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tfp.edward2.PixelCNN

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Create a random variable for PixelCNN.

tfp.edward2.PixelCNN(
    *args, **kwargs
)

See PixelCNN for more details.

Returns:

RandomVariable.

Original Docstring for Distribution

Construct Pixel CNN++ distribution.

Args:

  • image_shape: 3D TensorShape or tuple for the [height, width, channels] dimensions of the image.
  • conditional_shape: TensorShape or tuple for the shape of the conditional input, or None if there is no conditional input.
  • num_resnet: int, the number of layers (shown in Figure 2 of [2]) within each highest-level block of Figure 2 of [1].
  • num_hierarchies: int, the number of hightest-level blocks (separated by expansions/contractions of dimensions in Figure 2 of [1].)
  • num_filters: int, the number of convolutional filters.
  • num_logistic_mix: int, number of components in the logistic mixture distribution.
  • receptive_field_dims: tuple, height and width in pixels of the receptive field of the convolutional layers above and to the left of a given pixel. The width (second element of the tuple) should be odd. Figure 1 (middle) of [2] shows a receptive field of (3, 5) (the row containing the current pixel is included in the height). The default of (3, 3) was used to produce the results in [1].
  • dropout_p: float, the dropout probability. Should be between 0 and 1.
  • resnet_activation: string, the type of activation to use in the resnet blocks. May be 'concat_elu', 'elu', or 'relu'.
  • use_weight_norm: bool, if True then use weight normalization (works only in Eager mode).
  • use_data_init: bool, if True then use data-dependent initialization (has no effect if use_weight_norm is False).
  • high: int, the maximum value of the input data (255 for an 8-bit image).
  • low: int, the minimum value of the input data.
  • dtype: Data type of the Distribution.
  • name: string, the name of the Distribution.