when i train my own dataset,the loss is not decrease and it is always about 6.97

[jackchowtop1]

the following is my solver.prototxt:
net: "examples/re_identification/DenseNet_201.prototxt"
test_iter: 5000
test_interval: 1000
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 1000
display: 20
max_iter: 10000
momentum: 0.9
weight_decay: 0.0005
snapshot: 1000
snapshot_prefix: "examples/re_identification/caffenet_train"
solver_mode: GPU

and i add some code in your DenseNet.prototxt
at the beginning:
name: "DENSENET_201"
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
crop_size: 224
mean_value: 90
mean_value: 102
mean_value: 100
mirror: true
}
data_param {
source: "/home/zhou/caffe/examples/re_identification/img_train_lmdb"
batch_size: 1
backend: LMDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 224
mean_value: 90
mean_value: 102
mean_value: 100
mirror: false
}
data_param {
source: "/home/zhou/caffe/examples/re_identification/img_test_lmdb"
batch_size: 1
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 64
bias_term: false
pad: 3
kernel_size: 7
stride: 2
}
...
...
...
at the end i add:
layer {
name: "fc6"
type: "Convolution"
bottom: "pool5"
top: "fc6"
convolution_param {
num_output: 1000
kernel_size: 1
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc6"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc6"
bottom: "label"
top: "loss"
}
and the label of my dataset is 0 to 999
i do not know why my loss is not decrease and keep the value about 6.9

[kouyoumin]

Try adding
weight_filler {
type: "msra"
}
to convolution_param