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_cov

get_cov()

Get full covariance matrix.

Returns:

Tensor of shape [n, n]. Represents all parameter-parameter correlations captured by this FisherFactor.

get_cov_var

get_cov_var()

Get variable backing this FisherFactor.

May or may not be the same as self.get_cov()

Returns:

Variable of shape self._cov_shape.

instantiate_cov_variables

instantiate_cov_variables()

Makes the internal cov variable(s).

instantiate_inv_variables

instantiate_inv_variables()

Makes the internal "inverse" variable(s).

left_multiply_matpower

left_multiply_matpower(
    x,
    exp,
    damping_func
)

Left multiplies 'x' by matrix power of this factor (w/ damping applied).

This calculation is essentially: (C + damping * I)exp * x where * is matrix-multiplication, is matrix power, I is the identity matrix, and C is the matrix represented by this factor.

x can represent either a matrix or a vector. For some factors, 'x' might represent a vector but actually be stored as a 2D matrix for convenience.

Args:

• x: Tensor. Represents a single vector. Shape depends on implementation.
• exp: float. The matrix exponent to use.
• damping_func: A function that computes a 0-D Tensor or a float which will be the damping value used. i.e. damping = damping_func().

Returns:

Tensor of same shape as 'x' representing the result of the multiplication.

make_covariance_update_op

make_covariance_update_op(ema_decay)

Constructs and returns the covariance update Op.

Args:

• ema_decay: The exponential moving average decay (float or Tensor).

Returns:

An Op for updating the covariance Variable referenced by _cov.

make_inverse_update_ops

make_inverse_update_ops()

Create and return update ops corresponding to registered computations.

register_matpower

register_matpower(
    exp,
    damping_func
)

right_multiply_matpower

right_multiply_matpower(
    x,
    exp,
    damping_func
)

Right multiplies 'x' by matrix power of this factor (w/ damping applied).

This calculation is essentially: x * (C + damping * I)exp where * is matrix-multiplication, is matrix power, I is the identity matrix, and C is the matrix represented by this factor.

Unlike left_multiply_matpower, x will always be a matrix.

Args:

• x: Tensor. Represents a single vector. Shape depends on implementation.
• exp: float. The matrix exponent to use.
• damping_func: A function that computes a 0-D Tensor or a float which will be the damping value used. i.e. damping = damping_func().

Returns:

Tensor of same shape as 'x' representing the result of the multiplication.