提交 c8091ad8 编写于 作者: L liaogang

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上级 4dada9c7
type: "nn"
layers {
name: "data"
type: "data"
size: 2304
active_type: ""
}
layers {
name: "__conv_0__"
type: "exconv"
size: 36864
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "___conv_0__.w0"
conv_conf {
filter_size: 3
channels: 1
stride: 1
padding: 1
groups: 1
filter_channels: 1
output_x: 48
img_size: 48
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
num_filters: 16
shared_biases: true
}
layers {
name: "__bilinear_interp_layer_0__"
type: "bilinear_interp"
size: 65536
active_type: ""
inputs {
input_layer_name: "__conv_0__"
bilinear_interp_conf {
out_size_x: 64
out_size_y: 64
num_channels: 16
}
}
}
layers {
name: "__pool_0__"
type: "pool"
size: 16384
active_type: ""
inputs {
input_layer_name: "__bilinear_interp_layer_0__"
pool_conf {
pool_type: "max-projection"
channels: 4
size_x: 2
stride: 2
output_x: 64
img_size: 128
padding: 0
size_y: 2
stride_y: 2
output_y: 64
img_size_y: 128
padding_y: 0
}
}
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 384
active_type: "tanh"
inputs {
input_layer_name: "__pool_0__"
input_parameter_name: "___fc_layer_0__.w0"
}
}
parameters {
name: "___conv_0__.w0"
size: 144
initial_mean: 0.0
initial_std: 0.471404522657
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___conv_0__.wbias"
size: 16
initial_mean: 0.0
initial_std: 0.0
dims: 16
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_0__.w0"
size: 6291456
initial_mean: 0.0
initial_std: 0.0078125
dims: 16384
dims: 384
initial_strategy: 0
initial_smart: true
}
input_layer_names: "data"
output_layer_names: "__fc_layer_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__conv_0__"
layer_names: "__bilinear_interp_layer_0__"
layer_names: "__pool_0__"
layer_names: "__fc_layer_0__"
input_layer_names: "data"
output_layer_names: "__fc_layer_0__"
is_recurrent_layer_group: false
}
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