--- /dev/null
+name: "residual_bn"
+layer {
+ name: "input"
+ type: "Input"
+ top: "data"
+ input_param {
+ shape {
+ dim: 1
+ dim: 3
+ dim: 31
+ dim: 31
+ }
+ }
+}
+layer {
+ name: "MaxPool2dBackward4"
+ type: "Pooling"
+ bottom: "data"
+ top: "MaxPool2dBackward4"
+ pooling_param {
+ pool: MAX
+ kernel_size: 3
+ stride: 2
+ pad: 1
+ }
+}
+layer {
+ name: "ConvNdBackward5"
+ type: "Convolution"
+ bottom: "MaxPool2dBackward4"
+ top: "ConvNdBackward5"
+ convolution_param {
+ num_output: 5
+ pad_h: 1
+ pad_w: 1
+ kernel_h: 3
+ kernel_w: 3
+ stride: 1
+ bias_term: false
+ }
+}
+layer {
+ name: "BatchNormBackward6_bn"
+ type: "BatchNorm"
+ bottom: "ConvNdBackward5"
+ top: "BatchNormBackward6"
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-05
+ }
+}
+layer {
+ name: "BatchNormBackward6_scale"
+ type: "Scale"
+ bottom: "BatchNormBackward6"
+ top: "BatchNormBackward6"
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "ThresholdBackward7"
+ type: "ReLU"
+ bottom: "BatchNormBackward6"
+ top: "BatchNormBackward6"
+}
+layer {
+ name: "ConvNdBackward8"
+ type: "Convolution"
+ bottom: "BatchNormBackward6"
+ top: "ConvNdBackward8"
+ convolution_param {
+ num_output: 3
+ pad_h: 1
+ pad_w: 1
+ kernel_h: 3
+ kernel_w: 3
+ stride: 1
+ bias_term: false
+ }
+}
+layer {
+ name: "BatchNormBackward9_bn"
+ type: "BatchNorm"
+ bottom: "ConvNdBackward8"
+ top: "BatchNormBackward9"
+ batch_norm_param {
+ use_global_stats: true
+ eps: 1e-05
+ }
+}
+layer {
+ name: "BatchNormBackward9_scale"
+ type: "Scale"
+ bottom: "BatchNormBackward9"
+ top: "BatchNormBackward9"
+ scale_param {
+ bias_term: true
+ }
+}
+layer {
+ name: "AddBackward11"
+ type: "Eltwise"
+ bottom: "BatchNormBackward9"
+ bottom: "MaxPool2dBackward4"
+ top: "AddBackward11"
+ eltwise_param {
+ operation: SUM
+ }
+}