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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