model.py 文件源码

def __init__(self,
               X_train_file='',
               Y_train_file='',
               batch_size=1,
               image_size=256,
               use_lsgan=True,
               norm='instance',
               lambda1=10.0,
               lambda2=10.0,
               learning_rate=2e-4,
               beta1=0.5,
               ngf=64
              ):
    """
    Args:
      X_train_file: string, X tfrecords file for training
      Y_train_file: string Y tfrecords file for training
      batch_size: integer, batch size
      image_size: integer, image size
      lambda1: integer, weight for forward cycle loss (X->Y->X)
      lambda2: integer, weight for backward cycle loss (Y->X->Y)
      use_lsgan: boolean
      norm: 'instance' or 'batch'
      learning_rate: float, initial learning rate for Adam
      beta1: float, momentum term of Adam
      ngf: number of gen filters in first conv layer
    """
    self.lambda1 = lambda1
    self.lambda2 = lambda2
    self.use_lsgan = use_lsgan
    use_sigmoid = not use_lsgan
    self.batch_size = batch_size
    self.image_size = image_size
    self.learning_rate = learning_rate
    self.beta1 = beta1
    self.X_train_file = X_train_file
    self.Y_train_file = Y_train_file

    self.is_training = tf.placeholder_with_default(True, shape=[], name='is_training')

    self.G = Generator('G', self.is_training, ngf=ngf, norm=norm, image_size=image_size)
    self.D_Y = Discriminator('D_Y',
        self.is_training, norm=norm, use_sigmoid=use_sigmoid)
    self.F = Generator('F', self.is_training, norm=norm, image_size=image_size)
    self.D_X = Discriminator('D_X',
        self.is_training, norm=norm, use_sigmoid=use_sigmoid)

    self.fake_x = tf.placeholder(tf.float32,
        shape=[batch_size, image_size, image_size, 3])
    self.fake_y = tf.placeholder(tf.float32,
        shape=[batch_size, image_size, image_size, 3])