tf.contrib.kfac.fisher_factors.ConvDiagonalFactor

Class ConvDiagonalFactor

Inherits From: DiagonalFactor

Defined in tensorflow/contrib/kfac/python/ops/fisher_factors.py.

FisherFactor for a diagonal approx of a convolutional layer's Fisher.

Properties

name

Methods

__init__

__init__(
    inputs,
    outputs_grads,
    filter_shape,
    strides,
    padding,
    data_format=None,
    dilations=None,
    has_bias=False
)

Creates a ConvDiagonalFactor object.

Args:

  • inputs: List of Tensors of shape [batch_size, height, width, in_channels]. Input activations to this layer. List index is towers.
  • outputs_grads: List of Tensors, each of shape [batch_size, height, width, out_channels], which are the gradients of the loss with respect to the layer's outputs. First index is source, second index is tower.
  • filter_shape: Tuple of 4 ints: (kernel_height, kernel_width, in_channels, out_channels). Represents shape of kernel used in this layer.
  • strides: The stride size in this layer (1-D Tensor of length 4).
  • padding: The padding in this layer (1-D of Tensor length 4).
  • data_format: None or str. Format of conv2d inputs.
  • dilations: None or tuple of 4 ints.
  • has_bias: Python bool. If True, the layer is assumed to have a bias parameter in addition to its filter parameter.

Raises:

  • ValueError: If inputs, output_grads, and filter_shape do not agree on in_channels or out_channels.
  • ValueError: If strides, dilations are not length-4 lists of ints.
  • ValueError: If data_format does not put channel last.

get_cholesky

get_cholesky(damping_func)

get_cholesky_inverse

get_cholesky_inverse(damping_func)

get_cov

get_cov()

get_cov_as_linear_operator

get_cov_as_linear_operator()

get_matpower

get_matpower(
    exp,
    damping_func
)

instantiate_cov_variables

instantiate_cov_variables()

Makes the internal cov variable(s).

instantiate_inv_variables

instantiate_inv_variables()

make_covariance_update_op

make_covariance_update_op(ema_decay)

make_inverse_update_ops

make_inverse_update_ops()

register_cholesky

register_cholesky(damping_func)

register_cholesky_inverse

register_cholesky_inverse(damping_func)

register_matpower

register_matpower(
    exp,
    damping_func
)