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Inverting Alexnet. Paper: Inverting Convolutional Networks with Convolutional Networks
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| name: "CaffeNet" | |
| layers { | |
| name: "data" | |
| type: DATA | |
| top: "data" | |
| data_param { | |
| source: "/misc/lmbraid10/dosovits/Datasets/ILSVRC2012/all/val_leveldb" | |
| backend: LEVELDB | |
| batch_size: 16 | |
| crop_size: 227 | |
| mean_file: "/misc/lmbraid10/dosovits/Datasets/ILSVRC2012/all/imagenet_mean.binaryproto" | |
| mirror: false | |
| } | |
| } | |
| layers { | |
| name: "conv1" | |
| type: CONVOLUTION | |
| bottom: "data" | |
| top: "conv1" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 11 | |
| stride: 4 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu1" | |
| type: RELU | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layers { | |
| name: "pool1" | |
| type: POOLING | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "norm1" | |
| type: LRN | |
| bottom: "pool1" | |
| top: "norm1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layers { | |
| name: "conv2" | |
| type: CONVOLUTION | |
| bottom: "norm1" | |
| top: "conv2" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 2 | |
| kernel_size: 5 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu2" | |
| type: RELU | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layers { | |
| name: "pool2" | |
| type: POOLING | |
| bottom: "conv2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "norm2" | |
| type: LRN | |
| bottom: "pool2" | |
| top: "norm2" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layers { | |
| name: "conv3" | |
| type: CONVOLUTION | |
| bottom: "norm2" | |
| top: "conv3" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu3" | |
| type: RELU | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layers { | |
| name: "conv4" | |
| type: CONVOLUTION | |
| bottom: "conv3" | |
| top: "conv4" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu4" | |
| type: RELU | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layers { | |
| name: "conv5" | |
| type: CONVOLUTION | |
| bottom: "conv4" | |
| top: "conv5" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu5" | |
| type: RELU | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layers { | |
| name: "pool5" | |
| type: POOLING | |
| bottom: "conv5" | |
| top: "pool5" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "Rconv6" | |
| type: CONVOLUTION | |
| bottom: "pool5" | |
| top: "Rconv6" | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| name: "Rrelu6" | |
| type: RELU | |
| bottom: "Rconv6" | |
| top: "Rconv6" | |
| } | |
| layers { | |
| name: "Rconv7" | |
| type: CONVOLUTION | |
| bottom: "Rconv6" | |
| top: "Rconv7" | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| } | |
| } | |
| layers { | |
| name: "Rrelu7" | |
| type: RELU | |
| bottom: "Rconv7" | |
| top: "Rconv7" | |
| } | |
| layers { | |
| name: "Rconv8" | |
| type: CONVOLUTION | |
| bottom: "Rconv7" | |
| top: "Rconv8" | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| } | |
| } | |
| layers { | |
| name: "Rrelu8" | |
| type: RELU | |
| bottom: "Rconv8" | |
| top: "Rconv8" | |
| } | |
| layers { | |
| name: "deconv4" | |
| type: DECONVOLUTION | |
| bottom: "Rconv8" | |
| top: "deconv4" | |
| deconvolution_param { | |
| output_channels: 256 | |
| output_height: 12 | |
| output_width: 12 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "relu_deconv4" | |
| type: RELU | |
| bottom: "deconv4" | |
| top: "deconv4" | |
| relu_param { | |
| negative_slope: 0.3 | |
| } | |
| } | |
| layers { | |
| name: "deconv3" | |
| type: DECONVOLUTION | |
| bottom: "deconv4" | |
| top: "deconv3" | |
| deconvolution_param { | |
| output_channels: 128 | |
| output_height: 24 | |
| output_width: 24 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "relu_deconv3" | |
| type: RELU | |
| bottom: "deconv3" | |
| top: "deconv3" | |
| relu_param { | |
| negative_slope: 0.3 | |
| } | |
| } | |
| layers { | |
| name: "deconv2" | |
| type: DECONVOLUTION | |
| bottom: "deconv3" | |
| top: "deconv2" | |
| deconvolution_param { | |
| output_channels: 64 | |
| output_height: 48 | |
| output_width: 48 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "relu_deconv2" | |
| type: RELU | |
| bottom: "deconv2" | |
| top: "deconv2" | |
| relu_param { | |
| negative_slope: 0.3 | |
| } | |
| } | |
| layers { | |
| name: "deconv1" | |
| type: DECONVOLUTION | |
| bottom: "deconv2" | |
| top: "deconv1" | |
| deconvolution_param { | |
| output_channels: 32 | |
| output_height: 96 | |
| output_width: 96 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "relu_deconv1" | |
| type: RELU | |
| bottom: "deconv1" | |
| top: "deconv1" | |
| relu_param { | |
| negative_slope: 0.3 | |
| } | |
| } | |
| layers { | |
| name: "deconv0" | |
| type: DECONVOLUTION | |
| bottom: "deconv1" | |
| top: "deconv0" | |
| deconvolution_param { | |
| output_channels: 3 | |
| output_height: 192 | |
| output_width: 192 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
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| name: "CaffeNet" | |
| layers { | |
| name: "data" | |
| type: DATA | |
| top: "data" | |
| data_param { | |
| source: "/misc/lmbraid10/dosovits/Datasets/ILSVRC2012/all/val_leveldb" | |
| backend: LEVELDB | |
| batch_size: 16 | |
| crop_size: 227 | |
| mean_file: "/misc/lmbraid10/dosovits/Datasets/ILSVRC2012/all/imagenet_mean.binaryproto" | |
| mirror: false | |
| } | |
| } | |
| layers { | |
| name: "conv1" | |
| type: CONVOLUTION | |
| bottom: "data" | |
| top: "conv1" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 11 | |
| stride: 4 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu1" | |
| type: RELU | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layers { | |
| name: "pool1" | |
| type: POOLING | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "norm1" | |
| type: LRN | |
| bottom: "pool1" | |
| top: "norm1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layers { | |
| name: "conv2" | |
| type: CONVOLUTION | |
| bottom: "norm1" | |
| top: "conv2" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 2 | |
| kernel_size: 5 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu2" | |
| type: RELU | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layers { | |
| name: "pool2" | |
| type: POOLING | |
| bottom: "conv2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "norm2" | |
| type: LRN | |
| bottom: "pool2" | |
| top: "norm2" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layers { | |
| name: "conv3" | |
| type: CONVOLUTION | |
| bottom: "norm2" | |
| top: "conv3" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu3" | |
| type: RELU | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layers { | |
| name: "conv4" | |
| type: CONVOLUTION | |
| bottom: "conv3" | |
| top: "conv4" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu4" | |
| type: RELU | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layers { | |
| name: "conv5" | |
| type: CONVOLUTION | |
| bottom: "conv4" | |
| top: "conv5" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu5" | |
| type: RELU | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layers { | |
| name: "pool5" | |
| type: POOLING | |
| bottom: "conv5" | |
| top: "pool5" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "fc6" | |
| type: INNER_PRODUCT | |
| bottom: "pool5" | |
| top: "fc6" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu6" | |
| type: RELU | |
| bottom: "fc6" | |
| top: "fc6" | |
| } | |
| layers { | |
| name: "fc7" | |
| type: INNER_PRODUCT | |
| bottom: "fc6" | |
| top: "fc7" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu7" | |
| type: RELU | |
| bottom: "fc7" | |
| top: "fc7" | |
| } | |
| layers { | |
| name: "fc8" | |
| type: INNER_PRODUCT | |
| bottom: "fc7" | |
| top: "fc8" | |
| blobs_lr: 0 | |
| blobs_lr: 0 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| inner_product_param { | |
| num_output: 1000 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "defc7" | |
| type: INNER_PRODUCT | |
| bottom: "fc8" | |
| top: "defc7" | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| } | |
| layers { | |
| name: "relu_defc7" | |
| type: RELU | |
| bottom: "defc7" | |
| top: "defc7" | |
| } | |
| layers { | |
| name: "defc6" | |
| type: INNER_PRODUCT | |
| bottom: "defc7" | |
| top: "defc6" | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| } | |
| layers { | |
| name: "relu_defc6" | |
| type: RELU | |
| bottom: "defc6" | |
| top: "defc6" | |
| } | |
| layers { | |
| name: "defc5" | |
| type: INNER_PRODUCT | |
| bottom: "defc6" | |
| top: "defc5" | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| } | |
| layers { | |
| name: "relu_defc5" | |
| type: RELU | |
| bottom: "defc5" | |
| top: "defc5" | |
| } | |
| layers { | |
| name: "reshape" | |
| type: RESHAPE | |
| bottom: "defc5" | |
| top: "reshape_defc5" | |
| reshape_param { | |
| channels: 256 | |
| height: 4 | |
| width: 4 | |
| } | |
| } | |
| layers { | |
| name: "deconv4" | |
| type: DECONVOLUTION | |
| bottom: "reshape_defc5" | |
| top: "deconv4" | |
| deconvolution_param { | |
| output_channels: 256 | |
| output_height: 8 | |
| output_width: 8 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "relu_deconv4" | |
| type: RELU | |
| bottom: "deconv4" | |
| top: "deconv4" | |
| relu_param { | |
| negative_slope: 0.3 | |
| } | |
| } | |
| layers { | |
| name: "deconv3" | |
| type: DECONVOLUTION | |
| bottom: "deconv4" | |
| top: "deconv3" | |
| deconvolution_param { | |
| output_channels: 128 | |
| output_height: 16 | |
| output_width: 16 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "relu_deconv3" | |
| type: RELU | |
| bottom: "deconv3" | |
| top: "deconv3" | |
| relu_param { | |
| negative_slope: 0.3 | |
| } | |
| } | |
| layers { | |
| name: "deconv2" | |
| type: DECONVOLUTION | |
| bottom: "deconv3" | |
| top: "deconv2" | |
| deconvolution_param { | |
| output_channels: 64 | |
| output_height: 32 | |
| output_width: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "relu_deconv2" | |
| type: RELU | |
| bottom: "deconv2" | |
| top: "deconv2" | |
| relu_param { | |
| negative_slope: 0.3 | |
| } | |
| } | |
| layers { | |
| name: "deconv1" | |
| type: DECONVOLUTION | |
| bottom: "deconv2" | |
| top: "deconv1" | |
| deconvolution_param { | |
| output_channels: 32 | |
| output_height: 64 | |
| output_width: 64 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "relu_deconv1" | |
| type: RELU | |
| bottom: "deconv1" | |
| top: "deconv1" | |
| relu_param { | |
| negative_slope: 0.3 | |
| } | |
| } | |
| layers { | |
| name: "deconv0" | |
| type: DECONVOLUTION | |
| bottom: "deconv1" | |
| top: "deconv0" | |
| deconvolution_param { | |
| output_channels: 3 | |
| output_height: 128 | |
| output_width: 128 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 2 | |
| } | |
| } | |
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