两个卷积层的卷积神经网络（CNN），C++实现
*第一层：输入层
<code> name: "face" input: "data" input_dim: 1 input_dim: 3 input_dim: 152 input_dim: 152</code>
*第二层：卷积层
layers { name: "conv1" type: CONVOLUTION bottom: "data" top: "conv1" convolution_param { num_output: 32 kernel_size: 11 stride: 1 }
}
}
*第三层：池层
layers { name: "pool2" type: POOLING bottom: "conv1" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 }
}
}
*第四层：卷积层
layers { name: "conv3" type: CONVOLUTION bottom: "pool2" top: "conv3" convolution_param { num_output: 16 kernel_size: 9 stride: 1 }
}
}
*第五层：LOCAL
layers { name: "local4" type: LOCAL bottom: "conv3" top: "local4" local_param { num_output: 16 kernel_size: 9 stride: 1 }
}
}
*第六层：LOCAL
layers { name: "local5" type: LOCAL bottom: "local4" top: "local5" local_param { num_output: 16 kernel_size: 7 stride: 2 }
}
}
*第七层：LOCAL
layers { name: "local6" type: LOCAL bottom: "local5" top: "local6" local_param { num_output: 16 kernel_size: 5 stride: 1 }
}
}
*第八层：内积
layers { name: "fc7" type: INNER_PRODUCT bottom: "local6" top: "fc7" inner_product_param { num_output: 4096 }
}
}
*第九层：内积
layers { name: "fc8" type: INNER_PRODUCT bottom: "fc7" top: "fc8" inner_product_param { num_output: 83 }
}
}
*第十层：SOFTMAX
layers { name: "prob" type: SOFTMAX bottom: "fc8" top: "prob" }
*第十一层：ARGMAX
layers { name: "argmax" type: ARGMAX bottom: "prob" top: "argmax" }
=====小结=====