DL之DCGAN：基于keras框架利用深度卷积对抗网络DCGAN算法对MNIST数据集实现图像生成

目录

基于keras框架利用深度卷积对抗网络DCGAN算法对MNIST数据集实现图像生成

设计思路

输出结果

核心代码

相关文章
DL之DCGAN：基于keras框架利用深度卷积对抗网络DCGAN算法对MNIST数据集实现图像生成
DL之DCGAN：基于keras框架利用深度卷积对抗网络DCGAN算法对MNIST数据集实现图像生成实现

基于keras框架利用深度卷积对抗网络DCGAN算法对MNIST数据集实现图像生成

设计思路

输出结果

  

   

    

     

    
    

     

      X像素取值范围是[-1.0, 1.0]
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      Layer (type) Output Shape Param # 
     
    
   

    

     

    
    

     

      =================================================================
     
    
   

    

     

    
    

     

      dense_1 (Dense) (None, 1024) 103424 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      activation_1 (Activation) (None, 1024) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      dense_2 (Dense) (None, 6272) 6428800   
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      batch_normalization_1 (Batch (None, 6272) 25088 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      activation_2 (Activation) (None, 6272) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      reshape_1 (Reshape) (None, 7, 7, 128) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      up_sampling2d_1 (UpSampling2 (None, 14, 14, 128) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      conv2d_1 (Conv2D) (None, 14, 14, 64) 204864 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      activation_3 (Activation) (None, 14, 14, 64) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      up_sampling2d_2 (UpSampling2 (None, 28, 28, 64) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      conv2d_2 (Conv2D) (None, 28, 28, 1) 1601 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      activation_4 (Activation) (None, 28, 28, 1) 0 
     
    
   

    

     

    
    

     

      =================================================================
     
    
   

    

     

    
    

     

      Total params: 6,763,777
     
    
   

    

     

    
    

     

      Trainable params: 6,751,233
     
    
   

    

     

    
    

     

      Non-trainable params: 12,544
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      Layer (type) Output Shape Param # 
     
    
   

    

     

    
    

     

      =================================================================
     
    
   

    

     

    
    

     

      conv2d_3 (Conv2D) (None, 28, 28, 64) 1664 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      activation_5 (Activation) (None, 28, 28, 64) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      max_pooling2d_1 (MaxPooling2 (None, 14, 14, 64) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      conv2d_4 (Conv2D) (None, 10, 10, 128) 204928 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      activation_6 (Activation) (None, 10, 10, 128) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      max_pooling2d_2 (MaxPooling2 (None, 5, 5, 128) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      flatten_1 (Flatten) (None, 3200) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      dense_3 (Dense) (None, 1024) 3277824   
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      activation_7 (Activation) (None, 1024) 0 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      dense_4 (Dense) (None, 1) 1025 
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      activation_8 (Activation) (None, 1) 0 
     
    
   

    

     

    
    

     

      =================================================================
     
    
   

    

     

    
    

     

      Total params: 3,485,441
     
    
   

    

     

    
    

     

      Trainable params: 3,485,441
     
    
   

    

     

    
    

     

      Non-trainable params: 0
     
    
   

    

     

    
    

     

      _________________________________________________________________
     
    
   

    

     

    
    

     

      2020-11-24 21:53:56.659897: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
     
    
   

    

     

    
    

     

      (25, 28, 28, 1)
     
    
  
 

核心代码

  

   

    

     

    
    

     

      def generator_model():
     
    
   

    

     

    
    

     

       model = Sequential()
     
    
   

    

     

    
    

     

       model.add(Dense(input_dim=100, units=1024)) # 1034 1024
     
    
   

    

     

    
    

     

       model.add(Activation('tanh'))
     
    
   

    

     

    
    

     

       model.add(Dense(128*7*7))
     
    
   

    

     

    
    

     

       model.add(BatchNormalization())
     
    
   

    

     

    
    

     

       model.add(Activation('tanh'))
     
    
   

    

     

    
    

     

       model.add(Reshape((7, 7, 128), input_shape=(128*7*7,)))
     
    
   

    

     

    
    

     

       model.add(UpSampling2D(size=(2, 2)))
     
    
   

    

     

    
    

     

       model.add(Conv2D(64, (5, 5), padding='same'))
     
    
   

    

     

    
    

     

       model.add(Activation('tanh'))
     
    
   

    

     

    
    

     

       model.add(UpSampling2D(size=(2, 2)))
     
    
   

    

     

    
    

     

       model.add(Conv2D(1, (5, 5), padding='same'))
     
    
   

    

     

    
    

     

       model.add(Activation('tanh'))
     
    
   

    

     

    
    

     
 return model
     
    
   

    

     

    
    

     

      def discriminator_model(): # 定义鉴别网络：输入一张图像，输出0(伪造)/1(真实)
     
    
   

    

     

    
    

     

       model = Sequential()
     
    
   

    

     

    
    

     

       model.add(
     
    
   

    

     

    
    

     

       Conv2D(64, (5, 5),
     
    
   

    

     

    
    

     

       padding='same',
     
    
   

    

     

    
    

     

       input_shape=(28, 28, 1))
     
    
   

    

     

    
    

     

       )
     
    
   

    

     

    
    

     

       model.add(Activation('tanh'))
     
    
   

    

     

    
    

     

       model.add(MaxPooling2D(pool_size=(2, 2)))
     
    
   

    

     

    
    

     

       model.add(Conv2D(128, (5, 5)))
     
    
   

    

     

    
    

     

       model.add(Activation('tanh'))
     
    
   

    

     

    
    

     

       model.add(MaxPooling2D(pool_size=(2, 2)))
     
    
   

    

     

    
    

     

       model.add(Flatten())
     
    
   

    

     

    
    

     

       model.add(Dense(1024))
     
    
   

    

     

    
    

     

       model.add(Activation('tanh'))
     
    
   

    

     

    
    

     

       model.add(Dense(1))
     
    
   

    

     

    
    

     

       model.add(Activation('sigmoid'))
     
    
   

    

     

    
    

     
 return model
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      g = generator_model()
     
    
   

    

     

    
    

     

      g.summary()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      d = discriminator_model()
     
    
   

    

     

    
    

     

      d.summary()
     
    
  
 

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原文链接：yunyaniu.blog.csdn.net/article/details/110097975