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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
}
}
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