innerproduct layer convert failed: caffe.LayerParameter" has no field named "weight_decay"

[northeastsquare]

Hello, when I use caffe2ncnn to convert caffe model, it failed:
tools/caffe/caffe2ncnn ./deploy.prototxt ./param.caffemodel numproto numbin

[libprotobuf ERROR google/protobuf/text_format.cc:299] Error parsing text-format caffe.NetParameter: 184:15: Message type "caffe.LayerParameter" has no field named "weight_decay".
read_proto_from_text failed

the prototxt as follows:
`name: "DeepFace_set001_net"
layer {
name: "mydata"
type: "MemoryData"
top: "data"
top: "label"
transform_param {
scale: 0.00390625
}
memory_data_param {
batch_size: 1
channels: 1
height: 64
width: 64
}
}

layer{
name: "conv1"
type: "Convolution"

convolution_param {
num_output: 12
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.1
}
}
bottom: "data"
top: "conv1"
}

layer{
name: "pool1"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 3
stride: 3
}
bottom: "conv1"
top: "pool1"
}

layer{
name: "slice1"
type:"Slice"
slice_param {
slice_dim: 1
}
bottom: "pool1"
top: "slice1_1"
top: "slice1_2"
}

layer{
name: "etlwise1"
type: "Eltwise"
bottom: "slice1_1"
bottom: "slice1_2"
top: "eltwise1"
eltwise_param {
operation: MAX
}
}

layer{
name: "conv2"
type: "Convolution"

convolution_param {
num_output: 24
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.1
}
}
bottom: "eltwise1"
top: "conv2"
}
layer{
name: "pool2"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 3
stride: 3
}
bottom: "conv2"
top: "pool2"
}

layer{
name: "slice2"
type:"Slice"
slice_param {
slice_dim: 1
}
bottom: "pool2"
top: "slice2_1"
top: "slice2_2"
}

layer{
name: "etlwise2"
type: "Eltwise"
bottom: "slice2_1"
bottom: "slice2_2"
top: "eltwise2"

eltwise_param {
operation: MAX
}
}

layer{
name: "conv3"
type: "Convolution"

convolution_param {
num_output: 32
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.1
}
}
bottom: "eltwise2"
top: "conv3"
}

layer{
name: "pool3"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
bottom: "conv3"
top: "pool3"
}

layer{
name: "slice3"
type:"Slice"
slice_param {
slice_dim: 1
}
bottom: "pool3"
top: "slice3_1"
top: "slice3_2"
}

layer{
name: "etlwise3"
type: "Eltwise"
bottom: "slice3_1"
bottom: "slice3_2"
top: "eltwise3"
eltwise_param {
operation: MAX
}
}

layer{
name: "fc1"
type: "InnerProduct"
weight_decay: 10
weight_decay: 10
inner_product_param{
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.1
}
}
bottom: "eltwise3"
top: "fc1"
}

layer{
name: "slice6"
type:"Slice"
slice_param {
slice_dim: 1
}
bottom: "fc1"
top: "slice6_1"
top: "slice6_2"
}

layer{
name: "eltwise6"
type: "Eltwise"
bottom: "slice6_1"
bottom: "slice6_2"
top: "eltwise6"
eltwise_param {
operation: MAX
}
}

layer {
name: "dropout1"
type: "Dropout"
bottom: "eltwise6"
top: "dropout1"
dropout_param {
dropout_ratio: 0.7
}
}

layer{
name: "fc2"
type: "InnerProduct"
weight_decay: 10
weight_decay: 10
inner_product_param{
num_output: 11
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
#value: 0.1
}
}
bottom: "dropout1"
top: "fc2"
}
`

[nihui]

try to upgrade your caffe prototxt and caffemodel to new style firstly
reference guide https://github.com/Tencent/ncnn/wiki/how-to-use-ncnn-with-alexnet

[northeastsquare]

Thank you for your fast reply, I think I will have a pleasant journey with ncnn because of the kind and open author.
After upgrade, I delete
weight_decay: 10
weight_decay: 10
, as they are used in training processing . Then it passed