Может ли какой-нибудь орган помочь мне, как конвертировать эту модель для использования в керасе. Я перевел вес кофеен в вес h5 керас. Но я не могу создать модель keras из этого файла prototxt.
Файл protxt выглядит следующим образом:
name: "VGG_CNN_M_2048"
input: "data"
input_dim: 10
input_dim: 3
input_dim: 224
input_dim: 224
layers {
bottom: "data"
top: "conv1"
name: "conv1"
type: CONVOLUTION
convolution_param {
num_output: 96
kernel_size: 7
stride: 2
}
}
layers {
bottom: "conv1"
top: "conv1"
name: "relu1"
type: RELU
}
layers {
bottom: "conv1"
top: "norm1"
name: "norm1"
type: LRN
lrn_param {
local_size: 5
alpha: 0.0005
beta: 0.75
k: 2
}
}
layers {
bottom: "norm1"
top: "pool1"
name: "pool1"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
bottom: "pool1"
top: "conv2"
name: "conv2"
type: CONVOLUTION
convolution_param {
num_output: 256
pad: 1
kernel_size: 5
stride: 2
}
}
layers {
bottom: "conv2"
top: "conv2"
name: "relu2"
type: RELU
}
layers {
bottom: "conv2"
top: "norm2"
name: "norm2"
type: LRN
lrn_param {
local_size: 5
alpha: 0.0005
beta: 0.75
k: 2
}
}
layers {
bottom: "norm2"
top: "pool2"
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
bottom: "pool2"
top: "conv3"
name: "conv3"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3"
top: "conv3"
name: "relu3"
type: RELU
}
layers {
bottom: "conv3"
top: "conv4"
name: "conv4"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4"
top: "conv4"
name: "relu4"
type: RELU
}
layers {
bottom: "conv4"
top: "conv5"
name: "conv5"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5"
top: "conv5"
name: "relu5"
type: RELU
}
layers {
bottom: "conv5"
top: "pool5"
name: "pool5"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
bottom: "pool5"
top: "fc6"
name: "fc6"
type: INNER_PRODUCT
inner_product_param {
num_output: 4096
}
}
layers {
bottom: "fc6"
top: "fc6"
name: "relu6"
type: RELU
}
layers {
bottom: "fc6"
top: "fc6"
name: "drop6"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
}
layers {
bottom: "fc6"
top: "fc7"
name: "fc7"
type: INNER_PRODUCT
inner_product_param {
num_output: 2048
}
}
layers {
bottom: "fc7"
top: "fc7"
name: "relu7"
type: RELU
}
layers {
bottom: "fc7"
top: "fc7"
name: "drop7"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
}
layers {
bottom: "fc7"
top: "fc8"
name: "fc8"
type: INNER_PRODUCT
inner_product_param {
num_output: 1000
}
}
layers {
bottom: "fc8"
top: "prob"
name: "prob"
type: SOFTMAX
}
Я знаю о Conv2D и Relu и Max Pooling, но не уверен, что это будет работать или нет