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numbers_deploy.prototxt
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name: "SpeakerNet"
layer {
name: "data"
type: "Input"
top: "data"
input_param { shape: { dim: 1 dim: 3 dim: 512 dim: 512 } }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
# learning rate and decay multipliers for the filters
param { lr_mult: 1 decay_mult: 1 }
# learning rate and decay multipliers for the biases
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
# learning rate and decay multipliers for the filters
param { lr_mult: 1 decay_mult: 1 }
# learning rate and decay multipliers for the biases
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
# learning rate and decay multipliers for the filters
param { lr_mult: 1 decay_mult: 1 }
# learning rate and decay multipliers for the biases
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
# learning rate and decay multipliers for the filters
param { lr_mult: 1 decay_mult: 1 }
# learning rate and decay multipliers for the biases
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
# learning rate and decay multipliers for the filters
param { lr_mult: 1 decay_mult: 1 }
# learning rate and decay multipliers for the biases
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 256
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
# learning rate and decay multipliers for the filters
param { lr_mult: 1 decay_mult: 1 }
# learning rate and decay multipliers for the biases
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 128
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
# learning rate and decay multipliers for the filters
param { lr_mult: 1 decay_mult: 1 }
# learning rate and decay multipliers for the biases
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 24
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "fc8"
top: "prob"
}