Python get_celeb示例

示例#1

def celeb():
    X = util.get_celeb()

    dim = 64
    colors = 3

    d_sizes = {
        'conv_layers': [(64, 5, 2, False), (128, 5, 2, True),
                        (256, 5, 2, True), (512, 5, 2, True)],
        'dense_layers': []
    }

    g_sizes = {
        'z':
        100,
        'projection':
        512,
        'bn_after_project':
        True,
        'conv_layers': [(256, 5, 2, True), (128, 5, 2, True), (64, 5, 2, True),
                        (colors, 5, 2, False)],
        'dense_layers': [],
        'output_activation':
        tf.tanh
    }

    gan = DCGAN(dim, colors, d_sizes, g_sizes)
    gan.fit(X)

示例#2

def celeb():
    X = util.get_celeb()
    # just loads a list of filenames, we will load them in dynamically
    # because there are many
    dim = 64
    colors = 3

    # for celeb
    d_sizes = {
        'conv_layers': [(64, 5, 2, False), (128, 5, 2, True),
                        (256, 5, 2, True), (512, 5, 2, True)],
        'dense_layers': [],
    }
    g_sizes = {
        'z':
        100,
        'projection':
        512,
        'bn_after_project':
        True,
        'conv_layers': [(256, 5, 2, True), (128, 5, 2, True), (64, 5, 2, True),
                        (colors, 5, 2, False)],
        'dense_layers': [],
        'output_activation':
        tf.tanh,
    }

    # setup gan
    # note: assume square images, so only need 1 dim
    gan = DCGAN(dim, colors, d_sizes, g_sizes)
    gan.fit(X)

示例#3

文件： dcgan_tf.py 项目： ShuvenduBikash/machine_learning_examples

def celeb():
  X = util.get_celeb()
  # just loads a list of filenames, we will load them in dynamically
  # because there are many
  dim = 64
  colors = 3

  # for celeb
  d_sizes = {
    'conv_layers': [
      (64, 5, 2, False),
      (128, 5, 2, True),
      (256, 5, 2, True),
      (512, 5, 2, True)
    ],
    'dense_layers': [],
  }
  g_sizes = {
    'z': 100,
    'projection': 512,
    'bn_after_project': True,
    'conv_layers': [
      (256, 5, 2, True),
      (128, 5, 2, True),
      (64, 5, 2, True),
      (colors, 5, 2, False)
    ],
    'dense_layers': [],
    'output_activation': tf.tanh,
  }

  # setup gan
  # note: assume square images, so only need 1 dim
  gan = DCGAN(dim, colors, d_sizes, g_sizes)
  gan.fit(X)