Loss = -nan

[3ntr0phy]

Hi,
I'm trying to train my own data.
This is the solver :
net: "train_val.prototxt"
#test_initialization: false
#test_iter: 100
#test_interval: 1000
display: 20
average_loss: 20
base_lr: 0.000001
lr_policy: "poly"
power: 1.0
max_iter: 500
momentum: 0.9
weight_decay: 0.0001
snapshot: 100
snapshot_prefix: "mobilenet"

this is the train_val :
name: "MOBILENET"

transform_param {

scale: 0.017

mirror: false

crop_size: 224

mean_value: [103.94,116.78,123.68]

}

layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mean_file: "imagenet_mean.binaryproto"
mirror:false
}
image_data_param {
source: "./train.txt"
batch_size: 16
new_height: 256
new_width: 256
root_folder: "/"
}
}

layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv1/bn"
type: "BatchNorm"
bottom: "conv1"
top: "conv1"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv1/scale"
type: "Scale"
bottom: "conv1"
top: "conv1"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "conv2_1/dw"
type: "Convolution"
bottom: "conv1"
top: "conv2_1/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
group: 32
engine: CAFFE
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_1/dw/bn"
type: "BatchNorm"
bottom: "conv2_1/dw"
top: "conv2_1/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv2_1/dw/scale"
type: "Scale"
bottom: "conv2_1/dw"
top: "conv2_1/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu2_1/dw"
type: "ReLU"
bottom: "conv2_1/dw"
top: "conv2_1/dw"
}
layer {
name: "conv2_1/sep"
type: "Convolution"
bottom: "conv2_1/dw"
top: "conv2_1/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_1/sep/bn"
type: "BatchNorm"
bottom: "conv2_1/sep"
top: "conv2_1/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv2_1/sep/scale"
type: "Scale"
bottom: "conv2_1/sep"
top: "conv2_1/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu2_1/sep"
type: "ReLU"
bottom: "conv2_1/sep"
top: "conv2_1/sep"
}
layer {
name: "conv2_2/dw"
type: "Convolution"
bottom: "conv2_1/sep"
top: "conv2_2/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 64
bias_term: false
pad: 1
kernel_size: 3
group: 64
engine: CAFFE
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_2/dw/bn"
type: "BatchNorm"
bottom: "conv2_2/dw"
top: "conv2_2/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv2_2/dw/scale"
type: "Scale"
bottom: "conv2_2/dw"
top: "conv2_2/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu2_2/dw"
type: "ReLU"
bottom: "conv2_2/dw"
top: "conv2_2/dw"
}
layer {
name: "conv2_2/sep"
type: "Convolution"
bottom: "conv2_2/dw"
top: "conv2_2/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 128
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_2/sep/bn"
type: "BatchNorm"
bottom: "conv2_2/sep"
top: "conv2_2/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv2_2/sep/scale"
type: "Scale"
bottom: "conv2_2/sep"
top: "conv2_2/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu2_2/sep"
type: "ReLU"
bottom: "conv2_2/sep"
top: "conv2_2/sep"
}
layer {
name: "conv3_1/dw"
type: "Convolution"
bottom: "conv2_2/sep"
top: "conv3_1/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
group: 128
engine: CAFFE
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_1/dw/bn"
type: "BatchNorm"
bottom: "conv3_1/dw"
top: "conv3_1/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv3_1/dw/scale"
type: "Scale"
bottom: "conv3_1/dw"
top: "conv3_1/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu3_1/dw"
type: "ReLU"
bottom: "conv3_1/dw"
top: "conv3_1/dw"
}
layer {
name: "conv3_1/sep"
type: "Convolution"
bottom: "conv3_1/dw"
top: "conv3_1/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 128
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_1/sep/bn"
type: "BatchNorm"
bottom: "conv3_1/sep"
top: "conv3_1/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv3_1/sep/scale"
type: "Scale"
bottom: "conv3_1/sep"
top: "conv3_1/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu3_1/sep"
type: "ReLU"
bottom: "conv3_1/sep"
top: "conv3_1/sep"
}
layer {
name: "conv3_2/dw"
type: "Convolution"
bottom: "conv3_1/sep"
top: "conv3_2/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
group: 128
engine: CAFFE
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_2/dw/bn"
type: "BatchNorm"
bottom: "conv3_2/dw"
top: "conv3_2/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv3_2/dw/scale"
type: "Scale"
bottom: "conv3_2/dw"
top: "conv3_2/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu3_2/dw"
type: "ReLU"
bottom: "conv3_2/dw"
top: "conv3_2/dw"
}
layer {
name: "conv3_2/sep"
type: "Convolution"
bottom: "conv3_2/dw"
top: "conv3_2/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 256
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_2/sep/bn"
type: "BatchNorm"
bottom: "conv3_2/sep"
top: "conv3_2/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv3_2/sep/scale"
type: "Scale"
bottom: "conv3_2/sep"
top: "conv3_2/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu3_2/sep"
type: "ReLU"
bottom: "conv3_2/sep"
top: "conv3_2/sep"
}
layer {
name: "conv4_1/dw"
type: "Convolution"
bottom: "conv3_2/sep"
top: "conv4_1/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
group: 256
engine: CAFFE
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_1/dw/bn"
type: "BatchNorm"
bottom: "conv4_1/dw"
top: "conv4_1/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv4_1/dw/scale"
type: "Scale"
bottom: "conv4_1/dw"
top: "conv4_1/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu4_1/dw"
type: "ReLU"
bottom: "conv4_1/dw"
top: "conv4_1/dw"
}
layer {
name: "conv4_1/sep"
type: "Convolution"
bottom: "conv4_1/dw"
top: "conv4_1/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 256
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_1/sep/bn"
type: "BatchNorm"
bottom: "conv4_1/sep"
top: "conv4_1/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv4_1/sep/scale"
type: "Scale"
bottom: "conv4_1/sep"
top: "conv4_1/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu4_1/sep"
type: "ReLU"
bottom: "conv4_1/sep"
top: "conv4_1/sep"
}
layer {
name: "conv4_2/dw"
type: "Convolution"
bottom: "conv4_1/sep"
top: "conv4_2/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
group: 256
engine: CAFFE
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_2/dw/bn"
type: "BatchNorm"
bottom: "conv4_2/dw"
top: "conv4_2/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv4_2/dw/scale"
type: "Scale"
bottom: "conv4_2/dw"
top: "conv4_2/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu4_2/dw"
type: "ReLU"
bottom: "conv4_2/dw"
top: "conv4_2/dw"
}
layer {
name: "conv4_2/sep"
type: "Convolution"
bottom: "conv4_2/dw"
top: "conv4_2/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_2/sep/bn"
type: "BatchNorm"
bottom: "conv4_2/sep"
top: "conv4_2/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv4_2/sep/scale"
type: "Scale"
bottom: "conv4_2/sep"
top: "conv4_2/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu4_2/sep"
type: "ReLU"
bottom: "conv4_2/sep"
top: "conv4_2/sep"
}
layer {
name: "conv5_1/dw"
type: "Convolution"
bottom: "conv4_2/sep"
top: "conv5_1/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_1/dw/bn"
type: "BatchNorm"
bottom: "conv5_1/dw"
top: "conv5_1/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_1/dw/scale"
type: "Scale"
bottom: "conv5_1/dw"
top: "conv5_1/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_1/dw"
type: "ReLU"
bottom: "conv5_1/dw"
top: "conv5_1/dw"
}
layer {
name: "conv5_1/sep"
type: "Convolution"
bottom: "conv5_1/dw"
top: "conv5_1/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_1/sep/bn"
type: "BatchNorm"
bottom: "conv5_1/sep"
top: "conv5_1/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_1/sep/scale"
type: "Scale"
bottom: "conv5_1/sep"
top: "conv5_1/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_1/sep"
type: "ReLU"
bottom: "conv5_1/sep"
top: "conv5_1/sep"
}
layer {
name: "conv5_2/dw"
type: "Convolution"
bottom: "conv5_1/sep"
top: "conv5_2/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_2/dw/bn"
type: "BatchNorm"
bottom: "conv5_2/dw"
top: "conv5_2/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_2/dw/scale"
type: "Scale"
bottom: "conv5_2/dw"
top: "conv5_2/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_2/dw"
type: "ReLU"
bottom: "conv5_2/dw"
top: "conv5_2/dw"
}
layer {
name: "conv5_2/sep"
type: "Convolution"
bottom: "conv5_2/dw"
top: "conv5_2/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_2/sep/bn"
type: "BatchNorm"
bottom: "conv5_2/sep"
top: "conv5_2/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_2/sep/scale"
type: "Scale"
bottom: "conv5_2/sep"
top: "conv5_2/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_2/sep"
type: "ReLU"
bottom: "conv5_2/sep"
top: "conv5_2/sep"
}
layer {
name: "conv5_3/dw"
type: "Convolution"
bottom: "conv5_2/sep"
top: "conv5_3/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_3/dw/bn"
type: "BatchNorm"
bottom: "conv5_3/dw"
top: "conv5_3/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_3/dw/scale"
type: "Scale"
bottom: "conv5_3/dw"
top: "conv5_3/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_3/dw"
type: "ReLU"
bottom: "conv5_3/dw"
top: "conv5_3/dw"
}
layer {
name: "conv5_3/sep"
type: "Convolution"
bottom: "conv5_3/dw"
top: "conv5_3/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_3/sep/bn"
type: "BatchNorm"
bottom: "conv5_3/sep"
top: "conv5_3/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_3/sep/scale"
type: "Scale"
bottom: "conv5_3/sep"
top: "conv5_3/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_3/sep"
type: "ReLU"
bottom: "conv5_3/sep"
top: "conv5_3/sep"
}
layer {
name: "conv5_4/dw"
type: "Convolution"
bottom: "conv5_3/sep"
top: "conv5_4/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_4/dw/bn"
type: "BatchNorm"
bottom: "conv5_4/dw"
top: "conv5_4/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_4/dw/scale"
type: "Scale"
bottom: "conv5_4/dw"
top: "conv5_4/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_4/dw"
type: "ReLU"
bottom: "conv5_4/dw"
top: "conv5_4/dw"
}
layer {
name: "conv5_4/sep"
type: "Convolution"
bottom: "conv5_4/dw"
top: "conv5_4/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_4/sep/bn"
type: "BatchNorm"
bottom: "conv5_4/sep"
top: "conv5_4/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_4/sep/scale"
type: "Scale"
bottom: "conv5_4/sep"
top: "conv5_4/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_4/sep"
type: "ReLU"
bottom: "conv5_4/sep"
top: "conv5_4/sep"
}
layer {
name: "conv5_5/dw"
type: "Convolution"
bottom: "conv5_4/sep"
top: "conv5_5/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_5/dw/bn"
type: "BatchNorm"
bottom: "conv5_5/dw"
top: "conv5_5/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_5/dw/scale"
type: "Scale"
bottom: "conv5_5/dw"
top: "conv5_5/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_5/dw"
type: "ReLU"
bottom: "conv5_5/dw"
top: "conv5_5/dw"
}
layer {
name: "conv5_5/sep"
type: "Convolution"
bottom: "conv5_5/dw"
top: "conv5_5/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_5/sep/bn"
type: "BatchNorm"
bottom: "conv5_5/sep"
top: "conv5_5/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_5/sep/scale"
type: "Scale"
bottom: "conv5_5/sep"
top: "conv5_5/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_5/sep"
type: "ReLU"
bottom: "conv5_5/sep"
top: "conv5_5/sep"
}
layer {
name: "conv5_6/dw"
type: "Convolution"
bottom: "conv5_5/sep"
top: "conv5_6/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_6/dw/bn"
type: "BatchNorm"
bottom: "conv5_6/dw"
top: "conv5_6/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_6/dw/scale"
type: "Scale"
bottom: "conv5_6/dw"
top: "conv5_6/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_6/dw"
type: "ReLU"
bottom: "conv5_6/dw"
top: "conv5_6/dw"
}
layer {
name: "conv5_6/sep"
type: "Convolution"
bottom: "conv5_6/dw"
top: "conv5_6/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 1024
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_6/sep/bn"
type: "BatchNorm"
bottom: "conv5_6/sep"
top: "conv5_6/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv5_6/sep/scale"
type: "Scale"
bottom: "conv5_6/sep"
top: "conv5_6/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu5_6/sep"
type: "ReLU"
bottom: "conv5_6/sep"
top: "conv5_6/sep"
}
layer {
name: "conv6/dw"
type: "Convolution"
bottom: "conv5_6/sep"
top: "conv6/dw"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 1024
bias_term: false
pad: 1
kernel_size: 3
group: 1024
engine: CAFFE
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6/dw/bn"
type: "BatchNorm"
bottom: "conv6/dw"
top: "conv6/dw"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv6/dw/scale"
type: "Scale"
bottom: "conv6/dw"
top: "conv6/dw"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu6/dw"
type: "ReLU"
bottom: "conv6/dw"
top: "conv6/dw"
}
layer {
name: "conv6/sep"
type: "Convolution"
bottom: "conv6/dw"
top: "conv6/sep"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 1024
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6/sep/bn"
type: "BatchNorm"
bottom: "conv6/sep"
top: "conv6/sep"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
batch_norm_param {
use_global_stats: true
eps: 1e-5
}
}
layer {
name: "conv6/sep/scale"
type: "Scale"
bottom: "conv6/sep"
top: "conv6/sep"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
filler {
value: 1
}
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "relu6/sep"
type: "ReLU"
bottom: "conv6/sep"
top: "conv6/sep"
}
layer {
name: "pool6"
type: "Pooling"
bottom: "conv6/sep"
top: "pool6"
pooling_param {
pool: AVE
global_pooling: true
}
}
layer {
name: "fc7"
type: "Convolution"
bottom: "pool6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 42
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc7"
bottom: "label"
top: "loss"
}
layer {
name: "top1/acc"
type: "Accuracy"
bottom: "fc7"
bottom: "label"
top: "top1/acc"
include {
phase: TEST
}
}
layer {
name: "top5/acc"
type: "Accuracy"
bottom: "fc7"
bottom: "label"
top: "top5/acc"
include {
phase: TEST
}
accuracy_param {
top_k: 5
}
}

I tried also to replace the mean as you said in your tutorial but also I get

I0421 13:23:50.226541 2808 solver.cpp:218] Iteration 0 (-1.82169e-44 iter/s, 40.202s/20 iters), loss = -nan
I0421 13:23:50.226686 2808 solver.cpp:237] Train net output #0: loss = -nan (* 1 = -nan loss)
I0421 13:23:50.226697 2808 sgd_solver.cpp:105] Iteration 0, lr = 1e-06

I really don't understand why loss goes to -nan

[zhangnn016]

I got this problem too, have you find the reason?

[TerryBryant]

@Jacoppy @zhangnn016 try to remove all use_global_stats: true in the batch_norm_param, let the BatchNorm layers be in a default value

[NarcissusInMirror]

@Jacoppy @zhangnn016 try to remove all use_global_stats: true in the batch_norm_param, let the BatchNorm layers be in a default value

Thank you very much, it works for me!