Sreejith Balakrishnan
Oct 23, 2016, 12:27:24 PM10/23/16
to Caffe Users
Hi everyone,
My caffee training (both via caffe command and via pycaffe) is getting stuck at creation of one of the input layers. Please find the png file attached for details. I have waited for up to 2 hours before deciding to force quit.
Is it related to the error message "system entropy source not available"? What does that mean?
Please do give me some hints to proceed.
katari...@gmail.com
Jan 24, 2017, 7:00:15 AM1/24/17
to Caffe Users
Hi,
I have similar problem - printout is below. After I run caffe it stops with msg caffe.exe has stopped working.
Did you find a solution? Do you know what might be wrong?
Kind Regards,
Katarina
C:\Projects\caffeBinaries\caffe\bin>caffe train -solver=C:\Projects\caffe\examples\mnist\lenet_solver.prototxt
I0124 12:51:37.279693 12828 caffe.cpp:211] Use CPU.
I0124 12:51:37.279693 12828 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: "C:\\Projects\\caffe\\examples\\mnist\\lenet"
solver_mode: CPU
net: "C:\\Projects\\caffe\\examples\\mnist\\lenet_train_test.prototxt"
train_state {
level: 0
stage: ""
}
I0124 12:51:37.279693 12828 solver.cpp:91] Creating training net from net file: C:\Projects\caffe\examples\mnist\lenet_train_test.prototxt
I0124 12:51:37.279693 12828 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer mnist
I0124 12:51:37.279693 12828 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0124 12:51:37.295320 12828 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"
}
I0124 12:51:37.295320 12828 layer_factory.cpp:58] Creating layer mnist
I0124 12:51:37.295320 12828 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I0124 12:51:37.295320 12828 net.cpp:100] Creating Layer mnist
I0124 12:51:37.295320 12828 net.cpp:408] mnist -> data
I0124 12:51:37.295320 12828 net.cpp:408] mnist -> label
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