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Jack Yu 7 years ago committed by GitHub
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input: "data"
input_dim: 1
input_dim: 3
input_dim: 160
input_dim: 40
layer {
name: "conv0"
type: "Convolution"
bottom: "data"
top: "conv0"
convolution_param {
num_output: 32
bias_term: true
pad_h: 1
pad_w: 1
kernel_h: 3
kernel_w: 3
stride_h: 1
stride_w: 1
}
}
layer {
name: "bn0"
type: "BatchNorm"
bottom: "conv0"
top: "bn0"
batch_norm_param {
moving_average_fraction: 0.99
eps: 0.001
}
}
layer {
name: "bn0_scale"
type: "Scale"
bottom: "bn0"
top: "bn0"
scale_param {
bias_term: true
}
}
layer {
name: "relu0"
type: "ReLU"
bottom: "bn0"
top: "bn0"
}
layer {
name: "pool0"
type: "Pooling"
bottom: "bn0"
top: "pool0"
pooling_param {
pool: MAX
kernel_h: 2
kernel_w: 2
stride_h: 2
stride_w: 2
pad_h: 0
pad_w: 0
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "pool0"
top: "conv1"
convolution_param {
num_output: 64
bias_term: true
pad_h: 1
pad_w: 1
kernel_h: 3
kernel_w: 3
stride_h: 1
stride_w: 1
}
}
layer {
name: "bn1"
type: "BatchNorm"
bottom: "conv1"
top: "bn1"
batch_norm_param {
moving_average_fraction: 0.99
eps: 0.001
}
}
layer {
name: "bn1_scale"
type: "Scale"
bottom: "bn1"
top: "bn1"
scale_param {
bias_term: true
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "bn1"
top: "bn1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "bn1"
top: "pool1"
pooling_param {
pool: MAX
kernel_h: 2
kernel_w: 2
stride_h: 2
stride_w: 2
pad_h: 0
pad_w: 0
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
convolution_param {
num_output: 128
bias_term: true
pad_h: 1
pad_w: 1
kernel_h: 3
kernel_w: 3
stride_h: 1
stride_w: 1
}
}
layer {
name: "bn2"
type: "BatchNorm"
bottom: "conv2"
top: "bn2"
batch_norm_param {
moving_average_fraction: 0.99
eps: 0.001
}
}
layer {
name: "bn2_scale"
type: "Scale"
bottom: "bn2"
top: "bn2"
scale_param {
bias_term: true
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "bn2"
top: "bn2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "bn2"
top: "pool2"
pooling_param {
pool: MAX
kernel_h: 2
kernel_w: 2
stride_h: 2
stride_w: 2
pad_h: 0
pad_w: 0
}
}
layer {
name: "conv2d_1"
type: "Convolution"
bottom: "pool2"
top: "conv2d_1"
convolution_param {
num_output: 256
bias_term: true
pad_h: 0
pad_w: 0
kernel_h: 1
kernel_w: 5
stride_h: 1
stride_w: 1
}
}
layer {
name: "batch_normalization_1"
type: "BatchNorm"
bottom: "conv2d_1"
top: "batch_normalization_1"
batch_norm_param {
moving_average_fraction: 0.99
eps: 0.001
}
}
layer {
name: "batch_normalization_1_scale"
type: "Scale"
bottom: "batch_normalization_1"
top: "batch_normalization_1"
scale_param {
bias_term: true
}
}
layer {
name: "activation_1"
type: "ReLU"
bottom: "batch_normalization_1"
top: "batch_normalization_1"
}
layer {
name: "conv2d_2"
type: "Convolution"
bottom: "batch_normalization_1"
top: "conv2d_2"
convolution_param {
num_output: 256
bias_term: true
pad_h: 3
pad_w: 0
kernel_h: 7
kernel_w: 1
stride_h: 1
stride_w: 1
}
}
layer {
name: "conv2d_3"
type: "Convolution"
bottom: "batch_normalization_1"
top: "conv2d_3"
convolution_param {
num_output: 256
bias_term: true
pad_h: 2
pad_w: 0
kernel_h: 5
kernel_w: 1
stride_h: 1
stride_w: 1
}
}
layer {
name: "conv2d_4"
type: "Convolution"
bottom: "batch_normalization_1"
top: "conv2d_4"
convolution_param {
num_output: 256
bias_term: true
pad_h: 1
pad_w: 0
kernel_h: 3
kernel_w: 1
stride_h: 1
stride_w: 1
}
}
layer {
name: "conv2d_5"
type: "Convolution"
bottom: "batch_normalization_1"
top: "conv2d_5"
convolution_param {
num_output: 256
bias_term: true
pad_h: 0
pad_w: 0
kernel_h: 1
kernel_w: 1
stride_h: 1
stride_w: 1
}
}
layer {
name: "batch_normalization_2"
type: "BatchNorm"
bottom: "conv2d_2"
top: "batch_normalization_2"
batch_norm_param {
moving_average_fraction: 0.99
eps: 0.001
}
}
layer {
name: "batch_normalization_2_scale"
type: "Scale"
bottom: "batch_normalization_2"
top: "batch_normalization_2"
scale_param {
bias_term: true
}
}
layer {
name: "batch_normalization_3"
type: "BatchNorm"
bottom: "conv2d_3"
top: "batch_normalization_3"
batch_norm_param {
moving_average_fraction: 0.99
eps: 0.001
}
}
layer {
name: "batch_normalization_3_scale"
type: "Scale"
bottom: "batch_normalization_3"
top: "batch_normalization_3"
scale_param {
bias_term: true
}
}
layer {
name: "batch_normalization_4"
type: "BatchNorm"
bottom: "conv2d_4"
top: "batch_normalization_4"
batch_norm_param {
moving_average_fraction: 0.99
eps: 0.001
}
}
layer {
name: "batch_normalization_4_scale"
type: "Scale"
bottom: "batch_normalization_4"
top: "batch_normalization_4"
scale_param {
bias_term: true
}
}
layer {
name: "batch_normalization_5"
type: "BatchNorm"
bottom: "conv2d_5"
top: "batch_normalization_5"
batch_norm_param {
moving_average_fraction: 0.99
eps: 0.001
}
}
layer {
name: "batch_normalization_5_scale"
type: "Scale"
bottom: "batch_normalization_5"
top: "batch_normalization_5"
scale_param {
bias_term: true
}
}
layer {
name: "activation_2"
type: "ReLU"
bottom: "batch_normalization_2"
top: "batch_normalization_2"
}
layer {
name: "activation_3"
type: "ReLU"
bottom: "batch_normalization_3"
top: "batch_normalization_3"
}
layer {
name: "activation_4"
type: "ReLU"
bottom: "batch_normalization_4"
top: "batch_normalization_4"
}
layer {
name: "activation_5"
type: "ReLU"
bottom: "batch_normalization_5"
top: "batch_normalization_5"
}
layer {
name: "concatenate_1"
type: "Concat"
bottom: "batch_normalization_2"
bottom: "batch_normalization_3"
bottom: "batch_normalization_4"
bottom: "batch_normalization_5"
top: "concatenate_1"
concat_param {
axis: 1
}
}
layer {
name: "conv_1024_11"
type: "Convolution"
bottom: "concatenate_1"
top: "conv_1024_11"
convolution_param {
num_output: 1024
bias_term: true
pad_h: 0
pad_w: 0
kernel_h: 1
kernel_w: 1
stride_h: 1
stride_w: 1
}
}
layer {
name: "batch_normalization_6"
type: "BatchNorm"
bottom: "conv_1024_11"
top: "batch_normalization_6"
batch_norm_param {
moving_average_fraction: 0.99
eps: 0.001
}
}
layer {
name: "batch_normalization_6_scale"
type: "Scale"
bottom: "batch_normalization_6"
top: "batch_normalization_6"
scale_param {
bias_term: true
}
}
layer {
name: "activation_6"
type: "ReLU"
bottom: "batch_normalization_6"
top: "batch_normalization_6"
}
layer {
name: "conv_class_11"
type: "Convolution"
bottom: "batch_normalization_6"
top: "conv_class_11"
convolution_param {
num_output: 84
bias_term: true
pad_h: 0
pad_w: 0
kernel_h: 1
kernel_w: 1
stride_h: 1
stride_w: 1
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "conv_class_11"
top: "prob"
}
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