| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188 | name: "upconv_7"layer {  name: "input"  type: "Input"  top: "input"  input_param { shape: { dim: 1 dim: 3 dim: 142 dim: 142 } }}layer {  name: "conv1_layer"  type: "Convolution"  bottom: "input"  top: "conv1"  convolution_param {    num_output: 32    kernel_size: 3    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }  }}layer {  name: "conv1_relu_layer"  type: "ReLU"  bottom: "conv1"  top: "conv1"  relu_param {    negative_slope: 0.1  }}layer {  name: "conv2_layer"  type: "Convolution"  bottom: "conv1"  top: "conv2"  convolution_param {    num_output: 32    kernel_size: 3    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }  }}layer {  name: "conv2_relu_layer"  type: "ReLU"  bottom: "conv2"  top: "conv2"  relu_param {    negative_slope: 0.1  }}layer {  name: "conv3_layer"  type: "Convolution"  bottom: "conv2"  top: "conv3"  convolution_param {    num_output: 64    kernel_size: 3    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }  }}layer {  name: "conv3_relu_layer"  type: "ReLU"  bottom: "conv3"  top: "conv3"  relu_param {    negative_slope: 0.1  }}layer {  name: "conv4_layer"  type: "Convolution"  bottom: "conv3"  top: "conv4"  convolution_param {    num_output: 64    kernel_size: 3    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }  }}layer {  name: "conv4_relu_layer"  type: "ReLU"  bottom: "conv4"  top: "conv4"  relu_param {    negative_slope: 0.1  }}layer {  name: "conv5_layer"  type: "Convolution"  bottom: "conv4"  top: "conv5"  convolution_param {    num_output: 128    kernel_size: 3    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }  }}layer {  name: "conv5_relu_layer"  type: "ReLU"  bottom: "conv5"  top: "conv5"  relu_param {    negative_slope: 0.1  }}layer {  name: "conv6_layer"  type: "Convolution"  bottom: "conv5"  top: "conv6"  convolution_param {    num_output: 128    kernel_size: 3    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }  }}layer {  name: "conv6_relu_layer"  type: "ReLU"  bottom: "conv6"  top: "conv6"  relu_param {    negative_slope: 0.1  }}layer {  name: "conv7_layer"  type: "Deconvolution"  bottom: "conv6"  top: "conv7"  convolution_param {    num_output: 3    kernel_size: 4    stride: 2    pad: 1    weight_filler {      type: "gaussian"      std: 0.01    }  }}layer {  name: "target"  type: "MemoryData"  top: "target"  top: "dummy_label2"  memory_data_param {    batch_size: 1    channels: 3    height: 142    width: 142  }  include: { phase: TRAIN }}layer {  name: "loss"  type: "EuclideanLoss"  bottom: "conv7"  bottom: "target"  top: "loss"  include: { phase: TRAIN }}
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