tf.contrib.kfac.fisher_factors.ConvInputKroneckerFactor

Class ConvInputKroneckerFactor

Inherits From: InverseProvidingFactor

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

Kronecker factor for the input side of a convolutional layer.

Estimates E[ a a^T ] where a is the inputs to a convolutional layer given example x. Expectation is taken over all examples and locations.

Equivalent to Omega in https://arxiv.org/abs/1602.01407 for details. See Section 3.1 Estimating the factors.

Properties

name

Methods

__init__

__init__(
    inputs,
    filter_shape,
    padding,
    strides=None,
    dilation_rate=None,
    data_format=None,
    extract_patches_fn=None,
    has_bias=False
)

Initializes ConvInputKroneckerFactor.

Args:

• inputs: List of Tensors of shape [batch_size, ..spatial_input_size.., in_channels]. Inputs to layer. List index is tower.
• filter_shape: List of ints. Contains [..spatial_filter_size.., in_channels, out_channels]. Shape of convolution kernel.
• padding: str. Padding method for layer. "SAME" or "VALID".
• strides: List of ints or None. Contains [..spatial_filter_strides..] if 'extract_patches_fn' is compatible with tf.nn.convolution(), else [1, ..spatial_filter_strides, 1].
• dilation_rate: List of ints or None. Rate for dilation along each spatial dimension if 'extract_patches_fn' is compatible with tf.nn.convolution(), else [1, ..spatial_dilation_rates.., 1].
• data_format: str or None. Format of input data.
• extract_patches_fn: str or None. Name of function that extracts image patches. One of "extract_convolution_patches", "extract_image_patches", "extract_pointwise_conv2d_patches".
• has_bias: bool. If True, append 1 to in_channel.

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.

get_eigendecomp

get_eigendecomp()

Creates or retrieves eigendecomposition of self._cov.

get_inverse

get_inverse(damping_func)

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()

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_inverse

register_inverse(damping_func)

register_matpower

register_matpower(
    exp,
    damping_func
)

Registers a matrix power to be maintained and served on demand.

This creates a variable and signals make_inverse_update_ops to make the corresponding update op. The variable can be read via the method get_matpower.

Args:

• exp: float. The exponent to use in the matrix power.
• 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().

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.