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September 1, 2016 19:26
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| name: "sanchitNet: GoogleNet+C3D, v2" | |
| # ----- video/label input ----- | |
| # ----- for training | |
| layer { | |
| name: "data" | |
| type: "VideoData" | |
| top: "data" | |
| top: "label" | |
| video_data_param { | |
| #source: "dextro_benchmark_2016_06_17_train_c3d_valid.txt" | |
| source: "dextro_benchmark_2016_06_17_train_c3d_valid_subsampled.txt" | |
| batch_size: 20 | |
| new_height: 128 | |
| new_width: 171 | |
| new_length: 16 | |
| shuffle: true | |
| } | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| crop_size: 112 | |
| mirror: true | |
| mean_value: 104 | |
| mean_value: 117 | |
| mean_value: 123 | |
| } | |
| } | |
| # ----- for validation | |
| layer { | |
| name: "data" | |
| type: "VideoData" | |
| top: "data" | |
| top: "label" | |
| video_data_param { | |
| #source: "dextro_benchmark_2016_06_17_val_c3d_valid.txt" | |
| source: "dextro_benchmark_2016_06_17_val_c3d_valid_subsampled.txt" | |
| batch_size: 5 | |
| new_height: 128 | |
| new_width: 171 | |
| new_length: 16 | |
| shuffle: true | |
| } | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| crop_size: 112 | |
| mirror: false | |
| # instead of using mean cube, use a mean pixel | |
| # (90,98,102) is based on UCF-101 training data | |
| mean_value: 104 | |
| mean_value: 117 | |
| mean_value: 123 | |
| } | |
| } | |
| # original googleNet uses crop_size of 224, but in conjuction with C3D, we | |
| # use crop_size of 112 in order to reduce #computations | |
| # so, in this convolution, we use stride of 1 in spatial dimensions (instead of | |
| # 2 in the original googleNet) | |
| layer { | |
| name: "conv1/7x7x3_s2" | |
| type: "NdConvolution" | |
| bottom: "data" | |
| top: "conv1/7x7x3_s2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #pad: 3 | |
| #kernel_size: 7 | |
| #stride: 2 | |
| kernel_shape { dim: 3 dim: 7 dim: 7 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 3 dim: 3 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/relu_7x7x3" | |
| type: "ReLU" | |
| bottom: "conv1/7x7x3_s2" | |
| top: "conv1/7x7x3_s2" | |
| } | |
| layer { | |
| name: "pool1/3x3x1_s2" | |
| type: "NdPooling" | |
| bottom: "conv1/7x7x3_s2" | |
| top: "pool1/3x3x1_s2" | |
| pooling_param { | |
| pool: MAX | |
| #kernel_size: 3 | |
| #stride: 2 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 2 dim: 2 } | |
| } | |
| } | |
| # LRN layer is not friendly to temporal dimension, and its benefit is minimal | |
| # hence ignored in this implementation | |
| #layer { name: "pool1/norm1" type: "LRN" bottom: "pool1/3x3_s2" top: "pool1/norm1" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 } } | |
| layer { | |
| name: "conv2/3x3x1_reduce" | |
| type: "NdConvolution" | |
| bottom: "pool1/3x3x1_s2" | |
| top: "conv2/3x3x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/relu_3x3x1_reduce" | |
| type: "ReLU" | |
| bottom: "conv2/3x3x1_reduce" | |
| top: "conv2/3x3x1_reduce" | |
| } | |
| layer { | |
| name: "conv2/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "conv2/3x3x1_reduce" | |
| top: "conv2/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "conv2/3x3x3" | |
| top: "conv2/3x3x3" | |
| } | |
| #layer { name: "conv2/norm2" type: "LRN" bottom: "conv2/3x3" top: "conv2/norm2" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 } } | |
| layer { | |
| name: "pool2/3x3x3_s2" | |
| type: "NdPooling" | |
| bottom: "conv2/3x3x3" | |
| top: "pool2/3x3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| #kernel_size: 3 | |
| #stride: 2 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| stride_shape { dim: 2 dim: 2 dim: 2 } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/1x1x3" | |
| type: "NdConvolution" | |
| bottom: "pool2/3x3x3_s2" | |
| top: "inception_3a/1x1x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_1x1x3" | |
| type: "ReLU" | |
| bottom: "inception_3a/1x1x3" | |
| top: "inception_3a/1x1x3" | |
| } | |
| layer { | |
| name: "inception_3a/3x3x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "pool2/3x3x3_s2" | |
| top: "inception_3a/3x3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_3x3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3a/3x3x3_reduce" | |
| top: "inception_3a/3x3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_3a/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "inception_3a/3x3x3_reduce" | |
| top: "inception_3a/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "inception_3a/3x3x3" | |
| top: "inception_3a/3x3x3" | |
| } | |
| layer { | |
| name: "inception_3a/5x5x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "pool2/3x3x3_s2" | |
| top: "inception_3a/5x5x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_5x5x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3a/5x5x3_reduce" | |
| top: "inception_3a/5x5x3_reduce" | |
| } | |
| layer { | |
| name: "inception_3a/5x5x3" | |
| type: "NdConvolution" | |
| bottom: "inception_3a/5x5x3_reduce" | |
| top: "inception_3a/5x5x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| #pad: 2 | |
| #kernel_size: 5 | |
| kernel_shape { dim: 3 dim: 5 dim: 5 } | |
| pad_shape { dim: 1 dim: 2 dim: 2 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_5x5x3" | |
| type: "ReLU" | |
| bottom: "inception_3a/5x5x3" | |
| top: "inception_3a/5x5x3" | |
| } | |
| layer { | |
| name: "inception_3a/pool" | |
| type: "NdPooling" | |
| bottom: "pool2/3x3x3_s2" | |
| top: "inception_3a/pool" | |
| pooling_param { | |
| pool: MAX | |
| #pad: 3 | |
| #kernel_size: 1 | |
| #stride: 1 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 0 dim: 1 dim: 1 } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/pool_proj" | |
| type: "NdConvolution" | |
| bottom: "inception_3a/pool" | |
| top: "inception_3a/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_3a/pool_proj" | |
| top: "inception_3a/pool_proj" | |
| } | |
| layer { | |
| name: "inception_3a/output" | |
| type: "Concat" | |
| bottom: "inception_3a/1x1x3" | |
| bottom: "inception_3a/3x3x3" | |
| bottom: "inception_3a/5x5x3" | |
| bottom: "inception_3a/pool_proj" | |
| top: "inception_3a/output" | |
| } | |
| layer { | |
| name: "inception_3b/1x1x3" | |
| type: "NdConvolution" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/1x1x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_1x1x3" | |
| type: "ReLU" | |
| bottom: "inception_3b/1x1x3" | |
| top: "inception_3b/1x1x3" | |
| } | |
| layer { | |
| name: "inception_3b/3x3x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/3x3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_3x3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3b/3x3x3_reduce" | |
| top: "inception_3b/3x3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_3b/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "inception_3b/3x3x3_reduce" | |
| top: "inception_3b/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "inception_3b/3x3x3" | |
| top: "inception_3b/3x3x3" | |
| } | |
| layer { | |
| name: "inception_3b/5x5x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/5x5x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_5x5x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3b/5x5x3_reduce" | |
| top: "inception_3b/5x5x3_reduce" | |
| } | |
| layer { | |
| name: "inception_3b/5x5x3" | |
| type: "NdConvolution" | |
| bottom: "inception_3b/5x5x3_reduce" | |
| top: "inception_3b/5x5x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| #pad: 2 | |
| #kernel_size: 5 | |
| kernel_shape { dim: 3 dim: 5 dim: 5 } | |
| pad_shape { dim: 1 dim: 2 dim: 2 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_5x5x3" | |
| type: "ReLU" | |
| bottom: "inception_3b/5x5x3" | |
| top: "inception_3b/5x5x3" | |
| } | |
| layer { | |
| name: "inception_3b/pool" | |
| type: "NdPooling" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/pool" | |
| pooling_param { | |
| pool: MAX | |
| #pad: 3 | |
| #kernel_size: 1 | |
| #stride: 1 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 0 dim: 1 dim: 1 } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/pool_proj" | |
| type: "NdConvolution" | |
| bottom: "inception_3b/pool" | |
| top: "inception_3b/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_3b/pool_proj" | |
| top: "inception_3b/pool_proj" | |
| } | |
| layer { | |
| name: "inception_3b/output" | |
| type: "Concat" | |
| bottom: "inception_3b/1x1x3" | |
| bottom: "inception_3b/3x3x3" | |
| bottom: "inception_3b/5x5x3" | |
| bottom: "inception_3b/pool_proj" | |
| top: "inception_3b/output" | |
| } | |
| layer { | |
| name: "pool3/3x3x3_s2" | |
| type: "NdPooling" | |
| bottom: "inception_3b/output" | |
| top: "pool3/3x3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| #kernel_size: 3 | |
| #stride: 2 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| stride_shape { dim: 2 dim: 2 dim: 2 } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/1x1x3" | |
| type: "NdConvolution" | |
| bottom: "pool3/3x3x3_s2" | |
| top: "inception_4a/1x1x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_1x1x3" | |
| type: "ReLU" | |
| bottom: "inception_4a/1x1x3" | |
| top: "inception_4a/1x1x3" | |
| } | |
| layer { | |
| name: "inception_4a/3x3x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "pool3/3x3x3_s2" | |
| top: "inception_4a/3x3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_3x3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4a/3x3x3_reduce" | |
| top: "inception_4a/3x3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4a/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4a/3x3x3_reduce" | |
| top: "inception_4a/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 208 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "inception_4a/3x3x3" | |
| top: "inception_4a/3x3x3" | |
| } | |
| layer { | |
| name: "inception_4a/5x5x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "pool3/3x3x3_s2" | |
| top: "inception_4a/5x5x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_5x5x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4a/5x5x3_reduce" | |
| top: "inception_4a/5x5x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4a/5x5x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4a/5x5x3_reduce" | |
| top: "inception_4a/5x5x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| #pad: 2 | |
| #kernel_size: 5 | |
| kernel_shape { dim: 3 dim: 5 dim: 5 } | |
| pad_shape { dim: 1 dim: 2 dim: 2 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_5x5x3" | |
| type: "ReLU" | |
| bottom: "inception_4a/5x5x3" | |
| top: "inception_4a/5x5x3" | |
| } | |
| layer { | |
| name: "inception_4a/pool" | |
| type: "NdPooling" | |
| bottom: "pool3/3x3x3_s2" | |
| top: "inception_4a/pool" | |
| pooling_param { | |
| pool: MAX | |
| #pad: 3 | |
| #kernel_size: 1 | |
| #stride: 1 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 0 dim: 1 dim: 1 } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/pool_proj" | |
| type: "NdConvolution" | |
| bottom: "inception_4a/pool" | |
| top: "inception_4a/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4a/pool_proj" | |
| top: "inception_4a/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4a/output" | |
| type: "Concat" | |
| bottom: "inception_4a/1x1x3" | |
| bottom: "inception_4a/3x3x3" | |
| bottom: "inception_4a/5x5x3" | |
| bottom: "inception_4a/pool_proj" | |
| top: "inception_4a/output" | |
| } | |
| # remove singleton temporal dimension | |
| #layer { name: "reshape" type: "Reshape" bottom: "inception_4a/temporalpool_tmp" top: "inception_4a/temporalpool" reshape_param { shape { dim: 0 dim: 0 dim: 7 dim: 7 } } } | |
| # remove the first auxiliary side head | |
| #layer { name: "loss1/ave_pool" type: "Pooling" bottom: "inception_4a/temporalpool" top: "loss1/ave_pool" pooling_param { pool: AVE kernel_size: 5 stride: 3 } } | |
| #layer { name: "loss1/conv" type: "Convolution" bottom: "loss1/ave_pool" top: "loss1/conv" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } } } | |
| #layer { name: "loss1/relu_conv" type: "ReLU" bottom: "loss1/conv" top: "loss1/conv" } | |
| #layer { name: "loss1/fc" type: "InnerProduct" bottom: "loss1/conv" top: "loss1/fc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1024 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } } } | |
| #layer { name: "loss1/relu_fc" type: "ReLU" bottom: "loss1/fc" top: "loss1/fc" } | |
| #layer { name: "loss1/drop_fc" type: "Dropout" bottom: "loss1/fc" top: "loss1/fc" dropout_param { dropout_ratio: 0.7 } } | |
| #layer { name: "loss1/classifier" type: "InnerProduct" bottom: "loss1/fc" top: "loss1/classifier" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1000 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } | |
| #layer { name: "loss1/loss" type: "SoftmaxWithLoss" bottom: "loss1/classifier" bottom: "label" top: "loss1/loss1" loss_weight: 0.3 } | |
| #layer { name: "loss1/top-1" type: "Accuracy" bottom: "loss1/classifier" bottom: "label" top: "loss1/top-1" include { phase: TEST } } | |
| #layer { name: "loss1/top-5" type: "Accuracy" bottom: "loss1/classifier" bottom: "label" top: "loss1/top-5" include { phase: TEST } accuracy_param { top_k: 5 } } | |
| layer { | |
| name: "inception_4b/1x1x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/1x1x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_1x1x3" | |
| type: "ReLU" | |
| bottom: "inception_4b/1x1x3" | |
| top: "inception_4b/1x1x3" | |
| } | |
| layer { | |
| name: "inception_4b/3x3x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/3x3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 112 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_3x3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4b/3x3x3_reduce" | |
| top: "inception_4b/3x3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4b/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4b/3x3x3_reduce" | |
| top: "inception_4b/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 224 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "inception_4b/3x3x3" | |
| top: "inception_4b/3x3x3" | |
| } | |
| layer { | |
| name: "inception_4b/5x5x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/5x5x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_5x5x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4b/5x5x3_reduce" | |
| top: "inception_4b/5x5x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4b/5x5x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4b/5x5x3_reduce" | |
| top: "inception_4b/5x5x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #pad: 2 | |
| #kernel_size: 5 | |
| kernel_shape { dim: 3 dim: 5 dim: 5 } | |
| pad_shape { dim: 1 dim: 2 dim: 2 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_5x5x3" | |
| type: "ReLU" | |
| bottom: "inception_4b/5x5x3" | |
| top: "inception_4b/5x5x3" | |
| } | |
| layer { | |
| name: "inception_4b/pool" | |
| type: "NdPooling" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/pool" | |
| pooling_param { | |
| pool: MAX | |
| #pad: 3 | |
| #kernel_size: 1 | |
| #stride: 1 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 0 dim: 1 dim: 1 } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/pool_proj" | |
| type: "NdConvolution" | |
| bottom: "inception_4b/pool" | |
| top: "inception_4b/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4b/pool_proj" | |
| top: "inception_4b/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4b/output" | |
| type: "Concat" | |
| bottom: "inception_4b/1x1x3" | |
| bottom: "inception_4b/3x3x3" | |
| bottom: "inception_4b/5x5x3" | |
| bottom: "inception_4b/pool_proj" | |
| top: "inception_4b/output" | |
| } | |
| layer { | |
| name: "inception_4c/1x1x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/1x1x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_1x1x3" | |
| type: "ReLU" | |
| bottom: "inception_4c/1x1x3" | |
| top: "inception_4c/1x1x3" | |
| } | |
| layer { | |
| name: "inception_4c/3x3x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/3x3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_3x3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4c/3x3x3_reduce" | |
| top: "inception_4c/3x3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4c/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4c/3x3x3_reduce" | |
| top: "inception_4c/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "inception_4c/3x3x3" | |
| top: "inception_4c/3x3x3" | |
| } | |
| layer { | |
| name: "inception_4c/5x5x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/5x5x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_5x5x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4c/5x5x3_reduce" | |
| top: "inception_4c/5x5x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4c/5x5x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4c/5x5x3_reduce" | |
| top: "inception_4c/5x5x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #pad: 2 | |
| #kernel_size: 5 | |
| kernel_shape { dim: 3 dim: 5 dim: 5 } | |
| pad_shape { dim: 1 dim: 2 dim: 2 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_5x5x3" | |
| type: "ReLU" | |
| bottom: "inception_4c/5x5x3" | |
| top: "inception_4c/5x5x3" | |
| } | |
| layer { | |
| name: "inception_4c/pool" | |
| type: "NdPooling" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/pool" | |
| pooling_param { | |
| pool: MAX | |
| #pad: 3 | |
| #kernel_size: 1 | |
| #stride: 1 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 0 dim: 1 dim: 1 } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/pool_proj" | |
| type: "NdConvolution" | |
| bottom: "inception_4c/pool" | |
| top: "inception_4c/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4c/pool_proj" | |
| top: "inception_4c/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4c/output" | |
| type: "Concat" | |
| bottom: "inception_4c/1x1x3" | |
| bottom: "inception_4c/3x3x3" | |
| bottom: "inception_4c/5x5x3" | |
| bottom: "inception_4c/pool_proj" | |
| top: "inception_4c/output" | |
| } | |
| layer { | |
| name: "inception_4d/1x1x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/1x1x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 112 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_1x1x3" | |
| type: "ReLU" | |
| bottom: "inception_4d/1x1x3" | |
| top: "inception_4d/1x1x3" | |
| } | |
| layer { | |
| name: "inception_4d/3x3x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/3x3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 144 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_3x3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4d/3x3x3_reduce" | |
| top: "inception_4d/3x3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4d/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4d/3x3x3_reduce" | |
| top: "inception_4d/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 288 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "inception_4d/3x3x3" | |
| top: "inception_4d/3x3x3" | |
| } | |
| layer { | |
| name: "inception_4d/5x5x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/5x5x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_5x5x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4d/5x5x3_reduce" | |
| top: "inception_4d/5x5x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4d/5x5x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4d/5x5x3_reduce" | |
| top: "inception_4d/5x5x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #pad: 2 | |
| #kernel_size: 5 | |
| kernel_shape { dim: 3 dim: 5 dim: 5 } | |
| pad_shape { dim: 1 dim: 2 dim: 2 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_5x5x3" | |
| type: "ReLU" | |
| bottom: "inception_4d/5x5x3" | |
| top: "inception_4d/5x5x3" | |
| } | |
| layer { | |
| name: "inception_4d/pool" | |
| type: "NdPooling" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/pool" | |
| pooling_param { | |
| pool: MAX | |
| #pad: 3 | |
| #kernel_size: 1 | |
| #stride: 1 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 0 dim: 1 dim: 1 } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/pool_proj" | |
| type: "NdConvolution" | |
| bottom: "inception_4d/pool" | |
| top: "inception_4d/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4d/pool_proj" | |
| top: "inception_4d/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4d/output" | |
| type: "Concat" | |
| bottom: "inception_4d/1x1x3" | |
| bottom: "inception_4d/3x3x3" | |
| bottom: "inception_4d/5x5x3" | |
| bottom: "inception_4d/pool_proj" | |
| top: "inception_4d/output" | |
| } | |
| # remove second first auxiliary side head | |
| #layer { name: "loss2/ave_pool" type: "Pooling" bottom: "inception_4d/output" top: "loss2/ave_pool" pooling_param { pool: AVE kernel_size: 5 stride: 3 } } | |
| #layer { name: "loss2/conv" type: "Convolution" bottom: "loss2/ave_pool" top: "loss2/conv" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } } } | |
| #layer { name: "loss2/relu_conv" type: "ReLU" bottom: "loss2/conv" top: "loss2/conv" } | |
| #layer { name: "loss2/fc" type: "InnerProduct" bottom: "loss2/conv" top: "loss2/fc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1024 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } } } | |
| #layer { name: "loss2/relu_fc" type: "ReLU" bottom: "loss2/fc" top: "loss2/fc" } | |
| #layer { name: "loss2/drop_fc" type: "Dropout" bottom: "loss2/fc" top: "loss2/fc" dropout_param { dropout_ratio: 0.7 } } | |
| #layer { name: "loss2/classifier" type: "InnerProduct" bottom: "loss2/fc" top: "loss2/classifier" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1000 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } | |
| #layer { name: "loss2/loss" type: "SoftmaxWithLoss" bottom: "loss2/classifier" bottom: "label" top: "loss2/loss1" loss_weight: 0.3 } | |
| #layer { name: "loss2/top-1" type: "Accuracy" bottom: "loss2/classifier" bottom: "label" top: "loss2/top-1" include { phase: TEST } } | |
| #layer { name: "loss2/top-5" type: "Accuracy" bottom: "loss2/classifier" bottom: "label" top: "loss2/top-5" include { phase: TEST } accuracy_param { top_k: 5 } } | |
| layer { | |
| name: "inception_4e/1x1x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/1x1x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_1x1x3" | |
| type: "ReLU" | |
| bottom: "inception_4e/1x1x3" | |
| top: "inception_4e/1x1x3" | |
| } | |
| layer { | |
| name: "inception_4e/3x3x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/3x3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_3x3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4e/3x3x3_reduce" | |
| top: "inception_4e/3x3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4e/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4e/3x3x3_reduce" | |
| top: "inception_4e/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 320 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "inception_4e/3x3x3" | |
| top: "inception_4e/3x3x3" | |
| } | |
| layer { | |
| name: "inception_4e/5x5x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/5x5x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_5x5x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4e/5x5x3_reduce" | |
| top: "inception_4e/5x5x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4e/5x5x3" | |
| type: "NdConvolution" | |
| bottom: "inception_4e/5x5x3_reduce" | |
| top: "inception_4e/5x5x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #pad: 2 | |
| #kernel_size: 5 | |
| kernel_shape { dim: 3 dim: 5 dim: 5 } | |
| pad_shape { dim: 1 dim: 2 dim: 2 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_5x5x3" | |
| type: "ReLU" | |
| bottom: "inception_4e/5x5x3" | |
| top: "inception_4e/5x5x3" | |
| } | |
| layer { | |
| name: "inception_4e/pool" | |
| type: "NdPooling" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/pool" | |
| pooling_param { | |
| pool: MAX | |
| #pad: 3 | |
| #kernel_size: 1 | |
| #stride: 1 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 0 dim: 1 dim: 1 } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/pool_proj" | |
| type: "NdConvolution" | |
| bottom: "inception_4e/pool" | |
| top: "inception_4e/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4e/pool_proj" | |
| top: "inception_4e/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4e/output" | |
| type: "Concat" | |
| bottom: "inception_4e/1x1x3" | |
| bottom: "inception_4e/3x3x3" | |
| bottom: "inception_4e/5x5x3" | |
| bottom: "inception_4e/pool_proj" | |
| top: "inception_4e/output" | |
| } | |
| layer { | |
| name: "pool4/3x3x3_s2" | |
| type: "NdPooling" | |
| bottom: "inception_4e/output" | |
| top: "pool4/3x3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| #kernel_size: 3 | |
| #stride: 2 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| stride_shape { dim: 2 dim: 2 dim: 2 } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/1x1x3" | |
| type: "NdConvolution" | |
| bottom: "pool4/3x3x3_s2" | |
| top: "inception_5a/1x1x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_1x1x3" | |
| type: "ReLU" | |
| bottom: "inception_5a/1x1x3" | |
| top: "inception_5a/1x1x3" | |
| } | |
| layer { | |
| name: "inception_5a/3x3x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "pool4/3x3x3_s2" | |
| top: "inception_5a/3x3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_3x3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5a/3x3x3_reduce" | |
| top: "inception_5a/3x3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_5a/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "inception_5a/3x3x3_reduce" | |
| top: "inception_5a/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 320 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "inception_5a/3x3x3" | |
| top: "inception_5a/3x3x3" | |
| } | |
| layer { | |
| name: "inception_5a/5x5x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "pool4/3x3x3_s2" | |
| top: "inception_5a/5x5x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_5x5x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5a/5x5x3_reduce" | |
| top: "inception_5a/5x5x3_reduce" | |
| } | |
| layer { | |
| name: "inception_5a/5x5x3" | |
| type: "NdConvolution" | |
| bottom: "inception_5a/5x5x3_reduce" | |
| top: "inception_5a/5x5x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #pad: 2 | |
| #kernel_size: 5 | |
| kernel_shape { dim: 3 dim: 5 dim: 5 } | |
| pad_shape { dim: 1 dim: 2 dim: 2 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_5x5x3" | |
| type: "ReLU" | |
| bottom: "inception_5a/5x5x3" | |
| top: "inception_5a/5x5x3" | |
| } | |
| layer { | |
| name: "inception_5a/pool" | |
| type: "NdPooling" | |
| bottom: "pool4/3x3x3_s2" | |
| top: "inception_5a/pool" | |
| pooling_param { | |
| pool: MAX | |
| #pad: 3 | |
| #kernel_size: 1 | |
| #stride: 1 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 0 dim: 1 dim: 1 } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/pool_proj" | |
| type: "NdConvolution" | |
| bottom: "inception_5a/pool" | |
| top: "inception_5a/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_5a/pool_proj" | |
| top: "inception_5a/pool_proj" | |
| } | |
| layer { | |
| name: "inception_5a/output" | |
| type: "Concat" | |
| bottom: "inception_5a/1x1x3" | |
| bottom: "inception_5a/3x3x3" | |
| bottom: "inception_5a/5x5x3" | |
| bottom: "inception_5a/pool_proj" | |
| top: "inception_5a/output" | |
| } | |
| layer { | |
| name: "inception_5b/1x1x3" | |
| type: "NdConvolution" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/1x1x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_1x1x3" | |
| type: "ReLU" | |
| bottom: "inception_5b/1x1x3" | |
| top: "inception_5b/1x1x3" | |
| } | |
| layer { | |
| name: "inception_5b/3x3x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/3x3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_3x3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5b/3x3x3_reduce" | |
| top: "inception_5b/3x3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_5b/3x3x3" | |
| type: "NdConvolution" | |
| bottom: "inception_5b/3x3x3_reduce" | |
| top: "inception_5b/3x3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| #pad: 1 | |
| #kernel_size: 3 | |
| kernel_shape { dim: 3 dim: 3 dim: 3 } | |
| pad_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_3x3x3" | |
| type: "ReLU" | |
| bottom: "inception_5b/3x3x3" | |
| top: "inception_5b/3x3x3" | |
| } | |
| layer { | |
| name: "inception_5b/5x5x3_reduce" | |
| type: "NdConvolution" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/5x5x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_5x5x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5b/5x5x3_reduce" | |
| top: "inception_5b/5x5x3_reduce" | |
| } | |
| layer { | |
| name: "inception_5b/5x5x3" | |
| type: "NdConvolution" | |
| bottom: "inception_5b/5x5x3_reduce" | |
| top: "inception_5b/5x5x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #pad: 2 | |
| #kernel_size: 5 | |
| kernel_shape { dim: 3 dim: 5 dim: 5 } | |
| pad_shape { dim: 1 dim: 2 dim: 2 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_5x5x3" | |
| type: "ReLU" | |
| bottom: "inception_5b/5x5x3" | |
| top: "inception_5b/5x5x3" | |
| } | |
| layer { | |
| name: "inception_5b/pool" | |
| type: "NdPooling" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/pool" | |
| pooling_param { | |
| pool: MAX | |
| #pad: 3 | |
| #kernel_size: 1 | |
| #stride: 1 | |
| kernel_shape { dim: 1 dim: 3 dim: 3 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 0 dim: 1 dim: 1 } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/pool_proj" | |
| type: "NdConvolution" | |
| bottom: "inception_5b/pool" | |
| top: "inception_5b/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| #kernel_size: 1 | |
| kernel_shape { dim: 1 dim: 1 dim: 1 } | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_5b/pool_proj" | |
| top: "inception_5b/pool_proj" | |
| } | |
| layer { | |
| name: "inception_5b/output" | |
| type: "Concat" | |
| bottom: "inception_5b/1x1x3" | |
| bottom: "inception_5b/3x3x3" | |
| bottom: "inception_5b/5x5x3" | |
| bottom: "inception_5b/pool_proj" | |
| top: "inception_5b/output" | |
| } | |
| #layer { | |
| # name: "reshape" | |
| # type: "Reshape" | |
| # bottom: "inception_5b/output_tmp" | |
| # top: "inception_5b/output" | |
| # reshape_param { | |
| # shape { dim: 0 dim: 0 dim: 3 dim: -1 } | |
| # } | |
| #} | |
| # maxpooling on temporal dimension only | |
| layer { | |
| name: "pool5/1x1x3_s2" | |
| type: "NdPooling" | |
| bottom: "inception_5b/output" | |
| top: "pool5/1x1x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_shape { dim: 3 dim: 1 dim: 1 } | |
| stride_shape { dim: 1 dim: 1 dim: 1 } | |
| pad_shape { dim: 1 dim: 0 dim: 0 } | |
| } | |
| } | |
| layer { | |
| name: "pool5/7x7_s1" | |
| type: "Pooling" | |
| #bottom: "inception_5b/output" | |
| bottom: "pool5/1x1x3_s2" | |
| top: "pool5/7x7_s1" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 7 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool5/drop_7x7_s1" | |
| type: "Dropout" | |
| bottom: "pool5/7x7_s1" | |
| top: "pool5/7x7_s1" | |
| dropout_param { | |
| dropout_ratio: 0.4 | |
| } | |
| } | |
| layer { | |
| name: "loss3/classifier" | |
| type: "InnerProduct" | |
| bottom: "pool5/7x7_s1" | |
| top: "loss3/classifier" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 15 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss3/loss3" | |
| type: "SoftmaxWithLoss" | |
| bottom: "loss3/classifier" | |
| bottom: "label" | |
| top: "loss3/loss3" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "loss3/top-1" | |
| type: "Accuracy" | |
| bottom: "loss3/classifier" | |
| bottom: "label" | |
| top: "loss3/top-1" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "loss3/top-5" | |
| type: "Accuracy" | |
| bottom: "loss3/classifier" | |
| bottom: "label" | |
| top: "loss3/top-5" | |
| include { | |
| phase: TEST | |
| } | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| } |
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