347 1684 sgd_solver.cpp:106] Iteration 3800, lr = 0.007854
I1013 10:06:52.871928 1684 solver.cpp:228] Iteration 3900, loss = 0.0320845
I1013 10:06:52.874935 1684 solver.cpp:244] Train net output #0: loss = 0.0320845 (* 1 = 0.0320845 loss)
I1013 10:06:52.876941 1684 sgd_solver.cpp:106] Iteration 3900, lr = 0.00781158
I1013 10:06:54.979713 1684 solver.cpp:337] Iteration 4000, Testing net (#0)
I1013 10:06:56.354836 1684 solver.cpp:404] Test net output #0: accuracy = 0.9875
I1013 10:06:56.354836 1684 solver.cpp:404] Test net output #1: loss = 0.0353671 (* 1 = 0.0353671 loss)
I1013 10:06:56.370452 1684 solver.cpp:228] Iteration 4000, loss = 0.0140691
I1013 10:06:56.370452 1684 solver.cpp:244] Train net output #0: loss = 0.0140691 (* 1 = 0.0140691 loss)
I1013 10:06:56.370452 1684 sgd_solver.cpp:106] Iteration 4000, lr = 0.00776969
I1013 10:06:58.511303 1684 solver.cpp:228] Iteration 4100, loss = 0.0263123
I1013 10:06:58.511303 1684 solver.cpp:244] Train net output #0: loss = 0.0263123 (* 1 = 0.0263123 loss)
I1013 10:06:58.511303 1684 sgd_solver.cpp:106] Iteration 4100, lr = 0.00772833
I1013 10:07:00.652200 1684 solver.cpp:228] Iteration 4200, loss = 0.0117368
I1013 10:07:00.652200 1684 solver.cpp:244] Train net output #0: loss = 0.0117368 (* 1 = 0.0117368 loss)
I1013 10:07:00.652200 1684 sgd_solver.cpp:106] Iteration 4200, lr = 0.00768748
I1013 10:07:02.793052 1684 solver.cpp:228] Iteration 4300, loss = 0.0490961
I1013 10:07:02.793052 1684 solver.cpp:244] Train net output #0: loss = 0.0490961 (* 1 = 0.0490961 loss)
I1013 10:07:02.793052 1684 sgd_solver.cpp:106] Iteration 4300, lr = 0.00764712
I1013 10:07:04.933894 1684 solver.cpp:228] Iteration 4400, loss = 0.0143547
I1013 10:07:04.933894 1684 solver.cpp:244] Train net output #0: loss = 0.0143547 (* 1 = 0.0143547 loss)
I1013 10:07:04.933894 1684 sgd_solver.cpp:106] Iteration 4400, lr = 0.00760726
I1013 10:07:07.043498 1684 solver.cpp:337] Iteration 4500, Testing net (#0)
I1013 10:07:08.418617 1684 solver.cpp:404] Test net output #0: accuracy = 0.9875
I1013 10:07:08.418617 1684 solver.cpp:404] Test net output #1: loss = 0.039773 (* 1 = 0.039773 loss)
I1013 10:07:08.434267 1684 solver.cpp:228] Iteration 4500, loss = 0.00660795
I1013 10:07:08.434267 1684 solver.cpp:244] Train net output #0: loss = 0.00660791 (* 1 = 0.00660791 loss)
I1013 10:07:08.434267 1684 sgd_solver.cpp:106] Iteration 4500, lr = 0.00756788
I1013 10:07:10.575119 1684 solver.cpp:228] Iteration 4600, loss = 0.0135348
I1013 10:07:10.575119 1684 solver.cpp:244] Train net output #0: loss = 0.0135347 (* 1 = 0.0135347 loss)
I1013 10:07:10.575119 1684 sgd_solver.cpp:106] Iteration 4600, lr = 0.00752897
I1013 10:07:12.715939 1684 solver.cpp:228] Iteration 4700, loss = 0.00858051
I1013 10:07:12.715939 1684 solver.cpp:244] Train net output #0: loss = 0.00858048 (* 1 = 0.00858048 loss)
I1013 10:07:12.715939 1684 sgd_solver.cpp:106] Iteration 4700, lr = 0.00749052
I1013 10:07:14.856828 1684 solver.cpp:228] Iteration 4800, loss = 0.013837
I1013 10:07:14.856828 1684 solver.cpp:244] Train net output #0: loss = 0.013837 (* 1 = 0.013837 loss)
I1013 10:07:14.856828 1684 sgd_solver.cpp:106] Iteration 4800, lr = 0.00745253
I1013 10:07:16.997676 1684 solver.cpp:228] Iteration 4900, loss = 0.00716435
I1013 10:07:16.997676 1684 solver.cpp:244] Train net output #0: loss = 0.00716432 (* 1 = 0.00716432 loss)
I1013 10:07:16.997676 1684 sgd_solver.cpp:106] Iteration 4900, lr = 0.00741498
I1013 10:07:19.107244 1684 solver.cpp:454] Snapshotting to binary proto file examples/mnist/lenet_iter_5000.caffemodel
I1013 10:07:19.138531 1684 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_5000.solverstate
I1013 10:07:19.154156 1684 solver.cpp:337] Iteration 5000, Testing net (#0)
I1013 10:07:20.529271 1684 solver.cpp:404] Test net output #0: accuracy = 0.9886
I1013 10:07:20.529271 1684 solver.cpp:404] Test net output #1: loss = 0.0343976 (* 1 = 0.0343976 loss)
I1013 10:07:20.544936 1684 solver.cpp:228] Iteration 5000, loss = 0.046033
I1013 10:07:20.544936 1684 solver.cpp:244] Train net output #0: loss = 0.0460329 (* 1 = 0.0460329 loss)
I1013 10:07:20.544936 1684 sgd_solver.cpp:106] Iteration 5000, lr = 0.00737788
I1013 10:07:22.670121 1684 solver.cpp:228] Iteration 5100, loss = 0.0231957
I1013 10:07:22.670121 1684 solver.cpp:244] Train net output #0: loss = 0.0231957 (* 1 = 0.0231957 loss)
I1013 10:07:22.670121 1684 sgd_solver.cpp:106] Iteration 5100, lr = 0.0073412
I1013 10:07:24.810972 1684 solver.cpp:228] Iteration 5200, loss = 0.00935967
I1013 10:07:24.810972 1684 solver.cpp:244] Train net output #0: loss = 0.00935963 (* 1 = 0.00935963 loss)
I1013 10:07:24.826604 1684 sgd_solver.cpp:106] Iteration 5200, lr = 0.00730495
I1013 10:07:26.951828 1684 solver.cpp:228] Iteration 5300, loss = 0.00283169
I1013 10:07:26.951828 1684 solver.cpp:244] Train net output #0: loss = 0.00283165 (* 1 = 0.00283165 loss)
I1013 10:07:26.951828 1684 sgd_solver.cpp:106] Iteration 5300, lr = 0.00726911
I1013 10:07:29.092718 1684 solver.cpp:228] Iteration 5400, loss = 0.00842249
I1013 10:07:29.092718 1684 solver.cpp:244] Train net output #0: loss = 0.00842245 (* 1 = 0.00842245 loss)
I1013 10:07:29.092718 1684 sgd_solver.cpp:106] Iteration 5400, lr = 0.00723368
I1013 10:07:31.202320 1684 solver.cpp:337] Iteration 5500, Testing net (#0)
I1013 10:07:32.577424 1684 solver.cpp:404] Test net output #0: accuracy = 0.9883
I1013 10:07:32.577424 1684 solver.cpp:404] Test net output #1: loss = 0.0350875 (* 1 = 0.0350875 loss)
I1013 10:07:32.593072 1684 solver.cpp:228] Iteration 5500, loss = 0.00971781
I1013 10:07:32.593072 1684 solver.cpp:244] Train net output #0: loss = 0.00971777 (* 1 = 0.00971777 loss)
I1013 10:07:32.593072 1684 sgd_solver.cpp:106] Iteration 5500, lr = 0.00719865
I1013 10:07:34.733940 1684 solver.cpp:228] Iteration 5600, loss = 0.000905203
I1013 10:07:34.733940 1684 solver.cpp:244] Train net output #0: loss = 0.000905167 (* 1 = 0.000905167 loss)
I1013 10:07:34.733940 1684 sgd_solver.cpp:106] Iteration 5600, lr = 0.00716402
I1013 10:07:36.874794 1684 solver.cpp:228] Iteration 5700, loss = 0.00458089
I1013 10:07:36.874794 1684 solver.cpp:244] Train net output #0: loss = 0.00458086 (* 1 = 0.00458086 loss)
I1013 10:07:36.874794 1684 sgd_solver.cpp:106] Iteration 5700, lr = 0.00712977
I1013 10:07:39.000007 1684 solver.cpp:228] Iteration 5800, loss = 0.0429197
I1013 10:07:39.015626 1684 solver.cpp:244] Train net output #0: loss = 0.0429196 (* 1 = 0.0429196 loss)
I1013 10:07:39.015626 1684 sgd_solver.cpp:106] Iteration 5800, lr = 0.0070959
I1013 10:07:41.140871 1684 solver.cpp:228] Iteration 5900, loss = 0.00847424
I1013 10:07:41.140871 1684 solver.cpp:244] Train net output #0: loss = 0.0084742 (* 1 = 0.0084742 loss)
I1013 10:07:41.140871 1684 sgd_solver.cpp:106] Iteration 5900, lr = 0.0070624
I1013 10:07:43.280727 1684 solver.cpp:337] Iteration 6000, Testing net (#0)
I1013 10:07:44.657387 1684 solver.cpp:404] Test net output #0: accuracy = 0.9892
I1013 10:07:44.658390 1684 solver.cpp:404] Test net output #1: loss = 0.0333308 (* 1 = 0.0333308 loss)
I1013 10:07:44.672427 1684 solver.cpp:228] Iteration 6000, loss = 0.00297941
I1013 10:07:44.673431 1684 solver.cpp:244] Train net output #0: loss = 0.00297938 (* 1 = 0.00297938 loss)
I1013 10:07:44.675467 1684 sgd_solver.cpp:106] Iteration 6000, lr = 0.00702927
I1013 10:07:46.812116 1684 solver.cpp:228] Iteration 6100, loss = 0.00404553
I1013 10:07:46.814121 1684 solver.cpp:244] Train net output #0: loss = 0.0040455 (* 1 = 0.0040455 loss)
I1013 10:07:46.815125 1684 sgd_solver.cpp:106] Iteration 6100, lr = 0.0069965
I1013 10:07:48.949837 1684 solver.cpp:228] Iteration 6200, loss = 0.00796121
I1013 10:07:48.951807 1684 solver.cpp:244] Train net output #0: loss = 0.00796118 (* 1 = 0.00796118 loss)
I1013 10:07:48.953860 1684 sgd_solver.cpp:106] Iteration 6200, lr = 0.00696408
I1013 10:07:51.083505 1684 solver.cpp:228] Iteration 6300, loss = 0.00927992
I1013 10:07:51.085481 1684 solver.cpp:244] Train net output #0: loss = 0.0092799 (* 1 = 0.0092799 loss)
I1013 10:07:51.086510 1684 sgd_solver.cpp:106] Iteration 6300, lr = 0.00693201
I1013 10:07:53.220190 1684 solver.cpp:228] Iteration 6400, loss = 0.00616177
I1013 10:07:53.222162 1684 solver.cpp:244] Train net output #0: loss = 0.00616174 (* 1 = 0.00616174 loss)
I1013 10:07:53.224195 1684 sgd_solver.cpp:106] Iteration 6400, lr = 0.00690029
I1013 10:07:55.335819 1684 solver.cpp:337] Iteration 6500, Testing net (#0)
I1013 10:07:56.705461 1684 solver.cpp:404] Test net output #0: accuracy = 0.9892
I1013 10:07:56.707448 1684 solver.cpp:404] Test net output #1: loss = 0.0342351 (* 1 = 0.0342351 loss)
I1013 10:07:56.721467 1684 solver.cpp:228] Iteration 6500, loss = 0.00857477
I1013 10:07:56.722470 1684 solver.cpp:244] Train net output #0: loss = 0.00857473 (* 1 = 0.00857473 loss)
I1013 10:07:56.723474 1684 sgd_solver.cpp:106] Iteration 6500, lr = 0.0068689
I1013 10:07:58.860191 1684 solver.cpp:228] Iteration 6600, loss = 0.0264124
I1013 10:07:58.861191 1684 solver.cpp:244] Train net output #0: loss = 0.0264124 (* 1 = 0.0264124 loss)
I1013 10:07:58.863162 1684 sgd_solver.cpp:106] Iteration 6600, lr = 0.00683784
I1013 10:08:00.991823 1684 solver.cpp:228] Iteration 6700, loss = 0.00683724
I1013 10:08:00.993829 1684 solver.cpp:244] Train net output #0: loss = 0.00683721 (* 1 = 0.00683721 loss)
I1013 10:08:00.995842 1684 sgd_solver.cpp:106] Iteration 6700, lr = 0.00680711
I1013 10:08:03.131726 1684 solver.cpp:228] Iteration 6800, loss = 0.00408112
I1013 10:08:03.133730 1684 solver.cpp:244] Train net output #0: loss = 0.0040811 (* 1 = 0.0040811 loss)
I1013 10:08:03.135735 1684 sgd_solver.cpp:106] Iteration 6800, lr = 0.0067767
I1013 10:08:05.266402 1684 solver.cpp:228] Iteration 6900, loss = 0.00522403
I1013 10:08:05.268406 1684 solver.cpp:244] Train net output #0: loss = 0.00522401 (* 1 = 0.00522401 loss)
I1013 10:08:05.269409 1684 sgd_solver.cpp:106] Iteration 6900, lr = 0.0067466
I1013 10:08:07.395082 1684 solver.cpp:337] Iteration 7000, Testing net (#0)
I1013 10:08:08.769718 1684 solver.cpp:404] Test net output #0: accuracy = 0.9888
I1013 10:08:08.771723 1684 solver.cpp:404] Test net output #1: loss = 0.035025 (* 1 = 0.035025 loss)
I1013 10:08:08.784770 1684 solver.cpp:228] Iteration 7000, loss = 0.00657448
I1013 10:08:08.785768 1684 solver.cpp:244] Train net output #0: loss = 0.00657445 (* 1 = 0.00657445 loss)
I1013 10:08:08.787765 1684 sgd_solver.cpp:106] Iteration 7000, lr = 0.00671681
I1013 10:08:10.924448 1684 solver.cpp:228] Iteration 7100, loss = 0.0121463
I1013 10:08:10.926453 1684 solver.cpp:244] Train net output #0: loss = 0.0121463 (* 1 = 0.0121463 loss)
I1013 10:08:10.928458 1684 sgd_solver.cpp:106] Iteration 7100, lr = 0.00668733
I1013 10:08:13.061159 1684 solver.cpp:228] Iteration 7200, loss = 0.00267776
I1013 10:08:13.063134 1684 solver.cpp:244] Train net output #0: loss = 0.00267773 (* 1 = 0.00267773 loss)
I1013 10:08:13.064137 1684 sgd_solver.cpp:106] Iteration 7200, lr = 0.00665815
I1013 10:08:15.199861 1684 solver.cpp:228] Iteration 7300, loss = 0.0185436
I1013 10:08:15.201831 1684 solver.cpp:244] Train net output #0: loss = 0.0185435 (* 1 = 0.0185435 loss)
I1013 10:08:15.203866 1684 sgd_solver.cpp:106] Iteration 7300, lr = 0.00662927
I1013 10:08:17.338510 1684 solver.cpp:228] Iteration 7400, loss = 0.0036527
I1013 10:08:17.341522 1684 solver.cpp:244] Train net output #0: loss = 0.00365268 (* 1 = 0.00365268 loss)
I1013 10:08:17.343523 1684 sgd_solver.cpp:106] Iteration 7400, lr = 0.00660067
I1013 10:08:19.459148 1684 solver.cpp:337] Iteration 7500, Testing net (#0)
I1013 10:08:20.831836 1684 solver.cpp:404] Test net output #0: accuracy = 0.9892
I1013 10:08:20.832801 1684 solver.cpp:404] Test net output #1: loss = 0.0364178 (* 1 = 0.0364178 loss)
I1013 10:08:20.845861 1684 solver.cpp:228] Iteration 7500, loss = 0.00223585
I1013 10:08:20.846865 1684 solver.cpp:244] Train net output #0: loss = 0.00223582 (* 1 = 0.00223582 loss)
I1013 10:08:20.847841 1684 sgd_solver.cpp:106] Iteration 7500, lr = 0.00657236
I1013 10:08:22.981528 1684 solver.cpp:228] Iteration 7600, loss = 0.00394381
I1013 10:08:22.983520 1684 solver.cpp:244] Train net output #0: loss = 0.00394378 (* 1 = 0.00394378 loss)
I1013 10:08:22.985527 1684 sgd_solver.cpp:106] Iteration 7600, lr = 0.00654433
I1013 10:08:25.115223 1684 solver.cpp:228] Iteration 7700, loss = 0.0196834
I1013 10:08:25.117230 1684 solver.cpp:244] Train net output #0: loss = 0.0196834 (* 1 = 0.0196834 loss)
I1013 10:08:25.118197 1684 sgd_solver.cpp:106] Iteration 7700, lr = 0.00651658
I1013 10:08:27.252872 1684 solver.cpp:228] Iteration 7800, loss = 0.00327404
I1013 10:08:27.254878 1684 solver.cpp:244] Train net output #0: loss = 0.00327401 (* 1 = 0.00327401 loss)
I1013 10:08:27.255897 1684 sgd_solver.cpp:106] Iteration 7800, lr = 0.00648911
I1013 10:08:29.388586 1684 solver.cpp:228] Iteration 7900, loss = 0.00185404
I1013 10:08:29.390593 1684 solver.cpp:244] Train net output #0: loss = 0.001854 (* 1 = 0.001854 loss)
I1013 10:08:29.392597 1684 sgd_solver.cpp:106] Iteration 7900, lr = 0.0064619
I1013 10:08:31.501201 1684 solver.cpp:337] Iteration 8000, Testing net (#0)
I1013 10:08:32.873819 1684 solver.cpp:404] Test net output #0: accuracy = 0.9892
I1013 10:08:32.875864 1684 solver.cpp:404] Test net output #1: loss = 0.0335527 (* 1 = 0.0335527 loss)
I1013 10:08:32.889863 1684 solver.cpp:228] Iteration 8000, loss = 0.00614705
I1013 10:08:32.889863 1684 solver.cpp:244] Train net output #0: loss = 0.00614701 (* 1 = 0.00614701 loss)
I1013 10:08:32.890882 1684 sgd_solver.cpp:106] Iteration 8000, lr = 0.00643496
I1013 10:08:35.023572 1684 solver.cpp:228] Iteration 8100, loss = 0.0192059
I1013 10:08:35.024581 1684 solver.cpp:244] Train net output #0: loss = 0.0192059 (* 1 = 0.0192059 loss)
I1013 10:08:35.025543 1684 sgd_solver.cpp:106] Iteration 8100, lr = 0.00640827
I1013 10:08:37.159255 1684 solver.cpp:228] Iteration 8200, loss = 0.00787218
I1013 10:08:37.161262 1684 solver.cpp:244] Train net output #0: loss = 0.00787215 (* 1 = 0.00787215 loss)
I1013 10:08:37.162261 1684 sgd_solver.cpp:106] Iteration 8200, lr = 0.00638185
I1013 10:08:39.295898 1684 solver.cpp:228] Iteration 8300, loss = 0.0265738
I1013 10:08:39.296929 1684 solver.cpp:244] Train net output #0: loss = 0.0265737 (* 1 = 0.0265737 loss)
I1013 10:08:39.299909 1684 sgd_solver.cpp:106] Iteration 8300, lr = 0.00635568
I1013 10:08:41.430843 1684 solver.cpp:228] Iteration 8400, loss = 0.00670668
I1013 10:08:41.434828 1684 solver.cpp:244] Train net output #0: loss = 0.00670665 (* 1 = 0.00670665 loss)
I1013 10:08:41.436877 1684 sgd_solver.cpp:106] Iteration 8400, lr = 0.00632975
I1013 10:08:43.540426 1684 solver.cpp:337] Iteration 8500, Testing net (#0)
I1013 10:08:44.918090 1684 solver.cpp:404] Test net output #0: accuracy = 0.99
I1013 10:08:44.919092 1684 solver.cpp:404] Test net output #1: loss = 0.0330528 (* 1 = 0.0330528 loss)
I1013 10:08:44.933130 1684 solver.cpp:228] Iteration 8500, loss = 0.00646596
I1013 10:08:44.934134 1684 solver.cpp:244] Train net output #0: loss = 0.00646593 (* 1 = 0.00646593 loss)
I1013 10:08:44.936137 1684 sgd_solver.cpp:106] Iteration 8500, lr = 0.00630407
I1013 10:08:47.070852 1684 solver.cpp:228] Iteration 8600, loss = 0.000641635
I1013 10:08:47.072856 1684 solver.cpp:244] Train net output #0: loss = 0.000641601 (* 1 = 0.000641601 loss)
I1013 10:08:47.074916 1684 sgd_solver.cpp:106] Iteration 8600, lr = 0.00627864
I1013 10:08:49.204524 1684 solver.cpp:228] Iteration 8700, loss = 0.00248919
I1013 10:08:49.206532 1684 solver.cpp:244] Train net output #0: loss = 0.00248916 (* 1 = 0.00248916 loss)
I1013 10:08:49.207542 1684 sgd_solver.cpp:106] Iteration 8700, lr = 0.00625344
I1013 10:08:51.339238 1684 solver.cpp:228] Iteration 8800, loss = 0.00115433
I1013 10:08:51.341305 1684 solver.cpp:244] Train net output #0: loss = 0.0011543 (* 1 = 0.0011543 loss)
I1013 10:08:51.343212 1684 sgd_solver.cpp:106] Iteration 8800, lr = 0.00622847
I1013 10:08:53.474915 1684 solver.cpp:228] Iteration 8900, loss = 0.00148415
I1013 10:08:53.475916 1684 solver.cpp:244] Train net output #0: loss = 0.00148413 (* 1 = 0.00148413 loss)
I1013 10:08:53.477922 1684 sgd_solver.cpp:106] Iteration 8900, lr = 0.00620374
I1013 10:08:55.589537 1684 solver.cpp:337] Iteration 9000, Testing net (#0)
I1013 10:08:56.966166 1684 solver.cpp:404] Test net output #0: accuracy = 0.9886
I1013 10:08:56.968189 1684 solver.cpp:404] Test net output #1: loss = 0.0339002 (* 1 = 0.0339002 loss)
I1013 10:08:56.981241 1684 solver.cpp:228] Iteration 9000, loss = 0.0147503
I1013 10:08:56.982228 1684 solver.cpp:244] Train net output #0: loss = 0.0147502 (* 1 = 0.0147502 loss)
I1013 10:08:56.983245 1684 sgd_solver.cpp:106] Iteration 9000, lr = 0.00617924
I1013 10:08:59.115936 1684 solver.cpp:228] Iteration 9100, loss = 0.00737076
I1013 10:08:59.117923 1684 solver.cpp:244] Train net output #0: loss = 0.00737073 (* 1 = 0.00737073 loss)
I1013 10:08:59.119928 1684 sgd_solver.cpp:106] Iteration 9100, lr = 0.00615496
I1013 10:09:01.251560 1684 solver.cpp:228] Iteration 9200, loss = 0.00446405
I1013 10:09:01.252562 1684 solver.cpp:244] Train net output #0: loss = 0.00446402 (* 1 = 0.00446402 loss)
I1013 10:09:01.253566 1684 sgd_solver.cpp:106] Iteration 9200, lr = 0.0061309
I1013 10:09:03.386270 1684 solver.cpp:228] Iteration 9300, loss = 0.00824475
I1013 10:09:03.388242 1684 solver.cpp:244] Train net output #0: loss = 0.00824472 (* 1 = 0.00824472 loss)
I1013 10:09:03.389245 1684 sgd_solver.cpp:106] Iteration 9300, lr = 0.00610706
I1013 10:09:05.521981 1684 solver.cpp:228] Iteration 9400, loss = 0.0200841
I1013 10:09:05.523952 1684 solver.cpp:244] Train net output #0: loss = 0.020084 (* 1 = 0.020084 loss)
I1013 10:09:05.525956 1684 sgd_solver.cpp:106] Iteration 9400, lr = 0.00608343
I1013 10:09:07.638610 1684 solver.cpp:337] Iteration 9500, Testing net (#0)
I1013 10:09:09.004231 1684 solver.cpp:404] Test net output #0: accuracy = 0.9877
I1013 10:09:09.007218 1684 solver.cpp:404] Test net output #1: loss = 0.0394568 (* 1 = 0.0394568 loss)
I1013 10:09:09.021282 1684 solver.cpp:228] Iteration 9500, loss = 0.00323504
I1013 10:09:09.021282 1684 solver.cpp:244] Train net output #0: loss = 0.00323501 (* 1 = 0.00323501 loss)
I1013 10:09:09.023258 1684 sgd_solver.cpp:106] Iteration 9500, lr = 0.00606002
I1013 10:09:11.161974 1684 solver.cpp:228] Iteration 9600, loss = 0.00335854
I1013 10:09:11.163949 1684 solver.cpp:244] Train net output #0: loss = 0.00335851 (* 1 = 0.00335851 loss)
I1013 10:09:11.164966 1684 sgd_solver.cpp:106] Iteration 9600, lr = 0.00603682
I1013 10:09:13.296618 1684 solver.cpp:228] Iteration 9700, loss = 0.0024854
I1013 10:09:13.298666 1684 solver.cpp:244] Train net output #0: loss = 0.00248537 (* 1 = 0.00248537 loss)
I1013 10:09:13.300631 1684 sgd_solver.cpp:106] Iteration 9700, lr = 0.00601382
I1013 10:09:15.431372 1684 solver.cpp:228] Iteration 9800, loss = 0.0139184
I1013 10:09:15.433302 1684 solver.cpp:244] Train net output #0: loss = 0.0139184 (* 1 = 0.0139184 loss)
I1013 10:09:15.435307 1684 sgd_solver.cpp:106] Iteration 9800, lr = 0.00599102
I1013 10:09:17.568011 1684 solver.cpp:228] Iteration 9900, loss = 0.00603178
I1013 10:09:17.569984 1684 solver.cpp:244] Train net output #0: loss = 0.00603175 (* 1 = 0.00603175 loss)
I1013 10:09:17.570989 1684 sgd_solver.cpp:106] Iteration 9900, lr = 0.00596843
I1013 10:09:19.683639 1684 solver.cpp:454] Snapshotting to binary proto file examples/mnist/lenet_iter_10000.caffemodel
I1013 10:09:19.745410 1684 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_10000.solverstate
I1013 10:09:19.767470 1684 solver.cpp:317] Iteration 10000, loss = 0.00483315
I1013 10:09:19.768472 1684 solver.cpp:337] Iteration 10000, Testing net (#0)
I1013 10:09:21.132349 1684 solver.cpp:404] Test net output #0: accuracy = 0.9899
I1013 10:09:21.134388 1684 solver.cpp:404] Test net output #1: loss = 0.0316015 (* 1 = 0.0316015 loss)
I1013 10:09:21.136361 1684 solver.cpp:322] Optimization Done.
I1013 10:09:21.137379 1684 caffe.cpp:255] Optimization Done.