def _get_transformed_random_signs(self):
transformed_random_signs = []
for loss in self.layers.losses:
with tf.colocate_with(self.layers.loss_colocation_ops[loss]):
transformed_random_signs.append(
tf.sqrt(self.layers.loss_coeffs[loss])*loss.multiply_fisher_factor(
utils.generate_random_signs(loss.fisher_factor_inner_shape)))
return transformed_random_signs
def _get_transformed_random_signs(self):
if self.mat_type == "Fisher":
mult_func = lambda loss, index: loss.multiply_fisher_factor(index)
inner_shape_func = lambda loss: loss.fisher_factor_inner_shape
elif self.mat_type == "GGN":
mult_func = lambda loss, index: loss.multiply_ggn_factor(index)
inner_shape_func = lambda loss: loss.ggn_factor_inner_shape
transformed_random_signs = []
for loss in self.layers.losses:
with tf.colocate_with(self.layers.loss_colocation_ops[loss]):
value = mult_func(loss,
utils.generate_random_signs(inner_shape_func(loss)))
coeff = tf.cast(self.layers.loss_coeffs[loss], dtype=value.dtype)
transformed_random_signs.append(tf.sqrt(coeff) * value)
return transformed_random_signs
def _get_transformed_random_signs(self):
"""No docstring required."""
if self.mat_type == "Fisher":
mult_func = lambda loss, index: loss.multiply_fisher_factor(index)
inner_shape_func = lambda loss: loss.fisher_factor_inner_shape
elif self.mat_type == "GGN":
mult_func = lambda loss, index: loss.multiply_ggn_factor(index)
inner_shape_func = lambda loss: loss.ggn_factor_inner_shape
transformed_random_signs = []
for loss in self.layers.losses:
with tf.colocate_with(self.layers.loss_colocation_ops[loss]):
transformed_random_signs.append(
tf.sqrt(self.layers.loss_coeffs[loss]) * mult_func(
loss,
utils.generate_random_signs(inner_shape_func(loss))))
return transformed_random_signs