diff options
10 files changed, 30 insertions, 30 deletions
diff --git a/tensorflow/contrib/constrained_optimization/python/candidates.py b/tensorflow/contrib/constrained_optimization/python/candidates.py index ac86a6741b..66d7ebed74 100644 --- a/tensorflow/contrib/constrained_optimization/python/candidates.py +++ b/tensorflow/contrib/constrained_optimization/python/candidates.py @@ -204,7 +204,7 @@ def find_best_candidate_distribution(objective_vector, assert best_pp is not None # Throughout this loop, a maximum_violation of "lower" is not achievable, - # but a maximum_violation of "upper" is achiveable. + # but a maximum_violation of "upper" is achievable. while True: middle = 0.5 * (lower + upper) if (middle - lower <= epsilon) or (upper - middle <= epsilon): diff --git a/tensorflow/contrib/kfac/examples/convnet.py b/tensorflow/contrib/kfac/examples/convnet.py index d6b1a61b71..44e01e1aeb 100644 --- a/tensorflow/contrib/kfac/examples/convnet.py +++ b/tensorflow/contrib/kfac/examples/convnet.py @@ -202,7 +202,7 @@ def minimize_loss_single_machine(loss, accuracy: 0-D Tensor. Accuracy of classifier on current minibatch. layer_collection: LayerCollection instance describing model architecture. Used by K-FAC to construct preconditioner. - device: string, Either '/cpu:0' or '/gpu:0'. The covaraince and invserse + device: string, Either '/cpu:0' or '/gpu:0'. The covariance and inverse update ops are run on this device. session_config: None or tf.ConfigProto. Configuration for tf.Session(). @@ -470,7 +470,7 @@ def train_mnist_single_machine(data_dir, data_dir: string. Directory to read MNIST examples from. num_epochs: int. Number of passes to make over the training set. use_fake_data: bool. If True, generate a synthetic dataset. - device: string, Either '/cpu:0' or '/gpu:0'. The covaraince and inverse + device: string, Either '/cpu:0' or '/gpu:0'. The covariance and inverse update ops are run on this device. Returns: @@ -509,7 +509,7 @@ def train_mnist_multitower(data_dir, num_epochs, num_towers, num_epochs: int. Number of passes to make over the training set. num_towers: int. Number of CPUs to split inference across. use_fake_data: bool. If True, generate a synthetic dataset. - devices: string, Either list of CPU or GPU. The covaraince and inverse + devices: string, Either list of CPU or GPU. The covariance and inverse update ops are run on this device. Returns: @@ -621,7 +621,7 @@ def train_mnist_distributed_sync_replicas(task_id, data_dir: string. Directory to read MNIST examples from. num_epochs: int. Number of passes to make over the training set. op_strategy: `string`, Strategy to run the covariance and inverse - ops. If op_strategy == `chief_worker` then covaraiance and inverse + ops. If op_strategy == `chief_worker` then covariance and inverse update ops are run on chief worker otherwise they are run on dedicated workers. diff --git a/tensorflow/contrib/kfac/python/ops/estimator.py b/tensorflow/contrib/kfac/python/ops/estimator.py index 854f885c26..323234c403 100644 --- a/tensorflow/contrib/kfac/python/ops/estimator.py +++ b/tensorflow/contrib/kfac/python/ops/estimator.py @@ -97,8 +97,8 @@ class FisherEstimator(object): and to regularize the update direction by making it closer to the gradient. (Higher damping means the update looks more like a standard gradient update - see Tikhonov regularization.) - layer_collection: The layer collection object, which holds the fisher - blocks, kronecker factors, and losses associated with the + layer_collection: The layer collection object, which holds the Fisher + blocks, Kronecker factors, and losses associated with the graph. exps: List of floats or ints. These represent the different matrix powers of the approximate Fisher that the FisherEstimator will be able @@ -464,7 +464,7 @@ class FisherEstimator(object): def _get_grads_lists_empirical(self, tensors): # Passing in a list of loss values is better than passing in the sum as - # the latter creates unnessesary ops on the default device + # the latter creates unnecessary ops on the default device grads_flat = gradients_impl.gradients( self._layers.eval_losses(), nest.flatten(tensors), diff --git a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py index 3a5c8eb5f9..9fa6eb7dcd 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py @@ -870,7 +870,7 @@ class ConvKFCBasicFB(InputOutputMultiTower, KroneckerProductFB): Estimates the Fisher Information matrix's blog for a convolutional layer. - Consider a convoluational layer in this model with (unshared) filter matrix + Consider a convolutional layer in this model with (unshared) filter matrix 'w'. For a minibatch that produces inputs 'a' and output preactivations 's', this FisherBlock estimates, 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 diff --git a/tensorflow/contrib/kfac/python/ops/layer_collection.py b/tensorflow/contrib/kfac/python/ops/layer_collection.py index cbbfe7212c..43aa713edc 100644 --- a/tensorflow/contrib/kfac/python/ops/layer_collection.py +++ b/tensorflow/contrib/kfac/python/ops/layer_collection.py @@ -609,7 +609,7 @@ class LayerCollection(object): outputs, approx=None, reuse=VARIABLE_SCOPE): - """Registers a fully connnected layer. + """Registers a fully connected layer. Args: params: Tensor or 2-tuple of Tensors corresponding to weight and bias of @@ -975,7 +975,7 @@ class LayerCollection(object): block for this layer (which must have already been registered). If "VARIABLE_SCOPE", use tf.get_variable_scope().reuse. (Note that the word `use` here has a completely different meaning to "use in the graph" - as it perturns to the `inputs`, `outputs`, and `num_uses` arguments.) + as it pertains to the `inputs`, `outputs`, and `num_uses` arguments.) (Default: "VARIABLE_SCOPE") Raises: @@ -1045,7 +1045,7 @@ class LayerCollection(object): block for this layer (which must have already been registered). If "VARIABLE_SCOPE", use tf.get_variable_scope().reuse. (Note that the word `use` here has a completely different meaning to "use in the graph" - as it perturns to the `inputs`, `outputs`, and `num_uses` arguments.) + as it pertains to the `inputs`, `outputs`, and `num_uses` arguments.) (Default: "VARIABLE_SCOPE") Raises: @@ -1116,7 +1116,7 @@ class LayerCollection(object): block for this layer (which must have already been registered). If "VARIABLE_SCOPE", use tf.get_variable_scope().reuse. (Note that the word `use` here has a completely different meaning to "use in the graph" - as it perturns to the `inputs`, `outputs`, and `num_uses` arguments.) + as it pertains to the `inputs`, `outputs`, and `num_uses` arguments.) (Default: "VARIABLE_SCOPE") Raises: diff --git a/tensorflow/contrib/kfac/python/ops/loss_functions.py b/tensorflow/contrib/kfac/python/ops/loss_functions.py index 42d525c2c2..c8cebc42cb 100644 --- a/tensorflow/contrib/kfac/python/ops/loss_functions.py +++ b/tensorflow/contrib/kfac/python/ops/loss_functions.py @@ -214,7 +214,7 @@ class NegativeLogProbLoss(LossFunction): Here the 'Fisher' is the Fisher information matrix (i.e. expected outer- product of gradients) with respect to the parameters of the underlying - probability distribtion (whose log-prob defines the loss). Typically this + probability distribution (whose log-prob defines the loss). Typically this will be block-diagonal across different cases in the batch, since the distribution is usually (but not always) conditionally iid across different cases. @@ -238,7 +238,7 @@ class NegativeLogProbLoss(LossFunction): Here the 'Fisher' is the Fisher information matrix (i.e. expected outer- product of gradients) with respect to the parameters of the underlying - probability distribtion (whose log-prob defines the loss). Typically this + probability distribution (whose log-prob defines the loss). Typically this will be block-diagonal across different cases in the batch, since the distribution is usually (but not always) conditionally iid across different cases. @@ -262,7 +262,7 @@ class NegativeLogProbLoss(LossFunction): Here the 'Fisher' is the Fisher information matrix (i.e. expected outer- product of gradients) with respect to the parameters of the underlying - probability distribtion (whose log-prob defines the loss). Typically this + probability distribution (whose log-prob defines the loss). Typically this will be block-diagonal across different cases in the batch, since the distribution is usually (but not always) conditionally iid across different cases. diff --git a/tensorflow/contrib/kfac/python/ops/optimizer.py b/tensorflow/contrib/kfac/python/ops/optimizer.py index 03b9da7933..38605259b5 100644 --- a/tensorflow/contrib/kfac/python/ops/optimizer.py +++ b/tensorflow/contrib/kfac/python/ops/optimizer.py @@ -72,7 +72,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): (Higher damping means the update looks more like a standard gradient update - see Tikhonov regularization.) layer_collection: The layer collection object, which holds the fisher - blocks, kronecker factors, and losses associated with the + blocks, Kronecker factors, and losses associated with the graph. The layer_collection cannot be modified after KfacOptimizer's initialization. var_list: Optional list or tuple of variables to train. Defaults to the @@ -99,7 +99,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): placement_strategy: string, Device placement strategy used when creating covariance variables, covariance ops, and inverse ops. (Default: `None`) - **kwargs: Arguments to be passesd to specific placement + **kwargs: Arguments to be passed to specific placement strategy mixin. Check `placement.RoundRobinPlacementMixin` for example. Raises: @@ -120,7 +120,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): self._estimation_mode = estimation_mode self._colocate_gradients_with_ops = colocate_gradients_with_ops - # The below parameters are required only if damping needs to be adapated. + # The below parameters are required only if damping needs to be adapted. # These parameters can be set by calling # set_damping_adaptation_params() explicitly. self._damping_adaptation_decay = 0.95 @@ -574,7 +574,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): """Wrapper function for `self._compute_qmodel_hyperparams`. Constructs a list of preconditioned gradients and variables. Also creates a - op to asssign the computed q model change to `self._q_model_change`. + op to assign the computed q model change to `self._q_model_change`. Args: grads_and_vars: List of (gradient, variable) pairs. diff --git a/tensorflow/contrib/layers/python/layers/initializers.py b/tensorflow/contrib/layers/python/layers/initializers.py index 1192198ec2..655f038b18 100644 --- a/tensorflow/contrib/layers/python/layers/initializers.py +++ b/tensorflow/contrib/layers/python/layers/initializers.py @@ -111,7 +111,7 @@ def variance_scaling_initializer(factor=2.0, mode='FAN_IN', uniform=False, if not dtype.is_floating: raise TypeError('Cannot create initializer for non-floating point type.') if mode not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG']: - raise TypeError('Unknow mode %s [FAN_IN, FAN_OUT, FAN_AVG]', mode) + raise TypeError('Unknown mode %s [FAN_IN, FAN_OUT, FAN_AVG]', mode) # pylint: disable=unused-argument def _initializer(shape, dtype=dtype, partition_info=None): diff --git a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py index 9872c6f97c..8ebe45d851 100644 --- a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py +++ b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py @@ -158,7 +158,7 @@ class SDCAOptimizer(object): # exactly 2 (i.e., its shape should be [batch_size, column.dim]). check_rank_op = control_flow_ops.Assert( math_ops.less_equal(array_ops.rank(transformed_tensor), 2), - ['transformed_tensor shouls have rank at most 2.']) + ['transformed_tensor should have rank at most 2.']) # Reshape to [batch_size, dense_column_dimension]. with ops.control_dependencies([check_rank_op]): transformed_tensor = array_ops.reshape(transformed_tensor, [ @@ -172,7 +172,7 @@ class SDCAOptimizer(object): elif isinstance(column, layers.feature_column._BucketizedColumn): # pylint: disable=protected-access # A bucketized column corresponds to a sparse feature in SDCA. The # bucketized feature is "sparsified" for SDCA by converting it to a - # SparseFeatureColumn respresenting the one-hot encoding of the + # SparseFeatureColumn representing the one-hot encoding of the # bucketized feature. # # TODO(sibyl-vie3Poto): Explore whether it is more efficient to translate a @@ -220,7 +220,7 @@ class SDCAOptimizer(object): # occur multiple times for a single example. projected_ids = projection_length * example_ids + flat_ids - # Remove any redudant ids. + # Remove any redundant ids. ids, idx = array_ops.unique(projected_ids) # Keep only one example id per duplicated ids. example_ids_filtered = math_ops.unsorted_segment_min( |