def multiply_inverse(self, vector):
left_factor_inv = self._input_factor.get_damped_inverse(self._input_damping)
right_factor_inv = self._output_factor.get_damped_inverse(
self._output_damping)
reshaped_vector = utils.layer_params_to_mat2d(vector)
reshaped_out = math_ops.matmul(left_factor_inv,
math_ops.matmul(reshaped_vector,
right_factor_inv))
if self._renorm_coeff != 1.0:
reshaped_out /= math_ops.cast(
self._renorm_coeff, dtype=reshaped_out.dtype)
return utils.mat2d_to_layer_params(vector, reshaped_out)
def multiply(self, vector):
"""Approximate damped Fisher-vector product.
Args:
vector: Tensor or 2-tuple of Tensors. if self._has_bias, Tensor of shape
[input_size, output_size] corresponding to layer's weights. If not, a
2-tuple of the former and a Tensor of shape [output_size] corresponding
to the layer's bias.
Returns:
Tensor of the same shape, corresponding to the Fisher-vector product.
"""
reshaped_vect = utils.layer_params_to_mat2d(vector)
reshaped_out = reshaped_vect * (self._factor.get_cov() + self._damping)
return utils.mat2d_to_layer_params(vector, reshaped_out)
def multiply(self, vector):
left_factor = self._input_factor.get_cov()
right_factor = self._output_factor.get_cov()
reshaped_vector = utils.layer_params_to_mat2d(vector)
reshaped_out = (
math_ops.matmul(reshaped_vector, right_factor) +
self._output_damping * reshaped_vector)
reshaped_out = (
math_ops.matmul(left_factor, reshaped_out) +
self._input_damping * reshaped_out)
if self._renorm_coeff != 1.0:
reshaped_out *= math_ops.cast(
self._renorm_coeff, dtype=reshaped_out.dtype)
return utils.mat2d_to_layer_params(vector, reshaped_out)
def multiply(self, vector):
reshaped_vect = utils.layer_params_to_mat2d(vector)
reshaped_out = reshaped_vect * (self._factor.get_cov() + self._damping)
return utils.mat2d_to_layer_params(vector, reshaped_out)