contrib: autograph/constrained_optimization: minor spelling tweaks

tensorflow/contrib/autograph/converters/control_flow.py

@@ -46,7 +46,7 @@ def new_symbol(self, name_root, reserved_locals):
 
 
 class ControlFlowTransformer(transformer.Base):
-  """Transforms control flow structures like loops an conditionals."""
+  """Transforms control flow structures like loops and conditionals."""
 
   def _create_cond_branch(self, body_name, aliased_orig_names,
                           aliased_new_names, body, returns):

tensorflow/contrib/autograph/operators/control_flow.py

@@ -51,7 +51,7 @@ def for_stmt(iter_, extra_test, body, init_state):
   Args:
     iter_: The entity being iterated over.
     extra_test: Callable with the state as arguments, and boolean return type.
-        An additionnal loop condition.
+        An additional loop condition.
     body: Callable with the iterate and the state as arguments, and
         state as return type. The actual loop body.
     init_state: Tuple containing the initial state.

tensorflow/contrib/autograph/pyct/static_analysis/cfg.py

@@ -286,7 +286,7 @@ def __init__(self, label, context, transfer_fn=operator.or_):
 
   # TODO(alexbw): see if we can simplify by visiting breadth-first
   def visit(self, node):
-    """Depth-first walking the CFG, applying dataflow information propagtion."""
+    """Depth-first walking the CFG, applying dataflow information propagation."""
     # node.value is None only for the exit CfgNode.
     if not node.value:
       return

tensorflow/contrib/autograph/pyct/transformer.py

@@ -191,7 +191,7 @@ def after_visit(node):
 
   # TODO(mdan): Once we have error tracing, we may be able to just go to SSA.
   def apply_to_single_assignments(self, targets, values, apply_fn):
-    """Applies a fuction to each individual assignment.
+    """Applies a function to each individual assignment.
 
     This function can process a possibly-unpacked (e.g. a, b = c, d) assignment.
     It tries to break down the unpacking if possible. In effect, it has the same
@@ -219,7 +219,7 @@ def apply_to_single_assignments(self, targets, values, apply_fn):
           targets field of an ast.Assign node.
       values: an AST node.
       apply_fn: a function of a single argument, which will be called with the
-          respective nodes of each single assignment. The signaure is
+          respective nodes of each single assignment. The signature is
           apply_fn(target, value), no return value.
     """
     if not isinstance(targets, (list, tuple)):

tensorflow/contrib/constrained_optimization/README.md

@@ -46,7 +46,7 @@ document.
 Imagine that we want to constrain the recall of a binary classifier to be at
 least 90%. Since the recall is proportional to the number of true positive
 classifications, which itself is a sum of indicator functions, this constraint
-is non-differentible, and therefore cannot be used in a problem that will be
+is non-differentiable, and therefore cannot be used in a problem that will be
 optimized using a (stochastic) gradient-based algorithm.
 
 For this and similar problems, TFCO supports so-called *proxy constraints*,