tf.contrib.kfac.fisher_blocks.NaiveDiagonalFB

Class NaiveDiagonalFB

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

FisherBlock using a diagonal matrix approximation.

This type of approximation is generically applicable but quite primitive.

Note that this uses the naive "square the sum estimator", and so is applicable to any type of parameter in principle, but has very high variance.

Properties

num_registered_towers

Methods

__init__

__init__(
    layer_collection,
    params
)

Creates a NaiveDiagonalFB block.

Args:

  • layer_collection: The collection of all layers in the K-FAC approximate Fisher information matrix to which this FisherBlock belongs.
  • params: The parameters of this layer (Tensor or tuple of Tensors).

full_fisher_block

full_fisher_block()

instantiate_factors

instantiate_factors(
    grads_list,
    damping
)

multiply

multiply(vector)

Multiplies the vector by the (damped) block.

Args:

  • vector: The vector (a Tensor or tuple of Tensors) to be multiplied.

Returns:

The vector left-multiplied by the (damped) block.

multiply_cholesky

multiply_cholesky(
    vector,
    transpose=False
)

multiply_cholesky_inverse

multiply_cholesky_inverse(
    vector,
    transpose=False
)

multiply_inverse

multiply_inverse(vector)

Multiplies the vector by the (damped) inverse of the block.

Args:

  • vector: The vector (a Tensor or tuple of Tensors) to be multiplied.

Returns:

The vector left-multiplied by the (damped) inverse of the block.

multiply_matpower

multiply_matpower(
    vector,
    exp
)

register_additional_tower

register_additional_tower(batch_size)

Register an additional tower.

Args:

  • batch_size: The batch size, used in the covariance estimator.

register_cholesky

register_cholesky()

register_cholesky_inverse

register_cholesky_inverse()

register_inverse

register_inverse()

Registers a matrix inverse to be computed by the block.

register_matpower

register_matpower(exp)

tensors_to_compute_grads

tensors_to_compute_grads()