在caffe 中实现Generative Adversarial Nets（一）

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对抗生成网络（GAN）同时训练两个模型：能够得到数据分布的生成模型（generative model G）和能判够区别数据是生成的还是真实的判别模型（discriminative model D）。训练过程使得G生成的数据尽可能真实，同时又使得D尽可能能够区分生成的数据和真实的数据，最终G生成数据足以以假乱真，而D输出数据的概率均为0.5 。参考论文：Bengio大神的 Generative Adversarial Networks

2. GAN训练过程

这里写图片描述
注意：这里的loss在更新D梯度是上升方向，在caffe具体实现时，为了使得D模型梯度更新为梯度的下降方向，loss等价改为: loss=−[log(D(x(i))+log(1−D(G(z(i))))].

二、Deep Convolutional GANs (DCGAN)

1. DCGAN 网络结构

DCGAN 用卷积神经（CNN）代替GAN中用多层感知器（MLP）实现D和G，后生成图片效果显著提升。参考论文Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Neworks

DCGAN网络结构图
DCGAN结构内容

2. DCGAN caffe prototxt

1.train.prototxt

#  Create on: 2016/10/22 ShanghaiTech#  Author:    Yingying Zhangname: "gan_newface"layer {  name: "images"  type: "ImageData"  top: "face_images"  top: "label"  include {    phase: TRAIN  }  transform_param {    mirror: true    mean_value: [127.5]    scale: 0.00784314  }  image_data_param {    source: "data/face_data.txt"    root_folder: "data/"    batch_size: 128    new_height: 64    new_width: 64    is_color: true    shuffle: true  }}layer {  name: "rand_vec"  type: "RandVec"  top: "rand_vec"  rand_vec_param {    batch_size: 128    dim: 100    lower: -1.0    upper: 1.0    repeat: 3  }}layer {  name: "silence"  type: "Silence"  bottom: "label"}#----------- generative -----------layer {  name: "ip1"  type: "InnerProduct"  bottom: "rand_vec"  top: "ip1"  param {    name: "ip_w_g"    lr_mult: 1  }  param {    name: "ip_b_g"    lr_mult: 2  }  inner_product_param{    num_output: 16384    gen_mode: true    weight_filler {      type: "gaussian"      std: 0.02    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "ip1_reshape"  type: "Reshape"  bottom: "ip1"  top: "ip1_reshape"  reshape_param {    shape {      dim: 0      dim: 1024      dim: 4      dim: 4    }  }}layer {  name: "batch_norm_g1"  type: "BatchNorm"  bottom: "ip1_reshape"  top: "ip1_reshape"  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  batch_norm_param {    use_global_stats: false  }}layer {  name: "scale_batch_g1"  type: "Scale"  bottom: "ip1_reshape"  top: "ip1_reshape"  param {    name: "gen_s1"    lr_mult: 1    decay_mult: 1  }  param {    name: "gen_b1"    lr_mult: 1    decay_mult: 1  }  scale_param {    bias_term: true    gen_mode: true  }}layer {  name: "relu_g1"  type: "ReLU"  bottom: "ip1_reshape"  top: "ip1_reshape"}layer {  name: "gen_conv2"  type: "Deconvolution"  bottom: "ip1_reshape"  top: "gen_conv2"  param {    name: "gen_w_2"    lr_mult: 1  }  param {    name: "gen_b_2"    lr_mult: 2  }  convolution_param {    num_output: 512    pad: 2    kernel_size: 5    stride: 2    gen_mode: true    shape_offset: [1, 1]    weight_filler {      type: "gaussian"      std: 0.02    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "batch_norm_g2"  type: "BatchNorm"  bottom: "gen_conv2"  top: "gen_conv2"  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  batch_norm_param {    use_global_stats: false  }}layer {  name: "scale_batch_g2"  type: "Scale"  bottom: "gen_conv2"  top: "gen_conv2"  param {    name: "gen_s2"    lr_mult: 1    decay_mult: 1  }  param {    name: "gen_b2"    lr_mult: 1    decay_mult: 1  }  scale_param {    gen_mode: true    bias_term: true  }}layer {  name: "relu_g2"  type: "ReLU"  bottom: "gen_conv2"  top: "gen_conv2"}layer {  name: "gen_conv3"  type: "Deconvolution"  bottom: "gen_conv2"  top: "gen_conv3"  param {    name: "gen_w_3"    lr_mult: 1  }  param {    name: "gen_b_3"    lr_mult: 2  }  convolution_param {    num_output: 256    pad: 2    kernel_size: 5    stride: 2    gen_mode: true    shape_offset: [1, 1]    weight_filler {      type: "gaussian"      std: 0.02    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "batch_norm_g3"  type: "BatchNorm"  bottom: "gen_conv3"  top: "gen_conv3"  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  batch_norm_param {    use_global_stats: false  }}layer {  name: "scale_batch_g3"  type: "Scale"  bottom: "gen_conv3"  top: "gen_conv3"  param {    name: "gen_s3"    lr_mult: 1    decay_mult: 1  }  param {    name: "gen_b3"    lr_mult: 1    decay_mult: 1  }  scale_param {    gen_mode: true    bias_term: true  }}layer {  name: "relu_g3"  type: "ReLU"  bottom: "gen_conv3"  top: "gen_conv3"}layer {  name: "gen_conv4"  type: "Deconvolution"  bottom: "gen_conv3"  top: "gen_conv4"  param {    name: "gen_w_4"    lr_mult: 1  }  param {    name: "gen_b_4"    lr_mult: 2  }  convolution_param {    num_output: 128    pad: 2    kernel_size: 5    stride: 2    gen_mode: true    shape_offset: [1, 1]    weight_filler {      type: "gaussian"      std: 0.02    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "batch_norm_g4"  type: "BatchNorm"  bottom: "gen_conv4"  top: "gen_conv4"  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  batch_norm_param {    use_global_stats: false  }}layer {  name: "scale_batch_g4"  type: "Scale"  bottom: "gen_conv4"  top: "gen_conv4"  param {    name: "gen_s4"    lr_mult: 1    decay_mult: 1  }  param {    name: "gen_b4"    lr_mult: 1    decay_mult: 1  }  scale_param {    gen_mode: true    bias_term: true  }}layer {  name: "relu_g4"  type: "ReLU"  bottom: "gen_conv4"  top: "gen_conv4"}layer {  name: "gen_conv5"  type: "Deconvolution"  bottom: "gen_conv4"  top: "gen_conv5"  param {    name: "gen_w_5"    lr_mult: 1  }  param {    name: "gen_b_5"    lr_mult: 2  }  convolution_param {    num_output: 3    pad: 2    kernel_size: 5    stride: 2    gen_mode: true    shape_offset: [1, 1]    weight_filler {      type: "gaussian"      std: 0.02    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "tanh_g5"  type: "TanH"  bottom: "gen_conv5"  top: "gen_conv5"}#-----------   gan gate  ------------layer {  name: "gan_gate"  type: "GANGate"  bottom: "face_images"  bottom: "gen_conv5"  top: "dis_input"}#----------- discrimitive -----------layer {  name: "dis_conv_d1"  type: "Convolution"  bottom: "dis_input"  top: "dis_conv_d1"  param {    name: "dis_w_1"    lr_mult: 1  }  param {    name: "dis_b_1"    lr_mult: 2  }  convolution_param {    num_output: 128    pad: 2    kernel_size: 5    stride: 2    dis_mode: true    weight_filler {      type: "gaussian"      std: 0.02    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "batch_norm_d1"  type: "BatchNorm"  bottom: "dis_conv_d1"  top: "dis_conv_d1"  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  batch_norm_param {    use_global_stats: false  }}layer {  name: "scale_batch_d1"  type: "Scale"  bottom: "dis_conv_d1"  top: "dis_conv_d1"  param {    name: "dis_s1"    lr_mult: 1    decay_mult: 1  }  param {    name: "dis_b1"    lr_mult: 1    decay_mult: 1  }  scale_param {    dis_mode: true    bias_term: true  }}layer {  name: "relu_d1"  type: "ReLU"  bottom: "dis_conv_d1"  top: "dis_conv_d1"  relu_param {    negative_slope: 0.2  }}layer {  name: "dis_conv_d2"  type: "Convolution"  bottom: "dis_conv_d1"  top: "dis_conv_d2"  param {    name: "dis_w_2"    lr_mult: 1  }  param {    name: "dis_b_2"    lr_mult: 2  }  convolution_param {    num_output: 256    pad: 2    kernel_size: 5    stride: 2    dis_mode: true    weight_filler {      type: "gaussian"      std: 0.02    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "batch_norm_d2"  type: "BatchNorm"  bottom: "dis_conv_d2"  top: "dis_conv_d2"  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  batch_norm_param {    use_global_stats: false  }}layer {  name: "scale_batch_d2"  type: "Scale"  bottom: "dis_conv_d2"  top: "dis_conv_d2"  param {    name: "dis_s2"    lr_mult: 1    decay_mult: 1  }  param {    name: "dis_b2"    lr_mult: 1    decay_mult: 1  }  scale_param {    dis_mode: true    bias_term: true  }}layer {  name: "relu_d2"  type: "ReLU"  bottom: "dis_conv_d2"  top: "dis_conv_d2"  relu_param {    negative_slope: 0.2  }}layer {  name: "dis_conv_d3"  type: "Convolution"  bottom: "dis_conv_d2"  top: "dis_conv_d3"  param {    name: "dis_w_3"    lr_mult: 1  }  param {    name: "dis_b_3"    lr_mult: 2  }  convolution_param {    num_output: 512    pad: 2    kernel_size: 5    stride: 2    dis_mode: true    weight_filler {      type: "gaussian"      std: 0.02    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "batch_norm_d3"  type: "BatchNorm"  bottom: "dis_conv_d3"  top: "dis_conv_d3"  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  batch_norm_param {    use_global_stats: false  }}layer {  name: "scale_batch_d3"  type: "Scale"  bottom: "dis_conv_d3"  top: "dis_conv_d3"  param {    name: "dis_s3"    lr_mult: 1    decay_mult: 1  }  param {    name: "dis_b3"    lr_mult: 1    decay_mult: 1  }  scale_param {    dis_mode: true    bias_term: true  }}layer {  name: "relu_d3"  type: "ReLU"  bottom: "dis_conv_d3"  top: "dis_conv_d3"  relu_param {    negative_slope: 0.2  }}layer {  name: "dis_conv_d4"  type: "Convolution"  bottom: "dis_conv_d3"  top: "dis_conv_d4"  param {    name: "dis_w_4"    lr_mult: 1  }  param {    name: "dis_b_4"    lr_mult: 2  }  convolution_param {    num_output: 1024    pad: 2    kernel_size: 5    stride: 2    dis_mode: true    weight_filler {      type: "gaussian"      std: 0.02    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "batch_norm_d4"  type: "BatchNorm"  bottom: "dis_conv_d4"  top: "dis_conv_d4"  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  param {    lr_mult: 0    decay_mult: 0  }  batch_norm_param {    use_global_stats: false  }}layer {  name: "scale_batch_d4"  type: "Scale"  bottom: "dis_conv_d4"  top: "dis_conv_d4"  param {    name: "dis_s4"    lr_mult: 1    decay_mult: 1  }  param {    name: "dis_b4"    lr_mult: 1    decay_mult: 1  }  scale_param {    dis_mode: true    bias_term: true  }}layer {  name: "relu_d4"  type: "ReLU"  bottom: "dis_conv_d4"  top: "dis_conv_d4"  relu_param {    negative_slope: 0.2  }}layer {  name: "score"  type: "InnerProduct"  bottom: "dis_conv_d4"  top: "score"  param {    name: "score_w"    lr_mult: 1  }  param {    name: "score_b"    lr_mult: 2  }  inner_product_param{    num_output: 1    dis_mode: true    weight_filler {      type: "gaussian"      std: 0.0002    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "sigmoid"  type: "Sigmoid"  bottom: "score"  top: "score"}layer {  name: "gan_loss"  type: "GANDGLoss"  bottom: "score"  top: "gan_loss"  gan_loss_param {    dis_iter: 1    gen_iter: 2  }}

2.solver.prototxt

#  Create on: 2016/10/22 ShanghaiTech#  Author:    Yingying Zhangnet: "gan_configs/train.prototxt"debug_info: falsedisplay: 10solver_type: ADAMaverage_loss: 100base_lr: 2e-4lr_policy: "fixed"max_iter: 15000momentum: 0.5snapshot: 100gan_solver: truesnapshot_prefix: "models/gan_"

3.生成结果

这里写图片描述

未完待续！！！第二部分为caffe具体源码！

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