зеркало из https://github.com/microsoft/caffe.git
219 строки
2.9 KiB
Plaintext
219 строки
2.9 KiB
Plaintext
# Fully convolutional network version of CaffeNet.
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name: "CaffeNetConv"
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input: "data"
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input_shape {
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dim: 1
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dim: 3
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dim: 451
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dim: 451
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}
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layer {
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name: "conv1"
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type: "Convolution"
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bottom: "data"
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top: "conv1"
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convolution_param {
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num_output: 96
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kernel_size: 11
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stride: 4
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}
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}
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layer {
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name: "relu1"
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type: "ReLU"
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bottom: "conv1"
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top: "conv1"
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}
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layer {
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name: "pool1"
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type: "Pooling"
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bottom: "conv1"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "norm1"
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type: "LRN"
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bottom: "pool1"
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top: "norm1"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "conv2"
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type: "Convolution"
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bottom: "norm1"
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top: "conv2"
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convolution_param {
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num_output: 256
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pad: 2
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kernel_size: 5
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group: 2
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}
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}
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layer {
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name: "relu2"
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type: "ReLU"
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bottom: "conv2"
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top: "conv2"
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}
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layer {
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name: "pool2"
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type: "Pooling"
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bottom: "conv2"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "norm2"
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type: "LRN"
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bottom: "pool2"
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top: "norm2"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "conv3"
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type: "Convolution"
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bottom: "norm2"
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top: "conv3"
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu3"
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type: "ReLU"
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bottom: "conv3"
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top: "conv3"
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}
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layer {
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name: "conv4"
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type: "Convolution"
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bottom: "conv3"
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top: "conv4"
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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group: 2
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}
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}
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layer {
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name: "relu4"
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type: "ReLU"
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bottom: "conv4"
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top: "conv4"
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}
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layer {
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name: "conv5"
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type: "Convolution"
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bottom: "conv4"
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top: "conv5"
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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group: 2
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}
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}
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layer {
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name: "relu5"
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type: "ReLU"
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bottom: "conv5"
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top: "conv5"
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}
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layer {
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name: "pool5"
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type: "Pooling"
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bottom: "conv5"
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top: "pool5"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "fc6-conv"
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type: "Convolution"
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bottom: "pool5"
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top: "fc6-conv"
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convolution_param {
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num_output: 4096
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kernel_size: 6
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}
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}
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layer {
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name: "relu6"
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type: "ReLU"
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bottom: "fc6-conv"
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top: "fc6-conv"
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}
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layer {
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name: "drop6"
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type: "Dropout"
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bottom: "fc6-conv"
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top: "fc6-conv"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layer {
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name: "fc7-conv"
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type: "Convolution"
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bottom: "fc6-conv"
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top: "fc7-conv"
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convolution_param {
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num_output: 4096
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kernel_size: 1
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}
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}
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layer {
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name: "relu7"
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type: "ReLU"
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bottom: "fc7-conv"
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top: "fc7-conv"
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}
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layer {
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name: "drop7"
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type: "Dropout"
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bottom: "fc7-conv"
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top: "fc7-conv"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layer {
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name: "fc8-conv"
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type: "Convolution"
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bottom: "fc7-conv"
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top: "fc8-conv"
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convolution_param {
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num_output: 1000
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kernel_size: 1
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}
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}
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layer {
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name: "prob"
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type: "Softmax"
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bottom: "fc8-conv"
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top: "prob"
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}
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