diff options
Diffstat (limited to 'tensorflow/contrib/kfac/python/ops/fisher_factors.py')
| -rw-r--r-- | tensorflow/contrib/kfac/python/ops/fisher_factors.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/tensorflow/contrib/kfac/python/ops/fisher_factors.py b/tensorflow/contrib/kfac/python/ops/fisher_factors.py index b43232dfaf..afa2fd1ca7 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_factors.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_factors.py @@ -71,15 +71,15 @@ _MAX_NUM_OUTER_PRODUCTS_PER_COV_ROW = 1 # factor. This parameter is used only if `_SUB_SAMPLE_INPUTS` is True. _INPUTS_TO_EXTRACT_PATCHES_FACTOR = 0.5 -# If True, then subsamples the tensor passed to compute the covaraince matrix. +# If True, then subsamples the tensor passed to compute the covariance matrix. _SUB_SAMPLE_OUTER_PRODUCTS = False -# If True, then subsamples the tensor passed to compute the covaraince matrix. +# If True, then subsamples the tensor passed to compute the covariance matrix. _SUB_SAMPLE_INPUTS = False # TOWER_STRATEGY can be one of "concat" or "separate". If "concat", the data # passed to the factors from the blocks will be concatenated across towers -# (lazilly via PartitionedTensor objects). Otherwise a tuple of tensors over +# (lazily via PartitionedTensor objects). Otherwise a tuple of tensors over # towers will be passed in, and the factors will iterate over this and do the # cov computations separately for each one, averaging the results together. TOWER_STRATEGY = "concat" @@ -309,7 +309,7 @@ def _subsample_for_cov_computation(array, name=None): def _random_tensor_gather(array, max_size): - """Generates a random set of indices and gathers the value at the indcices. + """Generates a random set of indices and gathers the value at the indices. Args: array: Tensor, of shape `[batch_size, dim_2]`. @@ -1762,8 +1762,8 @@ class FullyConnectedMultiKF(FullyConnectedKroneckerFactor): # Might need to enforce symmetry lost due to numerical issues. invsqrtC0 = (invsqrtC0 + array_ops.transpose(invsqrtC0)) / 2.0 - # The following line imposses the symmetry assumed by "Option 1" on C1. - # Stangely the code can work okay with this line commented out, + # The following line imposes the symmetry assumed by "Option 1" on C1. + # Strangely the code can work okay with this line commented out, # depending on how psd_eig is defined. I'm not sure why. C1 = (C1 + array_ops.transpose(C1)) / 2.0 |